215 results about "Fuzzy set" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention discloses a fine-grained access control method and system based on risk identification, and belongs to the technical field of information security. According to the method, user subject attributes, behavior attributes and system environment attribute information are collected in real time, a standardized decision matrix is constructed, an interval type-2 fuzzy set (IT2FS) is used for conducting fuzzy modeling on the attributes, and an upper membership matrix and a lower membership matrix are generated. And calculating the dynamic weight of the attribute index in combination with a CRITIC method, introducing a time decay factor to dynamically correct a risk score through an improved TOPSIS method, calculating the Euclidean distance between an access request and a positive / negative ideal solution, and generating a normalized risk closeness degree. And based on the risk score and a preset threshold value, dynamically matching a hierarchical permission strategy, and adopting a static rule and a priority coverage mechanism to eliminate permission conflicts. According to the method, multi-dimensional risk assessment and dynamic weight adjustment are fused, the problems of insufficient real-time performance, subjective weight dependence and weak uncertainty processing capability in a traditional method are solved, the accuracy and security of access control are remarkably improved, and the method is suitable for scenes with high security requirements such as cloud computing and finance.

Provided in the present application is an energy optimization method for a multi integrated energy system (MIES) including shared energy storage. A unified shared energy storage power station is established by the MIES, thereby saving on the initial investment costs of subsystems respectively establishing energy storage apparatuses; and shared energy storage is more conducive to electric energy transmission inside the system, thereby saving operation costs. In the present application, the optimization method for an MIES including shared energy storage is provided, i.e., a two-stage three-layer optimization model is established: the upper layer is a shared energy storage power station capacity configuration model, and the lower layer is an MIES optimization scheduling model. In the present application, an uncertain fuzzy set is constructed on the basis of a Wasserstein distance and moment information, such that first-order moment information is merged into a Wasserstein distance fuzzy set, thereby making the establishment of the fuzzy set more accurate. By means of the present application, an MIES including shared energy storage can be constructed, thereby saving on the initial investment costs of subsystems respectively establishing energy storage apparatuses; and shared energy storage is more conducive to electric energy transmission inside the system, thereby saving operation costs.

The invention relates to a quality risk assessment method for a highway bridge engineering construction stage and a medium, and the method comprises the steps: determining the quality influence factor indexes of the highway bridge engineering construction stage, carrying out the dimension reduction through employing a PCA method, and constructing a quality risk index system, wherein the quality risk index system comprises a risk factor layer and a corresponding index layer; based on the quality risk index system, adopting a DEMATEL method to calculate the subjective influence weight of each index; based on the quality risk index system, an improved CRITIC method is adopted to calculate the objective weight of each index; based on the subjective influence weight and the objective weight, determining a comprehensive weight of each index by adopting a minimum identification information theory; and based on the comprehensive weight, combining the determined grade standard interval, and based on a variable fuzzy set theory, carrying out quality risk assessment, and outputting a quality risk assessment result. Compared with the prior art, the method has the advantages of improving the reliability of risk grade evaluation and the like.

The invention discloses a method for effectively studying and judging false alarm of a fire alarm system in a transformation power station in a multi-dimensional mode, and the method comprises the steps: collecting and fusing multi-channel sensor data, such as smoke concentration, temperature, humidity, electromagnetic field intensity, grounding state and the like, and constructing a scenarized data set in combination with an environment label and an operation and maintenance log; through multi-level feature processing such as normalization, wavelet denoising and dimension reduction, the data quality and discriminability are improved; according to the method, the space-time diagram neural network is used for achieving multi-period environment subarea and typical signal mode division, multi-period fusion confidence judgment is conducted on alarm signals based on the fuzzy set theory and Bayesian reasoning, dynamic judgment of false alarms and real alarms is achieved, and the accuracy and robustness of alarm judgment in the complex environment are effectively improved.

