391 results about "Fault model" patented technology

An optical cable multi-dimensional state monitoring and fault prediction method and system are provided, and the method comprises the steps: obtaining optical cable environment characteristic data, historical fault distribution probability and operation risk indexes, selecting key nodes, deploying state sensors, and collecting multi-dimensional state data to generate an initial state data set; establishing a fault judgment model based on the initial data set, and calculating multi-dimensional state feature data and determining an initial distribution range in combination with historical fault probability and environment features; arranging the physical state value and the communication state value according to a time sequence, and generating a dynamic threshold range of the key node by combining dynamic correction of environment characteristic data; and when the feature data exceed the threshold range, marking abnormity, calculating a judgment parameter through a fault model, and comparing the judgment parameter with a preset value to confirm a fault.

The invention discloses a PLC controller fault detection system, and relates to the technical field of industrial control equipment fault detection.The system is characterized in that PLC operation environment data is acquired through a multi-physical-quantity holographic acquisition module, and after the PLC operation environment data is cleaned and subjected to feature fusion through a data preprocessing module, a fault model is constructed through a multi-physical-quantity fusion model module; the self-adaptive threshold value judgment module dynamically calculates and judges a threshold value and evaluates a state; the fault traceability analysis module constructs a propagation path diagram based on a model and historical cases, and realizes accurate traceability of a fault source and a propagation process; according to the invention, multi-physical-quantity holographic acquisition and feature fusion algorithms are integrated, multi-dimensional parameters are monitored synchronously, a comprehensive feature model is constructed, the fault identification precision is improved, and misjudgment is avoided; dynamic optimization is achieved through self-adaptive threshold judgment, the early warning accuracy is improved, meanwhile, accurate traceability is achieved through an element fault propagation algorithm, a path is optimized in combination with historical cases, the downtime is shortened through full-process intelligent support, and the maintenance cost is reduced.

The invention provides a dry-type transformer turn-to-turn short circuit multi-physical field simulation analysis method considering insulation aging and harmonic influence, and relates to the technical field of simulation analysis, and the method comprises the following steps: constructing a physical simulation model of a to-be-tested dry-type transformer; setting physical parameters and multi-physical field operation conditions of the physical simulation model; calculating a corresponding physical field operation calculation result under each physical field operation condition; performing post-processing on the physical field operation calculation result; by establishing a detailed simulation model and accurately assigning physical property parameters of each component, physical property changes of the turn-to-turn insulating material at different aging stages and under different temperature and frequency conditions are emphatically considered; turn-to-turn short circuit fault modeling is further introduced, magnetic-thermal-flow coupling simulation calculation is carried out under multi-harmonic excitation, the multi-physical field distribution characteristics of the dry-type transformer during different turn-to-turn short circuit faults are explored, and a reliable basis is provided for design optimization and safety evaluation.

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 automatic control method of a molded case circuit breaker operating mechanism, and particularly relates to the technical field of molded case circuit breakers. Electrical parameters are monitored in real time and transmitted to a control system, and comparative analysis is performed based on a preset threshold value and a fault model, so that faults are detected in time and a control instruction is generated, and the system can identify and sort multiple fault scenes, automatically execute switching-on or switching-off actions and ensure the reliability and stability of a power system; a judgment mechanism based on the circuit recovery condition is introduced, after the circuit is disconnected, whether recovery operation is carried out or not is reasonably judged according to the state of the circuit breaker and the recovery condition of the power system, the self-recovery capacity and the fault diagnosis capacity of the power system are effectively improved, the method can adapt to the influences of high loads, complex environments and power grid fluctuation, and the reliability of the power system is improved. And the reliability and the precision of an automatic control system are obviously improved.

The invention provides a rotating machine fault diagnosis method and a related system. The rotating machine fault diagnosis method comprises the following steps: S1, constructing a rotor vibration equation according to a vibration behavior of a rotor system under an operation condition; s2, acquiring real-time information of a to-be-diagnosed rotating machine, performing modal analysis based on a rotor vibration equation according to the real-time information of the to-be-diagnosed rotating machine to determine a sensitive frequency band and key features, and forming multi-dimensional feature data; s3, carrying out denoising processing on the multi-dimensional feature data and carrying out signal reconstruction to obtain a reconstructed signal; s4, inputting the reconstruction signal into a pre-trained rotating machine fault model to obtain a fault diagnosis result; and the rotating machine fault model is fused by adopting a neural network ordinary differential equation and a convolutional neural network-gating cycle unit. The method can accurately simulate and predict the continuous dynamic change of the system, improves the diagnosis precision, and enhances the real-time performance and robustness of the model.

