123 results about "Fuzzy entropy" patented technology

The invention discloses a multi-region collaborative power grid planning system and method based on an improved multi-target particle swarm optimization algorithm, relates to the technical field of power system planning, and solves the problems of multi-target coupling and cross-region coordination in traditional power grid planning by constructing an economical, environment-friendly and reliable multi-dimensional target function and introducing a game theory method to quantify a multi-target constraint relation. The system comprises a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm algorithm execution module, a collaborative decision module and a result output module, the improved particle swarm algorithm adopts dynamic adaptive inertia weight, time-varying acceleration coefficient and differential mutation operation, and the convergence speed and Pareto frontier distribution quality are remarkably improved; and the collaborative decision-making module realizes cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and a fuzzy entropy theory. According to the method, collaborative optimization of calculation efficiency and scheme balance is realized in multi-regional power grid collaborative planning, and technical support is provided for scientific planning of a complex power grid system.

The invention discloses a transformer substation on-line monitoring system based on big data analysis, and relates to the technical field of power system state monitoring, the system obtains data through a data acquisition module, and after the data is processed by a signal preprocessing and feature window extraction module, a system response entropy calculation module calculates a multi-scale permutation entropy, a multi-scale fuzzy entropy and a transfer entropy; the method comprises the following steps of: constructing a composite entropy feature vector, establishing a working condition self-adaptive health entropy baseline by an entropy feature baseline management module by utilizing a machine learning algorithm, comparing a current entropy feature with the health baseline by a degradation evaluation and critical early warning module, performing analysis by combining various abnormal judgment rules and indexes based on a critical moderation theory, and outputting an evaluation and early warning result; according to the method, the functional out-of-order of the equipment can be sensed in advance, the dynamic interaction health degree is evaluated in a non-intrusive mode, effective early warning is provided for critical transformation such as system instability, and the reliability and safety of operation of the transformer substation are remarkably improved.

The invention discloses an industrial equipment intelligent control method and system, and the method comprises the following steps: S1, collecting and preprocessing equipment operation data, and constructing a state data set; s2, establishing a trend prediction model by adopting Gaussian process regression, and outputting a state prediction value; s3, constructing a graph structure representation model, and combining a graph convolutional network and a semi-supervised learning method to carry out joint training and identify a working condition category and an operation level; s4, based on the equipment operation data, calculating a fuzzy membership degree and a fuzzy entropy index, and generating an entropy distribution curve; s5, fusing the state prediction value, the working condition category, the operation grade and the fuzzy entropy index to generate a state evaluation result; s6, adjusting control strategy parameters according to a state evaluation result, and issuing a control instruction to an equipment control system; and S7, collecting feedback data, updating the state data set, and realizing closed-loop iterative optimization. The method has the advantages of accurate prediction, stable identification, flexible evaluation, fast control, self-learning and the like, and is suitable for complex and changeable industrial application scenes.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention discloses a distribution box fault detection method. The method comprises the following steps: collecting current signals of a distribution box in different fault states; performing VMD decomposition on transient fault current signals in different fault states to obtain IMF components in different frequency bands, calculating permutation entropy of the IMF components, denoising the IMF components when the permutation entropy is greater than or equal to a preset value, and not denoising if the permutation entropy is less than the preset value; calculating the fuzzy entropy value of each IMF for the de-noised IMF components and the IMF components which are not de-noised; and taking the fuzzy entropy as a fault feature vector, clustering the fault feature vector through a GG clustering algorithm, and identifying different fault types. VMD decomposition is combined with permutation entropy screening and denoising, fault feature vectors are extracted based on multi-scale fuzzy entropy, fault classification is achieved through a GG clustering algorithm, the problems that a traditional method is low in feature extraction precision and poor in clustering adaptability are solved, and the method has the advantages of improving fault signal feature extraction precision and clustering accuracy.