The invention discloses a quantum fuzzy neural network adaptive to high-dimensional input and a classification method, and relates to the field of quantum calculation and fuzzy neural networks and the field of computer vision. The network input layer receives high-dimensional data, amplitude coding, forward and reverse enhanced chain entanglement layer, parameterized quantum transformation and fuzzy set mapping are carried out through a quantum fuzzy feature extraction module, and dynamic dimension fuzzy features are output; high-dimensional neural features are extracted through a DNN feature extraction module to adapt to quantum fuzzy feature dimensions; dynamically distributing the weights of the quantum fuzzy features and the classic neural features through an adaptive feature fusion module; and carrying out Softmax classification on the fusion features through a classifier, and outputting a category probability. According to the method, the high-dimensional data coding efficiency can be effectively improved, the complex fuzzy logic relation learning capability of the quantum part and the quantum state correlation stability are enhanced, the uncertainty of the data is represented, and accurate classification of high-dimensional uncertainty images is realized while noise interference is reduced.

The invention relates to an electric energy dispatching method for virtual power plant multi-subject system game, which comprises the following steps: acquiring and constructing a Wasserstein distance fuzzy set of electricity price and renewable energy output based on historical power plant data, and respectively establishing a superior power grid electricity price predicted value and a fuzzy set constraint of renewable energy output; establishing a VPP multi-subject collaborative transaction optimization model among an operator, an electric vehicle charging station and a producer, and reconstructing the VPP multi-subject collaborative transaction optimization model based on a Wasserstein distance fuzzy set and a maximum harsh lower bound method; and for the reconstructed VPP multi-agent collaborative transaction optimization model, establishing a game equilibrium model based on a non-cooperative game theory, and solving through a stationary point optimization method to obtain an electric energy scheduling scheme among multiple agents of the virtual power plant. Compared with the prior art, the method has the advantages that the robustness of game decision results is improved, and the flexibility and intelligence of electric energy utilization are improved.

The invention discloses a flexible resource distributed optimization scheduling method and system considering wind power uncertainty, and the method comprises the steps: constructing a distribution robust opportunity constraint according to a wind power prediction error fuzzy set, and carrying out the convex approximation of the distribution robust opportunity constraint through introducing a conditional value-at-risk, carrying out linearization processing on the distribution robust opportunity constraint after convex approximation; based on the distribution robust opportunity constraint subjected to linear processing, multi-element flexible resource differentiation operation characteristics of an energy storage system, an electric vehicle and an adjustable load are considered, and a flexible resource collaborative scheduling model considering the wind power uncertainty distribution robust opportunity constraint is constructed; adopting a consensus alternating direction multiplier algorithm to reconstruct the flexible resource collaborative scheduling model considering the wind power uncertainty distribution robust opportunity constraint into a distributed optimal scheduling model; and improving a consensus alternating direction multiplier method through a branch and bound method, processing an adjustable load discrete variable, and solving the distributed optimization scheduling model to obtain a flexible resource distributed optimization scheduling strategy.

The invention discloses an image classification method based on distributed robust optimization, particularly relates to the technical field of image processing and image classification, and aims to construct a first-moment constraint fuzzy set based on a distributed robust optimization theory and fusion of multi-dimensional statistical characteristics of training data so as to realize stable discrimination and robust optimization of a model on a target category. Meanwhile, the upper bound of the loss is dynamically adjusted in combination with a dual optimization strategy, and the classification and discrimination precision of the model in a complex environment is improved through weighted solution of expected risks and worst case risks. The method can be widely applied to the fields of medical images, industrial visual inspection and the like, and has relatively high distribution offset adaptive capacity and abnormal sample discrimination capacity.

