541 results about "Dependability analysis" patented technology

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 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 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 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 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.

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 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.

The invention discloses an electrical equipment operation reliability analysis method based on RCM, and belongs to the technical field of electrical equipment operation and maintaining.The method comprises the steps that electrical equipment is decomposed into main function nodes and auxiliary function nodes through a function tree analysis method, a function performance standard library is constructed, and quantitative indexes of all the nodes and an incidence relation graph of the quantitative indexes are stored; constructing a failure mode library based on the function performance standard library, fusing a physical failure mechanism with historical fault data to generate a failure mode-function node mapping table, calculating a system-level function failure probability by adopting a Monte Carlo simulation method, and outputting a risk level matrix; using the risk level matrix to match the risk level to the node logic of the RCM decision tree, and generating a hybrid strategy set; and according to an execution result of the mixed strategy set, calculating a comprehensive health degree through an equipment health index model, and iteratively updating a failure mode library and a decision tree threshold parameter in combination with a reinforcement learning algorithm.

The invention provides an all-component multi-source solid waste asphalt mixture design method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of obtained test proportion data of a solid waste asphalt mixture, and generating a mixture data set; screening the mixture data set by using a preset constraint condition to obtain a plurality of candidate matching points meeting the performance index characteristics; the performance index characteristics comprise gradation structure characteristics related to particle size, bonding system characteristics related to high-temperature fluidity and water sensitivity, molding volume and construction characteristics related to porosity and saturation, and context embedding characteristics related to a service scene; splicing the performance index features and inputting the spliced performance index features into a trained gating expert network to obtain a corresponding performance prediction mean value and a performance fluctuation mean value; and performing reliability analysis on the candidate matching points based on the performance prediction mean value and the performance fluctuation mean value, and determining the candidate matching points of which the reliability is greater than a preset threshold value as target matching points.

The invention discloses a non-Gaussian random vibration signal generation method and system for controlling kurtosis and a peak factor. The method comprises the following steps: setting a reference condition for generating a non-Gaussian random vibration signal; a stable non-Gaussian random vibration signal model is constructed, a non-Gaussian random vibration signal is generated through the zero-mean Gaussian random signal and an impact signal, and the impact signal is used for providing a plurality of peaks with different amplitudes for the non-Gaussian random vibration signal; based on a reference condition, through control cooperation iteration of kurtosis and a peak factor, various parameters in the model are solved, and an impact signal is obtained; correcting the power spectral density of the zero-mean Gaussian random signal based on the power spectral density of the impact signal; and synthesizing the zero-mean Gaussian random signal and the impact signal to generate a non-Gaussian random vibration signal. The invention innovatively provides a non-Gaussian random vibration signal generation scheme capable of accurately controlling the kurtosis and the peak factor, and has important practical significance for improving the reliability analysis and test level of an engineering system.

The embodiment of the invention relates to the technical field of packaging interconnection reliability analysis, and provides a construction method and device for an electro-thermal migration effect circuit model of an interconnection line, and the method comprises the steps: converting an electro-thermal migration effect into a circuit parameter change based on a Korhonen control equation and boundary conditions, capturing the significant influence of temperature and temperature gradient on atom migration, and carrying out the analysis of the reliability of the interconnection line. According to the method, an equivalent circuit model of the interconnection line under the electro-thermal migration effect is deduced, a circuit matrix equation corresponding to the equivalent circuit model is determined by using an improved node analysis method, dimension reduction processing is performed on the circuit matrix equation, and an electro-thermal migration effect circuit model of the interconnection line is obtained, so that the system dimension is effectively compressed; therefore, the calculation complexity is effectively reduced, the solving efficiency is remarkably improved, the stress evolution in the complex multi-branch interconnection line can be rapidly analyzed, meanwhile, the cavity length evolution is dynamically simulated, and the precision requirement and the efficiency requirement are both considered.

The invention relates to a characteristic interval fitting and modeling method for breakdown voltage between GIS (Gas Insulated Switchgear) disconnecting switches, which is characterized in that on the basis of an actually measured scatter diagram of breakdown voltage, optimal distribution fitting is carried out on the scatter diagram, the distribution interval is predicted, and upper and lower boundary curves are calculated, so that subsequent modeling is facilitated. On the basis of an original simulation model, randomness of breakdown voltage is introduced to be combined with a reignition arc module to form a new model, and the new model is combined with an arc quenching module, a switch control module and an arc resistance module to form a new GIS disconnecting switch dynamic reignition arc simulation model. According to the invention, simulation of switch gap arc reignition randomness and extinguishing phenomenon is realized, and then the peak value distribution condition and the maximum peak value of the VFTO are analyzed. According to the method, the simulation precision is improved, the influence of the switching operation on the VFTO can be evaluated more accurately, and the reliability analysis of an electrical system is enhanced.

