769 results about "Industrial control system" patented technology

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The embodiment of the invention provides a security authentication method, device and system for a test platform, and a storage medium, and relates to the technical field of network security. The method comprises the following steps: performing fingerprint acquisition on virtual and real equipment to generate a unique identifier, dynamically slicing based on a software defined network to form an isolated test environment, and injecting a zero-trust access strategy associated with the fingerprint; and based on the zero-trust access policy and the device fingerprint, allocating a minimum permission through an attribute-based access control model, continuously evaluating device behaviors to calculate a dynamic trust score, and adaptively updating the permission according to the trust score. By constructing a full-process security certification system and through multi-dimensional mechanism cooperation, dynamic risk perception and timely strategy adjustment, fine-grained control of virtual and real equipment access in an industrial control scene is realized, the dynamic adaptability and risk resistance of a test platform in a complex network environment are improved, and the test efficiency is improved. Equipment access safety, authority distribution minimization and behavior monitoring continuity are ensured, and the safety risk of the industrial control system is reduced.

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

The invention discloses a hydraulic engineering intelligent safety monitoring system based on an industrial control system, and belongs to the technical field of industrial control and intelligent monitoring. The system aims at solving the technical problems that existing hydraulic engineering monitoring data are heterogeneous, information is isolated, passive early warning is achieved, and intelligent control is difficult. According to the system, multi-source heterogeneous data are deeply fused, a high-fidelity digital twin model is constructed, an artificial intelligence algorithm is used for real-time analysis, prediction and decision making, and the system is linked with an industrial control system, so that sensing, analysis, decision making and execution closed-loop management of a water conservancy project are realized. The safety margin, the operation efficiency and the intelligent level of the water conservancy project are effectively improved.

The invention relates to the technical field of security auditing, in particular to a security auditing method and system for an industrial control system, and the method comprises the following steps: aiming at a key control task, a communication task and a security task of a real-time operating system, collecting a period, a starting timestamp, a finishing timestamp, a central processing unit occupied time slice and peak memory usage amount data. According to the method, the period, timestamp, central processing unit occupation and memory usage data of a key task of a real-time operating system are collected, a task execution time boundary and a resource consumption envelope are set, and then an expected relation rule set of a task time sequence and resource consumption is constructed by applying historical data statistics and logic rule deduction; and meanwhile, the key length of symmetric and asymmetric encryption, initialization vector generation, hash algorithm selection, key derivation parameters and encryption operation context are stipulated, so that a comprehensive and specific ICS behavior specification baseline is established.

The invention relates to a test case generation method and system based on an industrial agent. The method comprises the steps that an industrial data set is acquired, and a system dependency model is constructed; generating a path coverage test sequence through the first agent; generating a boundary value test data set through the second agent; generating abnormal test scene information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test data set and the abnormal test scene information. In conclusion, by constructing the system dependency model and utilizing the multi-agent collaborative analysis process logic path, boundary condition and dependency relationship, the multi-surface coverage of the industrial control system test scene is realized, the automation of the test case generation is realized, the effects of improving the generation efficiency and enhancing the comprehensiveness of the test coverage are achieved, and the method is suitable for industrial control system test. Especially for the coverage of boundary conditions, abnormal scenes and concurrent paths, the manual dependence is reduced, and the reliability and consistency of the test are improved.

The invention discloses a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

The embodiment of the invention provides a test case sample cutting method, device and system and a storage medium, and relates to the technical field of network security testing. The method comprises the following steps: constructing an industrial control system attack tactical library; wherein the tactical library comprises classified and arranged attack techniques and corresponding security attributes; performing information analysis and semantic analysis on the test case sample to generate structured data of the test case sample and / or function points in the test case sample; and cutting the test case sample according to the security attribute and / or the structured data and / or the function point to obtain a test fragment corresponding to the security attribute and / or the function point. According to the method, classification-based attack techniques and security attributes are cut by constructing a tactical library, key information can be accurately extracted and test intentions can be understood through information analysis and semantic analysis, redundant tests are avoided, cut test fragments can concentrate on specific security attributes or function points, the test coverage range is more accurate, and the test efficiency is improved. And the distributed security test effect is improved.

