327 results about "Fault modeling" patented technology

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

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 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.

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 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 topology awareness priori driven mobile energy storage power distribution network real-time scheduling method and a topology awareness priori driven mobile energy storage power distribution network real-time scheduling system under ice disaster toughness. The method comprises the following steps: constructing a line fault space-time distribution model changing along with icing evolution, and forming a multi-period and multi-scene ice disaster dynamic fault set; on the basis of the fault set and the multi-scene power flow, combining the topology and toughness indexes of the power distribution network, and screening by using a graph convolutional network to obtain a scale-controlled mobile energy storage candidate access node set; a random scheduling model is established under the power grid-road integrated constraint, and a mobile energy storage arrangement position, a driving path and a charging and discharging plan are jointly optimized. According to the method, ice disaster dynamic fault modeling is combined with candidate set screening assisted by topology perception learning, the dimensionality of a large-scale random mixed integer optimization model containing mobile energy storage is effectively reduced while line time sequence failure characteristics are fully reflected, the calculation burden is relieved, and the calculation efficiency is improved. And the toughness of the power distribution network in a multi-ice disaster scene and the timeliness of pre-disaster scheduling are improved.

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 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 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 discloses a combined fault monitoring method and device based on a large model, a medium and equipment. The method comprises the following steps: collecting and preprocessing multi-dimensional data; extracting multi-source feature data based on a convolutional neural network and a long-short term memory network; performing redundancy elimination and fusion on the multi-source feature data; and cluster collaborative fault modeling is carried out, and global health assessment is carried out. According to the method, a deep learning large model is established through a convolutional neural network and a long-short term memory network, and effective fault features are automatically extracted from high-dimensional heterogeneous sensor data; through feature fusion of multi-source feature data, dimensionality reduction and fusion are carried out on features extracted from the sensor data, redundant information is removed, and a unified feature vector is generated; through the cluster collaborative fault modeling method, the identification problem of an equipment fault propagation cluster-level modeling path is solved, the globality and comprehensiveness of a fault monitoring system are improved, and fault monitoring of a cluster system is achieved.

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 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 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.

The invention relates to the technical field of gold mine geological modeling exploration, in particular to a gold mine modeling and reserve estimation method under multi-source information constraint. Geological images and exploration data of the target mining area are collected; performing interpolation operation according to the geological image in combination with the elevation data and the drilling data to establish a geological model; establishing a fracture model by adopting a deterministic modeling method according to the geological image; constructing a lithologic model by adopting an envelope surface modeling method according to the drilling data and the geophysical exploration data; constructing a gold ore body model by adopting an envelope surface modeling method according to the elevation data and the geophysical exploration data; obtaining mineralization information to construct a variation function model, and establishing a grade model by adopting a Kriging difference method according to the variation function parameter value of each fault; and estimating the gold ore reserves of the target mining area based on the established model. According to the method, the model can be established in combination with multi-source information, and the ore body structure can be accurately represented, so that the estimation precision of the gold ore is effectively improved.

The invention belongs to the technical field of earthquake simulation tests, and particularly relates to a fault dislocation testing device and method for a three-dimensional earthquake test, and the fault dislocation testing device is composed of two beveling models which are in the same form and are beveled on a cube along diagonal lines of an upper square surface and a lower square surface of the cube. The contact surface of the two beveled models, namely the opposite surface formed by beveling the two beveled models along the diagonal line, is a fault surface, and the fault models are arranged according to different test methods. The experimental device comprises the fault models, a strain observation system, an acoustic emission monitoring system, an active acoustic emission observation system and a laser total reflection fault monitoring system; according to the invention, a laboratory earthquake experiment can be realized, a laser total reflection fault observation system is researched and developed on the basis of a stress-strain observation device and an active and passive acoustic emission device, and synchronous monitoring of fault stress-strain, acoustic emission signals, displacement and fault surface contact conditions can be realized at the same time.

The invention relates to the field of integrated circuit testing, provides an SRAM (Static Random Access Memory) fault testing structure, and particularly relates to an IEEE (Institute of Electrical and Electronic Engineers) 1687 standard and an SRAM self- According to the method, a common fault model of an SRAM (Static Random Access Memory) is analyzed, improvement is performed on the basis of a March C-algorithm, and a March C-pro algorithm capable of covering most common faults of the SRAM is derived; a modularized SRAM self-test structure is designed based on a March C-pro algorithm, so that the design cost caused by built-in self-test of a memory and possible additional interface requirements are avoided; a multi-layer network structure based on the IEEE 1687 standard is designed, access to an embedded instrument is achieved, an SRAM self-test module is connected, and an SRAM test in the SOC is achieved.

