226 results about "Reliability model" patented technology

The invention discloses a real-time monitoring and fault response control device for an industrial and commercial liquid cooling energy storage system, and particularly relates to the technical field of liquid cooling energy storage fault management. Temperature, pressure, vibration and flow data of key components in a liquid cooling system are collected and preprocessed; a time sequence analysis and signal processing technology is utilized to extract feature vectors reflecting key component states from the multi-dimensional operation data set, the saliency of weak abnormal signals is enhanced, and a key feature set containing weak abnormal features is generated; constructing an anomaly recognition model by adopting a long-short-term memory network, inputting a key feature set, outputting an anomaly probability corresponding to the key component, and setting an early warning threshold value of the anomaly probability to trigger early warning; and predicting a fault maintenance time window of the key component based on a Weibull reliability model, and dynamically adjusting the priority of maintaining the key component and executing preventive maintenance based on a fault maintenance time window prediction result and an abnormal probability.

The invention provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention belongs to the technical field of wind turbine generator maintenance, and particularly relates to an offshore wind turbine generator two-stage rolling maintenance method considering subsystem correlation, which comprises the following steps: S1, constructing a comprehensive reliability model; s2, taking the minimization of the total maintenance cost in the operation cycle of the wind turbine generator as a target, and forming a target optimization problem based on the comprehensive reliability model; solving the target optimization problem to form a first-stage long-term maintenance strategy; s3, when an operation window meeting the storm accessibility condition appears, determining a subsystem of which the current reliability is lower than a preventive maintenance threshold value; s4, the opportunity maintenance benefit ratio is calculated, and if the opportunity maintenance benefit ratio is larger than a processing threshold value, advanced maintenance of the subsystem is triggered; and S5, after the advanced maintenance is executed each time, the optimal preventive maintenance time point is solved again so as to dynamically update the subsequent maintenance plan. According to the method, the fault coupling and degradation evolution law between subsystems can be accurately described, and the storm accessibility window is actively utilized to dynamically adjust the maintenance plan.

The invention discloses an air cushion belt dynamic balance control system and a management and control method, and belongs to the field of air cushion belt conveyors. Original signals of a sensor are obtained, a Fluent noise filtering algorithm is used for eliminating airflow interference, synchronous tension and air pressure data are aligned through timestamps, a reliability model is used for filling temperature field missing values, and standardized data streams are formed; based on the standardized data flow, analyzing an air pressure gradient by means of a fluid mechanics equation, extracting abnormal vibration modal characteristics of the carrier roller, calculating a fluctuation coefficient in combination with tension simulation, and obtaining a multi-dimensional parameter set containing dynamic distribution of a pressure field and a belt deviation angle; inputting the multi-dimensional parameter set into the digital twin, updating the fatigue damage parameters of the belt through Fluent simulation driving, rendering the visualization result of the flow field of the air chamber in real time, and outputting a simulation result containing the dynamic cloud picture of the pressure field of the air chamber and the fatigue damage distribution of the belt; and calculating the pressure balance degree of the air chamber according to a simulation result.

The invention discloses a railway signal equipment station level reliability analysis method, which belongs to the technical field of railway signal system operation and maintenance, and comprises the following steps of: calculating to obtain annual average temperature data and annual average humidity data of a region where a railway line is located; constructing an action coefficient of the environment on railway signal equipment degradation; constructing a degradation process model of the railway signal equipment; calculating to obtain a reliability value of the railway signal equipment; establishing a topological structure of the railway signal equipment system according to the railway signal station layout diagram and the interlocking table; equidistant discretization based on time scale is carried out on the reliability state of the railway signal equipment, and a station level reliability model of the railway signal equipment is constructed; and representing the station-level reliability of the railway signal equipment by using an indicative function, and calculating to obtain a station-level reliability value of the railway signal equipment. According to the invention, the problem that the equipment-level and station-level reliability evaluation of the outdoor railway signal equipment is difficult to carry out in a complex operation environment on a railway site is solved.