The invention relates to an embedded safety management method based on digital RMB industrial control equipment, and belongs to the technical field of industrial control system safety. The method comprises the following steps: during first deployment, carrying out information binding on an asymmetric key pair of the industrial control equipment and an equipment identity identifier to generate an equipment digital certificate; the digital signature is verified step by step from a private key of the asymmetric key pair during power-on starting through a safe starting process; when communication is established, performing digital signature on a communication key by calling a signature function of the hardware security module based on a challenge-response mechanism; when a digital RMB transaction is carried out, the trusted execution environment and the digital RMB hardware wallet module carry out secure interaction, and a transaction instruction is analyzed; during the operation period of the equipment, key data streams are continuously collected and analyzed by deploying an exception monitoring module. Reading, querying and related business transactions of the digital RMB on the industrial control equipment are achieved, and it is ensured that the identity is not forged and the instruction is not tampered.

The invention discloses a multi-element coupling heat supply monitoring system based on a meteorological condition prediction model, belongs to an industrial control system, realizes deep coupling of meteorological data and an equipment operation state, and solves a fusion error problem caused by fixed meteorological element weight in traditional monitoring to a certain extent. By establishing the efficiency evaluation model containing the environment compensation mechanism, the interference of external temperature fluctuation on equipment performance evaluation is eliminated, and the accuracy of the operation efficiency index is improved. A multi-dimensional health assessment system is adopted to integrate equipment maintenance data and real-time operation parameters, potential fault risks can be recognized in advance, and data support is provided for equipment preventive maintenance.

The invention relates to the technical field of industrial control systems, in particular to an ink path control system of a full-color printer, which comprises the following specific implementation steps of: measuring a plurality of physical quantities in a controlled physical process in real time, and outputting the physical quantities as state variables and external disturbance signals; receiving a state variable and an external disturbance signal, performing future state prediction through a nonlinear dynamic process model, and generating a prediction result; constructing a process performance index function, optimizing the performance index function through a sparrow search optimization algorithm based on a prediction result, and generating an optimal target set value; through a model based on Bayesian reasoning, parameters of a PID control law are updated in a self-adaptive mode according to external disturbance signals; the controller is used for receiving the optimal target set value and the adaptive PID control law parameters, executing the PID control law in combination with a preset undisturbed switching module, and generating a final control instruction; and receiving a final control instruction, and converting the final control instruction into driving signals acting on a plurality of physical actuators so as to realize closed-loop feedback control of the controlled physical process.

The invention discloses an industrial control protocol intelligent fuzzy test method and system based on a multi-agent large model, and relates to an industrial information security test method and system.According to the industrial control protocol intelligent fuzzy test method and system based on the multi-agent large model, the large model is enhanced by integrating the field of the retrieval enhancement generation (RAG) and the low-rank quantization adaptation (QLoRA) technology, and a multi-agent cooperation mechanism is combined; and full-automatic, high-precision and end-to-end fuzzy testing of the industrial protocol is realized. The system generates seed data meeting protocol specifications through the seed generation agent, the test case generation agent executes field, structure and semantic variation based on the seed data, efficient test cases are dynamically generated, a test strategy is optimized in real time through the feedback analysis and strategy adjustment agent, and the vulnerability discovery capability is improved. By dynamically adjusting the variation strategy and optimizing the generation of the test case, the coverage, the accuracy and the efficiency of the test are improved, and the security and the vulnerability detection capability of the industrial control system are remarkably improved.

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 data filtering method and system for a digital twin industrial control safety target range. The method comprises the following steps: firstly, marking an IP address of an industrial control system network in a segmented manner; feature extraction is carried out on the network behavior data of each IP segment, and splicing is carried out on the IP segment label to serve as a feature sample for constructing an industrial control network security identification model; a plurality of inverted residual modules with the same structure are adopted as core construction units, network behavior features in the feature samples are extracted and learned, and classification training is carried out; deploying the trained industrial control network security identification model to a pre-filtering module of a digital twin industrial control security target range, and carrying out classification, identification and filtering on network behavior data flowing in each IP segment in real time; and the misjudged sample is subjected to backtracking analysis, and the IP marking rule is updated. According to the technical scheme, the accuracy of network behavior data identification can be improved, so that the effectiveness of industrial control system simulation is improved.

