1113 results about "Industrial control system" patented technology

The invention provides a pressure sensor data precision compensation method and system for industrial control, and the method comprises the steps: firstly obtaining an original pressure signal sequence outputted by a target pressure sensor in a preset collection period, the original pressure signal sequence containing a plurality of pressure signals arranged according to a timestamp; meanwhile, a real-time environment parameter set, covering temperature, humidity and mechanical vibration parameters in an acquisition period are detected, then the real-time environment parameter set is input into a pre-trained compensation parameter generation model, a corresponding dynamic compensation parameter set is generated, then each signal of an original pressure signal sequence is adjusted step by step according to the dynamic compensation parameter set, and a dynamic compensation parameter set is generated; and finally, the compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an execution mechanism, so that the data precision of the pressure sensor can be dynamically and accurately compensated by integrating multiple environmental factors, and the accuracy and stability of industrial control are improved.

The invention relates to the technical field of data management, in particular to an industrial control system remote monitoring data synchronization method, which comprises the following steps of: 1, generating a dynamic synchronization time window based on an equipment state sudden change frequency and a network fluctuation degree; 2, dividing transmission priorities according to the influence degree of data on equipment security control; 3, quality characteristics of available transmission paths are evaluated, data of different priorities are transmitted in a fragmented mode according to a differentiation strategy, and data streams are recombined at a receiving end based on time sequence identification; 4, after network disconnection is recovered, synchronous consistency guarantee is achieved through data segment cascade hash verification and control instruction logic conflict detection; and 5, optimizing a time window generation algorithm and a path evaluation rule according to the historical synchronization effect data. Through accurate control, priority division, network adaptability enhancement and consistency guarantee of data transmission, an efficient and safe industrial control system remote monitoring data synchronization scheme with adaptive capability is provided.

The invention relates to the technical field of industrial control systems, in particular to a PET extruder multi-zone intelligent temperature regulation and control system, which comprises a temperature field parameter acquisition module for acquiring the input power, the real-time temperature value, the temperature change rate value and the temperature difference value between adjacent temperature zones of an extruder to obtain a temperature zone associated state set; and summarizing and mapping the temperature zone association state set, and establishing temperature field response characteristics. According to the method, through correlation analysis of the temperature states of the multiple areas, independent adjustment of a single area is upgraded to dynamic optimization based on the thermodynamic coupling relation, and the coordination of temperature adjustment of the multiple areas is improved. Dynamic parameters required by temperature regulation and control can be calculated in real time in combination with the heat transfer rate, the temperature response delay time and the temperature response gain coefficient of the temperature interval instead of depending on a fixed set value, different operation working conditions are adapted, and the regulation and control flexibility is improved.

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.

A hybrid approach involving the mix of communication patterns and passive fingerprinting is used to identify unknown device types, manufacturers, and models of devices of digital control systems. ANDVI implementation maps the identified devices to their known vulnerabilities. According to one example, to identify how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system, model-checking based A2G2V is employed. According to another example, an A2G2V algorithm uses existing model-checking tools, an architecture description tool, and code to generate an attack-graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. In yet another example, identification of label-cuts within an attack-graph automatically identifies a set of critical-attacks that, when blocked, renders the system secure. A linear complexity approximation utilizing SCCs helps identify the minimum label-cut representing a critical-attacks set.

The invention discloses an industrial equipment fault detection method fusing a complex relation and space-time dependence, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: constructing a plurality of adjacent matrixes, carrying out the weighted fusion to form an enhanced adjacent matrix, and comprehensively and accurately describing the complex multi-dimensional relation between industrial equipment; designing a spatial-temporal feature extraction module, extracting spatial features in parallel by using a graph convolutional neural network and a random graph attention network, extracting time features through time convolution and a multi-head attention mechanism, and dynamically fusing the spatial-temporal features by means of a gating mechanism to generate graph-level features; a state judgment layer composed of a plurality of node-level binary classifiers and a voting mechanism are adopted to comprehensively judge classification results of all nodes, so that the stability and reliability of judgment of the overall state of the industrial control system are enhanced, and the risk of misjudgment is reduced; the problems of equipment relation modeling and multi-dimensional information fusion are effectively solved, features are extracted and fused more accurately, and the accuracy and adaptability of anomaly detection are improved.