The invention discloses a method for establishing a risk management model of a hydrogen-doped natural gas pipeline. Firstly, a Bow-Tie model system is used for analyzing a failure reason and an accident consequence development scene of the hydrogen-doped natural gas pipeline; then, the model is mapped into a Bayesian network by utilizing GeNIe software; and calculating the prior probability of the basic event through an expert evaluation method and a fuzzy set theory. Setting a conditional probability table of the Bayesian network by using a fault tree mapping method and a historical data calculation method; on the basis, simulating an equipment component failure process through a Markov model, introducing hydrogen corrosion and hydrogen-assisted fatigue crack propagation models to carry out hydrogen-related dynamic analysis, and finishing parameter setting of a dynamic Bayesian network; and finally, calculating the failure probability of the hydrogen-doped natural gas pipeline and a potential life loss result when an accident occurs in combination with a model output result. The method adopts the dynamic Bayesian network to predict the pipeline failure probability, is more in line with the actual operation condition, and can be widely applied to the risk management process of the hydrogen-doped natural gas pipeline.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The present application discloses a distributed power supply configuration method and apparatus for an energy storage system, and a device. The method comprises: on the basis of an optimization objective, constructing a configuration optimization model of an energy storage system; performing objective unification processing on the configuration optimization model to generate a fuzzy optimization model; and acquiring distributed power supply location information of the energy storage system, and performing calculation on the distributed power supply location information by means of the fuzzy optimization model, so as to perform distributed power supply configuration on the energy storage system. When constructing the configuration optimization model, a plurality of optimization objectives are considered, comprising a minimum grid loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value, thereby helping implement comprehensive optimization of an energy storage system, and improving the operating efficiency and stability of a power grid. Objective unification processing is performed on the configuration optimization model by means of fuzzy set theory, thereby preventing conflicts among sub-objectives. Power supply configuration is performed on the basis of a calculation result, so that power flow distribution of a power grid can be optimized, thereby reducing grid loss and voltage deviation.

The present application relates to the technical field of supply chain management, and in particular to a supply chain resilience path identification method based on fuzzy set qualitative comparative analysis. The method comprises: acquiring historical operation data of nodes in a supply chain and response data under a sudden incident; on the basis of the similarity of the historical operation data between the nodes, obtaining the degree of operational correlation between the nodes; grouping all the nodes to obtain a plurality of node group clusters; on the basis of the response consistency of the nodes under the sudden incident, screening out a key node group cluster; on the basis of the distribution characteristics of the response data of different nodes, obtaining the response priority of each node; on the basis of the change difference of the response data between each node in the key node group cluster and other nodes in the same batch, obtaining the degree of external interference of each node; and on the basis of the degree of external interference, correcting the response priority to obtain a corrected response priority. The present invention can improve the evaluation efficiency and decision support capability of supply chain resilience.

The invention discloses an intelligent financial risk assessment optimization method, which relates to the technical field of risk assessment and comprises the steps of integrating financial data information and constructing a probability decision model; probability fuzzy scores of experts are collected, a decision matrix is constructed based on the probability fuzzy scores and a probability decision model, and the comprehensive weight of each alternative scheme is calculated in combination with grey number correlation degree analysis and a fuzzy set theory; performing differential risk control strategy evaluation on the financial risk based on the comprehensive weight; and constructing an intelligent approval platform based on the differentiated risk control strategy. Through multi-source data fusion, uncertainty quantitative modeling and dynamic strategy generation, precision improvement in financial risk assessment is realized, a grey number probability hybrid model reduces errors caused by data missing and subjective judgment, a risk control strategy updating period is shortened, manual recheck cost is reduced through automatic approval and differentiated risk control, and the risk assessment efficiency is improved. And meanwhile, the default loss of high-risk customers is reduced.

The invention provides a steering-by-wire control method and system based on particle swarm-sliding mode control and a fuzzy radial basis function, and the method comprises the steps: carrying out the adaptive approximation of an uncertain item and unknown disturbance of a state-space equation based on a radial basis function neural network in combination with an adaptive law, and obtaining the real-time estimation values of the uncertain item and the unknown disturbance; a sliding mode surface is obtained based on the wheel rotation angle error, the sliding mode surface is corrected and compensated based on the real-time estimation value, parameters of the sliding mode surface are optimized in combination with a particle swarm algorithm, and an optimized sliding mode surface is obtained; on the basis of a fuzzy logic controller, the optimized sliding mode surface and the change rate of the optimized sliding mode surface are converted into membership degrees of a fuzzy set for fuzzy logic reasoning, and the fuzzy set of control behaviors is obtained; obtaining a control signal based on the fuzzy set of control behaviors; and completing steering-by-wire control of the vehicle based on the control signal. According to the technical scheme, the response speed and the anti-interference capability of the steer-by-wire system can be improved.

