85 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.

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 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 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 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 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 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.

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.

The present application provides a kind of vital signs detection signal denoising method and device, it is related to physiological perception and signal processing technical field, including: based on millimeter wave radar's Doppler technique, obtains the original phase signal containing physiological signal;Original phase signal is carried out variational mode decomposition based on the search algorithm of variational mode decomposition super parameter optimization algorithm optimized by particle swarm optimization algorithm, obtain the vibration modal function information corresponding to the original phase signal;Wherein, the particle swarm optimization algorithm uses permutation entropy and fuzzy entropy as fitness function;After denoising processing is carried out to the vibration modal function, the vibration modal function after denoising processing is recombined, and high-precision denoising processing is obtained after physiological signal;From the high-precision denoising processing physiological signal, extract respiratory and heartbeat physiological signal.

The application discloses a partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals, comprising the following steps: S01, based on the RLS algorithm, the collected ultrasonic signals are subjected to noise reduction processing to eliminate white noise, and Fourier method is used to filter out interference signals below 20 kHz; S02, while the step 1 operation is being performed, the starting point of the collected ultrasonic signals is found out; S03, the original signal is down-sampled to 500 data points by using the discrete Fourier transform, the frequency spectrum is windowed and then inverse transformed, the fuzzy entropy of the down-sampled signal is calculated, and a first diagnosis model is constructed; S04, the sampling points of the original data are reduced to 3600 data points in the form of taking the maximum value at a certain interval. The partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals can effectively determine whether the switch cabinet has a partial discharge phenomenon, effectively reduces the false positive rate, and can be used for online detection and daily inspection of electrical equipment such as switch cabinets in multiple scenes.

The application discloses a kind of based on machine learning's hydrological data governance method and system, S1. Generation has unified space-time resolution hydrological monitoring data set;S2. Construction fuzzy entropy matrix of hydrological monitoring data;S3. Generation is represented in directed acyclic graph form causal model of hydrological monitoring data;S4. Real-time identification of abnormal hydrological monitoring data by dynamically adjusting anomaly detection threshold;S5. Generation is traced to abnormal candidate path, forms abnormal hydrological monitoring data causal chain;S6. Fuzzy entropy optimization processing is carried out to abnormal candidate path, according to the fuzzy entropy value of each causal chain in hydrological variable and its influence weight in causal chain, key abnormal causal path is screened out, and then the positioning of the root cause of abnormal hydrological monitoring data is positioned.The application provides a more efficient, intelligent technical solution for water resources management, disaster prevention and warning and environmental monitoring.

The invention relates to a transient electromagnetic signal denoising method based on self-adaptive fuzzy optimization singular spectrum analysis (SSA), which aims at denoising transient electromagnetic signals, and comprises the following specific steps of: 1, decomposing a noisy signal into a plurality of modes through a complementary ensemble empirical mode decomposition (CEEMD) method; and according to the sample entropy, classifying the modals into a signal-dominant category and a noise-dominant category. 2, taking a signal fuzzy entropy (FE) as a target function of each modal optimization, obtaining a strict boundary of each modal SSA reconstruction order by using a particle swarm optimization (PSO) algorithm, and taking a singular value in the strict boundary as a signal component; thirdly, solving loose boundaries of each modal reconstruction order through a singular value spectrum and a singular value difference spectrum, and generating a fuzzy interval by combining the loose boundaries with the strict boundaries; and 4, obtaining the membership degree of the singular value belonging to the signal or noise component in the fuzzy interval through a weighted fuzzy noise clustering algorithm (WFNC), and carrying out signal component reconstruction by taking the membership degree as a weight. And finally, adaptive reconstruction of each mode is carried out on the signal components by using an SSA method, and a de-noised signal is obtained through mode summation. According to the method, a low-signal-to-noise-ratio adaptive singular spectrum analysis denoising method is provided, the problem of wrong singular value selection in the reconstruction process is avoided, and the signal-to-noise ratio is effectively improved.

