641 results about "Dependability analysis" patented technology

The present application provides a digital twin-based power distribution network power supply reliability calculation method and system. The method comprises: obtaining basic data and operation data of a power distribution line; constructing a digital twin power grid on the basis of the digital twin technology as well as the basic data and the operation data; obtaining scores in respect of power distribution network reliability indexes of the power distribution line on the basis of the digital twin power grid, wherein the power distribution network reliability indexes comprise an element reliability index, a load point reliability index, a system reliability index, a series system reliability index, and a parallel system reliability index; and on the basis of a weighted fusion result of the scores of the power distribution network reliability indexes, determining the power distribution network power supply reliability to be calculated. The present application solves the problem that current methods for analyzing the power distribution network power supply reliability relate to treating entire power distribution networks as research objects and implementing manual sorting, and thus are time-consuming and labor-intensive, are difficult to ensure the accuracy of calculation results, and fail to conduct statistical analysis for weak links in the network distribution networks.

The invention relates to the technical field of power distribution system reliability analysis, in particular to a power distribution system reliability analysis method and system. The method comprises the following steps: performing multi-dimensional data acquisition on a power distribution system through a sensor network, and performing multi-dimensional data fusion to obtain multi-dimensional associated data; performing comprehensive operation data set construction on the multi-dimensional associated data to obtain a comprehensive operation data set; performing time sequence data segmentation according to the comprehensive operation data set to obtain a rapid fluctuation characteristic sequence and a slow change long-term trend sequence; performing time sequence decomposition on the rapid fluctuation characteristic sequence and the slow change long-term trend sequence to obtain high-frequency fluctuation characteristic data and long-term trend characteristic data; and performing feature component analysis on the high-frequency fluctuation feature data and the long-term trend feature data to obtain a multi-scale feature set. Through risk factor correlation analysis and reliability network model construction, the reliability analysis precision of the power distribution system is improved.

The invention discloses a reliability analysis method and system for a power system, and relates to the technical field of intelligent operation and maintenance of the power system, and the method comprises the steps: collecting data, and constructing a dynamic space-time atlas network of the power system, which comprises time dimension information and space dimension information; feature extraction is carried out based on a spatial-temporal feature learning framework constructed by a dual-channel graph attention network and a gated causal convolution network, and spatial-temporal fusion features containing complete spatial-temporal information are generated by using gated causal convolution and a time attention capture time sequence evolution mode; and converting the space-time fusion features into a local risk state and a global system state through a state feature mapping network, calculating a local component risk index and a system overall reliability index, generating a collaborative decision strategy, and realizing reliability analysis of the power system. According to the method, the intelligent operation and maintenance level of the power system is remarkably improved, the shutdown time and the maintenance cost of the system are reduced, and meanwhile, the system performance is continuously improved through preventive maintenance and an active optimization strategy.

The invention relates to the technical field of digital factories, and discloses a production data analysis method and system based on a digital factory, and the method comprises the steps: carrying out the space-time alignment and standardization processing of high-precision sensor data and low-precision sensor data in the digital factory, and obtaining a target production data tensor; carrying out production rule constraint fusion to obtain physical consistency production data and process parameter estimation values; performing reliability analysis to obtain a production data set with a confidence interval; a multi-stage production optimization problem is constructed according to the production data set with the confidence interval, the multi-stage production optimization problem is solved, an optimal production parameter strategy and an equipment adjustment scheme are obtained, the method achieves remarkable improvement in the aspects of product quality stability, energy efficiency, equipment adjustment frequency and comprehensive production efficiency, and the method is suitable for popularization and application. And effective technical support is provided for intelligent production management of a digital factory.

The invention belongs to the technical field of aviation equipment system modeling and analysis, and particularly relates to an aviation equipment system reliability analysis method and system based on MBSE model conversion, and the method comprises the steps: S1, building the function model reliability of an aviation equipment system through an MBSE model mapping conversion technology; s2, establishing a fault logic model of the aeronautical equipment system, and completing key fault identification and analysis evaluation of the aeronautical equipment system; s3, determining reliability index parameters of the aeronautical equipment system according to key fault identification and analysis results of the aeronautical equipment system; and S4, determining an aviation equipment system reliability analysis result according to the aviation equipment system reliability index parameters, and completing reliability analysis. According to the method, the model conversion method is adopted to analyze the reliability related indexes of the aeronautical equipment system, the situation that reliability analysis results of the same system are greatly differentiated due to manual intervention and do not conform to the actual system state can be avoided, and comprehensiveness and accuracy of fault logic relation analysis of the aeronautical equipment system are guaranteed.