The invention relates to the technical field of equal-strength design, and provides a high-reliability equal-strength design method and system for an intelligent cubic press, which considers structural characteristics of the cubic press, performs physical field analysis, modeling and simulation, determines deformation, temperature, stress distribution and stress concentration areas of key components, predicts structural crack initiation of the key components, and provides a high-reliability equal-strength design method and system for an intelligent cubic press. Crack propagation simulation is carried out; establishing a fatigue strength life prediction model based on deep learning and a simulation data set; constructing a fatigue strength reliability model, and establishing a mathematical association relationship among fatigue life, fatigue damage and reliability; a reliability calculation index is introduced into multi-objective optimization of the structure, a high-precision numerical solution of reliability robustness optimization design based on structure simulation is developed, and equal-strength design of the cubic press is achieved. According to the method, the optimal combination of design parameters can be obtained, the equal-strength design of parts and the whole machine structure is formed, and development of intelligent, high-precision and large-scale cubic press equipment is promoted.

The invention relates to the technical field of stress testing, in particular to a constant thermal stress sequential electric stress testing method, which comprises the following steps of: preparing a sample, calibrating equipment, applying linear increasing electric stress under constant thermal stress and monitoring failure in real time; calculating an electric stress acceleration factor, equivalent time and failure time; combined with the test junction temperature and the normal working condition, a comprehensive acceleration factor is calculated, and the failure time is converted; and constructing a three-parameter Weibull distribution reliability model based on the converted data, and estimating parameters by using maximum likelihood estimation and a Newton-Raphson algorithm to obtain a reliability index. According to the method, the failure can be efficiently induced under the condition of constant thermal stress, and the modeling reliability index is accurate, so that the efficiency, accuracy and engineering practicability of reliability evaluation are improved.

A deep foundation pit supporting structure design method based on system reliability analysis relates to the technical field of foundation pit structures, and obtains a supporting structure design method with high safety coefficient and low supporting cost through calculation and analysis, and comprises the following steps: S1, obtaining an engineering exploration report and a preliminary design scheme corresponding to a deep foundation pit; s2, determining a value range of a design variable x and statistical characteristics of a random variable y; s3, determining a failure mode of a deep foundation pit support design system according to engineering characteristics of the support corresponding to the deep foundation pit; s4, constructing a limit state function of each failure mode; s5, constructing a system reliability model through the system failure probability of the series system in the step S4, fusing a multi-objective optimization model into the system reliability model, and setting an objective function and a constraint condition; and S6, setting a multi-objective optimization algorithm in the deep foundation pit support design system, calculating the safety and cost of different support structures by using the algorithm, obtaining a Pareto solution set, and obtaining the corresponding deep foundation pit support structure design through comparison and selection.

The invention relates to the technical field of network communication, and discloses a network node reliability evaluation method based on spatio-temporal reliability, which comprises the following steps: S1, collecting operation state information and topological structure information of a plurality of nodes in a network; s2, based on the operation state information, establishing a time-varying reliability model of each node, and dynamically adjusting model parameters according to a recent operation state to obtain a time dimension reliability index of each node; s3, on the basis of the topological structure information, analyzing the incidence relation between the nodes, and obtaining the spatial dimension reliability index of each node in combination with the operation state information; and S4, synthesizing the time dimension reliability index and the space dimension reliability index to generate a comprehensive reliability evaluation result of each node. The limitation that a traditional static evaluation method cannot reflect the dynamic change of node states is overcome; comprehensive evaluation of node reliability in NFV and multi-domain network environments is realized, and the actual reliability condition of the node is accurately reflected through comprehensive analysis of two dimensions of time and space.

The invention discloses a compressor impeller fault life reliability analysis method, device and equipment and a medium. The method is characterized by comprising the steps that three-dimensional point cloud data of a target compressor are collected, three-dimensional modeling is conducted on the target compressor according to the three-dimensional point cloud data, and a compressor three-dimensional model is determined; performing particle erosion simulation and compressor crack propagation simulation on the compressor three-dimensional model, and determining an erosion reliability model; impeller fatigue simulation is conducted on the compressor three-dimensional model, and a fatigue reliability model is determined; and a compressor impeller reliability model is established according to the erosion reliability model and the fatigue reliability model, fault life reliability analysis is conducted on the target compressor based on the compressor impeller reliability model, and the impeller fault life reliability of the target compressor is determined. A comprehensive fault life reliability prediction model can be established for the compressor impeller, the reliability of the compressor impeller can be accurately predicted, and a reference function is provided for health management and maintenance of the compressor.

