115 results about "Wavelet basis functions" patented technology

The invention relates to the technical field of power supply or power distribution circuit systems, in particular to a photovoltaic module fault monitoring system and method, and the system comprises an acquisition module, a power supply dynamic compensation module, a control optimization module, a power supply module and a digital twin module. The acquisition module acquires multi-source data through the infrared thermal imaging sensor, the electroluminescent detection unit and the current and voltage characteristic curve acquisition module, the power supply dynamic compensation module dynamically adjusts wavelet basis function parameters based on an improved whale optimization algorithm, and the power supply module fuses hot spot distribution, current and voltage abnormity and aging trend data. And driving the dynamic adjustment of the protection threshold. And the digital twin module synchronizes real and virtual system data through transfer learning, rehearses a fault path to generate a maintenance instruction and feeds back the maintenance instruction to an optimization prediction model, so that a monitoring, protection and self-optimization closed-loop system is formed, and the fault diagnosis precision and the system reliability in a complex environment are improved.

The invention relates to the technical field of laser spectrum detection, in particular to an LIBS (Laser-induced Breakdown Spectroscopy) spectrum noise reduction method, system and equipment based on adaptive threshold wavelet transform and a storage medium. Acquiring an original spectral signal of the laser-induced breakdown spectroscopy; performing five-layer multi-layer wavelet decomposition on the original spectral signal by adopting a db4 wavelet basis function to obtain a high-frequency coefficient and a low-frequency coefficient of each layer; calculating a noise intensity standard deviation based on the detail coefficient of the highest decomposition layer; dynamically determining the optimal value of the regulation factor through a double-layer optimization strategy combining a grid search method and a golden section iterative optimization method; constructing an adaptive threshold value based on the noise intensity standard deviation and the adjustment factor; carrying out threshold value processing on the high-frequency coefficient by adopting a self-adaptive threshold value; and performing wavelet reconstruction on the processed high-frequency coefficient and low-frequency coefficient, and outputting a denoised spectral signal. While the LIBS spectral signal-to-noise ratio is remarkably improved, the spectral feature form is completely reserved, and reliable technical support is provided for laser-induced breakdown spectroscopy detection in a complex industrial environment.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

The invention relates to a GIS partial discharge optical detection method and device, and a medium. The method comprises the following steps: collecting a GIS partial discharge optical signal through a light guide rod, and carrying out the preprocessing of the GIS partial discharge optical signal; self-adaptive wavelet packet decomposition is carried out on the preprocessed signals, noise signals are separated through a self-adaptive threshold value adjustment algorithm, multi-scale time-frequency characteristics are extracted from the signals after noise separation, and according to the self-adaptive wavelet packet decomposition, a wavelet basis function is selected in a self-adaptive mode according to statistical indexes of the extracted signals so as to carry out wavelet packet decomposition; and constructing a lightweight convolutional neural network model, classifying the extracted multi-scale time-frequency features, and identifying a partial discharge signal. Compared with the prior art, the method has the advantages that the anti-interference capability is high, weak partial discharge signals can be effectively extracted, and the real-time requirement is met.

The invention provides a feature extraction method for an electroacoustic background interference signal of operation power transformation equipment, which belongs to the technical field of power transformation equipment, and comprises the following steps: acquiring an electroacoustic signal, preprocessing the electroacoustic signal, and generating a controlled interference signal at the same time; and performing time-frequency analysis on the preprocessed electroacoustic signal and the controlled interference signal, and optimizing the interference signal parameter to enable the interference signal parameter to be highly similar to the electroacoustic signal. And constructing a wavelet basis function library based on the optimized controlled interference signal, and performing wavelet packet decomposition on the electroacoustic signal to obtain a plurality of frequency band sub-signals. A self-adaptive threshold model is established by using a controlled interference signal, and soft threshold denoising processing is performed on sub-signals. Time-frequency features of the denoised sub-signals and the controlled interference signals are extracted, a feature mapping relation is established, and an initial feature set is obtained; and performing nonlinear dimensionality reduction on the initial feature set by adopting principal component analysis to obtain a dimensionality-reduced feature set. And an improved support vector machine model is adopted to evaluate the importance of dimension reduction features, and an optimal feature set is selected as an electroacoustic background interference signal feature.

