462 results about "Fault injection" patented technology

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

A method and system for analyzing fault data features of an active distribution network. The method comprises: analyzing a voltage variation after a fault in an active distribution network and analyzing a single-phase ground fault (S1); acquiring distribution network fault data by means of analog simulation (S2); and establishing a fault location prediction model to analyze fault data features and realize fault localization (S3). In the method, a voltage variation after a fault in an active distribution network is analyzed, the voltage variation is equivalently represented using an injected current, a single-phase ground fault is analyzed, and the localization of a fault point is realized. By simulating a current variation during an actual fault injection process by means of an injected current function, a model can more closely reflect an actual operating state, thereby enhancing the authenticity of fault simulation and the accuracy of prediction. A complex relationship between a current variation and node impedance is deeply analyzed by means of a complex information filtering function, and more refined fault features are extracted, thereby optimizing the precision of fault localization, and enhancing the adaptability of the model to different fault scenarios.

The invention provides a full-link dynamic monitoring and anomaly simulation test system and method. The system comprises a cross-level I / O load simulation engine module, a multi-level collaborative fault injection module, a full-link dynamic monitoring module and an intelligent root cause analysis module. The cross-level I / O load simulation engine module is used for simulating business pressure fluctuation, the multi-level collaborative fault injection module is used for synchronously injecting configurable anomalies and realizing cross-level fault conduction, and the full-link dynamic monitoring module realizes request-level end-to-end tracking and acquires monitoring data through a distributed probe. And the intelligent root cause analysis module positions a fault root and generates a repair suggestion in combination with LSTM time sequence prediction and Bayesian causal reasoning. According to the invention, the overall performance and fault response capability of the system can be comprehensively evaluated, and the accuracy and efficiency of system testing are improved.

The invention relates to the technical field of micro-service operation and maintenance, and discloses a full-link intelligent fault simulation and assessment defense method in a micro-service scene. The method comprises the following steps: constructing a dynamic dependency graph based on a service registration center and real-time communication traffic; performing matching backtracking on historical faults according to the atlas, and generating a fault injection point list and a propagation path set sensed by the atlas; in the isolated environment, driving the programmable agent to perform multi-dimensional fault injection, and synchronously acquiring full-amount system response signals; performing multi-dimensional deviation calculation on the signal and the base line to form an observation record containing deviation intensity and propagation rate; and training a graph neural network model with time sequence dependence understanding capability based on the record, and carrying out fault mode identification and evolution prediction on an online real-time link, and outputting a risk assessment report. According to the method, the authenticity of fault simulation and the initiative of risk prediction are improved.

The embodiment of the invention provides a simulation evaluation method and device for a multi-satellite cooperative task, and the method comprises the steps: building a unified time-space reference based on a standard time signal, and building a complete environment disturbance model comprising sunlight pressure, atmospheric resistance and geomagnetic field interference. A fault injection mechanism of load, attitude and orbit control is innovatively designed, and real simulation of multi-satellite cooperative motion is realized. According to the system, a system-level, subsystem-level and component-level multi-level evaluation index system is established, a dynamic weight calculation method based on a task execution state is adopted, and matching analysis of a three-dimensional error curve and a key event time sequence is combined, so that accurate evaluation of multi-satellite cooperative task performance is realized; and a reliable decision basis is provided for space task planning and system optimization.

According to the power transmission and distribution cable fault diagnosis simulation device and method, real-time adjustment of fault parameters is achieved through a programmable fault injector, various working conditions such as short circuit, open circuit, grounding, air gap discharge and arc faults can be accurately simulated, the diversity and authenticity of fault simulation are remarkably improved, and the fault diagnosis simulation efficiency is improved. Reliable test data is provided for subsequent diagnosis; the programmable load system dynamically adjusts load parameters, simulates complex load conditions in actual operation, combines an adaptive control function, and automatically adjusts the load according to a real-time feedback mechanism, thereby effectively improving the precision and reliability of fault simulation. A multi-modal sensor is arranged along a cable, a multi-dimensional monitoring system is established, precise positioning of the sensor and spatial continuity of data are realized in combination with a GIS technology, and the comprehensive sensing capability of the cable operation state is remarkably improved through real-time data acquisition and fusion.