The invention relates to the technical field of parameter extraction, and provides a reliability parameter extraction method based on a Lifetime equation and parameter mapping, and the method comprises the steps: S1, building a Lifetime degradation formula, and obtaining a Lifetime model capable of describing the degradation trend of a device based on a physical mechanism of the device; s2, judging whether a user has a parameter mapping formula or not, if so, extracting degradation parameters through an automatic reliability parameter extraction tool and a customized reliability model interface, and entering step S5; s3, if the user does not have the parameter mapping formula, collecting degradation data and extracting a parameter degradation curve through an automatic parameter extraction tool; s4, selecting a machine learning model to obtain a parameter mapping relationship, fusing physical constraints, taking a known physical relationship as an initial item input model, obtaining a reliability formula, and then entering the step S2; and S5, extracting a formula and verifying the formula, evaluating prediction precision in a test set, and performing extrapolation verification under a stress condition outside a training data range. The parameter degradation modeling time can be shortened, and the automatic parameter extraction efficiency can be improved.

The invention discloses a reliability-based titanium alloy medium plate welding optimization control system and method for a new energy vehicle, and the system comprises a data obtaining and preprocessing module, a performance prediction module, a reliability evaluation module and a process parameter optimization module, welding process parameters and the tensile strength of a welding joint, the welding width of the bottom surface of a welding seam and angular deformation under the corresponding welding process parameters are recorded; the performance prediction module is used for training and learning the sample data acquired in the data acquisition module, the trained performance prediction module is used for prediction, and the weight of an RBF neural network and the initial value of a Gaussian function parameter are optimized based on an improved genetic algorithm in the module; the reliability evaluation module is used for establishing a welding non-probabilistic reliability model based on an ellipsoid convex model and considering the tensile strength of a welding joint, the weld penetration depth and deformation, and evaluating the rationality of the current process parameter group by comparing the reliability obtained through calculation with a reliability threshold value; the process parameter optimization module is used for constructing a fitness function about the tensile strength of a welding joint, the weld width of the bottom surface of a welding seam and angular deformation by adopting a weight method, and optimizing an optimal process parameter group based on a particle swarm optimization algorithm of simulated annealing; according to the invention, efficient and reliable welding process optimization can be realized, the reliability and the performance stability of a welding joint are ensured, and the experiment trial and error cost is reduced.

The invention belongs to the technical field of multi-level RCM, and discloses a multi-level RCM analysis and reliability structure construction method and a related device. The multi-level RCM analysis and reliability structure construction method comprises the following steps: establishing a unit system function model after constructing a unit system database, performing multi-level classification on equipment of a unit system in combination with a fuzzy clustering algorithm, and then determining levels and weights of various types of equipment in the unit system function model, analyzing a failure mode of each type of equipment after multi-level classification through a harmfulness analysis method in combination with a fault tree analysis technology, establishing a correlation model between the equipment failure mode and a unit system function, determining an influence weight of the equipment failure mode on the reliability of the unit system, and establishing a unit system reliability model; inputting the unit system database into the unit system reliability model to obtain a unit system maintenance strategy library; the analysis result accuracy and the system reliability can be greatly improved.

The invention discloses a multi-source data fusion aerospace electronic system reliability evaluation method based on design and manufacturing process constraints, and belongs to the technical field of electronic system reliability evaluation. The problem that the reliability evaluation accuracy of the spaceflight electronic system is poor under the condition of incomplete information is solved. The method comprises the following steps: firstly, establishing a digital prototype model of the aerospace electronic system, and establishing a parameter degradation model of key components of the electronic system in combination with a failure physical model so as to obtain a performance degradation reliability function of each key electrical performance parameter of the electronic system; establishing an electronic system function failure propagation network model based on a logical relationship between an electronic system function structure and basic function network nodes, performing state updating on each function network node of the electronic system by adopting a Bayesian inference method, and obtaining a function failure reliability function of each function network node of the electronic system; and then based on the two reliability functions, constructing a system-level reliability model to obtain an overall reliability curve of the electronic system.