The invention discloses an automatic control method and system for transformer substation maintenance, operation and maintenance supervision based on data analysis, and belongs to the technical field of transformer substation maintenance, and the method comprises the steps: collecting multi-source monitoring data of a transformer substation, including voltage, current, transformer oil temperature, partial discharge signals, GIS equipment vibration frequency and insulating oil chromatographic data; performing time sequence processing on the multi-source monitoring data to generate time sequence monitoring data, performing filtering processing on power frequency noise in the time sequence monitoring data by adopting a Daubechies wavelet basis function, and performing feature extraction to obtain time sequence processing data; inputting the time sequence processing data into a pre-constructed long short-term memory network model for fault prediction to obtain an equipment fault probability prediction value; when the equipment fault probability prediction value exceeds a preset threshold value, triggering a multi-stage early warning signal and executing an automatic control operation; and according to the maintenance record data, calculating a fault recovery rate and an operation stability index, and generating a maintenance evaluation report.

The invention discloses a sampling signal noise reduction method and device based on a wavelet basis function, equipment and a medium, and belongs to the field of signal processing, and the method comprises the steps: carrying out the preprocessing of an original sampling signal after the original sampling signal is obtained, and obtaining a processing signal; performing wavelet decomposition on the processing signal to obtain a wavelet coefficient, and correcting the wavelet coefficient to obtain a correction coefficient; and performing inverse wavelet transform on the correction coefficient to obtain a noise reduction signal. According to the invention, after the original sampling signal is obtained, the preset composite power quality disturbance signal is added to the original sampling signal, and the noise reduction processing is carried out according to the wavelet coefficient of the signal combined with the noise to obtain the noise reduction signal. According to the invention, by adding the noise, high-efficiency noise suppression can be realized while key disturbance characteristics are reserved, so that the precision of the signal after noise reduction is improved; as the signal features of the original signals are reserved, a reliable preprocessing scheme can be provided for high-precision data acquisition scenes such as new energy monitoring of the power grid according to the original features subsequently.

The invention provides a multivariate time sequence prediction method and system and a medium, an input time sequence is adaptively decomposed into an approximate coefficient sequence and a detail coefficient sequence by adopting an adaptive discrete wavelet transform and inverse transform mode, and compared with the previous wavelet transform depending on a predefined wavelet basis function, the multivariate time sequence prediction method and system have the advantages that the time sequence prediction efficiency is improved; the mode is more flexible, and filtering kernels of discrete wavelet transform and inverse transform can be updated in a data driving mode in the training process according to the characteristics of the time sequence, so that the filtering kernels are more suitable for the current sequence. Wavelet transformation realized by carrying out convolution operation on a time dimension lacks extraction and modeling of correlation between global information and different variables in a multivariate time sequence; therefore, a coefficient mixing module and a wavelet domain attention channel enhancement module are provided to carry out supplementary modeling on an approximation coefficient and detail coefficient sequence obtained after wavelet transformation. The method can meet the prediction requirements of the time series in actual production such as power load.

The invention discloses a rotary machine fault diagnosis method based on PyramidNet and Transform, and relates to the field of fault diagnosis, and the method comprises the following steps: carrying out the normalization preprocessing and sliding window segmentation of an original vibration signal, and generating a time sequence data segment with a fixed length; carrying out discrete wavelet transform on the time sequence, carrying out multilayer decomposition by adopting a Daubechies 4 wavelet basis function, and converting a signal from a time domain to a frequency domain; a hybrid network model based on PyramidNet and Transform is constructed, local features are extracted by increasing the number of channels layer by layer, and channel attention and space attention optimization is carried out in combination with a CBAM module; performing time sequence modeling on the output features, and capturing a long-distance dependency relationship of a time sequence through a multi-head self-attention mechanism; according to the method, the extracted global features are classified, fault categories are output, feature extraction and fault classification are carried out by adopting an improved hybrid network architecture, the defects of a traditional method in feature extraction and global dependency modeling are overcome, and the detection precision and robustness of fault diagnosis are effectively improved.