The invention discloses an unmanned aerial vehicle automatic test method, which comprises the steps of building a high-fidelity semi-physical simulation platform which comprises an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator, a flight control module and a ground control station, and loading a complete machine fault model and a dynamic environment model; designing an intelligent multi-fault injection scheme, and defining a single fault scene, a combined fault scene and a fault evolution scheme; configuring a dynamic environment simulation scene including static environment parameters, dynamic environment elements and environment, and a fault interaction scene; executing a self-adaptive test process, automatically generating a test case, intelligently scheduling a test sequence and dynamically adjusting test parameters; and performing multi-source data intelligent analysis and evaluation, collecting test data, performing fusion analysis on fault influence, and generating an evaluation report. By constructing a whole machine fault and dynamic environment model, the scene authenticity is improved by 40%, and for example, the motor fault yaw deviation under strong wind is closer to a real machine compared with a traditional method.

The invention discloses an aircraft braking system evaluation and prediction method based on fault simulation and geometric features, and belongs to the technical field of electric data processing. The method comprises the following steps: constructing a simulation model of an aircraft brake system, and injecting faults into the simulation model according to different degrees of fault injection control parameters to generate a degradation monitoring data set of the whole life cycle of the system; dividing the degradation monitoring data set into a training data set and a test data set; training the GoogLeNet model by using the training data set to obtain a health assessment model; evaluating the health degree of the aircraft brake system by using the test data set; constructing a topological graph according to the multi-rack operation data and the health degree of the aircraft braking system; nodes in the topological graph are clustered and divided into K clusters; establishing a residual life prediction model for each cluster; and performing similarity matching on the sample and the K clusters, and selecting the best matched residual life prediction model to perform residual life prediction on the system. The method can accurately predict the remaining life of the aircraft brake system.

The invention provides a communication network operation accident simulation drilling method and device, and solves the problem that a standardized simulation drilling process with automatic deduction and objective quantitative evaluation capabilities is difficult to construct in the prior art. The communication network operation accident simulation drilling method comprises the following steps: acquiring historical operation data of a communication network, and generating at least one scene element data set according to the historical operation data; the scene element data set comprises disposal evaluation data; determining a scene element data set corresponding to the target accident scene, and constructing an analog simulation environment of a communication network operation accident; determining a preset timeline according to the fault injection parameter, and driving a simulation environment to evolve; and acquiring drill process data, and generating a drill evaluation result by combining with analysis of the disposal evaluation data. The invention further provides a device corresponding to the method. According to the method, historical data is converted into a structured drilling process, and measurable, reproducible and continuous optimization of the emergency capability is realized.

The invention belongs to the technical field of cloud computing and software testing, and particularly discloses an automatic fault injection and recovery testing method based on cloud native, which comprises the following steps: acquiring test basic data of a to-be-tested cloud native system; constructing a fault test execution model according to the test basic data; according to the fault test execution model, executing an automatic fault injection operation; generating an abnormal alarm log according to the system running state after the fault injection operation, and synchronously recording a fault injection operation log; according to the abnormal alarm log, executing automatic fault recovery operation; and generating a fault test report according to the fault injection operation log, the system monitoring data and the recovery operation process log. The invention aims to solve the problems of low automation degree, narrow scene coverage, weak monitoring evaluation and passive recovery in cloud native system fault test in the prior art, and provides a comprehensive, automatic, monitorable and evaluable fault injection and recovery test scheme.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention relates to an intelligent equipment function reliability verification method and device, equipment and a storage medium, and the method comprises the steps: obtaining operation environment parameters and equipment state data of intelligent equipment, and carrying out the construction evaluation of a redundancy verification architecture, and obtaining an initial verification reference; acquiring real-time operation data of the intelligent equipment, and performing function association analysis on the real-time operation data and the initial verification reference to obtain function abnormality features; acquiring state information of associated equipment of the intelligent equipment, performing cross validation on the state information of the associated equipment according to the function abnormality characteristics, and performing fault analysis on the state information of the associated equipment and the function abnormality characteristics to obtain an evolution fault injection scheme; and carrying out abnormal behavior identification on the real-time operation data to obtain an equipment repair requirement, and carrying out feasibility evaluation on the equipment repair requirement based on the function abnormal characteristics to obtain a dynamic repair strategy. According to the invention, a comprehensive guarantee mechanism can be provided for reliable operation of the intelligent equipment.

