158 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 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 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 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 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 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.

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.

The invention relates to the technical field of electric power information, and provides a frequency modulation market-oriented electric vehicle package design and parameter formulation method and system. The frequency modulation market-oriented electric vehicle package design and parameter formulation method comprises the following steps of: obtaining user historical data, and constructing a user preference fuzzy set of a user under an evaluation criterion according to the acceptance degree of the user on the evaluation criterion; based on all retail frequency modulation packages, calculating the conformity degree of the packages and the evaluation criteria, and constructing a package intuitionistic fuzzy set of each package under the evaluation criteria; matching the user with the package to obtain a similarity measurement matrix, and calculating the probability that the user selects a certain package to obtain a user package selection probability matrix; based on the user plan selection probability matrix, plan subsidy cost is determined; and constructing a target function based on the energy purchase cost, the package subsidy cost, the expected risk cost and the frequency modulation income, and solving an optimal scheme in combination with constraint conditions. And a user selection behavior can be simulated through the fuzzy set to formulate package parameters.

The invention discloses an auxiliary power device modeling method based on fuzzy set intelligent optimization. The method comprises the following steps: firstly, establishing models of all parts of the APU according to an aerodynamic thermodynamic law, and constructing a common working equation set; then, the particle swarm optimization process is divided into four typical states of exploration, depth, convergence and local optimum, and the current state is identified in real time by using a fuzzy estimator. And on the basis, optimizing hyper-parameters such as inertia weight, cognitive coefficient and social coefficient are adaptively adjusted according to the recognition state. And finally, taking a residual weighted least square function with a quadratic penalty term as a fitness index, and driving a particle swarm to solve an equation set. According to the method, through the state recognition and parameter self-adaption mechanism, the defects that a traditional Newton method is high in dependency on an initial value and a basic particle swarm algorithm is prone to falling into local optimum and slow in convergence are effectively overcome, and efficient and steady solution of the APU model is achieved.

This invention relates to a DNN fuzzy recognition model training method. The method involves extracting features from input sample objects using an object-oriented DNN fuzzy recognition model, mapping these features to obtain the object's classification probability distribution, and mapping these classification probabilities to membership degrees of fuzzy sets in a fuzzy sample model library. The model is trained by calculating a loss function value based on the classification probability distribution using sample annotations. After one generation of training, the model is adjusted by calculating a loss function value based on the fuzzy set proximity using sample annotations. If the result of either loss function calculation does not meet the preset loss value requirement, the samples in the fuzzy sample model library are randomly shuffled and reordered, and the calculation is iteratively repeated until the results of both loss function calculations meet the preset loss value requirement. This invention also relates to a method and apparatus for identifying hydrological and meteorological elements using a model trained using the above method. This method can simulate crew members automatically identifying hydrological and meteorological elements, improving the accuracy of identification and significantly reducing the workload of crew members.

The application discloses a two-stage distribution robust planning method of a comprehensive energy system considering flexibility, relates to the technical field of comprehensive energy system optimization planning and intelligent decision-making, and comprises the following steps: constructing a comprehensive energy system equipment and multi-network coupling model; establishing a random source and load model of photovoltaic available output, wind power available output, and electric load, heat load and gas load; extracting covariant information corresponding to a target scene, and constructing a conditional experience distribution and a Wasserstein conditional distribution fuzzy set based on historical error samples; establishing a two-stage distribution robust optimization model of one-stage capacity configuration and two-stage operation scheduling, which takes the minimum of annualized investment cost and expected operation cost under the worst distribution as a target and contains flexibility margin constraints; and obtaining an optimal capacity configuration scheme of the comprehensive energy system through a model reconstruction and decomposition algorithm. Compared with the prior art, the application can improve the flexibility, robustness and economy of the planning result of the comprehensive energy system under uncertain operation conditions.

The invention discloses a method and a device for participating in active and reactive power joint clearing of a power distribution network by energy storage under uncertainty, and relates to the technical field of power distribution network dispatching and energy system optimization, and the method comprises the steps: obtaining a topological structure, line parameters, distributed resource parameters and source load historical data of the power distribution network; inputting source load historical data into the fuzzy set model, and inputting a power distribution network topological structure and line parameters into power distribution network physical constraints based on a linearization power flow model; embedding the node net load power uncertainty represented by the fuzzy set model into the physical constraint of the power distribution network by introducing a distributed robust opportunity constraint to obtain an opportunity constraint, and converting the opportunity constraint into a deterministic constraint; solving the active-reactive joint clearing model to obtain active and reactive scheduling plans of each node of the power distribution network by taking the minimization of the total cost of system operation as a target; power distribution node marginal electricity price considering uncertainty is obtained through calculation on the basis of dual variables of the active-reactive joint clearing model, and active electric energy and reactive support service in the market are settled on the basis of the uncertainty power distribution node marginal electricity price and active and reactive scheduling plans of all nodes of the power distribution network.