The invention relates to the field of communication signal processing, in particular to an air defense and disaster prevention early warning alarm real-time control system based on Beidou third-generation communication, which adopts a Daubechies wavelet family to perform five-layer discrete wavelet decomposition and decomposes the total delay of satellite signals into different scale coefficients. By accurately controlling the boundary scale, the system can effectively separate the ionosphere error from the troposphere error. And by inserting a zero value between filter coefficients, information loss caused by down-sampling operation is avoided. And aiming at different atmospheric error characteristics, the system adaptively selects an optimal wavelet basis function. A stable correction effect is kept under extreme weather conditions, and an efficient and practical air defense and disaster prevention real-time early warning control method is constructed.

The invention discloses a natural gas pipeline leakage detection method based on novel wavelet basis transformation and singular value decomposition in a two-dimensional convolutional neural network. Firstly, a sound signal collected by a sound wave sensor is converted into a digital signal; secondly, in the data preprocessing stage, singular value decomposition is carried out on the digital signals to effectively eliminate background noise interference, and then batch normalization is carried out on the processed data; then, converting the one-dimensional time sequence signal into a two-dimensional time-frequency image by adopting a self-defined Morlet wavelet basis function; and finally, based on the time-frequency images, constructing and training a 2D-CNN model for fault classification, and presenting a diagnosis result through a confusion matrix and a comparison graph. According to the method, 97.55% of fault recognition accuracy is obtained in a public data set, and compared with other competitive methods, the method shows more excellent noise robustness and classification performance, and has higher accuracy and wide application prospects in pipeline leakage diagnosis in a complex noise environment.

The invention discloses a photovoltaic access power distribution network fault diagnosis method based on fault identification, and particularly relates to the technical field of power grid fault diagnosis, and the method comprises the steps: synchronously obtaining multi-point electrical quantity monitoring data, photovoltaic system inverter operation data and environment data of a power distribution network side, and carrying out the abnormality elimination and delay compensation preprocessing; based on the switching frequency of the photovoltaic inverter and the line parameters of the power distribution network, adaptively selecting a wavelet basis function and a decomposition scale, and extracting a fusion fault feature vector; inputting the feature vectors into a two-channel hybrid neural network model for intelligent diagnosis, and outputting fault section positioning, type classification, severity level and occurrence time information; and finally generating a fault processing instruction according to the diagnosis result. According to the method, the problems of low diagnosis precision, slow response and the like caused by single data source, poor feature extraction self-adaption and simple model structure of the existing method are effectively solved, the accuracy and reliability of complex fault recognition are remarkably improved, and closed-loop automation of fault diagnosis and processing is realized.

The invention discloses a separation overload and impact signal identification method and system based on wavelet transform, and belongs to the technical field of mechanical engineering and signal processing.The method comprises the steps that S1, separation data are collected and preprocessed, and the separation data comprise separation acceleration data; s2, decomposing and separating the acceleration data through discrete wavelet transform to obtain a detail coefficient and an approximation coefficient; s3, reconstructing a high-frequency impact signal and a low-frequency overload signal based on the detail coefficient and the approximation coefficient, and obtaining an impact response positive spectrum and a negative spectrum; s4, setting different wavelet basis functions and decomposition layers, repeating the step S2 and the step S3, and separating impact and overload data through multi-objective optimization; and S5, according to the optimized separation impact and overload data, an environment test condition or a product structure strength design index is formulated. According to the method, the problems that complex trend terms cannot be accurately identified and validity criteria are lacked in the prior art are solved, and the signal separation precision and the engineering application reliability are remarkably improved.