The invention discloses an SDH communication equipment fault maintenance simulation teaching method based on digital twinning. The method comprises the following steps of S1, equipment simulation, S2, fault scene definition, S3, fault injection, S4, fault simulation, S5, fault diagnosis, S6, fault repair and S7, evaluation and feedback. The system has the advantages that a real SDH equipment fault scene can be simulated, fault checking and repairing training close to actual combat can be provided for students, excessive consumption and high purchase cost of entity equipment are avoided, virtual simulation of an SDH communication equipment fault mode is realized by constructing a high-precision SDH communication equipment digital twinborn body, and the simulation efficiency of the SDH communication equipment fault mode is improved. And fault processing training close to a real environment is provided for students. The invention provides an efficient, economic and safe SDH communication equipment fault maintenance skill training method so as to improve the practical ability and the troubleshooting efficiency of trainees.

The invention discloses an equipment anomaly tracing method and system based on a digital twinborn and graph neural network, and belongs to the technical field of industrial intelligent operation and maintenance and fault diagnosis. The invention provides an innovative solution integrating digital twin high-fidelity simulation and a graph structure deep learning algorithm, aiming at the technical bottlenecks that the generalization performance of an existing data driving method is insufficient under the conditions of fault sample scarcity and category imbalance and the traceability accuracy of unknown and composite faults is poor. The method comprises the following steps: constructing a high-fidelity digital twin integrating multi-dimensional physical attributes and a system topology structure; based on a fault mode, influence and harmfulness analysis method system, constructing a fault mode library comprising a plurality of single fault modes and composite fault modes, and generating an enhanced training data set with accurate labels through an automatic fault injection mechanism; training a graph neural network model with a multi-level attention mechanism by using the data set so as to learn a propagation rule of a fault in a complex system topology; and finally deploying the model to carry out abnormity traceability analysis on real-time industrial Internet of Things monitoring data. According to the method, the fault diagnosis generalization ability and the positioning precision under the sample imbalance condition are remarkably improved.

The invention provides a mixed reality industrial simulation training system which is composed of a space fusion perception unit, a multi-physics field simulation unit, a cooperative training management unit and a self-adaptive teaching engine. The space fusion sensing unit is composed of a real-time environment modeling and positioning subsystem and a physical interaction detection algorithm, and the real-time environment modeling and positioning subsystem dynamically constructs a three-dimensional space coordinate system of a real industrial scene through a visual sensor array and a radio frequency scanning device; generating a pose matrix of the virtual equipment model in the perspective display interface; the physical interaction detection algorithm realizes contact force feedback between virtual and real objects based on dynamic solution of collision volume, and when an operator triggers a virtual switch through a handheld controller, a real equipment control signal is synchronously driven; the multi-physics field simulation unit comprises a physical engine core module and a fault injection interface.

The invention belongs to the technical field of chassis testing, and particularly relates to a distributed HIL testing system and method for a linear chassis control system, and the method comprises the steps: a central coordination module receives a fault injection parameter configured by a user, and transmits a fault injection instruction to a fault injection module based on the fault injection parameter; the fault injection module receives a fault injection instruction of the central coordination module, analyzes the instruction to obtain a fault injection parameter, generates a fault signal meeting the input requirement of a target HIL node by using a pre-established mathematical model according to the fault injection parameter obtained through analysis, and sends the fault signal to the target HIL node; and the test management module acquires real-time state data of each subsystem, a system response curve after fault injection and key indexes in real time through the acquisition module, and generates a test report. The real-time data interaction mechanism of each HIL node enables the system to quickly respond to the change of the working condition and provide real-time feedback information, so that problems can be found in time.