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 relates to an industrial control system security policy adaptive configuration method and device and a readable storage medium. According to the method, a control instruction stream, a process variable sampling value and link layer message metadata are synchronously collected, an association hypergraph fusing network semantics, control semantics and physical semantics is constructed to represent a system state, and a sampling period is divided into a plurality of time slots with fixed lengths. In each time slot, self-supervised learning is utilized to extract a topology embedded vector group, and a system operation rule is represented. A robustness envelope interval is generated by calculating a statistical distance between a current time slot and a historical baseline, and is used for driving a reinforcement learning model to generate a security policy parameter set. And executing corresponding access control, traffic shaping and integrity verification operations in the next time slot, generating receipt vector feedback model update according to an execution result, outputting a micro-policy patch with a hash signature and a timestamp, and sending the micro-policy patch to the edge security gateway to realize deployment, thereby realizing closed-loop adaptive optimization of the security policy in the dynamic environment.

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.

An AI-powered system for optimizing industrial workflows and predictive maintenance, which includes the following: a network of sensors configured to collect real-time data from industrial plants, including temperature, pressure, vibration, and energy consumption metrics; a central control unit configured to process and analyze the collected data using machine learning algorithms that continuously improve by learning from both historical and real-time data to increase prediction accuracy; a predictive maintenance module configured to analyze data to predict potential equipment failures, schedule proactive maintenance tasks, and prioritize maintenance activities based on the predicted impact of the failure and the criticality of the equipment; a workflow optimization module configured to dynamically adjust operating parameters to optimize the process flow, including the detection of inefficiencies and the suggestion of improvements in real time; an integration module configured to connect and interact with existing industrial control systems and ERP (Enterprise Resource Planning) software to ensure a seamless flow of data between the platforms; a user interface configured to visualize data in real time, provide alerts and customizable dashboards, and is accessible via desktop and mobile devices for remote monitoring and management; an alarm and notification module configured to deliver critical alarms and maintenance notifications via email, SMS and in-app notifications to ensure a timely response; a cloud-based architecture configured to facilitate scalable data storage, remote access, and management across multiple industrial sites; a reporting module configured to generate comprehensive reports on plant performance, maintenance history, and workflow efficiency for analysis and decision-making; A security protocol to protect data integrity and ensure secure communication between all system components.

The invention provides an anomaly detection method and device based on adaptive multi-scale feature modeling, and relates to the technical field of data anomaly detection. The method combines time component decomposition and a gating network, and allocates suitable expert networks for data on different time scales. In an expert layer, a deep convolutional network and a cross-time-slice attention mechanism are utilized to mine mutual relations in different time periods and among the different time periods in a time sequence. Then, combining a graph structure, and modeling spatial interaction characteristics among variables; and finally, learning the potential representation of the data to generate reconstruction data, and calculating a reconstruction error and the KL divergence of a potential space to carry out model training, thereby carrying out efficient anomaly detection under an unsupervised condition. According to the method, the precision of anomaly detection in the industrial control system is improved, a more flexible and accurate anomaly detection tool is provided for practical application, and the method adapts to complex dynamic changes of time sequence data in the industrial control system under the condition that no label data exists.

The invention discloses an industrial control data processing system and method based on an embedded real-time operating system, and relates to the technical field of industrial automation control, and the industrial control data processing system comprises a dynamic weight evaluation module, a resource elastic distribution module and a dual self-healing verification module; and the dynamic weight evaluation module is used for generating a dynamic weight value according to the task type, the data security level, the deadline margin and the historical execution abnormal rate. According to the industrial control data processing system and method based on the embedded real-time operating system, the task criticality is quantified in real time through the dynamic weight evaluation module, and the real-time response capability and the resource utilization efficiency of the industrial control system are improved in combination with elastic resource allocation driven by load prediction. A dynamic weight mechanism ensures that the emergency task can instantly preempt resources, and the problem of low-priority task blocking caused by traditional static priority scheduling is avoided.