The invention discloses an intelligent valve terminal monitoring management system of a hydraulic station, which relates to the technical field of industrial automation control and comprises a data acquisition module, a feature extraction module, a time-frequency domain combined diagnosis module, a fault diagnosis decision module, an alarm module, a remote monitoring module, a data storage module and a system control module. The method has the advantages that the adaptive wavelet packet decomposition technology is adopted to perform multi-band feature extraction on the operating parameters of the hydraulic station valve terminal, and the signal local energy entropy is combined to dynamically select the optimal wavelet basis function and the decomposition layer number, so that sensitive band features corresponding to faults such as valve element abrasion and valve internal leakage can be accurately identified; according to the system, through dynamic optimization of decomposition parameters and frequency band energy analysis, the false alarm rate is remarkably reduced, meanwhile, dependence on artificial experience is avoided, the system has the self-adaptive diagnosis capacity for complex working conditions, and therefore the accuracy and reliability of fault detection are improved.

The invention discloses a partial discharge signal feature extraction method and system, and the method comprises the following steps: collecting a current signal on a grounding loop cable of detected electrical equipment, and carrying out the preprocessing of the current signal; converting the preprocessed signal into a two-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain a principal component; matrix reconstruction is carried out based on the principal component to generate a de-noising matrix, and the de-noising matrix is restored to a time domain signal sequence; performing discrete wavelet transform on the time domain signal sequence to obtain a multi-level approximation coefficient and a detail coefficient; the discrete wavelet transform selects a wavelet basis function from the candidate wavelet basis set based on a preset adaptive wavelet basis selection mechanism, and adaptively determines the number of wavelet decomposition layers based on the signal dominant frequency characteristics; and inputting the approximation coefficient and the detail coefficient into an inverse wavelet transform module to obtain a final partial discharge signal. According to the method, efficient, accurate and universal partial discharge signal feature extraction can be realized.

The invention provides a fault identification method for a photovoltaic energy storage power distribution system, and relates to the technical field of photovoltaic system fault identification, and the method comprises the steps: S1, setting a fault threshold standard; s2, collecting real-time voltage and current signals in the photovoltaic energy storage power distribution system by using a signal source; s3, selecting a proper wavelet basis function; s4, performing convolution operation on the collected signals and the selected wavelet basis function to obtain wavelet coefficients of the signals at different scales and positions; s5, analyzing the characteristics of the wavelet coefficient; and S6, according to the extracted features, in combination with a preset fault threshold, judging whether the system has a fault or not. Real-time voltage / current signals are obtained by means of a signal source to provide data support in the subsequent discrete wavelet transform process, the signals are decomposed into approximate coefficients and detail coefficients by means of wavelet transform, and the fault state existing in the power distribution process of the photovoltaic energy storage system is determined according to the high-frequency and low-frequency conditions of the approximate coefficients and the detail coefficients. And early detection of potential fault hidden dangers is facilitated.

