272 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 relates to a cardiac arrest risk grade prediction method and system based on a fuzzy logic method, and the method comprises the steps: obtaining real-time vital sign data of a patient, the real-time vital sign data comprising at least one of heart rate, blood pressure, oxyhemoglobin saturation and respiratory rate; on the basis of the real-time vital sign data, feature selection and importance evaluation processing are carried out through a machine learning algorithm, and target features most related to the sudden cardiac arrest risk are obtained; performing fuzzification processing based on fuzzy C-means clustering on the basis of the target features so as to convert the accurate numerical data into a fuzzy set; according to a fuzzy logic principle, in combination with a case-based reasoning method, performing matching calculation on the fuzzy set and known sudden cardiac arrest case characteristics to obtain a sudden cardiac arrest risk level; based on the cardiac arrest risk level, risk level evaluation is carried out, and a corresponding early warning mechanism is triggered. The implementation of the method can improve the accuracy and comprehensiveness of prediction.

The invention discloses a T-S fuzzy and TD3-based slow dynamic unknown coal-fired power generation system coordination control method, which comprises the following steps: firstly, decomposing a coal-fired power generation system into a fast subsystem and a slow subsystem by using a singular perturbation theory, and decomposing an original control task into a stabilization task of the fast subsystem and a tracking task of the slow subsystem; for the fast subsystem, a fuzzy set is selected on a definition domain of the fast subsystem by using the slow variation characteristic of steam pressure, and a T-S fuzzy model is constructed; a fast subsystem controller is obtained by solving algebraic Riccati equations corresponding to a plurality of linear systems. For a slow subsystem, a double-Q network is adopted to reduce overestimation of a Q value, a dynamic learning rate and a batch size adjustment mechanism are introduced to accelerate training convergence, and learning is carried out under a TD3 algorithm framework to obtain control input of the slow subsystem. The fast subsystem and the slow subsystem control inputs to be combined and then act on the original system to obtain state information at the next moment, and the intelligent agent interacts with the original system to complete collaborative optimization.

The invention provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises the following steps: collecting real-time state data and real-time alarm data of a target part, and obtaining external environment data and map navigation data; preprocessing the data, fusing the preprocessed data to obtain a multi-source data set target feature, inputting the target feature extracted from the multi-source data set into an adaptive neural fuzzy inference system, mapping the target feature to a plurality of fuzzy sets, and fuzzifying by using a member function; calculating the rule weight of each target feature to reflect the fuzzy intensity; and training an inference system by adopting a neural network learning algorithm, adjusting a member function and a rule weight, and converting fuzzy output into a specific numerical value or a fault level by utilizing an output member function in a defuzzification process so as to judge the health state of the part. According to the invention, high-interpretability fault diagnosis output is provided, the accuracy is improved, and fault diagnosis can be carried out in a wide vehicle operation environment.

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 the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust optimization configuration method, device and equipment and a medium. Establishing a power grid side wind and light energy storage optimization configuration model considering inertia constraint; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving; converting the two-stage distribution robust optimization configuration model into a mixed integer linear programming model by using a linearization technology; and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device. According to the method, the economical efficiency and load fluctuation can be balanced, and the problems of frequency instability and the like of a large-scale wind and light energy grid-connected power grid are avoided by considering inertia constraint.

The invention provides a cooling and heating equipment energy consumption positioning and fault prediction method under an EMS energy consumption management system, and the method comprises the steps: S1, collecting state original data of target equipment to construct a comprehensive evaluation data set, employing a source data extraction function to enable indexes to be matched to a fuzzy set, forming a fuzzy vector, and carrying out the clustering, sorting and recognition of the important features, thereby achieving the energy consumption positioning and fault prediction of the target equipment; generating device-level time series data; s2, training a health score prediction model and a fault classification model based on the time sequence data vector; and S3, performing health scoring and fault classification prediction on the real-time data by using the training model, and generating early warning information. By defining the fuzzy set and the source data extraction function and extracting key features through clustering analysis, important features are further screened by using the random forest algorithm, so that the accuracy of target equipment performance evaluation is improved, a more accurate basis is provided for subsequent fault diagnosis, and the purposes of simplifying the model, improving the analysis efficiency and reducing the cost are achieved. And remarkable economic benefits are brought to enterprise partners.