The invention discloses a travel time reliability evaluation method based on post-earthquake emergency traffic demands, and belongs to the technical field of emergency traffic. Comprising the following steps: S1, based on a road network loading time-varying curve of various types of demands in a post-earthquake emergency period, carrying out multi-stage demand accumulative loading on the post-earthquake emergency period by taking 12 hours as a cycle; s2, on the basis of the S1, traffic system post-earthquake emergency demand prediction is carried out by adopting a correction four-stage method; s3, based on S2 and road vulnerability analysis, establishing a post-earthquake road section travel time reliability model based on a BPR formula by adopting an analytical method; and S4, on the basis of the S3, establishing a post-earthquake road network travel time reliability evaluation model based on the Bayesian network by using the bidirectional reasoning technology of the Bayesian network and taking the road section travel time reliability as a prior probability.

The invention relates to a numerical control machine tool reliability modeling method and system considering a fault trend. The method comprises the following steps: checking the homogeneity of a numerical control machine tool based on the fault trend; constructing an AMSAA model of the multiple samples subjected to truncation at unequal time; evaluating the precision of the reliability model; aiming at the problem of insufficient reliability model precision of a multi-sample condition of a numerical control machine tool, a multi-sample AMSAA model modeling method of the numerical control machine tool considering a fault trend is provided, sample data classification is carried out through trend inspection and homotype inspection, and fault data of same-type machine tools are preprocessed by adopting a fault total time method; adopting a maximum likelihood method to estimate AMSAA model parameters of the same-type machine tool, and using Cramer-Von Mises to test the goodness of fit of the model; evaluating the precision of the reliability model by taking an average absolute percentage error (MAPE) of an instantaneous MTBF point estimation value and an MTBF observation value as an index; compared with a multi-sample AMSAA model established by a direct maximum likelihood method, the method is higher in prediction precision.

The present invention provides a dual-redundant servo valve and its application, belonging to the field of electro-hydraulic servo control; comprising a housing, two sets of electro-hydraulic servo valves and a hydraulic pressure sensor; the two sets of electro-hydraulic servo valves are connected in parallel and arranged in the housing, respectively defined as a left valve and a right valve; the hydraulic pressure of the left valve is monitored by the hydraulic pressure sensor; the left valve is set as the main valve, and the right valve is set as the backup valve; when the right valve is a hot backup, the two sets of electro-hydraulic servo valves are started at the same time to control the supply pressure, and if one of them fails, the other can independently complete the control target; when the right valve is a cold backup, the backup valve is started only when the main valve fails to complete the control target. The dual-redundant servo valve proposed in the present invention is composed of a pair of twin electro-hydraulic servo valves on a common housing. The reliability model of the left valve and the right valve of the valve system is a parallel relationship. As long as one of the electro-hydraulic servo valves is normal, the dual-redundant servo valve can complete the regulation and control of the output pressure.

The application relates to a failure prediction maintenance method and device with a preventable strategy, and relates to the field of equipment prediction and maintenance technology. In view of the problems in the prior art that a traditional two-state reliability model is difficult to accurately describe the degradation degree, failure condition and maintenance state of a system in a service life, the calculation process is relatively single, and the result error is relatively large, the technical scheme is as follows: the failure prediction maintenance method with the preventable strategy comprises the following steps: collecting a probability distribution obeyed by the residual life of a system; collecting maintenance time and maintenance cost; establishing an optimization model; iteratively processing the model to obtain an optimal failure prediction interval and a residual life threshold value; when the optimal failure prediction interval is reached, the residual life of the system is predicted, the residual life is compared with the threshold value, and if the residual life is lower than the threshold value, the system is subjected to corrective maintenance, and if the residual life is higher than the threshold value, the system is subjected to preventive maintenance. The application is suitable for application in preventive maintenance work, and establishes a new research basis for the research of preventive maintenance work.