The invention provides an electrical equipment operation state real-time statistical analysis method and system, and belongs to the field of electrical equipment operation state monitoring, and the method comprises the steps: obtaining multi-dimensional time sequence sensor data during the operation of electrical equipment, selecting a proper wavelet basis function based on Shannon information entropy to carry out wavelet packet decomposition, and extracting time-frequency domain features; constructing a dynamic relation graph which takes a sensor as a node and takes a Granger causal relationship as a weight, and learning node space features by using a graph convolutional network in combination with time-frequency features; the node features are input into a gating circulation unit, and electrical equipment state sequence codes fused with space-time dependence are generated; and carrying out Viterbi decoding by using the state transition cost matrix, and reasoning an optimal electrical equipment operation state path. According to the method, the uncertainty of a prediction result can be quantified, clear confidence evaluation is provided for a final analysis conclusion, and the reliability of decision making is improved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention provides a battery pack anomaly detection method and system based on average deviation and wavelet analysis, and relates to the technical field of new energy vehicle power battery pack detection, and the method comprises the steps: obtaining the voltage data of each single battery in a power battery pack in real time, and carrying out the preprocessing; converting the preprocessed voltage data into a time sequence format, and compressing and storing by adopting a time partitioning strategy; calling the stored voltage time sequence data of each battery cell, and obtaining the average voltage deviation of each single battery cell by using a voltage deviation calculation method; carrying out wavelet transformation on the average voltage deviation of each single battery cell, carrying out multi-scale decomposition by adopting a wavelet basis function, obtaining high-frequency components of different frequency bands, analyzing energy distribution of the high-frequency components, calculating statistical characteristics of high-frequency energy, setting an energy threshold value of the single battery, and comparing the statistical characteristics with the energy threshold value, so as to obtain the high-frequency energy of the single battery cell. And determining whether the battery cell is abnormal or not.

The invention discloses a power load prediction method, device and equipment and a medium, and relates to the technical field of power load prediction.The method comprises the steps that firstly, a historical original load sequence is obtained; then, performing signal separation on the historical original load sequence to obtain a high-frequency signal and a low-frequency signal; performing modal decomposition on the high-frequency signal and the low-frequency signal by using an improved variational modal decomposition algorithm to obtain a residual signal and a plurality of intrinsic modal components; decomposing the residual signal by using a wavelet basis function to obtain a wavelet coefficient corresponding to each intrinsic mode component; for each intrinsic mode component, inputting the intrinsic mode component and the wavelet coefficient corresponding to the intrinsic mode component into a trained load prediction model to obtain a prediction result corresponding to each intrinsic mode component; and finally, a final load prediction result is determined according to the prediction results corresponding to all the intrinsic mode components, and the power load prediction precision is improved.

The invention discloses an SOH prediction method based on a filter and a DLi near model, and relates to the technical field of battery life prediction, and the method comprises the steps: S1, data preprocessing, S2, data standardization, S3, wavelet denoising, and S4, DLi near model prediction. According to the method, effective components and high-frequency noise of the signals can be accurately distinguished through multi-scale decomposition, nonlinear threshold processing and wavelet denoising; specifically, the low-frequency component is reserved to reflect the long-term attenuation trend of the battery capacity, and the high-frequency noise component is effectively eliminated through threshold screening; compared with a traditional Fourier transform or linear filtering method, the method has the advantages that global features and local abrupt change details of signals can be captured at the same time due to the tight supporting performance and the high vanishing moment characteristic of the wavelet basis function, and the problem that the signals are fuzzy or key information is lost is avoided; according to the design, the data quality is improved, a purer input signal is provided for a subsequent model, and therefore interference of noise on prediction precision is reduced.

The invention relates to the technical field of welding quality intelligent control, and discloses a friction stir welding method and system based on ultrasonic detection.The friction stir welding method based on the ultrasonic detection.The friction stir welding method based on the ultrasonic detection.The friction stir welding method based on the ultrasonic detection.The friction stir welding method comprises the following steps that the material density and the sound velocity are obtained, and acoustic impedance is calculated and matched with a wavelet basis function; pre-welding ultrasonic detection is carried out based on the function, and a defect-free reference signal is constructed; real-time ultrasonic signals and temperature and stress information are collected in the welding process; extracting features and inputting the features into a neural network, and outputting a defect probability graph; constructing a state vector and inputting the state vector into a reinforcement learning module; outputting and executing a welding parameter adjusting action; and predicting the performance by using a physical information neural network, and triggering parameter resetting when the performance is lower than a threshold value. The method for constructing the defect probability graph based on the ultrasonic data is adopted, the ultrasonic detection signals are mapped into the space probability distribution graph, visual perception of the defects in the welding seam is achieved, and the technical effect that potential defects in the welding area can be accurately recognized without damaging a sample piece is achieved.