The invention provides an overhead transmission line fault loss assessment method based on spherical fuzzy and entropy weight methods, and relates to the technical field of transmission line fault loss assessment. Comprising the following steps: S1, acquiring natural disaster data of a to-be-evaluated line area; s2, dividing the fault loss into a plurality of evaluation dimensions, and constructing a hierarchical fault loss evaluation system by taking natural disasters as influence factors; s3, based on the disaster data, calculating fault loss cost of each evaluation dimension under different natural disasters; s4, using a fuzzy set theory to quantify the influence degree of the natural disaster on each evaluation dimension, and calculating a subjective weight; s5, calculating the objective weight of each evaluation dimension by adopting an entropy weight method; and S6, determining a comprehensive weight through the subjective and objective weight fusion model, and evaluating the fault loss of the overhead transmission line based on the comprehensive weight. In the whole life cycle cost calculation, fault loss evaluation is added, accurate quantification of multi-dimensional loss is realized, and the reliability of the line is improved in the later line design process.

The invention discloses a village-town crossing road maintenance construction safety risk dynamic evaluation method, and belongs to the field of artificial intelligence modeling and road engineering safety, and the method comprises the steps: building a risk index system comprising multi-level indexes; based on the risk indicator system, a dynamic Bayesian network model is constructed, and the model comprises a root node and a conditional dependency relationship between the nodes; determining the fuzzy probability of each root node in the dynamic Bayesian network model by adopting a fuzzy set theory in combination with an expert scoring method; the conditional probability of a non-root node in the dynamic Bayesian network model is corrected on the basis of a Leak Noise-or Gate expansion model; forward reasoning is carried out through a dynamic Bayesian network model, and a dynamic probability curve of the town-crossing highway maintenance construction safety risk is generated; backward reasoning is carried out through a dynamic Bayesian network model, and the posterior probability of the key risk factors is calculated. According to the method, the safety in the village-town-penetrating maintenance construction process can be guaranteed.

A method and device for detecting security of Internet of Things data of market transaction, and an electronic apparatus are provided. The method includes: calculating at least one first trapezoidal fuzzy set having a number of attacks of data; calculating at least one second trapezoidal fuzzy set of an error rate of the data; calculating at least one third trapezoidal fuzzy set of a repetition rate of the data; calculating at least one fourth trapezoidal fuzzy set of miss rate of the data; determining a security detection value according to the first trapezoidal fuzzy set; determining a false detection value according to the second trapezoidal fuzzy set, the third trapezoidal fuzzy set and the fourth trapezoidal fuzzy set; and determining security of the Internet of Things data of the market transaction according to the security detection value and the false detection value.

The invention discloses an equipment failure risk analysis method based on a probability hesitant fuzzy evidence theory, and relates to the field of failure risk analysis, and the method comprises the steps: S1, building a fault tree structure, and constructing a reference failure rate set of basic events in a fault tree; s2, acquiring evaluation data of an expert on a reference failure rate set of the basic event, and obtaining an adjustment score and a tendency degree of the basic event; s3, converting the adjustment score into an evaluation probability by using a score conversion rule; s4, establishing a qualification weight according to the qualification of the expert; quantizing and adjusting the uncertainty of the score and the tendency degree to obtain an evaluation uncertainty weight, and carrying out weighted summation on the two weights to synthesize a total weight; s5, based on the evaluation probability and the total weight, synthesizing a final evaluation probability through a Dempster-Shafer evidence theory; and S6, performing fault tree analysis based on the final evaluation probability. According to the method, the probability hesitant fuzzy set and the Dempster-Shafer evidence theory are introduced, the diversity and uncertainty of expert evaluation are reserved, and conflicts and consistency between evidences are effectively processed.

The invention relates to the technical field of compressed air energy storage, in particular to a thermal power coupling compressed air energy storage system robust scheduling method, device and equipment, and the method comprises the steps: constructing an operation model of each subsystem in a topological structure of the thermal power coupling compressed air energy storage system; a comprehensive operation model of the whole thermal power coupling compressed air energy storage system is established based on the operation model of each subsystem, thermal power resources can be utilized to the maximum extent, the energy storage efficiency is improved, the comprehensive model serves as a thermoelectric system hub, a power flow model on the power grid side and a heat supply network model on the heat supply network side are combined, an optimal scheduling model is established, and the energy storage efficiency is improved. A new energy output fuzzy set is obtained to cope with uncertain factors in actual operation, fuzzy set parameters are set according to actual data of a power grid in a target region, and the parameters are input into an optimal scheduling model to output an optimal scheduling strategy, so that the system operation cost is effectively reduced, and the system performance is improved. And the adaptability to new energy fluctuation and the risk management capability are enhanced.