The invention discloses a data recovery test method, which comprises the following steps of: performing fault injection on a storage system to enable the storage system to enter a recovery state; determining a target test scene in the recovery state, and performing a performance recovery test on the storage system in the target test scene; estimating a target duration for the storage system to enter a stable state in the performance recovery test process; after waiting for the target duration, collecting target performance data of the storage system for verification, and determining a test result of the target test scene according to a verification result; and determining a data recovery test result according to the test result of each target test scene. By applying the technical scheme provided by the invention, more efficient and accurate data recovery performance testing can be realized for the storage system. The invention also discloses a data recovery test device, electronic equipment and a computer readable storage medium, which also have the above technical effects.

A code analyzer implements machine learning to detect vulnerabilities in computer code. The code analyzer trains a machine learning model using training vectors that characterize vulnerable programming patterns. The code analyzer evaluates a topological representation of the computer code using the machine learning model to identify a potential vulnerability. The potential vulnerability corresponds to a portion of the computer code where an attack can be used to bypass a security procedure. The code analyzer tests the potential vulnerability by emulating a fault injection during execution of the portion of the computer code. Upon confirming that the potential vulnerability can be exploited via a fault injection, the code analyzer generates a training vector that characterizes a vulnerable programming pattern associated with the portion of the computer code. The training vector can be used to further train the machine learning model.

The invention discloses a parameter optimization method for a chip electromagnetic fault injection test, and the method comprises the steps: collecting a large number of electromagnetic fault injection test data of a training chip, and constructing a training data set; training a plurality of models by using the constructed training data set, and establishing a mapping relationship between the electromagnetic fault injection parameter and the attack success rate; collecting a small amount of electromagnetic fault injection test data of the novel-type chip to be tested; carrying out transfer learning on the trained model by using data of a chip to be tested with a new model, and selecting an optimal model from the models after transfer learning; determining a value range of electromagnetic fault injection parameters of a chip to be detected of a new type, constructing a multi-dimensional parameter space, and generating uniformly distributed parameter combinations by using a Latin hypercube sampling method; and inputting the parameter combinations into the optimal model, predicting the attack success rate of each parameter combination, and selecting the optimal parameter combination of the electromagnetic fault injection test. According to the method, the electromagnetic fault injection parameter combination can be automatically optimized, and the manual tuning cost is reduced.

The invention discloses an automatic model strengthening method based on the interpretability of a neural network. The method comprises the following steps: carrying out secondary development on PytorchFI, carrying out fault injection based on an IEEE 754 standard, building a fault test environment, and developing a batch fault injection tool and a redundancy reinforcement tool for model weight; constructing a model based on reinforcement learning, and designing a standard Markov decision process of interaction between an agent and a neural network model, including contents of a state space, an action space, a reward function and the like; an SAC algorithm is used as a neural network architecture search algorithm, and the redundancy proportion of the convolution kernel in the layer is explored; based on a neuron integral gradient method, importance of convolution kernels is calculated and sorted; and combining an SAC algorithm and neural network interpretability to design an automatic model reinforcement framework, and performing redundancy reinforcement on important convolution kernels. According to the method, model reinforcement based on the interpretability of the neural network is realized, the importance and redundancy requirements of the convolution kernel can be accurately identified, the fault-tolerant capability and robustness of the model are improved, the fault detection rate is higher, the space-time overhead is lower, and the method is suitable for various convolutional neural network architectures.