The invention provides a terahertz time-domain spectral signal noise reduction method and system for transformer aged insulating oil, and the method comprises the steps: collecting a terahertz time-domain spectral signal of an aged insulating oil sample, and carrying out the parameter optimization of variational mode decomposition through a whale optimization algorithm, according to the method, an optimized variational mode decomposition algorithm is adopted to decompose terahertz time-domain spectral signals of aged insulating oil into a series of intrinsic mode function components, then independent component analysis is adopted to separate the intrinsic mode function components into noise and effective signals, and finally the effective signals are screened out by calculating information entropy of independent components. And reconstructing the terahertz time-domain spectral signal of the aged insulating oil after noise reduction. The technical problems that the wavelet basis function and the decomposition layer number of wavelet noise reduction are difficult to determine, the noise reduction effect is restricted by the endpoint effect and mode aliasing problem of empirical mode decomposition, and the noise reduction effect of variational mode decomposition is poor are solved.

The invention discloses an overhead transmission line fault detection and classification method based on discrete wavelet transform and a time sequence convolutional network, and relates to the technical field of electronics. The method comprises the following steps: acquiring original signals of three-phase current and grounding current of the overhead transmission line, and carrying out normalization processing and moving average filtering preprocessing; discrete wavelet transform (DWT) of a Daubechies wavelet basis function Db4 is adopted to perform multi-scale decomposition on the preprocessed signal, a high-frequency detail coefficient is extracted, and a multi-channel time sequence input vector is constructed; and inputting the input vector into a time sequence convolutional neural network (TCN). According to the method, fault features are efficiently extracted through DWT, time sequence data are accurately modeled through TCN, the problems that a traditional method is low in accuracy, slow in response and weak in anti-interference capacity in a complex fault scene are solved, the fault classification accuracy is 99.9%, the parallel computing capacity is high, training is stable, the method can adapt to different fault conditions, and the method is suitable for large-scale popularization and application. And the operation reliability and the intelligent protection level of the power system are effectively improved.

The present application relates to the technical field of composite material structure monitoring, in particular to a composite material structure damage identification method based on acoustic emission wavelet packet energy characteristics, which comprises: S1, obtaining acoustic emission signal data of the composite material and performing pretreatment; S2, using a wavelet base function and a discrete wavelet inverse transform to perform wavelet packet decomposition on the denoised acoustic emission signal data of the composite material; S3, obtaining the processed acoustic emission signal data of the composite material, and extracting the characteristic energy of each acoustic emission signal data; S4, using a clustering method to perform clustering analysis and completing the composite material structure damage identification. The present application uses acoustic emission signals as the basis for analysis, uses wavelet transform and k-means clustering to accurately realize the structure damage identification and judgment of the composite material, can realize dynamic and continuous monitoring, is not affected by the complexity of the structure, has wide applicability, and is suitable for various composite materials.

The present application relates to the technical field of data analysis, and more particularly, to a solid-state battery performance test method and system based on data analysis, comprising: obtaining voltage time series data of a solid-state battery, and removing a direct current component to obtain a fluctuating voltage sequence; based on a ratio of a second-order difference to a first-order difference of the fluctuating voltage sequence and a signal-to-noise ratio, calculating a local transient response index at each time, which is used to represent a local steepness of voltage fluctuation. The present application aims at the problem that solid-state battery micro-short circuit waveforms are diverse and easily covered by noise, and through constructing a local transient response index, the steepness of voltage fluctuation is quantified in real time, and a small deformation wavelet basis function highly matched with the current signal form is dynamically generated, and a feature value calculation method with a form proximity penalty term is introduced, so that precise matching and enhancement of weak short circuit signals are realized, non-fault interference is effectively eliminated, and the signal-to-noise ratio and accuracy of detection are significantly improved.