The invention relates to the technical field of industrial control systems, and discloses an industrial and commercial energy storage battery management system and method.The industrial and commercial energy storage battery management method comprises the steps that a battery main loop switching device is controlled to superpose micro-disturbance signals, a voltage response sequence is collected in real time, the approximate entropy value of the voltage response sequence is calculated, the battery operation mode is determined according to entropy value threshold value comparison, and a corresponding control strategy is executed; according to the method, the electrochemical stability in the battery is directly perceived through order degree analysis of the voltage response sequence, model-free state recognition is achieved, meanwhile, a resource self-adaptive mechanism that macroscopic monitoring triggers microscopic calculation is constructed, the decision reliability is improved through electrochemical and thermodynamic signal cross validation under the complex working condition, and the method is suitable for large-scale popularization and application. And a dynamic control system with both real-time performance and safety is formed.

The invention discloses a power grid industrial control system security protection scheme based on a block chain, and belongs to the field of power grid automation security. Aiming at the problems of high centralized authentication risk, weak physical layer protection and the like of a traditional industrial control system, a trusted execution environment and intelligent contract response system is constructed through fusion of a block chain distributed account book and a hardware security module (HSM). According to the scheme, equipment physical identity binding and vibration monitoring are achieved through an HSM, the consensus efficiency is optimized through an improved PBFT algorithm, instruction legality, parameter compliance and execution logic rationality are dynamically verified in combination with an intelligent contract, a security baseline is generated in real time, and the instruction stream deviation degree is monitored. The method can dynamically balance the security and real-time performance of the instruction, reduce manual intervention, improve the abnormal response efficiency to a second level, is suitable for industrial control systems such as SCADA (Supervisory Control And Data Acquisition) and the like, remarkably improves the protection capability in scenes such as DDoS attack resistance and physical tampering detection, and provides an extensible intelligent protection normal form for power grid security.

The invention discloses a PLC behavior measurement method, device and equipment based on trusted 3.0 and national cryptographic algorithms, and relates to the field of safety control in an industrial control system.The method comprises the steps that a hardware trust root module installed on a PLC in advance is used for conducting trusted verification on the PLC during starting, and the module is constructed based on the trusted computing 3.0 standard; after verification succeeds, a behavior feature vector of the PLC operation state is obtained based on trusted computing 3.0, wherein the vector comprises at least one of an integrity verification parameter, a resource consumption parameter, an operation parameter and an associated parameter; generating behavior measurement data based on trusted computing 3.0, and measuring a vector through an Euclidean distance algorithm in combination with behavior baseline data; comparing the measurement result with a preset Euclidean distance threshold value, encrypting the measurement result by adopting a domestic key algorithm, and transmitting a comparison result to a security management platform; according to the method, trusted computing 3.0 (TC3.0) and the national cryptographic algorithm are combined, accurate behavior measurement is carried out on the operation state, and the equipment safety and the attack resistance can be enhanced.

The invention discloses a PLC variable association relationship analysis method and device based on weight calculation and electronic equipment. The method comprises the following steps: constructing a program dependency graph representing a PLC variable dependency relationship; according to the program dependency graph, quantifying association strength between PLC variables, and calculating to obtain a dependency edge weight; according to the dependency edge weight, calculating an association relationship score between the PLC variables, and identifying to obtain the association relationship score between the PLC variables; and determining potential attack point risk information of the industrial control system according to the identified association score, and guiding a defense system to discover an intrusion point or a fault source. According to the embodiment of the invention, the strong association relationship before the variable can be comprehensively and reliably identified; in addition, direct dependence between variables can be accurately recognized, indirect influence relations are captured, and the method can be widely applied to the technical field of computers.