The invention relates to an offshore wind speed prediction method based on a TVFEMD-FE-TCN-Transform model, and the method comprises the steps: obtaining historical offshore wind speed data, carrying out the preprocessing of the data, decomposing the original wind speed data through TVFEMD to obtain a plurality of IMF components, improving the data stability, and carrying out the prediction of the offshore wind speed. According to the method, four types of signals including a high-frequency signal, an intermediate-frequency signal, a low-frequency signal and a trend signal are generated through reconstruction according to IMF component complexity through fuzzy entropy FE, calculation complexity is reduced, a time domain convolutional network TCN is adopted to extract reconstructed signal features, fusion is performed, the reconstructed signal features are input into Transform for wind speed prediction, meanwhile, Transform model parameters are optimized through MWOA, and the wind speed prediction accuracy is improved. Predicting the offshore wind speed by using the optimal parameter combination Transform model to obtain a final offshore wind speed prediction value; according to the method provided by the invention, the problems of insufficient signal decomposition, weak feature extraction capability and low prediction precision of a single prediction model are effectively solved, and powerful support is provided for operation and maintenance management and power grid dispatching of the offshore wind power plant under the condition that the offshore wind speed has intermittent and fluctuation characteristics.

The invention discloses a robot joint motor dynamic load self-adjusting method based on fuzzy entropy optimization. The robot joint motor dynamic load self-adjusting method comprises the following steps: S1, acquiring operation parameter data of a robot joint motor by adopting sensors arranged at key joints of a robot; s2, generating a corresponding adaptive adjustment factor data set; s3, forming a load probability distribution model parameter data set; s4, applying the load probability distribution model parameter data set to real-time load prediction, determining the load trend of a robot joint motor in a future period of time, and outputting predicted load distribution data; and S5, dynamically adjusting control parameters of a robot joint motor according to the predicted load distribution data and the adaptive adjustment factor data set to form a new control strategy data set. According to the method, the control parameters of the robot joint motor are dynamically adjusted in different load states, so that the oscillation effect of the robot in a complex load environment can be effectively reduced, and energy consumption is optimized under the condition that the motion precision is not affected.

The invention provides a GIS equipment detection system and method based on partial discharge-vibration signal fusion, and relates to the technical field of GIS equipment detection. The method has the flexibility of adapting to different data conditions, and vibration signals and partial discharge signals at a contact and a basin-type insulator are respectively obtained through blind source separation of collected multi-source signals for GIS equipment fault detection; the respective sequential relationship of the vibration signal and the partial discharge signal is considered, the multi-scale fuzzy entropy of the vibration signal and the partial discharge signal is calculated, the richness represented by the vibration characteristic and the partial discharge characteristic is further enhanced, the problems of poor reliability and large detection error of a fault detection method based on a single technology are effectively solved, and the fault detection efficiency is improved. The problems that the characteristics of an existing vibration diagnosis system and the characteristics of an existing partial discharge diagnosis system are prone to omission and low in accuracy are solved. Meanwhile, a three-dimensional digital twinborn visual model of the GIS equipment is also established, and the operation of the GIS equipment can be monitored in real time in combination with fault information.

The invention relates to the field of image data processing, in particular to a part defect detection method and system based on machine vision, and the method comprises the steps: obtaining a gray image of the surface of an automobile part, carrying out the preprocessing, and extracting a surface texture image of a part region; using a fuzzy entropy method to block the surface texture image, and obtaining the gray difference degree and the abnormal degree in each block according to the gray value change and the texture expression in each block; dynamically adjusting the number of iterations of the sub-blocks based on the abnormal degree of the blocks; and optimizing an existing fuzzy entropy method according to the number of iterations to obtain a significant fuzzy entropy value of each block, and judging whether the automobile part has a bubble defect area or not based on the significant fuzzy entropy values. According to the method, the gray difference degree and the texture change in each block are analyzed, and the fuzzy entropy method is combined, so that the potential bubble defect area can be identified more accurately.