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 discloses a digital twinning-based thermal heat supply system defense cooperative protection method and system, particularly relates to the technical field of industrial control system safety, and is used for solving the problems of hidden attack missing detection and response delay caused by splitting of energy flow and data flow in an existing protection mechanism. Physical-network behavior collaborative verification is realized by constructing a digital twinborn model, and precise protection is realized by quantifying coupling failure characteristics of energy flow and instruction flow. The method specifically comprises the following steps: collecting production data, industrial control logs and network traffic; constructing a digital twin model to simulate expected behaviors of equipment, communication and physical states; comparing and detecting abnormal data in real time; extracting a thermodynamic potential gradient characteristic quantity and a path integral characteristic quantity from the abnormal data; calculating a covariant difference value of the two characteristic quantities, and judging an external attack when the covariant difference value exceeds a threshold value; when an attack is triggered, a micro-isolation gateway is deployed, dynamic access control is implemented based on an equipment fingerprint, a user behavior sequence and an environment fingerprint, and an attack chain is blocked.

The invention relates to an industrial vision adaptive illumination compensation system and method based on Bayesian optimization, and the system comprises an environment perception module which is used for collecting the multi-dimensional environment state data of an industrial site in real time, and collecting an original image; the intelligent preprocessing module is used for enhancing the original image according to the parameter combination output by the Bayesian optimization module to generate an enhanced image; the model reasoning module is used for identifying and detecting the enhanced image through a bimodal algorithm to generate a detection result; the Bayesian optimization module is used for performing objective function calculation to generate an optimization objective score, performing iterative optimization according to the optimization objective score and a historical observation data set, generating a candidate parameter combination, and generating a global optimal parameter and a configuration template based on the candidate parameter combination and a current environment feature; and the industrial interface module is used for transmitting the global optimal parameter, the detection result and the multi-dimensional environment state data to an industrial control system. According to the invention, the efficiency and adaptability of illumination compensation are improved, and the data calculation amount is reduced.

The invention discloses a power grid abnormal flow detection and identification method based on a multi-mode machine learning algorithm. The method comprises the following steps: step 1, acquiring industrial control protocol message data: capturing a communication message of network flow of an electric power industrial control system through a data collector; step 2, training a multi-modal auto-encoder: training the auto-encoder based on the network traffic characteristics of the normal service; 3, abnormal traffic monitoring: detecting and collecting abnormal traffic by taking the decoding reduction degree under the normal service as a baseline; step 4, abnormal data analysis modeling: training a multi-modal neural network model in combination with the abnormal traffic and the coding result of the baseline model for classifying the abnormal traffic; according to the method, some abnormal traffic existing in the network traffic of the power industrial control system can be detected, and the abnormal traffic in the industrial control system is classified by analyzing the data features and the service scenes of the abnormal traffic.

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 industrial control system for intelligent valve production, and belongs to the field of valve production industrial control systems, and the system comprises a multi-dimensional sensing module which is used for collecting the assembling angle, the assembling depth and the assembling height data in the valve assembling process in real time; the central control module is in communication connection with the multi-dimensional sensing module and is used for comparing the real-time assembly data with a deviation value of a preset process standard and generating an assembly qualification judgment result; the traceability analysis module responds to the assembly disqualification judgment result, and traces at least one process link defect causing assembly disqualification based on the distribution characteristics of the deviation values and the time sequence relevance; and the adjustment execution mechanism is controlled by the central control module and dynamically corrects the operation parameters of the assembly equipment according to the deviation value. According to the method, whether assembly is qualified or not can be analyzed in a multi-aspect and high-precision mode from the assembly angle, the assembly depth and the assembly height, and dynamic correction is conducted in time when the assembly is unqualified.

The invention relates to an intelligent scheduling management method and system for aluminum profile production, and the method comprises the following steps: obtaining and cleaning the original data of a to-be-produced order, and obtaining standardized preprocessing production order data; extracting production characteristic data in the orders, and performing priority ranking on the orders according to the production characteristic data; task time windows are distributed to all the process link devices based on the sorting result, and a task time window distribution table is generated; resource matching analysis between the equipment and the task is carried out based on the table, and an equipment-task adaptation degree matrix is obtained; finally, the adaptation degree matrix is sent to an industrial control system, the production line is driven to automatically execute corresponding machining tasks, and the technical problem that a traditional aluminum profile production line mostly depends on artificial experience to conduct task scheduling and equipment arrangement and is difficult to adapt to the market trend of multiple varieties, small batches and fast delivery time, and consequently the production efficiency is low is solved.