The embodiment of the invention provides a reliability analysis method and system of a body model, a medium, equipment and a product. The method comprises the following steps: receiving a target video of a target agent executing an operation task generated by the body model; performing one or more of motion consistency analysis, visual consistency analysis and logic rationality analysis on the target video to obtain one or more of a motion consistency index value, a visual consistency index value and a logic rationality index value; and determining a reliability analysis result of the body model based on one or more of the motion consistency index value, the visual consistency index value and the logic rationality index value. According to the embodiment of the invention, the key index is obtained by analyzing the target video of the operation task executed by the target agent generated by the body model, so that the reliability of the body model in an actual task scene can be effectively evaluated.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a power supply reliability analysis method and system based on operation and maintenance characteristics of a power distribution network, and particularly relates to the technical field of power supply analysis of the power distribution network. Harmonic data, including harmonic current, harmonic voltage and frequency components, of each node in a power distribution network system are collected in real time; in combination with the topological structure of the power distribution network, the sparse linear equation set is solved through L1 norm optimization, and the main source of harmonic waves is identified; the contribution degree of each nonlinear load to the harmonic distortion of a power distribution network system is calculated, a list of key harmonic sources is screened out through Pareto analysis, a filter configuration optimization model is constructed based on the identified key harmonic sources, and the harmonic distortion level and the filter installation cost are used as constraint functions to optimize the filter configuration. The installation position and the parameter configuration of the filter serve as decision variables, an optimization algorithm is adopted for solving, and the recommended installation position and the parameter configuration of the filter are output; the harmonic suppression effects under different load conditions are evaluated, and the problem that the power supply reliability is reduced due to harmonic distortion is effectively solved.

The present disclosure relates to the technical field of autonomous driving. Provided are a method and apparatus for determining a failure probability in an autonomous driving simulation test. The method comprises: performing high-coverage uniform sampling in a high-dimensional parameter space, so as to obtain a set of first test scenarios; performing a simulation test on each first test scenario in the set of first test scenarios, so as to obtain a first test index value; determining a scenario weight of each first test scenario, and determining a first test failure probability on the basis of the scenario weight and the first test index value; if the first test failure probability does not meet an accuracy requirement, performing sensitivity analysis on a first scenario parameter that affects the first test index value, so as to determine sensitive scenario parameters, and then reducing the high-dimensional parameter space to a low-dimensional parameter space formed by means of the sensitive scenario parameters; and performing reliability analysis on the low-dimensional parameter space, so as to determine a final failure probability. By using the method and apparatus for determining a failure probability in an autonomous driving simulation test, the problem of the efficiency of calculation of related methods for determining a failure probability being low is solved.

The invention provides a turbine case connection part reliability analysis and verification method considering uncertainty, and the method comprises the steps: building a finite element analysis model of a local connection structure of a turbine case, and achieving the representation of the uncertainty of connection rigidity; establishing a turbine casing structure uncertainty analysis model considering the connection rigidity uncertainty; determining a failure mode of the turbine case connecting part, defining a failure performance function, and performing reliability analysis on the turbine case structure; according to the finite element analysis result of the turbine case, designing a feature simulation piece of the connection part; processing a characteristic simulation part of the turbine case connecting part and carrying out a fatigue test; and obtaining a finite element analysis result of the turbine case of the processed characteristic simulation piece, and verifying the processed characteristic simulation piece in combination with the dispersibility of the fatigue life data. According to the method, the precision and engineering of the reliability analysis of the connection part of the turbine case are realized, and the precision of reliability evaluation is improved.

The invention discloses a mechanical structure reliability analysis method and system based on an active learning agent model, and belongs to the technical field of mechanical structure reliability analysis, and the method comprises the steps: analyzing the working condition and load condition of a mechanical structure to be subjected to reliability analysis, and determining a performance function of the mechanical structure in combination with the safety working requirements of the structure; based on the principle of equal probability conversion, the input variables are converted into a standard normal space, and Monte Carlo reliability analysis samples are generated; in a standard normal space, using a center cross method to generate a 4n initial fitting sample, calculating a function value of the sample, obtaining an initial response matrix, and fitting an initial proxy model; and establishing a distance constraint learning function based on the existing agent model and the fitting sample, and iteratively updating the agent model, the fitting sample, the response value matrix and the failure probability in combination with a prior reliable circular ring sample reduction method until a reliability analysis result with enough precision is obtained, and ending the reliability analysis system.