The present invention discloses a method for generating a rotor fault diagnosis model based on multiple transform domains and a diagnosis method. The former includes: collecting vibration signal data of a modular rotor fault of a gas turbine in multiple categories to construct a training and test data set; transforming each data to the short-time Fourier time-frequency domain using the STFT transform domain, transforming it to the Morlet wavelet time-frequency domain using the Morlet wavelet transform domain, and transforming it to the complex network phase space domain using the complex network recurrence plot method; inputting the training data set transformed by the three transform domains into a preset model, and using the CNN network layer for feature extraction; using the LSTM network to perform feature structure learning on the output features, and training to obtain a rotor fault model; using the test data set transformed by the three transform domains to verify and adjust the rotor fault model to obtain a rotor fault diagnosis model for fault detection. The present invention can realize automatic data processing, reduce manual input, and improve the efficiency and accuracy of fault diagnosis.

The present invention belongs to the technical field of electromechanical equipment fault diagnosis, and discloses an online fault diagnosis method for a wind turbine transmission chain. The specific steps are as follows: S1: using a SCADA system to collect and preprocess the environmental, electrical and vibration multi-source monitoring parameters of the wind turbine in real time, effectively improving the accuracy and availability of the data; S2: designing a multi-source feature association fusion module, making full use of the complementarity of environmental, electrical and vibration parameters, and improving the accuracy and reliability of fault diagnosis; S3: constructing a teacher-student network framework, realizing the construction and real-time diagnosis of an online fault model, and improving the real-time and intelligent level of fault diagnosis. The application of a class-balanced sampling mechanism and a positive-negative joint learning strategy eliminates the influence of continuous state label offset and effectively reduces the pseudo-label noise interference in online samples, thereby improving the model's adaptive diagnostic capability in scenarios with dynamic changes in working conditions.

The invention relates to a mechanical arm actuator fault tolerance control method based on sliding mode control and a neural network, and belongs to the field of industrial robots. The method comprises the steps that modeling is conducted on the fault condition of a mechanical arm actuator, and a fault model is established; defining a joint angle tracking error, designing a sliding mode controller, and establishing a sliding mode surface of the sliding mode controller as linear sliding; designing a mechanical arm dynamic form according to the linear sliding surface and the fault model; estimating a total dynamic item by adopting an RBFNN neural network to obtain an estimated value of a system dynamic item, and taking the estimated value as an output quantity of the RBFNN neural network; designing a control law; calculating and generating a corresponding control input quantity according to a control law by adaptively updating a fault matrix of the weight matrix; and the mechanical arm actuator controls the mechanical arm according to the control input quantity so as to achieve fault compensation. According to the method, high-precision trajectory tracking is achieved under the condition that dynamics and actuator faults are unknown, and an accurate fault model or a fault detection system is not needed.

The invention discloses a tunnel face rock mass three-dimensional structure estimation method, and the method comprises the steps: obtaining point cloud data describing tunnel face space information, recognizing rock mass structural surface occurrence and two-dimensional space distribution information based on the point cloud data, calculating the inter-group spacing of each dominant group of the structural surface, and estimating the three-dimensional structure of the tunnel face rock mass. Based on the geometric center coordinate, the occurrence and the average elevation of the structural plane, calculating the inter-group spacing of each dominant group; calculating the joint fissure diameter and the joint fissure center coordinate; a joint fissure three-dimensional model is established by combining the diameter, occurrence and center coordinates of the joint fissure, and the representation form is disc-shaped fissures distributed in the rock mass; building a rock stratum layer three-dimensional model and a fault layer three-dimensional model by combining the two-dimensional trace position and occurrence, wherein the expression form is fracture of transverse rock mass; and coupling the joint fissure three-dimensional model, the rock stratum surface three-dimensional model, the fault model three-dimensional model and a bedrock model based on actual working conditions to obtain a discrete fissure network model, namely a rock mass three-dimensional structure.

The invention discloses a cascade fault analysis method based on digital twinning and physical power and related components. The method comprises the following steps: establishing a double-layer coupling network model through a digital twinning virtual network and a physical power network; setting a cascade fault propagation process through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy so as to construct a multi-layer network cascade fault model; updating the multi-layer network cascade fault model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascade fault model; and performing cascade fault analysis on a specified power system by using the target cascade fault model. According to the method, the target cascade fault model is constructed based on the coupling symbiosis between the digital twin virtual network and the physical power network in combination with the communication delay, and the analysis accuracy and reliability of the cascade fault of the power system can be effectively improved through the target cascade fault model.