A seismic wave identification method based on deep learning comprises the following steps: step 1, acquiring seismic wave signals to obtain original data, and performing center interception and normalization processing to obtain unified signals; 2, decomposing a unified signal by using CEEMDAN to obtain a plurality of orders of intrinsic mode functions and residual components, carrying out coarse graining processing on each order of mode function according to a preset time scale parameter to divide into subsequences, constructing a similarity criterion through a fuzzy membership function, calculating fuzzy entropy values of the subsequences, and integrating the fuzzy entropy values into a feature matrix; and 4, inputting the feature matrix after the dimension reduction into a CTCM-1D-CNN model optimized by a tribe competition and member cooperation algorithm, predicting the type of an output seismic wave, and completing seismic wave identification. Therefore, the design has good seismic wave identification precision and stability.

The invention discloses a particle filtering fusion positioning method based on indoor and outdoor fuzzy judgment, which comprises the following steps of: firstly, dividing a region to be positioned into dynamic grid units, constructing a space-time signal distribution matrix, and measuring the matching degree of the space-time signal distribution matrix and a standard scene through weighted Jaccard similarity; secondly, designing a dual-channel adaptive membership function based on a fuzzy theory, and converting hard decision into probabilistic weight output; and then a four-parameter joint optimization mechanism based on environmental difference, model uncertainty, fuzzy entropy and confidence is constructed, a classification threshold and a confidence interval are dynamically adjusted, finally an improved particle filtering fusion model is constructed, a hybrid dynamics model is constructed to predict the positions of particles, and a dynamic resampling strategy is combined to improve the particle utilization rate. And the positioning accuracy and stability are improved. According to the invention, the problem that the positioning precision is reduced or the positioning result jumps frequently when indoor and outdoor scenes are switched is solved.

The invention discloses a residual chlorine determination method, and relates to the technical field of monitoring. The method comprises the following steps: immersing a potential type residual chlorine sensor into a standard residual chlorine solution, collecting a potential response value, calculating a response coefficient, and correcting a potential signal of the sensor; denoising the corrected signal by using a multi-scale fuzzy entropy algorithm to obtain a processed signal; color development time, spectral absorbance and environmental parameter data are collected, the processed signals are combined, and four-dimensional historical data are generated through multi-dimensional quantitative fusion; constructing a four-dimensional residual chlorine concentration detection model based on the three-dimensional curved surface model, and training the model by using four-dimensional historical data; inputting four-dimensional real-time data to the trained model, and outputting predicted residual chlorine concentration; and comparing the predicted value with the actual concentration, directly outputting if the error is smaller than a preset threshold value, fusing real-time data into a historical data retraining model if the error is larger than the threshold value, and outputting after iterative optimization. According to the invention, the four-dimensional residual chlorine concentration detection model is constructed, so that the refined determination of the residual chlorine content is realized.

The invention discloses a short-term photovoltaic power deep learning prediction method based on decomposition sequence complexity evaluation and clustering reconstruction. The short-term photovoltaic power deep learning prediction method mainly comprises the following steps: step 1, decomposing a photovoltaic power time sequence by using successive variational mode decomposition; 2, calculating the complexity of each decomposition component by using a fuzzy entropy algorithm; 3, dividing the components with different complexities by using Gaussian hybrid clustering, and reconstructing the components into high-frequency, intermediate-frequency and low-frequency components; step 4, using a chaos evolution optimization algorithm to optimize hyper-parameters of the bidirectional gating cycle unit (BIGRU) prediction model of each component; 5, performing short-term photovoltaic power prediction on the reconstructed high-frequency component, the reconstructed intermediate-frequency component and the reconstructed low-frequency component by adopting a BIGRU model; and step 6, superposing the predicted values of the components to obtain a photovoltaic power point prediction result. And step 7, using kernel density estimation to form a confidence interval based on a point prediction result, and finally obtaining a photovoltaic power probability interval prediction result. The method has the beneficial effects that the complexity of each decomposition component is evaluated and reconstructed by utilizing the fuzzy entropy algorithm, and comprehensive optimization of each link including model training is realized on the basis of powerful capabilities of capturing context information and processing long sequence data based on the BIGRU.