The invention relates to the technical field of information security, in particular to a key management system and method based on a medical block chain, and the system comprises a state evaluation module, a reverse tracking module, a foresight analysis module, an intelligent alarm threshold adjustment module, a resource priority scheduling module, a reliability analysis and fault tolerance module, and a process optimization and decision support module. According to the method, risk assessment and classification are effectively carried out and a key access control strategy is adjusted by adopting a support vector machine, a decision tree algorithm and a deep learning algorithm, a key use trend is predicted by utilizing an autoregression integral moving average model and a long-short-term memory network, and the key access control strategy is optimized through an isolated forest and a deep learning network. An alarm threshold value is dynamically adjusted, the early warning capability of the system for potential security threats is improved, a key management process is optimized through a genetic algorithm and a particle swarm optimization technology, a security decision and a response strategy are executed in combination with a rule engine and a machine learning model, and the response efficiency and the decision accuracy are improved.

The invention relates to the technical field of filter film testing, and discloses filter film thermal deformation testing equipment and a testing method. The method comprises the following steps: based on a test environment, carrying out a temperature cycle test on the filter film, and collecting a triple data set at the same time; performing image preprocessing and displacement field construction on the triple data set, and decomposing a displacement field into a linear combination of a standard deformation mode to obtain a thermal deformation response function; constructing a hierarchical probability graph model based on a thermal deformation response function, and extracting thermal deformation feature structural representation through variational inference; and performing deviation compensation on the structural representation of the thermal deformation characteristics to obtain environment self-adaptive thermal deformation characteristics, and calculating a thermal deformation reliability evaluation result comprising a thermal expansion coefficient matrix, a thermal deformation anisotropy index and thermal ripple strength. And comprehensive evaluation and reliability analysis of the thermal deformation characteristic of the filter film are realized.

The invention discloses a fractional order thermal network model modeling and parameter identification method for an IGBT module, and the method is characterized in that the method comprises the following steps: S1, building a fractional order thermal network model of the thermal impedance of the IGBT module based on the mathematical function operation relation between the heat transfer mechanism of the IGBT module and the fractional order calculus; s2, based on a junction-shell transient thermal impedance curve given in a data manual of an IGBT module of any model, identifying a fractional order thermal capacity C, an order alpha, a thermal resistance R and a time constant tau in the fractional order thermal network model; the fractional order thermal network model provided by the invention can accurately describe a time-varying curve of thermal impedance of the IGBT module, so that a reference basis can be provided for design and reliability analysis of a circuit system containing such elements.

The invention discloses a multi-tolerance coupling mechanism precision analysis and tolerance distribution method, which comprises the following steps of: analyzing an assembly structure of a mechanism to be processed: analyzing part composition of the mechanism and an assembly relationship among all parts, and determining key geometric features which influence functional requirement poses; determining the small displacement spinor of the key geometrical characteristics of each assembly surface, establishing a mechanism error transfer matrix, solving a functional requirement pose error accumulation transfer function, and analyzing the mechanism precision; fitting a function relationship between the tolerance and the actual variation interval of the small displacement spinor; establishing a reliability performance function of the tolerance, and carrying out reliability analysis; carrying out primary distribution on each tolerance when the output function requirement is known; and a PSO algorithm is adopted to optimize the tolerance, and a final tolerance value is determined, so that the to-be-machined mechanism is designed. While the final precision of the mechanism is predicted, the tolerance selection problem of each assembly key geometric feature can be guided, and finally the tolerance-machining cost optimization is realized.

The invention relates to the technical field of millimeter-wave radars, and discloses a millimeter-wave radar material level monitoring method, device and equipment and a storage medium. The method comprises the steps that a plurality of millimeter-wave radar sensors are installed at the top of a coal feeder bin, a reflection point cloud data set is collected, Euclidean clustering is carried out, and an effective point cloud clustering set is obtained; performing multi-stage information screening to obtain an effective material level cluster set of each millimeter wave radar sensor; carrying out signal detection alternating-to-parallel ratio and reliability analysis to generate fused material level data; inputting the fused material level data into a multi-model interaction and unscented Kalman filter model for material level dynamic tracking to obtain a time sequence material level curve and a prediction confidence interval; and iterative second-order cone programming calculation is executed based on the time sequence material level curve and the prediction confidence interval, and a coal feeder control parameter sequence is obtained. According to the method, the problem of radar point cloud disorder caused by a complex environment in the coal feeder is effectively solved, and efficient control parameter optimization under different load conditions is realized.