The present invention belongs to the field of intelligent transportation technology, and specifically discloses a channel resource allocation method and system based on hierarchical reinforcement learning. The present invention proposes a fairness model based on Jain's fairness index, which is used to measure the fairness of resource allocation and ensure that the resource allocation between different vehicles is relatively balanced; a reliability model is established, which measures the probability of successful transmission of information within a specified time by introducing the concept of interruption probability, thereby ensuring the communication needs of the V2V link; a delay model is designed based on the information age AOI model, which is used to evaluate the time it takes for information to be generated and received, so as to ensure timely transmission of information. The present invention also constructs a cellular vehicle network channel resource allocation model based on hierarchical reinforcement learning. Through hierarchical design, the model decomposes the cellular vehicle network channel resource allocation problem into an upper-layer clustering problem and a lower-layer intra-cluster channel resource optimization problem, thereby ensuring fair and efficient resource allocation in a complex environment.

This invention provides a high-end scientific instrument reliability analysis and design method based on SysML, belonging to the field of instrument reliability analysis technology. The method includes: constructing a system model of the target instrument using SysML, the system model including a requirements diagram, internal block diagram, and attribute library; extracting all failure modes from all failure mode libraries and assigning them global numbers to establish a failure mode mapping matrix; performing failure mode and impact analysis (FMEA) for the target instrument based on the system model to obtain a failure mode and impact analysis table; establishing a parameterized reliability model for each functional path; calculating the reliability constraints of each functional path based on the system model and the impact analysis table; and performing reliability analysis based on the measured attributes and reliability constraints of sub-components to generate an optimization scheme. The advantages of this invention are improved accuracy, consistency, and automation of reliability analysis.

The invention discloses an underwater vehicle control system competitive failure reliability evaluation method, belongs to the technical field of underwater vehicle reliability evaluation, and is used for evaluating the underwater vehicle control system competitive failure reliability and preprocessing multi-source monitoring of an underwater vehicle control system. Constructing an underwater vehicle control system reliability model; constructing a non-time-delay parameter and a time-delay related parameter, and calculating estimated values of the non-time-delay parameter and the time-delay related parameter by using a least square method, a statistical inference method and a time-delay structure identification method; and introducing an alternating iterative optimization mechanism to carry out parameter estimation, and calculating the reliability of the underwater vehicle control system. According to the method, accurate modeling and reliability evaluation of the competitive failure degradation process and the impact coupling effect of the underwater vehicle control system are realized by constructing a dynamic adaptive feature extraction model and a time-delay related degradation theory and adopting a parameter estimation and alternate iteration optimization strategy.

A closed-loop selective attention system for resolving conflicts in multi-source or multi-speaker environments, including a plurality of internal attention models, each outputting a probability distribution over candidate sources and an associated confidence score, a fuser detecting conflicts when two or more of said attention models output high-confidence predictions that disagree, a selective sampling policy querying one or more external agents, wherein each external agent possesses a knowledge base, a reliability model, and a communication protocol, a trust and reliability module assigning and updating dynamic trust scores for internal and external agents based on past performance, an efficiency optimizer minimizing communication overhead and decision delay by balancing token usage cost and latency cost, and a dynamical system formulator ensuring convergence of the conflict resolution process under bounded trust, decaying step size, and limited sampling.

The present invention discloses a method and system for allocating production and maintenance coupled tasks that considers the operating status of equipment. The system includes: a digital twin platform that simulates the allocation scheme obtained by the task allocation decision module to verify its feasibility; an equipment model management module that establishes a reliability model for the status of the equipment and establishes a numerical model for the production process of the equipment; an order receiving module that decomposes the order into production tasks for each equipment; a maintenance strategy management module that manages the maintenance strategy and selects the maintenance strategy corresponding to the equipment based on the current equipment reliability; a task allocation decision module that unifies the planning of production tasks and maintenance tasks and provides an allocation scheme; and an allocation result interaction module that outputs the final allocation scheme and interacts with the physical production line to provide production guidance for the physical production line. The present invention solves the problem of conflict between production and maintenance in the use of time resources, and achieves simultaneous and efficient production and maintenance.

The invention discloses an open source big data system software reliability model with fault removal efficiency obeying Weibull distribution, and belongs to the technical field of software reliability analysis. Performance fitting and prediction experiments are carried out through eight fault data sets and nine software reliability models, 95% confidence interval analysis is carried out, and results show that the model provided by the invention has better fault fitting and fault prediction performance than other models. The model provided by the invention can effectively and accurately evaluate the reliability of the open source big data system software, and has certain guiding significance for actual software development and testing.