The invention relates to the technical field of industrial safety, in particular to a safety instrument system SIL evaluation method considering human factor failure. The method comprises the steps that firstly, human factor influence factors of personnel, equipment, environment and management are objectively quantified through a triangular fuzzy entropy weight method; then human factor failure probability models of three stages of observation, judgment and execution are constructed; and finally, the quantized human factor failure probability is used as a series link to be coupled into a system PFD calculation model, so that an SIL level evaluation result closer to an actual operation condition is obtained. According to the method, quantitative evaluation of human factor failure is realized, a quantitative basis containing human factor improvement measures can be provided for system redundancy configuration and test cycle optimization, and the accuracy and engineering guidance value of SIL evaluation are remarkably improved.

The invention relates to the technical field of industrial internet and digital transformation, and discloses a modular digital technology deployment system and method based on dynamic adaptation, and the system comprises a data collection layer, an edge calculation layer, a dynamic adaptation module library and a cloud platform layer, the data collection layer is in communication connection with the edge calculation layer, and the edge calculation layer is in communication connection with the dynamic adaptation module library. The edge computing layer is in two-way communication with the dynamic adaptation module library, and the dynamic adaptation module library is in two-way communication with the cloud platform layer; the data acquisition layer is used for acquiring production data in real time and preprocessing the production data; and the edge calculation layer is used for deploying a lightweight algorithm to execute a local real-time decision. On-demand calling and combination of technical components are realized by constructing a dynamic adaptation module library, and in combination with an edge-cloud collaborative optimization mechanism, the problems of resource waste and insufficient real-time performance in a traditional architecture are effectively solved. Wherein the dynamic adaptation algorithm dynamically generates an optimal module combination scheme based on a fuzzy entropy evaluation model, and the edge layer lightweight algorithm ensures that local real-time decision response delay is remarkably reduced.

The application provides an audio signal denoising method based on improved variational mode decomposition and a related product. The method comprises the following steps: obtaining a target input audio signal, and using an improved particle swarm optimization algorithm with a local optimal solution jump-out mechanism and a fuzzy entropy search variational mode decomposition to search for a target component number and a target penalty factor; decomposing the target input audio signal according to the target component number and the target penalty factor to obtain a plurality of first target components; calculating the fuzzy entropy of each first target component, and using a wavelet threshold denoising algorithm to denoise the first target components with fuzzy entropy higher than a preset fuzzy entropy threshold to obtain first components; and obtaining a target output audio signal according to the first target components with fuzzy entropy not higher than the preset fuzzy entropy threshold and the first components. The application can solve the problem of poor denoising effect caused by unreasonable parameter setting in the prior art, thereby realizing more efficient and accurate audio signal denoising processing.

This invention discloses a lithology identification method based on hybrid mode decomposition and fuzzy entropy-PSO optimization of drilling signals, belonging to the field of integrated deep earth space exploration and drilling geophysical exploration technology. The method first acquires real-time seismic monitoring signals during drilling; then, it uses an empirical and variational hybrid mode decomposition (EVMD) method to decompose the original signal, removing strong noise disturbances; finally, it employs a particle swarm optimization (PSO) algorithm combined with adaptive optimization using fuzzy entropy as the objective function to obtain the optimal parameter combination [K, α] of EVMD, and decomposes it to obtain the optimal modal data and construct a multi-dimensional feature vector, completing the real-time identification and judgment of strata lithology and rock mass structural characteristics. This invention effectively solves the technical problems of traditional methods, such as difficulty in separating strong noise, fixed parameters that cannot be adaptively adapted, mode aliasing, and poor real-time performance. It achieves extremely high lithology identification accuracy and is suitable for deep earth space development, deep resource exploration, and intelligent construction of underground engineering projects.