The invention discloses an intelligent fault injection and performance quantification system and method for a helicopter engineering simulator, and belongs to the technical field of flight simulation and testing, and the system comprises a hierarchical combinable fault model library module which is used for the flexible construction from a part level to a system level to a composite fault; the intelligent fault injection management module is used for realizing an intelligent fault injection strategy; the multi-dimensional performance index quantification module is used for collecting data before and after fault injection in real time and setting a performance evaluation index system; the automatic test and visual analysis module is used for comparing various quantitative indexes of different control laws or different parameters of the same control law in the same fault scene to generate a comprehensive evaluation report; according to the intelligent fault injection and performance quantification system and method for the helicopter engineering simulator, efficient, objective and deep testing of robustness, stability and efficiency of an advanced flight control law in a special condition state is realized through a hierarchical fault model, an intelligent injection strategy and automatic quantitative evaluation.

The invention relates to the technical field of fault testing, in particular to a fault injection method and device, a fault testing method and device, computer equipment and a medium. The method comprises the steps of obtaining a fault modification request, wherein parameters of the fault modification request at least comprise a target signature, a target identifier, a fault type, modification content and an injection deadline; calling a fault injector to generate a fault program corresponding to the fault modification request; a fault program is injected into the target function through the fault injector, and when the target function is called by the target process, the fault program is used for processing input parameters and / or return parameters of the target function; the target function is a function corresponding to the target signature, and the target process is a service process corresponding to the target identifier. By adopting the method, the fault test efficiency can be improved.

The invention discloses a software flashing fault detection method and device and a medium, and belongs to the technical field of software test.The method comprises the steps that test equipment serves as a flashing upper computer or only serves as a data monitoring tool; when the testing device serves as an upper computer, the testing device is connected with the programmable power source and the tested piece through the control panel, a complete flashing process is executed, process data are recorded, and the fault situation is simulated in the flashing process; the response of the tested piece in the flashing process is monitored through the test equipment, whether the response meets the design requirement or not is verified, rapid positioning and reason analysis are conducted on the wrong response, and simulation, fault injection testing and test result analysis of the DoIP flashing process are achieved.

A computer-implemented method, according to one embodiment, includes causing a first fault injection configuration to be injected into a first node of a first edge site of a cloud environment, where the first edge site includes a plurality of nodes, and the cloud environment includes a second edge site. The method further includes computing a first minimum cost maximum flow value for the first fault injection configuration, and causing a second fault injection configuration to be injected into a second node of the second edge site for causing a second fault injection. A second minimum cost maximum flow value is computed for the second fault injection configuration. In response to a determination that a difference of the minimum cost maximum flow values is less than a predetermined threshold, one of the fault injection configurations is retained and the other fault injection configuration is discarded.

The invention discloses a simulation data enhanced controllable-pitch propeller small sample fault diagnosis meta-learning method, and relates to the field of fault diagnosis, and the method comprises the steps: carrying out the simulation modeling and fault injection based on the working mechanism of a controllable-pitch propeller, and building a system fault model; constructing a fault simulation enhancement data set; constructing a variable-length time series data classification network model, wherein the model is connected with a multimode encoder and a triple loss function in combination with a residual error; training the variable-length time series data classification network model by utilizing a three-stage meta-learning training process, wherein the three-stage meta-learning training process comprises a meta-learning training process by utilizing an open source data set and a fault simulation enhancement stage by utilizing a fault simulation enhancement data set; and applying the trained variable-length time series data classification network model to an actually collected fault small sample data set for small sample task training. According to the method, the model is pre-trained by using the historical related data set, so that the current task data is reduced, training samples are reduced, and higher diagnosis precision is achieved.