The invention provides a rolling quality parameter uncertainty quantification method based on mixed entropy-fuzzy clustering. The method comprises the following steps: S1, multi-source data fusion: integrating rolling compaction parameters, meteorological data and real-time monitoring data in an engineering construction process, and constructing a multi-dimensional feature matrix; s2, calculating mixed entropy, namely quantifying the randomness and fuzziness of parameter distribution in combination with information entropy and fuzzy entropy; s3, performing dynamic fuzzy clustering, namely, optimizing a clustering center based on an improved firefly algorithm, and dividing parameter uncertainty levels; step S4, uncertainty contribution degree analysis: quantifying the influence weight of each parameter on the rolling quality through an entropy weight-grey correlation method; according to the method, the information entropy and the fuzzy entropy are fused, and dynamic clustering and an intelligent optimization algorithm are combined, so that precise quantification and hierarchical management and control of the rolling parameter uncertainty are realized.

The invention discloses a power transmission and transformation project economic evaluation method, system, equipment and medium fusing adaptive fuzzy entropy weighting and a multi-target grey wolf optimization algorithm, and belongs to the technical field of power system economic analysis, and the method comprises the steps: constructing an economic index system, building a fuzzy membership matrix, and calculating an index weight through combining fuzzy entropy and information entropy; a comprehensive weight is generated by adopting a self-adaptive fusion mechanism, then a multi-target weighted evaluation model is constructed, and finally multi-target search is performed by utilizing a swarm intelligence optimization algorithm. According to the invention, by constructing a self-adaptive weighting mechanism fusing the fuzzy entropy and the information entropy and combining the global search capability of the multi-target grey wolf optimization algorithm, multi-index weight dynamic optimization and multi-target cooperative solution in the economic evaluation of the power transmission and transformation project are realized; the method effectively overcomes the limitation of a traditional method in the aspects of weight distribution subjectivity, insufficient index coupling processing and multi-target balance, and forms a closed-loop evaluation system from index processing to intelligent decision making.

The invention discloses a system and a method for monitoring the running state of a fan blade based on voiceprint recognition. The method comprises the following steps: acquiring voiceprint data and environment data during running of the fan blade through a data acquisition module; performing noise reduction processing by adopting an improved VMD method, and adaptively optimizing a decomposition modal number and a penalty factor through fuzzy entropy and an energy ratio; constructing a plurality of branch input data according to the voiceprint data; inputting the two types of branch data into a pre-trained multi-branch model, carrying out weighted fusion on similar results by combining a voiceprint signal-to-noise ratio and a defect confidence difference, and outputting a state result of a defect type and confidence; and when the confidence exceeds a threshold value, integrating the environment data, the blade parameters and the GAP features to generate positioning analysis data, determining a defect position through a defect positioning model, and triggering an alarm. According to the method, the feature quality is improved through adaptive noise reduction, the feature fusion effect is optimized through dynamic weight, the defect recognition accuracy is enhanced through confidence fusion, efficient positioning is achieved, and the monitoring efficiency and reliability are remarkably improved.

The invention discloses a rolling linear guide pair performance degradation state identification method under variable working conditions, and relates to the technical field of rolling linear guide pair performance state monitoring. Vibration signals at different operation speeds and different positions in the whole-life periodic performance degradation process of the rolling linear guide rail pair are collected; the vibration signals are preprocessed by using a combined denoising method based on CEEMDAN and a wavelet adaptive threshold value; extracting time domain, frequency domain and multi-scale fuzzy entropy features from the denoised vibration signals to construct a feature set; constructing a field adaptive degradation state recognition model based on a one-dimensional convolutional neural network; and carrying out degradation state identification tests of multiple groups of variable working conditions, randomly selecting data of at least two groups of working conditions as labeled source domain training data, taking data of other working conditions as unlabeled target domain test data, and identifying the performance degradation state of the rolling linear guide rail pair by utilizing a trained model. The method is suitable for linear guide pair performance degradation state recognition under various working conditions.