The invention relates to the technical field of construction risk management of constructional engineering, in particular to a subway station deep foundation pit construction risk assessment method based on bilateral probability language, which comprises the following steps: step 1, constructing an evaluation set in a bilateral probability language term set form; 2, performing objective standardization processing on evaluation information; 3, determining a combination weight of subjective and objective combination; step 4, hierarchical information aggregation based on a weighted average operator; and step 5, risk quantification and grade determination. According to the method, a bilateral probability language term set (DPLTS) is introduced to completely describe expert group opinion distribution, and an LCM objective standardization method and a fuzzy entropy-cross entropy-BWM combined weighting model are combined to construct a risk assessment system which is complete in information, scientific in weight and robust in decision making; the defects of the prior art in the aspects of expressing complex uncertainty and fusing subjective and objective information are effectively overcome, and the reliability, the distinction degree and the practical value of a risk assessment result are remarkably improved.

The invention provides a signal noise reduction method for acoustic monitoring of a tool wear state in a milling process, which relates to the technical field of intelligent manufacturing, and is characterized in that main shaft noise is eliminated through spectral characteristics of IMF components obtained through adaptive noise complete set empirical mode decomposition in combination with spectral characteristics of signals acquired by a multi-scene milling test; first-stage noise reduction is realized; performing fast independent component analysis on the residual IMF components after the first-stage noise reduction to realize blind source separation, and identifying and removing random noise components in combination with a fuzzy entropy threshold criterion to realize second-stage noise reduction; reconstructing a de-noised signal by using the residual signal components after the second-stage noise reduction, and screening out features conforming to a tool wear trend through a Kendall rank correlation coefficient; and inputting the screened features conforming to the tool wear trend into a convolutional neural network to realize high-precision intelligent monitoring of the tool wear state based on the sound signal. According to the invention, stable denoising performance is maintained under different working conditions.

The application belongs to the field of electroencephalogram signal processing, and is a fatigue driving classification method and system based on electroencephalogram multi-scale fuzzy entropy features of CEEMDAN. The method comprises the following steps: collecting electroencephalogram data, and labeling part of the data; pre-processing the electroencephalogram signal to remove artifacts in the electroencephalogram signal; extracting features from the pre-processed electroencephalogram signal, constructing and training an SVM classifier, classifying unmarked data using the trained SVM classifier to obtain pseudo-labeled data; performing CEEMDAN processing and scale transformation processing on the pseudo-labeled data and the previously obtained labeled data to obtain electroencephalogram multi-scale fuzzy entropy features based on CEEMDAN, and establishing a fatigue state classification model to classify the electroencephalogram signal. The collection method is simple, is more suitable for application in an intelligent driving system, and has a high fatigue state recognition rate.

The invention provides a computing network infrastructure efficiency evaluation method, device, equipment and program product, and relates to the technical field of infrastructure evaluation. The method comprises the following steps: acquiring intuitionistic fuzzy scores of a plurality of evaluation experts on efficiency evaluation indexes of the computing network infrastructure; determining the intuitionistic fuzzy entropy of each evaluation expert and an initial weight matrix based on the intuitionistic fuzzy score; determining the confidence of each evaluation expert based on the intuitionistic fuzzy entropy; based on the confidence coefficient of each evaluation expert and the initial weight matrix, determining a corrected target weight matrix; and based on the value of the to-be-evaluated computing network infrastructure in each efficiency evaluation index and the corresponding weight, determining an efficiency evaluation result of the to-be-evaluated computing network infrastructure. The method is used for accurately evaluating the efficiency of the computing network infrastructure.

The invention discloses a metal plate defect imaging method based on a gold washing optimization algorithm combined with SVMD and multi-scale fuzzy entropy. The method comprises the following steps: acquiring an original excitation response signal of a lossless metal plate; obtaining an optimal balance parameter of successive variational mode decomposition through a gold washing optimization algorithm; performing successive variational mode decomposition on the original excitation response signal based on the optimal balance parameter to obtain a plurality of intrinsic mode function components and performing reconstruction to obtain a reconstructed signal; calculating a multi-scale fuzzy entropy value of the reconstructed signal to obtain a multi-scale fuzzy entropy value of the lossless metal plate; obtaining a multi-scale fuzzy entropy value of the damaged metal plate through all the steps, and obtaining a damage index based on the multi-scale fuzzy entropy value of the damaged metal plate and the multi-scale fuzzy entropy value of the damaged metal plate; and generating a metal plate defect image based on the damage index. The method is higher in image signal-to-noise ratio, better in imaging quality and more accurate in defect positioning in different noise environments, and is more suitable for metal plate defect detection in engineering application.