The invention relates to a vegetation leaf dry matter content remote sensing high-precision inversion method. The method comprises the following steps: S1, constructing a vegetation leaf sample data set; s11, constructing a blade actual measurement data set; s12, generating an analog data set and / or an analog data set added with noise; s13, dividing the actual measurement data set into an actual measurement training set and an actual measurement verification set; s2, dry matter weak information features are extracted through continuous wavelet transform; s21, carrying out multi-scale analysis calculation on dry matter weak information by using a continuous wavelet transform method; s22, performing wavelet basis function transformation on the original reflection spectrum of each leaf sample to obtain wavelet coefficient characteristics; s23, carrying out correlation analysis calculation on the wavelet coefficient characteristics and the LMA; s24, a threshold value is set, and wavelet coefficient characteristics with sensitivity to LMA spectrum weak information are screened out; and S3, constructing an LMA inversion model based on wavelet coefficient coupling machine learning. The method is high in inversion precision and strong in noise robustness.

The present application relates to the technical field of electric vehicle reducer fatigue analysis, and particularly relates to a method for preparing a frequency domain of a gear fatigue load spectrum of an electric vehicle reducer based on CCWOA, comprising: S1: constructing a bending stress load spectrum of a reducer gear of an electric vehicle; S2: generating an optimal wavelet base function based on discrete wavelet parameter optimization of an energy leakage criterion; S3: performing threshold optimization based on a CCWOA algorithm to obtain an optimal threshold; the CCWOA algorithm introduces a Logistic-Tent chaotic mapping mechanism and a cosine iteration strategy in the WOA algorithm; S4: performing discrete wavelet transform on the bending stress load spectrum of the reducer gear of the electric vehicle based on the optimal wavelet base function and performing load spectrum frequency domain coding through the optimal threshold; and S5: realizing fatigue analysis of the electric vehicle reducer based on a gear fatigue acceleration load spectrum of the electric vehicle reducer. The present application realizes high-precision compression of the reducer gear load spectrum and equivalent retention of fatigue damage, and provides a reliable scheme for fatigue analysis of the electric vehicle reducer.

This invention relates to a wavelet threshold-based method and system for denoising cold test vibrations, comprising the following steps: constructing a vibration parameter database based on the acquired diesel engine cold test vibration signal; performing wavelet decomposition on the vibration parameters in the database according to the selected wavelet basis function and decomposition level to obtain wavelet coefficients; performing wavelet reconstruction on the vibration parameters based on a threshold function to obtain denoised cold test vibration parameters, specifically: shrinking the wavelet coefficients within the threshold range to zero, while keeping the wavelet coefficients outside the threshold unchanged; and processing the acquired diesel engine cold test vibration signal using the denoised cold test vibration parameters to obtain a denoised cold test vibration signal. The threshold function is used to solve the oscillation and distortion problems existing in the denoising process, thereby improving the denoising effect of the cold test vibration signal.

The application discloses a kind of lithium battery energy storage station based on edge computing data preprocessing method.It includes the following steps: obtaining wavelet base function is discretized, and discrete wavelet function is obtained;Discrete wavelet transform is carried out to arbitrary input acoustic signal, and approximate value and noise value are decomposed to discrete wavelet;Threshold size is determined according to sample estimation;Wavelet coefficient is reorganized according to threshold, and the inverse transform of wavelet energy spectrum after wavelet coefficient processing is carried out, reconstructs time signal;Denoising evaluation index function is established, for the one that fails to meet, reconstructed signal is returned again decomposition, and second threshold determination is carried out again, and reconstruction is carried out again, if the signal after twice reconstruction still cannot meet, it will be discarded;The reconstructed signal after wavelet denoising and meeting the requirement signal-to-noise ratio evaluation index is extracted by principal component analysis PCA eigenvalue.The beneficial effects of the application are: it can realize denoising dimension reduction processing to obtain eigenvalue.