The invention relates to the technical field of malicious software identification and analysis, and discloses an electric power industrial control system malicious software identification and analysis method based on artificial intelligence, and the method comprises the steps: collecting static operation sample data and dynamic operation sample data of software in an electric power working condition system in a software test environment, and carrying out the feature extraction; performing feature fusion on the static feature sequence and the dynamic feature sequence by using a physical information neural network based on an improved feature dimension reduction strategy; and classifying the fusion features of the software by adopting a local mean neighbor classification model of a fusion heuristic algorithm to obtain software categories corresponding to the fusion features. According to the method, feature adaptive fusion is realized by combining improved feature dimension reduction and a physical information neural network, and higher classification precision and robustness are realized under the support of optimal subset and neighbor number selection by combining a local mean neighbor classification method of a heuristic algorithm, so that the identification performance of the malicious software of the electric power industrial control system is effectively improved.

The invention relates to the technical field of industrial control, and discloses an industrial communication protocol adaptation method and system based on OpenHarmony. The method comprises the following steps: performing distributed soft bus deployment on a plurality of industrial devices in an industrial control network to obtain a distributed device connection architecture; carrying out multi-protocol adapter loading on the industrial automation runtime service of the distributed equipment connection architecture to obtain a multi-protocol runtime service instance; performing communication protocol identification and configuration on the plurality of industrial devices according to the multi-protocol runtime service instance to obtain standardized device data; and establishing a data transmission channel according to the standardized device data, and performing global state synchronization and cooperative control instruction distribution on the plurality of industrial devices to generate a distributed cooperative control network. The deployment and maintenance work of the industrial control system is simplified, the accuracy and consistency of system configuration are improved, the risk of manual configuration errors is reduced, and the development cost and maintenance complexity of industrial application are reduced.

The invention relates to the technical field of industrial control, and discloses an industrial control system task scheduling method and system based on dynamic priority. The method comprises the following steps: acquiring equipment inclination angle data and performing data cleaning; analyzing a state change trend according to the cleaned data, extracting dynamic features, and mapping the feature values into risk levels by applying a risk assessment rule base; if the risk level exceeds a threshold value, determining a task emergency degree in combination with the risk level and equipment importance, and generating a task redistribution instruction; a task queue is adjusted according to the instruction and the emergency degree, and an adjusted task scheduling sequence is obtained after it is verified that resources are sufficient; and the queue state is continuously monitored to realize closed-loop adjustment. According to the method, by establishing closed-loop association of physical risk perception and task scheduling, the problem that a traditional static scheduling strategy cannot cope with dynamic changes of equipment is solved, and the safety and the operation efficiency of the industrial control system are remarkably improved.

The invention discloses a data transmission method and device, an industrial control system, a storage medium and a computer program product, and the method comprises the steps: initializing a continuous physical memory, dividing the continuous physical memory into memory blocks with a fixed size, recording the initial address, size, idle or occupied state of the memory blocks through a red-black tree, and forming a dynamic address pool; the dynamic address pool can realize on-demand allocation of memory blocks and merging and dynamic expansion of adjacent free blocks; the method comprises the following steps: acquiring a load index in real time through an FPGA (Field Programmable Gate Array), triggering interruption based on load grading and predicting a memory demand in combination with a machine learning model, and pre-fetching a memory block from a dynamic address pool to a user mode mapping region before a real-time task initiates a memory allocation request; a user mode atomic operation annular queue is designed, direct data interaction between the X86 and the FPGA is achieved through memory mapping, and a Burst transmission and cache consistency protocol of a hardware bus is combined. According to the scheme, the problem of memory fragmentation is solved, the data transmission delay is reduced, and the CPU occupancy rate is greatly reduced.

Described systems and methods may enable a cloud-computing system to use data model-based analysis to manage maintenance of an industrial automation system based on life stage predictions and real-time evaluations. Based on the data model-based analyses, the cloud-computing system may instruct an industrial control system of the industrial automation system to implement recommended adjustments and / or to confirm a part replacement occurred in situ. The recommended adjustment may include a part replacement. The cloud-computing system may generate instructions to cause shipment of the part to be installed as part of the part replacement to the industrial automation system.