The invention discloses a cable accessory fault accurate positioning method and system based on multi-physics field cooperation, and belongs to the technical field of cable fault detection, and the method comprises the steps: installing and deploying a plurality of signal monitoring sensors in a key region of a cable accessory, and forming a multi-physics field sensing matrix; the method comprises the following steps: monitoring a cable accessory in real time by adopting heterogeneous sampling frequency, collecting a multi-physics signal, extracting a multi-physics signal feature in the multi-physics signal based on a time-frequency domain joint feature algorithm, performing signal analysis on the multi-physics signal feature, and obtaining a multi-physics signal analysis result; a multi-physical field fault model is constructed based on a multi-field coupling positioning algorithm, and a fault point in the cable accessory is positioned according to a multi-physical field signal analysis result, so that the fault of the cable accessory can be quickly and accurately identified and positioned, and the reliability and stability of a power system are improved.

The invention relates to an intelligent server fault detection method based on a BMC, and the method comprises the steps: adjusting a to-be-detected server to a debug mode of the BMC to obtain original data, judging whether the data received by the BMC is sensor fault data or not, and when the BMC receives an alarm that a sensor is abnormal; constructing a fault model and carrying out training and optimization; acquiring specified data of a to-be-detected server in real time, preprocessing the specified data, inputting the preprocessed specified data into the optimized fault model, automatically classifying the preprocessed specified data by the fault model, judging whether the to-be-detected server has a fault or a fault tendency, if so, starting an automatic repair strategy, and if not, starting an automatic repair strategy. According to the automatic decision, which repair measure is taken is determined according to the severity and the influence range of the fault, and the repair operation is executed through the automatic script or tool of the automatic operation. Through deep mining of BMC underlying data value and fusion of an intelligent model and an automatic repair mechanism, full-process optimization of server fault detection and processing is realized.

The invention, which relates to the processing and manufacturing field, discloses an intelligent processing and manufacturing management system comprising a data acquisition module, a data transmission and processing module, a production planning and scheduling module, an equipment management module, a quality management module, an inventory management module and a personnel management module. And the equipment management module is used for carrying out full-life-cycle management on the processed and manufactured equipment, monitoring the running state of the equipment in real time, and predicting the fault occurrence probability of the equipment through analysis of the running data of the equipment. According to the management system for intelligent processing and manufacturing, refined and intelligent management of the whole life cycle of equipment from access to scrapping can be realized, the reliability, the available duration and the operation efficiency of the equipment can be effectively improved, common fault models can be identified through the constructed diagnosis model, and the reliability of the equipment can be improved. And faults caused by multiple factors can be accurately positioned and analyzed.

Systems and methods for detecting and diagnosing machine faults are discussed. An exemplary system includes at least one sensor node to sense a signal indicative of an operation status of a machine part, and a machine health analyzer circuit to generate a computational machine fault model comprising an autoencoder (AE) network and an associative module. The AE network encodes the sensed signal into signal features in a latent feature space, and decodes the signal features to produce a reconstructed signal. The associative module transforms the encoded signal features into an associative output using a dynamically updatable codebook. The machine health analyzer circuit detects a presence or absence of fault in the machine part based on reconstruction losses determined respectively from the reconstructed signal and the associative output. The detected fault can be presented to a user or to a process such as fault diagnosis or fault correction.

The invention relates to the technical field of circuit board detection, and discloses a circuit board detection method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting historical design parameters and simulation results of a circuit board, processing the design parameters and the simulation results, and forming a data set # imgabs0 #; executing a prediction model training strategy according to the data set, and generating an output prediction model of the circuit board; obtaining current design parameters of the circuit board, and applying the design parameters to the output prediction model to obtain a model result; executing the simulation dynamic adjustment strategy, and dynamically adjusting the simulation process of the simulation tool; historical fault design parameters of the circuit board are collected, a fault model training strategy is executed, and a fault prediction model of the circuit board is generated; applying the current design parameters of the circuit board to the fault prediction model to obtain a fault probability; setting a fault probability threshold; if the fault probability is not less than the fault probability threshold. And performance prediction, simulation optimization and fault early warning and correction in the circuit board design and manufacturing process are improved.