The invention relates to the technical field of data processing, in particular to a chemical product leakage rapid early warning method, which comprises the following steps of: acquiring a plurality of pressure data in a chemical product conveying pipeline, obtaining each pressure data window, further obtaining the data fluctuation degree in each pressure data window, and calculating the data fluctuation degree of each pressure data window; the data fluctuation degree in each pressure data window is adjusted, the data potential abnormal degree in each pressure data window is obtained, the adjustment similar tolerance limit between the pressure data sub-windows is obtained according to the data potential abnormal degree, and the pressure data is obtained according to the adjustment similar tolerance limit between the pressure data sub-windows. According to the method, the fuzzy entropy of each piece of pressure data is obtained, then the abnormal condition of the data in each pressure data window is judged, whether the chemical product conveying pipeline leaks or not is determined, and the accuracy of anomaly detection is improved.

The invention relates to the technical field of image data processing, in particular to a visual sense-based battery case stamping surface quality detection method, which comprises the following steps of: extracting a collected surface image of a battery case by utilizing semantic segmentation to obtain a battery case area, and equally dividing the battery case area into a plurality of image blocks; for any image block, constructing a gray level histogram of each pixel point; determining the gray level special degree of each pixel point; determining the gray abnormal degree of the target pixel point; obtaining a weighted fuzzy membership degree used for optimizing the fuzzy entropy algorithm; and obtaining a fuzzy entropy value used for determining the burr defect degree of each image block so as to realize quality detection of the stamping surface of the battery shell. According to the method, the gray level of each image block of the battery shell area is analyzed to obtain the gray level special degree and the gray level abnormal degree, so that the fuzzy membership degree between the pixel points is optimized, and the problem that tiny burrs and printing parameter areas are easily confused by a traditional fuzzy entropy algorithm is effectively solved.

The invention belongs to the technical field of health diagnosis, and particularly relates to a full-life-cycle health diagnosis method of a variable-frequency water supply unit for water resource management, which comprises the following steps: S1, acquiring time sequence data of a multi-source sensor in the operation process of the variable-frequency water supply unit; calculating a health state baseline compensation coefficient through a pre-trained logarithmic function model based on the equipment accumulative operation time; variational mode decomposition and Hilbert-Huang transform are carried out on time series data of the multi-source sensor, instantaneous energy, kurtosis and fuzzy entropy are extracted to form multi-dimensional features, and a three-dimensional feature tensor is constructed; and S2, training a deep self-encoding network composed of a double-flow asymmetric encoder and a reconstruction decoder by using the three-dimensional feature tensor. According to the method, diagnosis misinformation caused by frequent change of the working condition of the water supply unit is reduced, the reliability of the model is enhanced, and early weak fault detection and health state evaluation of equipment are realized.

This invention discloses a method for monitoring the state trends of hydropower units based on two-stage signal decomposition and the IBiLSTM model. The method involves collecting vibration signals from the hydropower units for preprocessing; constructing the ITGCOA optimization algorithm and designing a fitness function; using the ITGCOA optimization algorithm to adaptively optimize the SVMD and BAACMD models, achieving initial decomposition of the preprocessed signal and secondary decomposition of the sub-mode component with the highest center frequency obtained from the initial decomposition; calculating the fuzzy entropy values ​​of the remaining sub-mode components for reconstruction; and fusing the high-frequency feature sub-sequences obtained from the secondary decomposition with the reconstructed feature sub-sequences to construct the input sequence of the prediction model. This input sequence is then used to construct the IBiLSTM prediction model for monitoring the state trends of hydropower units, achieving high-precision prediction. Compared with existing technologies, this invention improves the efficiency and accuracy of hydropower unit state trend prediction, accurately warns of abnormal unit operating conditions, ensures the safe and stable operation of the units, and improves the overall efficiency of the power plant.