The invention discloses a safety construction management method based on digitization, and belongs to the field of construction management. The safety construction management method comprises the steps that disk saw operation data of a construction site are collected; preprocessing the collected disk saw operation data; according to the preprocessed disk saw operation data, the safe operation state of the disk saw is judged through a support vector machine (SVM) algorithm; according to the safe operation state, performing risk assessment by adopting a risk assessment method based on combination of an evidence theory and a fuzzy set theory to obtain a safety risk level; and according to the safe operation state and the safety risk level, carrying out safety management on the disk saw through a multi-level early warning control strategy based on reinforcement learning. In view of low risk assessment precision in disk saw construction in the prior art, the risk assessment precision is improved through risk assessment combining an evidence theory and a fuzzy set theory and by using a reinforcement learning early warning control strategy and the like.

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 belongs to the technical field of complex system evaluation, and provides a complex system evaluation method based on fuzzy multi-objective decision, which comprises the following steps: S1, constructing a multi-level and multi-dimensional complex system evaluation model; s2, performing fuzzification processing on the indexes under each evaluation target by using a fuzzy set theory; s3, introducing a dynamic weight distribution mechanism, and dynamically adjusting the weight of each target by adopting an intelligent algorithm according to the relative importance among the targets and the current system state; according to the method, a multi-level and multi-dimensional evaluation model is constructed, uncertainty and fuzziness in the evaluation process are processed by utilizing a fuzzy set theory, a dynamic weight distribution mechanism and an intelligent algorithm are introduced to optimize weights, an improved multi-objective decision algorithm is applied to perform comprehensive analysis, and finally optimization suggestions are proposed; according to the method, the overall condition of the complex system can be reflected more accurately, the timeliness and pertinence of evaluation are ensured, and the scientificity and accuracy of evaluation 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 provides a power grid dispatching AI test data credibility evaluation method and device. The method comprises the following steps: preprocessing power grid raw data; arranging the preprocessed data into a plurality of data pairs according to a power grid AI application scene; according to the plurality of data pairs, constructing a fuzzy set of distribution robust optimization based on a Wasserstein distance; and according to the fuzzy set, measuring a data distribution offset degree, and according to the data distribution offset degree, evaluating the credibility of the power grid dispatching AI test data. According to the method, credible factors except for data missing and abnormal problems in the data generation and transmission process of the power grid AI test data are explained through data offset, and the data offset is measured through a credibility index based on the Wasserstein distance, so that credibility evaluation of the power grid dispatching AI test data is achieved. According to the method, the credibility index is designed through the distributed robust optimization framework, so that robustness and interpretability can be provided for a power grid AI test data set and subsequent power grid dispatching AI.

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 invention provides a flow control method and device for optimizing fuzzy PID based on particle swarm optimization, and relates to the technical field of intelligent control, and the method comprises the steps: taking a sample flow error and a sample error change rate as sample input variables, taking a first control parameter Kp, a second control parameter Ki and a third control parameter Kd as sample output parameters, and creating a PID controller model; performing fuzzification processing on a current input variable to generate a fuzzy set, matching a weighting rule based on the fuzzy set, inputting the fuzzy set into the PID controller model to complete defuzzification, and obtaining a corrected output parameter; optimizing the corrected output parameter by adopting a particle swarm algorithm to obtain a target output parameter; and determining a flow control parameter in the current gas flow control environment based on the target output parameter. According to the invention, the fuzzy PID controller is designed and optimization is carried out by using the particle swarm optimization, so that a more accurate and stable control effect is realized in gas flow control.