The invention discloses a human-computer interface application system and method for an industrial control system, electronic equipment, a medium and a program product. The system comprises an HMI module, an AI interaction entrance is embedded in the HMI module, and a bidirectional channel between the HMI module and the AI module is constructed based on an open interface of the HMI module; the HMI module is used for receiving character or voice question description from a user based on the AI interaction entrance and providing the question description to the AI module; receiving intelligent answers or suggestions returned by the AI module, and presenting the intelligent answers or suggestions through the AI interaction entrance; the AI module is used for receiving the question description, calling an AI model based on the question description, receiving intelligent answers or suggestions from the AI model, and returning the intelligent answers or suggestions to the HMI module; wherein the AI model is injected into the industrial knowledge base through fine tuning or question word engineering. According to the technical scheme, dependence on experience of operators can be reduced.

The invention provides an electric power industrial control system abnormal behavior detection method, system, device and medium, and relates to the technical field of electric power system security, and the method comprises the steps: collecting a communication message, a device state and an operation instruction, extracting a message fingerprint vector to construct a feature matrix, calculating a state transition feature to form a feature correlation tensor, and obtaining a feature correlation tensor; and performing time domain fragmentation on the tensor to construct a graph structure, extracting time sequence graph features by using a graph convolutional network, training a twinning neural network to construct a discrimination boundary, and realizing real-time detection of abnormal behaviors. According to the method, the abnormal behaviors in the complex electric power industrial control system can be accurately identified, the detection accuracy is improved, and the false alarm rate is reduced.

The invention relates to the technical field of industrial control system security testing, and provides a construction method and device for a dynamic attack and defense testing environment of an industrial control system. The method comprises the following steps: realizing virtualization operation and behavior modeling of industrial control firmware through a hardware system simulation technology based on logic self-learning; constructing a meta-aggregation data resource pool to realize intelligent scheduling and automatic deployment of industrial control component resources; when it is detected that the behavior of the industrial equipment is abnormal, deviation behavior data are complemented based on a causal relationship reasoning mechanism, and a simulation model is driven to be adjusted; constructing a digital-analog fusion model supporting space-time driving and data synchronization, and realizing data consistency and linkage between a virtual environment and a real environment; an AI agent mechanism is introduced, an attack path is automatically deduced, a defense strategy is optimized, and intelligent evaluation of a drilling process is completed; attack protection scene configuration and control are carried out through an interactive user interface, and simulation and verification operations in a complex dynamic attack and defense environment are realized.

The invention discloses a single-classification industrial control system anomaly detection method based on double-view information bottleneck fusion, which belongs to the technical field of industrial control system anomaly detection and comprises the following steps of: in a training stage, simultaneously constructing a time sequence view and a pseudo image view; respectively extracting characterization through a time dynamic encoder and a pre-trained visual backbone; performing cross-view compression in the variational information bottleneck fusion module and establishing projection branches only depending on sequence representation; a normal subspace is learned around a center vector, an accurate hypersphere is established to describe normal data, and a center distance is used as an abnormal score and a quantile threshold is used to complete discrimination; in the detection stage, online recognition can be performed only by inputting a time sequence view. According to the method, dual-view prior and variational information bottleneck are fused, end-to-end collaborative optimization is carried out on the dual-view prior and the variational information bottleneck and deep support vector data description, an accurate decision boundary can be established under the condition of a small number of samples, and a solution for industrial control system anomaly detection under the condition that the samples are limited is provided.

The invention discloses an ICS-oriented efficient multi-source data interaction method and system, and relates to the technical field of intelligent power plant ICS data communication. According to the method, interaction variables are limited through a backboard bus agent, and the data interaction security is improved in combination with a dual-access security control mechanism; a multi-link communication architecture fusing optical fiber, 5G and industrial Ethernet is constructed, transmission tasks are dynamically allocated by using an intelligent scheduling algorithm, and a communication procedure and a coding strategy are adaptively adjusted, so that the data transmission efficiency and reliability in different network environments are improved; a distributed cache unit is deployed at each server node, and the data access delay is reduced in combination with a cache consistency regulation and an intelligent scheduling and prefetching mechanism; through an omnibearing fault perception and parallel rapid switching algorithm, rapid takeover under a fault condition is realized, and service continuity is ensured in cooperation with data consistency; the method can be widely applied to industrial control systems with high requirements on data interaction safety, real-time performance and reliability, such as intelligent power plants.