The invention provides a comprehensive energy efficiency evaluation method and system for a smart park, and the method comprises the steps: constructing a nonlinear metabolism matrix reflecting the energy incidence relation between equipment, and solving the optimal metabolic flux distribution through mixed integer nonlinear programming; defining an interaction rule base with a metabolic network, dynamically triggering rules according to real-time sensor data, and adjusting real-time metabolic flux; calculating the quantized value of each current energy efficiency index according to the real-time metabolic flux, quantifying the uncertainty through a fuzzy entropy theory, dynamically distributing multi-target weights, and calculating a comprehensive energy efficiency score; and calculating the quantized value of each energy efficiency index in the optimal state according to the optimal metabolic flux distribution, comparing the fuzzy entropy, determining a to-be-optimized energy efficiency index, and identifying high risks and potential risks to formulate an optimization strategy. According to the method, various equipment and energy flow conditions in the park are considered, the energy efficiency level of the system is accurately reflected, dynamic adjustment and evaluation are carried out according to real-time data, uncertainty factors of the energy system are identified and solved, and efficient and stable operation of the system is guaranteed.

The invention provides a traffic special bottleneck monitoring method based on multi-source road data, and the method comprises the following steps: S1, collecting road data of a plurality of intelligent network connection data clients, and building a road information database of different road segments; s2, performing precision complementation and error ablation on the road information of the same road section of a plurality of intelligent network connection data clients, outputting multi-source road information, and calculating overall evaluation parameters of multi-dimensional deep road section ablation; s3, calculating a road data fuzzy matrix; s4, setting and calculating the critical entropy of the traffic event; calculating a fuzzy entropy in combination with the multi-source road information and the road data fuzzy matrix; and when the fuzzy entropy is greater than the critical entropy, constructing a special event list, and listing the special event list for continuous tracking. Through the multi-dimensional multi-source deep road data ablation algorithm, the deep road data fuzzy method and the road data fuzzy entropy fluctuation recognition algorithm, the accuracy of monitoring the special traffic events is improved from the way of processing the multi-source road data.

The invention belongs to the field of geological disaster prediction, and particularly provides a reservoir landslide displacement prediction method based on dynamic lag recognition and fuzzy entropy optimization, which comprises the following steps: acquiring and preprocessing landslide time sequence monitoring data; combining the distributed lag nonlinear model with the maximum information coefficient, dynamically analyzing the lag relationship between the displacement and the rainfall and reservoir water level through a sliding window, outputting a self-adaptive lag stage and constructing a lag feature set; adaptively decomposing the displacement sequence by using variation mode decomposition of fuzzy entropy optimization, determining the optimal mode number according to the minimum fuzzy entropy, and reconstructing the intrinsic mode function into trend, period and random items; the method comprises the following steps: extracting local features of a multi-lag feature space through CNN, inputting reconstructed displacement components into GRU to capture time dependence, introducing an attention mechanism to weight a key time step, and outputting a predicted value and a confidence interval through quantile regression; according to the method, dynamic lag capture, adaptive decomposition and CNN-GRU-Attention are fused, and high-precision and high-robustness prediction is realized.

The invention discloses a power distribution network multi-scale toughness evaluation method based on multi-disaster coupling modeling, and relates to the field of power system optimization scheduling, in particular to an active power distribution network toughness improvement method based on deep reinforcement learning. The method comprises the following steps: (1) establishing a typhoon wind field model of a region where the power distribution network is located; (2) establishing a typhoon path model of a region where the power distribution network is located; (3) power distribution network fault scene simulation; and (4) carrying out load reduction based on the load importance degree. (5) constructing a toughness evaluation index system; (6) based on the fuzzy entropy, carrying out weight calculation on the toughness indexes of all levels; and (7) carrying out adaptive combination weighting fusion.

The invention provides an emotional state recognition method and device based on electroencephalogram signals and electronic equipment. Firstly, a target electroencephalogram signal of a to-be-analyzed object is obtained; then, feature extraction is conducted on the target electroencephalogram signal, a delta-alpha frequency band power ratio set, a self-adaptive similar tolerance fuzzy entropy set and a phase lag index set are obtained, and each element in the delta-alpha frequency band power ratio set represents the collaboration degree between the activity of a delta frequency band and the activity of an alpha frequency band in the target electroencephalogram signal; each element in the adaptive similar tolerance fuzzy entropy set represents the complexity of the target electroencephalogram signals, and each element in the phase lag index set represents the phase synchronization degree of the target electroencephalogram signals of the two channels. And then fusing the delta-alpha frequency band power ratio set, the adaptive similar tolerance fuzzy entropy set and the phase lag index set to obtain a fused feature set. And finally, based on the fusion feature set, performing emotional state recognition on the to-be-analyzed object to obtain an emotional state recognition result.