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 relates to the technical field of slope engineering, and discloses a slope deformation damage scale prediction model construction method and device, and the method comprises the steps: defining m fuzzy factors affecting slope damage, and carrying out the quantification of the fuzzy factors; defining a fuzzy set of quantized fuzzy factors to obtain m fuzzy factor sets; performing rule base construction on the m fuzzy factor sets to obtain a rule base of fuzzy inference rules; and performing model construction based on the rule base by adopting a fuzzy neural network, and constructing a prediction model. According to the method, by introducing the fuzzy set theory and the fuzzy logic tool, the fuzzy factors in slope deformation and damage are quantified, the adaptability of the prediction model to the complex slope environment is improved, and the precision and reliability of the prediction result are improved; the theoretical model and monitoring data are effectively combined, the slope state can be dynamically evaluated in real time, a scientific decision basis is provided, precautionary measures can be taken in advance, and the slope instability risk can be reduced.

The invention discloses a new energy-energy storage combined operation strategy optimization method based on uncertainty modeling and distributed robust optimization. The method comprises the following steps: a) carrying out data acquisition and preprocessing on power generation data and power market prices of a new energy plant station; b) using a fuzzy set to express uncertainty factors in the data; c) based on the probability distribution of the uncertainty data, establishing an optimization model of new energy-energy storage joint operation through a distribution robust optimization method; d) modeling and solving the optimization problem by using a deep neural network or a convolutional neural network; and e) adjusting a scheduling strategy of the energy storage facility according to an optimization result, ensuring maximum consumption of new energy in an uncertain environment, reducing wind and light abandoning phenomena, and improving robustness of power grid scheduling. The method has the advantages that the intelligent level of scheduling is remarkably improved, and the risk and cost of a traditional scheduling method are reduced.

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 relates to a complex product multi-subject demand mining and importance analysis method. Determining a demand subject of the complex product based on the subject judgment element; constructing an information quality framework; performing noise filtering on the low-quality data based on a natural language processing technology; performing multi-subject demand fusion based on the semantic similarity and the demand subject weight; constructing a possibility degree model combining the hesitant fuzzy set and Kano; calculating a technical initial weight in combination with information entropy; and finally, the overall weight of the multi-subject demand is calculated based on combined weighting, and a multi-subject demand weight sorting result is obtained. According to the method, multiple technologies such as natural language processing, deep learning and Kano model improvement are combined, a technical scheme for carrying out complex product multi-subject demand mining and importance analysis is realized, and a decision basis is provided for enterprises to carry out product demand development and product design and manufacturing.

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 relates to a method for improving the power supply capacity of a cluster wind power and thermodynamic system in cold-wave weather, which belongs to the technical field of power system operation scheduling, and comprises the following steps: constructing an electrothermal coupling system model, considering the operation cost of each device of the system, and forming a comprehensive cost minimization objective function; considering the operation characteristics of the wind power plant under cold waves, introducing external reserve, and establishing a system safe operation constraint system; a Wasserstein distance is adopted to construct a fuzzy set, a probability distribution spherical set driven by sample data is utilized to describe the uncertainty of wind power prediction errors, a model is reconstructed, and a distribution robust optimization model is established; converting an inner-layer extreme value problem into a single-layer optimization problem by applying a duality theory, performing convex approximation processing on opportunity constraints in combination with a conditional value-at-risk theory, and constructing a solvable linear programming model; and designing a dynamic confidence level, and balancing system operation cost and power supply reliability in cold-wave weather by optimizing and coordinating economy and system risks.

According to the improved FMEA method based on the interval type-2 fuzzy set and the DEMATEL-VIKOR mixed multi-criterion decision, on the basis of traditional FMEA, a more perfect IMS key hardware risk assessment model is constructed by improving related methods. A related failure mode is obtained through a fault tree analysis method. According to the invention, the interval type-2 fuzzy set and the FMEA are combined to solve the problem of inaccurate RPN value in the traditional FMEA; calculating comprehensive weights of experts by utilizing a similarity aggregation method and combining personal backgrounds of the experts; in the aspect of risk factors, a combined weight method is adopted, so that the analysis precision can be improved, and the contribution of each factor to the overall risk can be evaluated more comprehensively by comprehensively considering the weights of a plurality of indexes; the DEMATEL-VIKOR hybrid multi-criterion decision-making method for preference relationship analysis fully considers the relevance between factors and the comprehensive influence of multiple criteria, and provides relatively comprehensive and visual decision-making support.