The invention provides a product design-oriented product recovery scheme prediction method and system. The method comprises the steps of defining evaluation influence factors of a product recovery scheme; evaluating the relative importance of each factor by using a comprehensive decision-making tool combining an analytic hierarchy process and a decision-making test and evaluation laboratory method, and calculating to obtain a comprehensive weight; the invention discloses recovery scheme probability prediction based on fuzzy comprehensive evaluation. The method comprises the following steps: establishing a factor set consisting of elements of factors influencing an evaluation object; establishing an evaluation set composed of different recovery schemes possibly made for the evaluation object; constructing a fuzzy evaluation matrix based on the factor set and the evaluation set; constructing a weight set fuzzy set of each influence factor based on the comprehensive weight; and obtaining a comprehensive evaluation model based on the weight set fuzzy set and the fuzzy evaluation matrix, obtaining the probability that a certain product part executes a set recovery scheme based on the comprehensive evaluation model, and obtaining a predicted product recovery scheme based on the obtained probability.

The invention discloses an infrared and visible light image multi-level fusion method and device based on fractional differential and an intuitive fuzzy set, and relates to the field of computer vision. The method comprises the following steps: respectively decomposing an infrared image and a visible light image based on an MDLatLRR decomposition method to obtain a corresponding basic component and a series of multi-scale detail components; fusing the detail components by using a rule based on an intuitionistic fuzzy set to obtain a detail fusion component; fusing the basic components by adopting a fractional order differential rule to obtain a basic fusion component; and obtaining a reconstructed image of the target object according to the detail fusion component and the basic fusion component. According to the method, the local texture richness is enhanced and the key feature extraction effect is improved through the rule of an intuitionistic fuzzy set, meanwhile, the overall feature of the image is effectively reserved through the global and memory features of fractional differential, and finally, the high-quality fusion effect is achieved through recombination of a detail fusion component and a basic fusion component.

The application discloses a kind of based on data-driven wasserstein fuzzy set active distribution network distribution robust day-ahead scheduling method, realize the construction compact DRO fuzzy set, and effectively reduce the conservativeness of day-ahead scheduling result;The method constructs the wasserstein condition generated adversarial network model (CWGAN-GP) based on gradient penalty norm, for wind, light output day-ahead scene generation, and proposes the abnormal sample identification method of improved DBSCAN clustering combined with CNN-BiGRU automatic encoder, to improve the credibility of generated scene set;Adopt the compact boundary of data support set determined based on non-parametric kernel density estimation (NKDE) confidence interval, and combined with wasserstein metric to construct DRO fuzzy set.The application compared with the existing DRO day-ahead scheduling method, realizes the deep combination of data-driven method and DRO model, effectively reduces the conservativeness of fuzzy set, and improves the economy of day-ahead scheduling scheme and the adaptability of coping with new energy output uncertainty under the premise of guaranteeing decision robustness.

This application relates to the field of stationary time series analysis and prediction technology, and discloses a fuzzy prediction method for non-stationary time series, including: determining the fuzzy posterior probability corresponding to the current sequence; updating the fuzzy parameters of the fuzzy set based on the fuzzy posterior probability; generating a rule base associated with the updated fuzzy parameters; performing probabilistic defuzzification prediction based on the rule base to obtain the prediction result; updating the model parameters of the Gaussian mixture model based on the prediction error set corresponding to the prediction result; determining the KL divergence corresponding to the prediction error set based on the parameters in the model parameters, the first half error set corresponding to the first half window, and the second half error set corresponding to the second half window; wherein the current window consists of a first half window and a second half window; the prediction error set consists of the first half error set and the second half error set; and performing prediction based on the KL divergence to obtain the output sequence. This solution can meet the practical prediction needs of non-stationary time series.