The present invention belongs to the technical field of designs of mechanical parameters or variables in electrical digital data processing. Disclosed is a method for optimizing the reliability of an ultra-deepwater pile hammer system, which method is used for performing reliability analysis on a pile hammer system. The method comprises: performing functional analysis and functional module division on a mechanical system, a power system, a hydraulic system, a pneumatic system and an electronic control system of an ultra-deepwater pile hammer system, viewing the five systems as five first-level subsystems, clarifying important parameters of the first-level subsystems, and further dividing the first-level subsystems into second-level subsystems; and performing criticality analysis on the second-level subsystems, and allocating reliability indexes of the first-level subsystems and the second-level subsystems of the ultra-deepwater pile hammer system and reliability indexes of failure modes of the second-level subsystems, so as to obtain an optimal reliability allocation scheme for the ultra-deepwater pile hammer system. In the present invention, various parameters of first-level systems and second-level systems are combined, and failure modes are taken into consideration, so as to obtain an optimal reliability allocation scheme.

The invention discloses a fault tree Boolean function equivalent mapping method based on an untrained neural network, and relates to the field of fault tree analysis. In order to solve the problems that in the prior art, a Boolean function mapping structure is not beneficial to parallel expansion, the calculation efficiency is limited, and the Boolean function mapping structure is difficult to efficiently realize on high-parallel platforms such as a GPU, the invention provides a method for generating topological structure data by analyzing a fault tree model; the basic events, the intermediate events and the top events are mapped into neurons of an input layer, a hidden layer and an output layer respectively, a feedforward network with fixed weight and bias is constructed, and a logic activation function is defined in nodes to realize Boolean logic propagation. The input layer receives a basic event state vector, outputs a top event result through forward propagation, and can realize large-scale Boolean function mapping on a parallel platform through batch input matrixes. The method is suitable for reliability analysis, minimum cut set simplification, top event probability calculation, parallelization fault tree solving and the like of a large-scale complex system.

The invention belongs to the technical field of reliability analysis, and discloses an electric energy meter adaptive residual life prediction method and system based on random degradation modeling, and the method comprises the steps: obtaining degradation data of an electric energy meter through an accelerated degradation test, removing failure data, and calculating a drift coefficient and a diffusion coefficient through a maximum likelihood estimation method; establishing a multi-source data fusion model of the drift coefficient and the temperature and humidity stress in combination with historical data, and fitting model parameters; expanding the model to a normal-temperature and normal-humidity condition, and calculating a corresponding drift coefficient; and based on the improved random degradation model, deriving a probability density function, an accumulative distribution function and a reliability function, and predicting the residual life of the electric energy meter at normal temperature and normal humidity. The system comprises a temperature and humidity test box, a power source table body and an upper computer, and is used for automatically collecting and analyzing data. Through multi-source data fusion and random degradation modeling, the prediction accuracy is improved, the test cost and time are reduced, and the method is suitable for electric energy meter reliability evaluation under complex working conditions.

The invention discloses an intelligent switch disconnecting link anti-misoperation method and system, and the method comprises the steps: collecting the electrical state information of a switch and a disconnecting link in real time, and constructing a power grid topological structure based on an improved particle swarm algorithm; based on the power grid topological structure, reliability analysis is carried out by adopting a minimum cut set method, and the failure probability of key nodes and a system reliability index are calculated; generating and dynamically adjusting an anti-misoperation strategy by utilizing a reinforcement learning model and combining historical operation data, a power grid topological structure, a reliability index and fault data; in the operation execution stage, the operation logic is verified through the machine learning model, and if it is detected that the operation violates an anti-error strategy or a reliability threshold, locking is triggered and an alarm is given; the state information of the power equipment is obtained through the real-time monitoring module, the equipment layout is optimized by combining topology analysis and the improved particle swarm optimization, and the response speed and accuracy of the system are improved.

The invention discloses a notched structure fatigue life prediction method based on probabilistic crystal plasticity, is applied to the field of reliability analysis, and provides a probabilistic crystal plasticity modeling framework for solving the problems of notched effect (local stress concentration) and material uncertainty generally existing in notched metal structural parts under the action of external loads. And the influence of material dispersibility (including material performance and microstructure) on the fatigue life of the notched part is considered. According to the method, a dual-scale modeling method is adopted, the mechanical response of a notched sample is simulated by adopting Chboche and crystal plastic constitutive simulation in a macro-micro mode, the randomness of the microstructure of the material and the dispersity of the mechanical property are considered by adopting a Voronoi algorithm and a Latin hypercube sampling method, and the fatigue life under different loads is predicted by adopting a fatigue indication factor; and finally describing the dispersibility of the fatigue life by using a probability model.

The invention relates to a power connector contact life analysis method based on a hybrid random process, and the method comprises the steps: carrying out the modeling of a degradation path of a power connector contact based on historical data, constructing a hybrid random process, and constructing an improved hybrid degradation model; establishing an acceleration model in the accelerated degradation process; obtaining an integral accelerated degradation model expression based on variables related to stress in the improved hybrid degradation model and an acceleration model; performing two-step estimation on unknown parameters of the overall accelerated degradation model; and deducing the service life distribution of the contacts of the power connector from a mixed random process. The method is more suitable for the actual application condition, the degradation model is more accurate, the actual performance of the equipment under different stress conditions can be reflected more truly, and the credible prediction capability of the residual life of the equipment is remarkably improved; important technical reference is provided for subsequent reliability analysis, and a feasible solution is provided for practical engineering application.