The present invention discloses a method and system for fatigue reliability analysis of welding joints of a floating platform. The method includes the following steps: Step S1, based on the notch strain method, establish a fatigue damage calculation program for the welding joints of the floating platform of an offshore floating wind turbine; Step S2, construct a fatigue reliability model of the welding joints and analyze and determine its deterministic parameters and random variables; Step S3, use the first-order reliability method to iteratively solve the design point of the fatigue reliability model; Step S4, calculate the fatigue reliability of the welding joints of the floating platform of the offshore floating wind turbine and the sensitivity of each design variable according to the design point. The fatigue reliability and sensitivity analysis carried out by the present invention can also effectively identify the influence of the uncertainty of each random variable on the fatigue reliability of the welding joints of the floating platform of the offshore floating wind turbine, so as to extend it to the design of the welding joints of the floating platform of the offshore floating wind turbine to improve the fatigue reliability of the welding joints of the floating platform of the offshore floating wind turbine.

The invention provides an unmanned system cluster task importance analysis method based on task reliability. The method comprises the following steps: constructing a task scene through unmanned system cluster constituent elements; based on the task scene, performing task reliability calculation according to the unmanned system cluster constituent elements to obtain an unmanned system cluster task reliability model; performing importance calculation on the unmanned system cluster task reliability model through an equipment state and a cluster killing chain to obtain an unmanned system cluster task importance analysis model; and performing maintenance optimization sorting according to the unmanned system cluster task reliability model and the unmanned system cluster task importance analysis model to obtain an unmanned system cluster optimization strategy. According to the method, reliability and importance analysis is carried out on the unmanned system cluster, and key task node identification and maintenance priority ranking are realized, so that the task execution flexibility, robustness and cost effectiveness of the unmanned system are remarkably improved.

The application discloses a kind of dynamic multi-energy coupling and conversion comprehensive energy system reliability evaluation method.Establish the multi-state reliability model of multi-energy production element considering dynamic multi-energy coupling, establish the multi-state reliability model of multi-energy conversion element considering dynamic multi-energy conversion, establish the multi-state reliability model of multi-energy transmission element considering dynamic multi-energy coupling, according to the multi-state reliability model of three kinds of elements, the multi-state reliability model of three kinds of subsystems is constructed;According to the multi-state reliability model of three kinds of subsystems, the reliability of comprehensive energy system considering multi-energy coupling and conversion is obtained by using improved general production function method, and reliability evaluation is realized.The dynamic characteristics of the comprehensive energy system can be more accurately characterized by using the multi-state model, and the reliability of the comprehensive energy system that meets the accuracy requirements is accurately calculated.

The application discloses a kind of multi-energy coupling and conversion comprehensive energy system multi-state reliability evaluation method.Establish the multi-state reliability model of multi-energy conversion element considering multi-energy conversion, using clustering algorithm reliability aggregation, and mapping formation aggregated multi-state reliability model;Multi-state reliability model of multi-energy coupling element considering multi-energy coupling is established, and then using Gaussian approximation and sampling approximation reliability approximation processing in sequence obtains multi-state approximate reliability model;The aggregated multi-state reliability model and multi-state approximate reliability model are processed according to the fast reliability evaluation method to obtain the reliability of the comprehensive energy system, and the reliability evaluation is realized.The application improves the traditional reliability evaluation method from time and accuracy, reduces the calculation time, and quickly and effectively realizes the estimation of system reliability.

This invention discloses a method for generating operation and maintenance schemes for deep-water explosive containers based on generative adversarial networks (CGANs), relating to the field of deep-water explosive container safety operation and maintenance technology. Based on the maximum strain prediction value output by the optimal GRU strain prediction model, the fuzziness of material performance parameters is first processed using a fuzzy interval reliability model to obtain a reliability interval. Then, using this reliability interval as input, a random interval hybrid reliability model is combined to fuse the randomness of the predicted strain, calculating a reliability index η used to quantify the container's safety status. This invention generates virtual data that conforms to the real distribution and has physical rationality through conditional generative adversarial networks (CGANs), effectively compensating for the lack of historical data under extreme conditions such as high explosive dosage and high water pressure, enhancing the generalization ability of data-driven models, and significantly improving strain prediction accuracy in scarce sample intervals.