The invention discloses a drinking water quality monitoring and early warning method and system based on an intelligent water rack, and relates to the technical field of water quality monitoring, and the method comprises the following steps: collecting water quality data to construct a time sequence, and carrying out nonlinear normalization processing on the time sequence to obtain a normalized sequence; calculating a predicted value of future time based on the normalized sequence, and performing monitoring and early warning by using a quadratic polynomial fitting trend model based on the predicted value of the future time; and classifying abnormal monitoring results by using K-means clustering, calculating time distribution density, and constructing a visual interface to display the monitoring and classification results. According to the method, the nonlinear normalization processing of the time sequence is realized through the dynamic scaling factor calculated by the generalized fuzzy entropy and the dynamic characteristic value, and through the combination of the value of the interpolation point and the change rate of the fractional derivative, the prediction capability of the early warning model under different water quality change situations is enhanced, and the accuracy of water quality monitoring and the timeliness of early warning in advance are improved.

The invention relates to a federated learning model aggregation weight dynamic allocation method based on fuzzy entropy in a heterogeneous environment, and belongs to the technical field of federated learning and intelligent weight allocation. The method comprises the following steps: continuously monitoring and recording operation state characteristics of each heterogeneous client participating in federated learning, and constructing a state characteristic time sequence; using a fuzzy entropy theory to quantify the uncertainty or volatility of the client operation state reflected by the state characteristic time sequence to obtain a fuzzy entropy value of each client; on the basis of the fuzzy entropy value, calculating a dynamic aggregation weight which is in negative correlation with the state stability of each client side for each client side; and the server performs weighted aggregation on the local model update submitted by each client according to the dynamic aggregation weight to generate a global model. According to the method, the negative influence of a client with a poor running state or an unstable running state on a global model training process is effectively reduced, and the model aggregation efficiency and robustness of a federated learning system in a heterogeneous environment are improved.

The invention discloses an electrical load anomaly detection method and system based on a multi-granularity fuzzy rough set, and relates to the technical field of power data analysis, and the method comprises the steps: obtaining a high-dimensional time sequence feature matrix from to-be-detected electrical load time sequence data through employing a long-short term memory network; gathering the high-dimensional time sequence characteristic matrix into a plurality of multi-granularity pellets through a multi-granularity pellet generation method; calculating a multi-granularity fuzzy relationship among the multi-granularity pellets, constructing a multi-granularity fuzzy rough set model, and calculating the fuzzy rough density of each multi-granularity pellet and the multi-granularity fuzzy entropy of each attribute based on the model; calculating an abnormal score of each multi-granularity particle ball, mapping the abnormal scores of the multi-granularity particle balls to corresponding samples in the electrical load time sequence data, and performing abnormal load judgment on the corresponding samples based on the abnormal scores; the multi-granularity information of the load data can be effectively utilized, the anti-noise capability is enhanced, and the accuracy of anomaly detection is improved by processing the uncertainty of the data.

The invention discloses a fault diagnosis method based on multi-source data fusion and a deep optimization network, and belongs to the technical field of wind turbine generator bearing fault diagnosis, and the method comprises the steps: obtaining multi-source sensor data of a wind turbine generator under different working conditions, and carrying out the preprocessing of the collected multi-source sensor data; designing a multi-source data feature fusion algorithm based on correlation variance contribution, fusing the preprocessed multi-source sensor data, and extracting a fuzzy entropy value from the fused multi-source sensor data as a feature vector input into the intelligent fault diagnosis model; constructing an intelligent fault diagnosis model DBE based on the optimized deep belief network, and training the DBE by adopting a greedy learning method and a hippocampus optimization algorithm; and performing fault diagnosis based on the trained DBE. According to the method, the problems of signal abnormal value and data missing and fault diagnosis reliability under the influence of variable working conditions and external noise interference of the wind turbine generator are solved, and the fault diagnosis accuracy of the wind turbine generator is improved.