The application discloses a heat management control method, system and computer equipment based on adaptive fuzzy logic. The application fuses adaptive fuzzy sets, multi-target game decision and extended state observer technology, realizes accurate disturbance tracing through constructing a disturbance causal graph, generates Pareto optimal control parameters with the aid of a four-party game architecture, and dynamically compensates control actions with a three-layer ESO. The implementation process includes signal monitoring and causal graph generation, game decision and parameter optimization, observer execution and compensation, and effect evaluation and self-tuning closed loop. The application significantly improves the dynamic balance capability of the system among stability, response speed and energy efficiency, and has strong adaptability and robustness.

The invention discloses a power grid operation optimization method and system based on power grid loss, and the method comprises the steps: obtaining power grid operation historical data, and constructing an uncertainty data set of renewable energy sources and loads in a power grid based on a prediction error of the power grid operation historical data; establishing a power grid loss mathematical model based on the linearized power flow equation; by taking minimization of the network loss as a target and combining with the network loss out-of-limit risk of the conditional value-at-risk constraint key line, constructing a distributed robust optimization model; and dynamically solving the distribution robust optimization model so as to adjust and optimize a power grid operation strategy in real time. According to the method, through covering the non-Gaussian uncertainty distribution of photovoltaic output and load, a distribution robust optimization model taking network loss minimization as a target is constructed, and a CVaR constraint out-of-limit risk is embedded; a closed-loop feedback mechanism is designed, a fuzzy set and a regulation and control strategy are updated in a rolling mode based on real-time data, distributed power supply output, energy storage charging and discharging and reactive compensation instructions are adjusted in a self-adaptive mode, power grid loss is reduced, and meanwhile voltage safety and line safety are ensured.

This invention discloses a packet loss detection method for unmanned surface vessel (USV) formations based on adaptive fuzzy membership filtering. The method includes: establishing state equations describing the motion of both the leader and follower USVs; linearizing these nonlinear equations to simplify the complex curvilinear motion relationships into linear mathematical expressions, thus obtaining simplified state equations for the entire formation system; introducing an adjustable measurement noise figure based on the simplified formation model, dynamically adjusting the filtering intensity according to the current measurement noise level, thereby more accurately estimating the real-time state of the USVs; and simultaneously detecting packet loss during state updates, combining preset communication quality indicators and real-time calculated noise levels to determine the reliability of received data. This invention achieves good detection results for packet loss in noisy channels and can be widely applied in the field of data detection.

The invention provides a hydroelectric power transmission project construction accident risk analysis method based on an RF-WA-PHFWFGSM operator, and the method comprises six core steps: data preprocessing, RF preliminary feature screening, improved expert simulation evaluation, WA-PHFWFGSM operator aggregation, comprehensive verification, and result output, and each step is progressive layer by layer to form a complete risk analysis closed loop of data, features, evaluation, verification, and application. Aiming at the problems of expert evaluation hesitation and information uncertainty, a probability hesitation fuzzy set (PHFS) is used for processing qualitative attribute feature evaluation, expert hesitation attitudes and fuzzy information are completely reserved through language terms and probability distribution and quantification of expert opinions, information loss caused by the fact that a traditional method is simplified into a determined numerical value is effectively avoided, and the method has the advantages of being high in accuracy and high in reliability. And the authenticity and integrity of expert evaluation results are improved.

The invention belongs to the field of cloud computing, and provides a computing power network work load resource mode prediction method, device, equipment and medium, and the method comprises the steps: determining a shape set according to a work load set, carrying out the processing through a membership function, obtaining the membership degree of the work load to a mode fragment with the combined multi-resource utilization rate, and obtaining the mode fragment with the combined multi-resource utilization rate; further determining time and space coupled workload distribution of the computing power network; determining a first-order dynamic neighbor load set according to the working load, and determining a non-stationary fuzzy set according to the first-order dynamic neighbor load set and the fuzzy set; determining fuzzy representation of competition of a working load resource mode in the target computing power network by adopting an r-hop space tuple and a link loss factor; and predicting the mode of the workload resource of the target computing power network in the next time slot by adopting a multi-order adaptive cross entropy loss function. The method has the beneficial effect that the prediction accuracy of the working load resource mode of the computing power network is improved.

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 configuration method and system based on information gap decision, and the method comprises the steps: taking the lowest comprehensive cost as an objective function, and building a power grid side wind and light energy storage optimal configuration model considering inertia constraint; processing the data by using a multivariate time sequence density peak value clustering algorithm based on a principal component analysis method; 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, considering the uncertainty of wind and light output prediction, and converting the distribution robust optimization configuration model based on an information gap decision theory; and converting the distributed robust optimal 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.