The invention relates to the technical field of structural reliability analysis, in particular to an adaptive structural reliability analysis method based on a Co-kriging proxy model, which comprises the following steps: determining a structural failure mode, acquiring corresponding high-fidelity and low-fidelity performance functions and inputting distribution information of variables; a high-fidelity sample set and a low-fidelity sample set are obtained through sampling; obtaining responses corresponding to the two fidelity samples, and constructing a Co-kriging agent model; carrying out structural reliability analysis by utilizing a Monte Carlo simulation method, and calculating a failure probability of each iteration; judging whether a convergence condition is met; adding new sample points into the training set by using a learning function, constructing a Co-kriging proxy model, and obtaining a final failure probability; according to the method, the Co-kriging proxy model is combined with the learning function MPO (x, m), multi-precision sample data can be fused, and on the premise that it is ensured that the failure probability estimation result has high precision, the calculation process is optimized, unnecessary calculation resource consumption is effectively reduced, and the overall calculation cost is remarkably reduced.

The invention discloses a high-low cycle composite fatigue life prediction method based on a particle swarm optimization physical information neural network, which aims at high-low cycle composite fatigue life prediction of turbine blades under small sample data characteristics, and comprises the following steps of: introducing a classical physical formula of a linear cumulative damage theory into a loss function as a constraint term; training fatigue test data by adopting a neural network model; deducing a constraint space of model parameters according to domain knowledge of high-low cycle composite fatigue life prediction, and converting training of a neural network into a constrained optimization problem; a particle swarm optimization algorithm is adopted to optimize the weight and the threshold value of the neural network, and the learning ability of the neural network under a small sample data set is further improved. The method provided by the invention can predict the fatigue life of the turbine component under the high-low cycle combined load, has high prediction precision under a small sample data set, and provides theoretical support for fatigue life prediction and reliability analysis of the engine turbine component.

The invention provides a non-probabilistic time-varying reliability analysis method based on ellipsoid correlation modeling. The method comprises the following steps: carrying out interval modeling on uncertainty input parameters; constructing a multi-dimensional ellipsoid uncertain domain based on correlation information among parameters; a system time-varying performance function is established, and an interval process of output response changing along with time is obtained through uncertainty propagation solution; identifying the maximum value point of the upper boundary and the minimum value point of the lower boundary in the interval process as reliability key points; respectively calculating non-probability reliability indexes based on the ellipsoid model at the moments of the two key points; and taking the smaller value of the two indexes as the non-probability time-varying reliability index of the system in the target time interval. According to the method, the problem that the evaluation result is conservative due to the fact that variable correlation is ignored in a traditional non-probability method is effectively solved, time-varying evolution of system performance can be accurately quantified, and an accurate and practical dynamic safety evaluation tool is provided for a complex engineering system with scarce data.

The invention belongs to the field of system reliability design analysis, and particularly relates to a common cause failure parameter-based fault analysis method, which comprises the following steps: acquiring a fault tree model of an analysis object, and fusing the fault tree model with a common cause failure parameter model; constructing a fault tree model with a common cause failure parameter model; performing common cause failure analysis by using the common cause failure parameter model; expanding the constructed fault tree model with the common cause failure parameter model into an explicit expression common cause failure fault tree model by adopting a universal parameter model expansion method; solving the extended explicit expression common cause failure fault tree model to obtain a top event occurrence probability; according to the method, the implicit common cause failure parameter model is expanded into explicit expression, the automation of the reliability analysis process of the complex system considering the common cause failure influence is realized, the modeling difficulty is reduced, and the reliability analysis working efficiency is improved.