The invention provides an industrial control and infrastructure defense cooperation method and system based on digital twinning, and relates to the technical field of defense cooperation. The method comprises the following steps: based on a digital twinning technology, collecting sensor data in an industrial control system, and identifying abnormal fluctuation of equipment operation to obtain abnormal activity information; the occurrence frequency and intensity of the deviation data are analyzed, and the network data transmission security of the industrial entity is optimized to obtain security protocol optimization configuration; reconfiguring the network access authority, and monitoring the new access mode and the data flow in real time to obtain an access control optimization state; monitoring network behaviors by using an isolated forest algorithm, and quantitatively evaluating potential internal and external threats to obtain a threat evaluation update log; and adjusting the data acquisition frequency and the processing process based on the update log, and obtaining a dynamic cooperative defense mechanism by updating network defense and response rules. According to the invention, the early warning and response capability of the industrial control system can be optimized.

The invention belongs to the crossing field of industrial control and edge computing, and particularly relates to an industrial personal computer data security dynamic protection method and system based on AI edge computing. Firstly, industrial control data encryption and standardization preprocessing are achieved through a hardware-level trusted execution environment; secondly, performing real-time anomaly detection on the encrypted data stream based on an AI model, generating a threat score by combining industrial control protocol feature analysis, and forming an edge layer intelligent detection capability; the authentication mode is dynamically switched based on the threat score, and self-adaptive strategy adjustment is realized in a fog layer; then optimizing edge resource allocation according to the threat index and the equipment load, and constructing a cloud elastic scheduling mechanism; and finally, a security system covering data encryption, real-time detection, dynamic protection and resource optimization is formed through grading component cooperation, and the method is suitable for real-time threat detection and self-adaptive response in an industrial internet scene.

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.

An intrusion detection system can be embedded in an industrial controller to detect an attack on an industrial control system. The intrusion detection system can detect an attack by determining if there are anomalies related to the network traffic in the industrial control system and / or by determining if the devices (e.g., sensors and actuators) connected to the industrial controllers of the industrial control system are not operating within expected ranges. To assist in determining if the devices of the industrial control system are not operating properly, the industrial controllers can also be networked together in a peer-to-peer network that permits each industrial controller to share its state information with the other industrial controllers.

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 belongs to the technical field of nuclear power, and particularly relates to an abnormal time series data detection method and device based on a multi-view space-time interactive attention network. According to the method and the device, complex spatial-temporal correlation dependency in the multi-source time series data of the industrial control system can be fully mined, so that time series data anomaly detection can be more comprehensively modeled. The method focuses on carrying out joint characterization learning on multi-view features such as global level and local level of time series data; constructing a time domain and space domain multi-head self-attention mechanism to perform space-time content interactive aggregation to obtain a content perception representation, and performing space-time association learning by using a plurality of stacked graph attention network layers to obtain an association perception representation; and finally, in a double attention intensified comparison reconstruction module, respectively adopting global and local multi-head cross attention mechanisms to further obtain a reconstruction time sequence, thereby effectively realizing multi-source time sequence data anomaly detection in the industrial control system.

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 invention belongs to the technical field of five-axis linkage numerical control machining, and particularly relates to a five-axis linkage numerical control machining dynamic error compensation system based on an industrial control system, which comprises a split-axis error detection module, a dynamic compensation model construction module, an industrial control real-time compensation execution module, an RTCP dynamic calibration unit and a self-adaptive learning optimization module. According to the invention, a real-time residual error monitoring and feedback mechanism is introduced to establish data connection with an original adaptive optimization module, so that parameter adaptive adjustment based on an actual compensation effect is realized, and the adaptability and compensation precision of the system under a complex dynamic processing condition are enhanced.

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.