The embodiment of the present invention provides a method, device, electronic device and readable medium for disaster recovery fault drill of distributed system based on chain tracking, which relates to the field of cloud computing technology. The method includes: inserting a custom observation probe into the source code; monitoring the dynamic context information of the program during operation through the custom observation probe; exporting the dynamic context information as a log file; capturing the changes of the log file by deploying an agent in the distributed system, and performing fault injection; recording the flow data of the production environment and saving it as mirrored flow; replaying the mirrored flow in the disaster recovery environment to generate a panoramic view of the business logic link; the panoramic view of the business logic link includes a normal business logic link panoramic view and an abnormal business logic link panoramic view; comparing the normal business logic link panoramic view with the abnormal business logic link panoramic view to perform fault diagnosis. The present invention can improve the fault coverage and the accuracy of fault discovery.

The invention discloses a fault injection and detection method based on RTL, and relates to the technical field of aerospace fault-tolerant calculation, the fault injection and detection method is compatible with three single-particle fault types of SEU, SET and SEL, the injection bit number, duration and position can be customized by a user, and based on random number file configuration, the problems of single fault type and parameter solidification of an existing tool are solved; a bypass injection mechanism controlled by a timer is adopted, and a fault data flow is switched in a specified clock period, so that a trunk data flow is not influenced, and the occupation of system resources is reduced; a forward detection module is designed based on (13, 8) extended Hamming codes, a receiving end quickly locates the fault type and position through a syndrome look-up table, original data does not need to be transmitted back, and detection delay is effectively controlled; a streamlined design is adopted, check bit generation, fault injection, syndrome generation and fault information output are executed in parallel, and hardware resource overhead is obviously optimized.

The invention discloses an automatic driving hardware-in-the-loop (HIL) test fault injection system and method, and relates to the technical field of vehicle testing, the system combines a precise trigger mechanism of a fault injection module and time sequence-causal correlation analysis of an influence tracking module through the graphical flexible configuration capability of a dynamic configuration module, and the fault injection module is used for performing fault injection on a vehicle. According to the method, the test scene coverage is remarkably improved, the dynamic probabilistic parameter setting can simulate a non-deterministic fault mode in the real world, the fault combination rule setting can verify the fault-tolerant capability of the system under a composite fault, and the requirement of a high-safety standard for a multi-fault superposition test is met; and meanwhile, a timestamp matching engine is used for eliminating misjudgment caused by clock asynchronization through high-precision alignment of faults and response signals, the influence weight of the faults on ECU decision can be quantified through a causal confidence coefficient algorithm, defect roots are accurately positioned, and the detection efficiency and accuracy of the automatic driving system are remarkably improved.

The invention discloses a distributed testing method and system based on micro-service architecture. The method comprises the steps of generating a test task and a fault injection task according to a test plan; distributing each test case to a plurality of test agents according to the test task, so that the plurality of test agents respectively execute the corresponding test cases, and the plurality of test agents and the plurality of micro-service instances of the tested system run on a plurality of service nodes in a one-to-one manner; injecting a corresponding fault in an execution environment of the tested system according to the fault injection task; and collecting and summarizing log data related to the test, and outputting a test result and the influence of the fault on the test result according to the log data. According to the embodiment of the invention, a complete test process from test plan generation to test result analysis of the tested system is supported, high cooperation of fault injection and test execution is realized through accurate control of fault injection, and meanwhile, the distributed test system constructed based on the micro-service architecture can be adaptively expanded according to the scale of the tested system.

The invention provides a model-based reusable vehicle fault joint simulation method, which comprises the following steps of: (1) creating fault mode information according to the composition and connection relationship of a system simulation model, and summarizing to form a fault mode information set associated with the model; (2) analyzing a logic causal relationship between fault events, and establishing a system-level fault mode library based on fault mode information; (3) aiming at a joint simulation requirement, screening out a fault mode needing to be injected from the fault mode library, establishing a relationship between fault mode information and a simulation model through a model grammar rule, and generating a fault model; and (4) according to the requirements of fault combination, fault time and fault injection probability in the simulation of the reusable vehicle, completing fault simulation setting, and carrying out fault joint analog simulation. According to the method, automatic mapping and injection of fault information based on the model are realized, and the analogue simulation requirements of the reusable vehicle under large-scale, cross-system and cross-linking coupling faults are met.