The invention relates to the technical field of unmanned aerial vehicle design, in particular to an unmanned aerial vehicle propeller definite reliability modeling method and system, a medium and a program product. The method comprises the following steps: firstly, constructing a demand model, a system context, a use case and an activity diagram of an unmanned aerial vehicle propeller based on SysML, and defining a mathematical function relationship of core performance parameters such as thrust, torque and rotating speed; then statistical fitting is carried out in combination with experimental data, a joint performance margin equation is constructed, a definite reliability theory is introduced, and the reliability value of the propeller is calculated through a standard normal cumulative distribution function; and finally, carrying out constraint binding on the constraint equation and the system structure model to realize dynamic response and model linkage under parameter change. According to the method, integration of reliability analysis in the MBSE model is realized, and the automation degree, the evaluation precision and the engineering application efficiency of reliability modeling of the propeller system are remarkably improved.

The invention relates to a non-probabilistic fatigue reliability analysis method for a controllable-pitch propeller hub mechanism under data driving, which comprises the following steps of: considering multiple uncertain sources such as elastic modulus and friction coefficient, modeling each part of the controllable-pitch propeller hub mechanism through parameterization to obtain a finite element model under different uncertain variable values; drawing a fatigue load spectrum by using a finite element simulation load result, and performing fatigue simulation by combining an S-N curve to obtain the fatigue life of each part; and obtaining a non-probability interval model of the fatigue life of each part by using a data-driven agent model and a Monte Carlo method under a limited fatigue life sample condition, and calculating the fatigue reliability of each part by using a non-probability area method. A data driving method is adopted, so that the problems of high cost and time consumption of obtaining enough data in actual engineering are avoided; fatigue reliability analysis is carried out by adopting a non-probabilistic method, so that uncertainty in engineering can be better dealt with, and the fatigue reliability of the controllable-pitch propeller hub mechanism can be more comprehensively evaluated.

The invention relates to the technical field of fatigue time-varying reliability analysis, in particular to a fatigue time-varying reliability calculation method and device, equipment, a storage medium and a product. The method comprises the following steps: performing crack propagation simulation on a three-dimensional plate shell structure based on a random propagation finite element method, and establishing a geometric model of a crack propagation path; based on the geometric model, calculating a stress intensity factor of the crack tip by adopting an interaction integral method, and determining change data of the stress intensity factor in the crack propagation process; based on the stress intensity factor of the crack tip and the change data of the stress intensity factor in the crack propagation process, performing numerical simulation on the crack propagation process by adopting an propagation finite element method; and based on a numerical simulation result, optimizing the crack propagation process by adopting an HLRF-BFGS optimization algorithm, and determining the reliability of fatigue crack propagation. According to the method, the fatigue reliability evaluation effect is improved.

The invention discloses a complex production line reliability analysis method combining a fault operation and maintenance knowledge graph with a fault tree, and the method comprises the steps: constructing a multi-level production line fault operation and maintenance knowledge graph based on a production line-equipment-fault-method, inputting fault information, carrying out the fault matching query of a production line layer, constructing an available equipment sub-graph, and carrying out the fault matching query of the equipment sub-graph; a production line fault tree is constructed by analyzing the logic relation between the production line faults and the factors causing the faults, and finally qualitative analysis is conducted on the production line fault tree, the production line fault rate is quantitatively calculated, and reliability analysis of the production line is achieved. The method is simple and easy to implement, reliability analysis of a current production system can be improved from an equipment level to a whole production line level considering coupling between equipment, a mode layer is reversely standardized and verified by utilizing characteristics of the production line and fault knowledge, production management personnel are helped to understand the rule of equipment or system fault generation, and the reliability of the production system is improved. And a reasonable maintenance strategy is formulated.

The invention discloses a neural network-based turbine blade high-low cycle composite fatigue reliability analysis method, which comprises the following steps of: obtaining a small amount of data samples of a turbine blade through finite element analysis, extracting the characteristics of the data samples by using a convolutional neural network, and inputting the extracted characteristics into a BP neural network optimized by an improved seagull optimization algorithm to obtain the high-low cycle composite fatigue reliability of the turbine blade. The method comprises the following steps: establishing a CNN-MSOA-BP neural network model of a turbine blade under a high-low cycle composite fatigue load, taking multi-source uncertainty such as a load parameter and a material parameter into consideration, sampling a large number of input samples through Monte Carlo, and obtaining a corresponding output response through the established CNN-MSOA-BP neural network model, thereby performing reliability analysis of the turbine blade. According to the method provided by the invention, the reliability of the turbine structure under complex loads such as high-low cycle compound fatigue can be analyzed, the method has relatively high precision and operation efficiency, and theoretical support is provided for reliability analysis and optimization of turbine blades.