The invention discloses a power distribution network grounding fault research and judgment analysis method, relates to the technical field of power distribution network fault analysis, and solves the technical problem that fault analysis is not accurate and deep enough due to the fact that fault features in a neutral point grounding complex fault scene cannot be comprehensively considered. According to the method, single-phase grounding fault models of a neutral point ungrounded system, an arc suppression coil grounded system and a small resistor grounded system are respectively constructed, grounding current, phase voltage and neutral point voltage expressions under the models are detailedly deduced, and characteristics of voltage and current before and after a fault point in the steady-state and transient-state processes are deeply analyzed; the method comprehensively considers various complex fault scenes, can more accurately grasp essential characteristics of faults in different grounding modes, and provides a more comprehensive and accurate basis for fault diagnosis and processing.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

The invention discloses a fault modeling method, device and equipment based on seismic data and a storage medium, relates to the field of geological modeling, and aims to process initial seismic data by adopting a dip angle-oriented median filtering method and effectively remove noise while retaining geological structure information such as faults and the like. And then the trained fault identification model is introduced to process the target seismic data, fault information can be rapidly extracted from massive seismic data, and the identification efficiency is greatly improved. And then similarity is calculated from two dimensions of spatial distance and direction attribute based on a fault identification result, clustering is carried out by using a spectral clustering algorithm, correlation of fault lines in spatial distribution and fault extension direction characteristics are considered, and more reasonable and accurate fault line groups are obtained. And finally, constructing an initial B-spline curved surface by utilizing a grouping result, and enabling the finally constructed three-dimensional fault model not only to fit actual seismic data, but also to accord with the smoothness and direction rule of a geological structure through variation energy minimization optimization, so as to accurately reflect the real form of an underground fault.

The invention relates to a preset performance specified time tracking control method for a mechanical arm, belongs to the technical field of intelligent robot control, and solves the technical problem of low control precision of the mechanical arm. Comprising the steps of establishing a dynamical model and a fault model, designing a preset performance specified time function, constructing a preset performance specified time tracking control law, and verifying the bounded stability of the preset performance specified time tracking control law. According to the control method, the specified time preset performance function is introduced into the control law design in an error transformation mode, synchronous adjustment of the convergence precision and the convergence speed is guaranteed, and the control method is used for the mechanical arm device and can also be used for locally spraying an end effector on the surface of an aircraft.

The invention provides a power-traffic coupling charging network toughness evaluation method and system, and belongs to the technical field of power system analysis. According to the method, a power-traffic coupling charging network is constructed, a typhoon path and a Holland wind field model are simulated in combination with a Bezier curve to calculate a wind speed, a dynamic traffic flow distribution, alternating current optimal power flow and cascade fault model is established, a fault data set is generated by utilizing Monte Carlo simulation, a fault prediction model is trained through a dynamic Bayesian network, and a fault prediction result is obtained. Inputting the actually measured wind speed, outputting a dynamic probability graph, and calculating a dynamic toughness index to evaluate the toughness of the power-traffic coupling charging network. According to the method, the problem of insufficient fault propagation and dynamic evaluation between systems is considered, and the toughness evaluation precision of the power-traffic coupling charging network under typhoon disasters is improved.

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 discloses a wind turbine blade fault diagnosis method based on aerodynamic noise and image recognition, and relates to the technical field of wind turbine blade fault diagnosis, and the method comprises the steps: building a plurality of typical fault models of a wind turbine blade based on a numerical simulation platform, and extracting simulation acoustic image data; the position of the aerodynamic noise source is positioned through an acoustic camera, and corresponding blade image information is collected by an unmanned aerial vehicle; and carrying out feature training on the image sample and the acoustic image data by using a deep neural network and executing fault matching judgment. According to the method, a fault acoustic image database under multiple working conditions is constructed through numerical simulation, outfield data acquisition is performed in combination with an acoustic camera and an unmanned aerial vehicle, and joint feature training and fault matching of acoustic frequency spectrums and images are realized by using a deep neural network. The method can break through the limitations of the prior art in the aspects of real-time performance, accuracy and intelligence, and achieves the efficient and reliable diagnosis of the fault of the wind turbine blade.