The invention discloses a multi-attribute decision-making method and system based on normal swing hesitant fuzzy information and a foreground theory. The method comprises the following steps: constructing an initial decision-making matrix and carrying out normalization processing; generating a multi-dimensional hesitant fuzzy decision matrix by calculating the Hamming distance, the chi-square distance and the trigonometric function distance between each scheme and the positive ideal solution, and converting the multi-dimensional hesitant fuzzy decision matrix into a normal swing hesitant fuzzy decision matrix; the comprehensive hesitant fuzzy entropy method and the logarithm percentage change driven target weighting method are fused to determine the comprehensive weight of the index; mapping the negative ideal solution distance into an income value and mapping the positive ideal solution distance into a loss value in combination with a foreground theory, and generating a foreground value decision matrix and normalizing the foreground value decision matrix; and calculating the correlation degree between the scheme and the dynamic ideal solution through a normal swing hesitant fuzzy weighting correlation coefficient, thereby realizing scheme sorting. According to the method, various kinds of uncertainty information and incomplete psychological characteristics of decision makers can be fully considered, and more accurate index weights and decision results are provided.

This application discloses a method and system for online condition assessment of permanent magnet motors based on multi-scale fuzzy entropy, relating to the technical field of permanent magnet motor fault diagnosis and reliability index analysis. It solves the technical problems of existing technologies that often rely on macroscopic parameters such as failure rate, making it difficult to quantify the degree of motor performance degradation and lacking consideration for real-time online condition assessment of permanent magnet motors, resulting in low accuracy and real-time performance of permanent magnet motor condition assessment. The method generates a radial air gap magnetic flux density difference signal based on the radial air gap magnetic flux density signal, and uses this to generate a multi-scale fuzzy entropy and a permanent magnet demagnetization assessment model. It generates the permanent magnet condition result based on the multi-scale fuzzy entropy corresponding to the online radial air gap magnetic flux density signal, and eliminates magnetic flux density background interference through the radial air gap magnetic flux density difference signal. By characterizing the subtle differences in signal complexity under different demagnetization states through multi-scale fuzzy entropy, it achieves a quantitative description of the degree of permanent magnet degradation, improving the real-time performance and accuracy of condition assessment.

The invention discloses a yaw variable pitch control method, device and system based on wind regime prediction. The method comprises the steps that an original sensor data stream related to wind regime prediction is pulled from a message queue; pre-processing the original sensor data stream to obtain pre-processed first data; performing data windowing on the preprocessed first data; calculating a fuzzy entropy corresponding to the first data after windowing; selecting a corresponding reference model based on the fuzzy entropy to determine a predicted wind direction and a predicted wind speed; formulating a yaw control strategy according to the predicted wind direction, and formulating a variable pitch control strategy according to the predicted wind speed; and performing yaw control according to the yaw control strategy, and performing variable pitch control according to the variable pitch control strategy. According to the method, the wind direction and the wind speed are determined through wind regime prediction, the yaw control action is assisted, the control hysteresis is reduced, the system response speed is increased, and the accuracy of yaw and variable pitch control is improved.

The invention relates to the technical field of image fusion, in particular to a hesitant fuzzy variable granularity dictionary learning-based multi-modal image fusion method, which comprises the following steps of: firstly, adaptively selecting division granularity according to image quality, and partitioning a source image into blocks; then extracting image block features and calculating hesitant fuzzy membership degrees of the image block features so as to quantitatively represent uncertainty information in the image; obtaining a joint over-complete dictionary and a sparse coefficient through dictionary learning, and fusing the hesitant fuzzy entropy and a granularity coefficient to construct an adaptive weight; and finally, fusing the sparse coefficient by using the weight and reconstructing a fused image. The problems of image fuzzy processing, structure multi-scale expression and insufficient adaptive feature extraction capability are effectively solved.