The application discloses a kind of based on measurement and knowledge water surface unmanned system operating state early warning method, belong to unmanned system safety monitoring technical field.To solve the problem of insufficient knowledge utilization, poor dynamic adaptability of existing early warning method, the method of the application includes: collecting position, attitude, ship body state and environment multi-source time series data, and obtaining text form expert knowledge;Adopt sliding window and standardization preprocessing data;Expert knowledge semantic vector is extracted using BERT model;Through Crossformer model coding time series features, knowledge vector is spliced and fused, and system state features are output;Risk membership degree is calculated based on fuzzy set theory, and early warning is triggered according to the maximum membership principle.The application realizes the deep integration of measurement data and expert knowledge, improves the early warning accuracy, robustness and generalization ability.

The present application discloses a method and apparatus for adjusting a memory correctable error threshold, a server, and a medium. The method comprises: setting a fuzzy control rule, wherein the fuzzy control rule comprises a correspondence between a fuzzy set of the number of memory correctable errors and a fuzzy set of the number of system interruptions, and a fuzzy set of a memory correctable error threshold; collecting statistics about information related to correctable errors within a unit period, and generating current memory correctable error information; performing fuzzy reasoning on the current memory correctable error information on the basis of the fuzzy control rule, and determining the current memory correctable error threshold having a nonlinear mapping relationship with the current memory correctable error information; and in response to a count of memory correctable errors generated by a counting register for a memory reaches the current memory correctable error threshold stored in a threshold register, triggering a system interruption.

This invention discloses a method, apparatus, equipment, and medium for the sub-Bruker optimal scheduling of hydropower stations, relating to the field of hydropower station scheduling technology. It includes: determining a fuzzy set of inflow error based on the historical estimated and actual historical inflow of at least one hydropower station; constructing a sub-Bruker optimal scheduling model for the hydropower station based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error; reconstructing the sub-Bruker optimal scheduling model based on conditional value-at-risk theory to obtain a reconstructed optimal scheduling model; and determining the target output, target head, and target power generation flow of each hydropower station based on the reconstructed optimal scheduling model and the target flow estimated value of at least one hydropower station. This solution reduces the water wastage of hydropower stations and improves their power generation and operational stability by determining the fuzzy set of inflow error and establishing a sub-Bruker optimal scheduling model.

The invention discloses a nuclear power plant reactor coolant system fault diagnosis method and a related device, and relates to the field of fault diagnosis, and the method comprises the steps: obtaining sensor data of target equipment, inputting the sensor data to a fuzzy CNN model, and obtaining a plurality of pooling feature intervals based on an interval type-2 fuzzy set; the fuzzy CNN model is determined according to the CNN model and an interval type-2 fuzzy set method; inputting all pooling feature intervals based on the interval type-2 fuzzy set into a fuzzy GRU model to obtain a plurality of hidden state intervals based on the interval type-2 fuzzy set; the fuzzy GRU model is determined according to the GRU model and an interval type-2 fuzzy set method; inputting the hidden state intervals of all the interval type-2 fuzzy sets into a fuzzy softmax model to obtain a fault diagnosis result of the target equipment; the fuzzy softmax model is determined according to the softmax model and an interval type-2 fuzzy set method. According to the invention, the fault identification precision can be improved in a complex working environment.

The invention provides a shaft coupling type whole vehicle in-loop simulation system confidence comprehensive evaluation method and device, and the method comprises the steps: putting forward a deterministic numerical evaluation index, a probability evaluation index and a semantic description evaluation index which represent the confidence level of a shaft coupling type whole vehicle in-loop simulation system around the shaft coupling type whole vehicle in-loop simulation system; detailed description of multi-dimensional features of the simulation system is realized, modeling and mapping conversion are performed on the three types of indexes and index weights represented by intervals by adopting a type-2 fuzzy set, and a confidence evaluation level corresponding to confidence is obtained by adopting an EKM algorithm so as to perform comprehensive evaluation on the confidence level of the shaft coupling type whole vehicle in-the-loop simulation system. According to the method provided by the invention, the core limitation of single evaluation dimension in the prior art is effectively broken, the uncertainty disturbance in the evaluation process is also remarkably reduced, and the scientific and comprehensive evaluation of the confidence level of the shaft coupling type whole vehicle in-loop simulation system is realized.