The present invention relates to a motor turn-to-turn short-circuit fault detection method based on variational modal decomposition fused with deep learning, which solves the defects of low accuracy and slow efficiency of turn-to-turn short-circuit fault diagnosis compared with the prior art. The present invention includes the following steps: establishing a permanent magnet synchronous motor fault model; extracting fault features by variational modal decomposition algorithm; optimizing variational modal decomposition parameters by sparrow search algorithm; establishing a deep pyramid pooling residual convolutional neural network model; training of SPP-ResCNN network; real-time diagnosis of permanent magnet synchronous motor turn-to-turn short-circuit fault. The present invention uses sparrow search algorithm to optimize variational modal decomposition algorithm, eliminates other harmonic components in fault features, extracts fault features of specific frequencies, and significantly improves the accuracy of fault features. At the same time, pyramid pooling network and residual network are used to optimize convolutional neural network, which improves the accuracy and efficiency of fault diagnosis.

The invention discloses a switch cabinet partial discharge fault identification method based on multi-fault information fusion, and the method comprises the steps: building different partial discharge fault models in a switch cabinet, and carrying out the data collection of partial discharge signals generated by different models through a plurality of sensors; dividing the collected multi-fault multi-source signal data into a training set and a test set; carrying out noise reduction processing on the signal data by using an LMS-wavelet transform method; constructing time domain feature parameters for the denoised data, and extracting features by using a sliding window method; performing dimension reduction processing on the features by using a kernel principal component analysis (KPCA) method; and training a PNN model based on the signal features to realize fault identification. According to the method, monitoring information of various sensors is fused, the LMS-wavelet transform method and the KPCA kernel principal component analysis method are introduced at the same time to carry out noise reduction and feature dimension reduction on complex multi-source data, the operation time of fault recognition is greatly shortened, and the efficiency of switch cabinet partial discharge fault detection is improved.

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 invention relates to the technical field of chip test vector intelligent generation scheme design, in particular to a failure analysis-oriented chip test vector intelligent generation method and system. The method comprises the following steps: acquiring a chip design netlist, a fault model and a known failure mode; analyzing potential design or manufacturing defects based on the information; and according to an analysis result, a targeted test vector capable of effectively exciting and isolating potential defects is automatically generated. The system comprises a corresponding input module, an analysis module and a generation module. According to the method, the design information, the fault model and the historical failure mode are intelligently fused, automatic and precise generation of the test vector is achieved, and the defects that a traditional method is insufficient in test coverage rate, low in failure positioning efficiency and limited in automation degree are overcome. According to the method, the test completeness and the failure analysis efficiency can be remarkably improved, the dependence on artificial experience is reduced, and an efficient technical means is provided for guaranteeing the reliability of the chip.

The invention provides a community power grid monitoring and autonomous decision scheduling system. The community power grid monitoring and autonomous decision scheduling system comprises a man-machine interaction module; the monitoring module is configured to acquire power grid operation data in real time, and the power grid operation data at least comprise voltage, current, power and environmental parameters; the fault diagnosis module is integrated with a fault prediction model based on machine learning, generates a fault feature library through historical data and real-time data training, and is used for recognizing potential faults in advance and triggering protection actions; the autonomous decision-making unit generates a scheduling strategy based on the fault diagnosis result and is linked with the power distribution equipment to execute a protection action; wherein the monitoring module and the fault diagnosis module work cooperatively through a distributed computing architecture, original data are preprocessed by utilizing an edge computing node, and dynamic optimization of a fault model is realized through a cloud computing platform. According to the method, the fault handling capacity, the operation efficiency and the user service quality of the community power grid can be remarkably improved, and key technical support is provided for intelligent power grid construction.

The invention discloses an electric power system safety assessment method and system considering multiple meteorological sensitive factors, and relates to the technical field of electric power system safety assessment, and the method comprises the steps: building a fault model under the influence of multiple meteorological factors based on the coupling effect of different meteorological factors on electric power system elements; and calculating the fault probability of each element of the power system under the extreme weather condition by using a multi-modal data fusion and neural network technology. And evaluating the safety of the power system through a Monte Carlo method in combination with the fault probability of the system element, and outputting a reliability index. According to the method, the dependence of traditional safety assessment on single-factor hypothesis, a static model and low-efficiency calculation is broken through, and a full-link innovation path of multi-factor dynamic perception-deep learning fusion modeling-global state probability assessment is realized; finally, the technical effects of improving the evaluation accuracy, adapting to extreme weather evolution and enhancing the emergency response capability of the power system are achieved, and the method has remarkable social value and engineering application prospects.