The invention discloses a multi-chip CCGA packaging welding column failure risk evaluation method, and belongs to the technical field of electronic packaging and reliability analysis, and the method comprises the steps: building a finite element analysis model through obtaining the geometric and material parameters of a multi-chip CCGA packaging structure; non-uniform working conditions are set, boundary conditions and constraints are applied, and non-uniform distribution thermal stress of the welding column is obtained through thermal cycle loading analysis; key data are extracted, the fatigue life of the welding column is calculated through a Coffin-Manson thermal fatigue model, and a life matrix is determined; the welding column damage amount is calculated based on a thermal fatigue model and a linear accumulation damage theory, and a damage matrix is obtained; welding column failure is judged according to the damage matrix, and a risk level matrix is obtained. According to the method, the stress distribution and failure mechanism of the welding column under the multi-chip coupling scene can be rapidly and accurately evaluated in the design and early verification stage according to the actual working condition, and scientific support is provided for research and development of high-reliability electronic products.

The invention relates to the technical field of high-dimensional reliability analysis, in particular to a high-dimensional reliability analysis method combining a physical information neural network and distance sampling, which comprises the following steps of: measuring a response value of a measured solid under a to-be-measured condition by adopting a sensor, and taking the to-be-measured condition and the corresponding response value as a transfer learning sample; obtaining a plurality of transfer learning samples; generating a plurality of configuration points, wherein the configuration points record space-time positions and randomly generated attributes of the tested solid; carrying out physical information learning based on the configuration points, and carrying out transfer learning training based on transfer learning samples to obtain a deep neural network reliability analysis model; based on a deep neural network reliability analysis model, calculating the reliability of the high-dimensional reliability assessment configuration point as a reliability analysis result; the method can improve the generalization performance of the physical information neural network under a small amount of sample data.

A Predictive Diagnostic Information Capability-Technology (PreDICT™) system (100) enables users including expert and nonexpert users to provide information regarding a condition of a subject and receive timely and accurate information regarding risk stratification, treatment options and other medical evaluation information. The illustrated system (100) generally includes a user device (102) for use by a user assisting a subject (104), a processing platform (108), and a network (106) for connecting the user device (102) to the processing platform (108). The system (100) may also involve an emergency response network (130) that includes public-safety answering points (PSAPs) (132). The processing platform (108) processes the sensor information and other information from the user device (102), determines risk stratification information as well as medical diagnosis and treatment option information based on machine learning technology, and provides output information to the user device to assist the user in treating the subject (104).

The invention discloses an aero-engine gear high-cycle fatigue reliability analysis method, which takes a certain aero-engine gear transmission system as an object, and establishes a gear finite element model by using an ANSYS parameterized design programming language; the method comprises the steps that after test data are obtained, an active learning Kriging proxy model is adopted to obtain a proxy finite element model, then the gear proxy finite element model is corrected through a QMC-based gradient deceleration particle swarm algorithm, and a corrected high-precision finite element model is obtained; in consideration of the influence of the uncertainty of the size, the material and the load on the reliability of the gear, fitting an S-N curve by adopting gear contact fatigue test data; then, correcting the S-N curve and establishing a life-based tooth surface contact fatigue limit state function; and finally, performing reliability analysis in combination with the corrected high-precision finite element model.

The invention belongs to the technical field of electric energy parameter monitoring, and particularly relates to an electric energy meter reliability analysis method and system, a computer device and a storage medium. The method comprises the steps of applying disturbance of a certain amplitude under a preset disturbance condition to original operation data of an electric energy meter to generate disturbance data under the corresponding disturbance condition, inputting the original operation data and the disturbance data under each disturbance condition into an inversion model to carry out parameter backstepping, and obtaining an inversion model under the corresponding disturbance condition. An inversion model under a corresponding disturbance condition is utilized to determine a predicted output value of the electric energy meter under the disturbance condition, the reliability of the electric energy meter is evaluated based on a residual error between a theoretical output value of the electric energy meter under a standard working condition and the predicted output value of the electric energy meter under each disturbance condition, and the inversion model is endowed with sensitive response capability to external fluctuation. The behavior change of the electric energy meter under a non-ideal condition can be observed, so that the performance change of the electric energy meter caused by disturbance can be identified.