The invention provides a remote fault maintenance method for a coffee machine, and relates to the technical field of coffee machine maintenance. The remote fault maintenance method for the coffee machine comprises the following steps: S1, module division and fault signal determination; s2, collecting test data, and carrying out fault analysis training by utilizing machine self-learning; s3, enabling a remote diagnosis function by the user; s4, fault signal detection is carried out on the fault model; s5, the user carries out self-recovery according to the diagnosis report; and S6, processing when the repair scheme is invalid. According to the invention, when the coffee machine of the user has a fault, the user can actively apply for a remote diagnosis service, the coffee machine can automatically upload historical whole machine operation data and a fault signal to the server, and the server rapidly analyzes the data through machine learning, generates a fault diagnosis report and provides a detailed fault reason and a repair scheme.

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.

The invention provides a double-time-scale medium-voltage distribution cable latent fault modeling method and system, and the method comprises the steps: building a carbonization channel resistance model which takes a cable latent fault as a time scale, and comprises a carbonization channel resistance of a carbonization part and a solid dielectric breakdown resistance of a sound part; a step function is used to describe a carbonization channel resistance value with a cable latent fault as a time scale, and a first expression is obtained; describing the solid dielectric breakdown resistance of the sound part by adopting the three-stage model based on the relationship between the current flowing through the solid dielectric and the external voltage to obtain a second expression; and obtaining a medium-voltage distribution cable latent fault model based on the first expression and the second expression.

The invention discloses a method for realizing a redundant function memory chip test, which relates to the technical field of memory chip test, and comprises the following steps: calculating hyperedge coupling strength based on a hypergraph fault model, and generating a test vector sequence and a memory unit test instruction by adopting a weighted flipping mode according to the hyperedge coupling strength; injecting the test vector sequence into a tested memory chip, executing memory unit test instructions one by one, and capturing a failure unit coordinate and a failure mode category at the same time; constructing a Hall triad based on the failure unit coordinates and the failure mode category, executing theorem proving on the Hall triad by using a theorem proving device to verify the test completeness, generating an increment test vector when the verification fails, executing theorem proving again, and recording the result of the test completeness; according to the method, the hypergraph fault model is established, so that the coverage rate of non-independent faults is increased, the number of redundant test instructions is reduced, the test time and resource consumption are reduced, and the defect detection efficiency is improved.

The invention provides a bakelite guide rail fault monitoring method and system, and relates to the technical field of fault detection, and the method comprises the steps: building a digital connection between a monitoring platform and a distributed sensor network; collecting multi-dimensional physical quantities during operation of the guide rail, and constructing a state information base; generating an abnormal state information base; calling the visual sensor group to collect a bakelite guide rail fault section image according to the abnormal sequence value, and obtaining a guide rail part image set; analyzing the guide rail part image set to obtain a guide rail fault cause library; performing correlation analysis according to the abnormal state information base and the guide rail fault cause base to obtain a guide rail fault model; and generating a fault maintenance scheme according to the guide rail fault model, and feeding back the fault maintenance scheme to the monitoring platform. The technical problems that in the prior art, a monitoring mode is not accurate and comprehensive enough, data processing capacity is lacked, judgment is slow and errors are prone to being generated due to the fact that a system mature fault model is lacked are solved.

The invention discloses an AGC information physical system reliability verification method and system, and relates to the technical field of power dispatching automation. The problems that an existing AGC system only adopts a static operation mode and an ideal communication condition in a simulation test, and coupling response of continuous operation and sudden disturbance of a power grid under a long time scale is difficult to reproduce truly are solved. The state evolution under event driving is realized by constructing three types of simulation event sources of a time sequence operation sample, a master station control instruction and external disturbance and setting a priority mechanism of external disturbance gt, master station instruction gt and time sequence propulsion; an uplink channel fault model is introduced to simulate on-off, delay and errors of measurement data, and a downlink channel fault model is introduced to simulate transmission abnormity of a control instruction; closed-loop simulation of frequency response and electrical quantity updating is realized by combining promotion of a rotor motion equation and dynamic load flow calculation; and finally, by analyzing indexes such as frequency deviation, tie line deviation and control response delay, comprehensive reliability evaluation of the AGC system in a complex information physical environment is completed.