607 results about "White noise" patented technology

The invention discloses a global ultra-precision strain measurement method, and relates to the technical field of strain measurement. The method comprises the following steps: continuously shooting moving images of a target area by using a high-frame-rate high-definition camera, and recording accurate time; and carrying out image correction, feature extraction and tracking and data normalization. And after Gaussian white noise is added, singular value decomposition and reconstruction are carried out. And a Lucas-Kanade optical flow method is adopted to calculate the displacement of the feature points in the reconstruction matrix. And a displacement smoothing stage: smoothing the displacement data by using an LOESS method. And carrying out global strain calculation by adopting a window sliding method, and calculating the strain through the spatial derivative of the displacement field. According to the method, the quantization effect can be relieved, the continuity of displacement calculation is improved, low-energy noise and irrelevant features are removed, main motion information is reserved, meanwhile, smoothness is enhanced, local features are captured in a high-precision mode, and measurement sensitivity is improved. The method is suitable for strain measurement of large-scale structures such as bridges and buildings.

The invention discloses a GNSS real-time deception jamming detection method and system based on deep learning, and belongs to the field of satellite navigation signal processing, and the method comprises the steps: collecting Doppler frequency shift signals of a GNSS receiver in real time, and obtaining an initial training data set; an LSTM prediction model with an incremental learning mechanism is constructed, the LSTM prediction model comprises a double-layer LSTM network and a full connection layer, a gating mechanism is introduced into the double-layer LSTM network, and the initial training data set is adopted to add Gaussian white noise to the prediction model for model training; performing sliding window processing on the initial training data set, and extracting a multi-dimensional feature vector; inputting the trained prediction model for prediction to obtain a model prediction value; calculating a residual error between a model predicted value and a real value, calculating a standard deviation of the residual error after eliminating a preheating period, and dynamically setting a threshold value based on the standard deviation of the residual error; and when the absolute value of the residual exceeds a threshold value and the sampling point processed by the sliding window is located in a preset interference time window, judging that a signal interference event occurs.

The invention provides a high-voltage circuit breaker voiceprint denoising method based on data enhancement and a storage medium, and the method comprises the steps: processing a collected original voiceprint data sequence, extracting stable and effective Mel-frequency cepstrum coefficient features, and constructing a two-dimensional feature matrix; a parallel mixed data enhancement strategy is adopted to generate a positive sample pair, and an encoder is trained in combination with a contrast learning mechanism, so that the representation robustness of the model under different voiceprint change conditions is improved. The method comprises the following steps: decomposing an original signal containing noise fringes into a plurality of modal components by using variational modal decomposition, extracting low-frequency effective components, introducing Gaussian white noise, constructing a corrosion target signal as a decoder training target, learning through a denoising automatic encoder, and finally outputting a denoised voiceprint feature signal. The method has the advantages of high robustness, high noise suppression capability, excellent feature expression capability and the like, is suitable for the field of online monitoring and intelligent diagnosis of the state of high-voltage circuit breaker equipment, and has good application prospect and engineering value.

A distribution network fault positioning method based on particle filter algorithm multi-information fusion comprises the following steps: extracting transient information, and extracting a transient zero-sequence current high-frequency component by using discrete wavelet transform; calculating a similar entropy and a skewness coefficient of a transient zero-sequence current high-frequency component of each measurement point to obtain a respective corresponding initial fault section position; fusing the two preliminary fault section positions by using a particle filtering algorithm to obtain a fault section position; determining head and tail fault steady-state zero-sequence voltage and power of the fault section; and cutting a fault section, obtaining the zero-sequence voltage and power of a next cutting point by using a particle filter algorithm, carrying out iterative cutting until a threshold condition is met, and outputting a fault position. According to the method, transient zero-sequence current high-frequency components and steady-state zero-sequence voltage power serve as fault characteristic quantities, and the fault positioning function under the conditions of different fault positions, fault closing angles and white noise interference of the active distribution network is achieved.

The invention discloses a park heterogeneous load prediction method based on EEMD and Kmeans adaptive optimization. The method comprises the following steps: carrying out missing value filling, abnormal value elimination and feature coding on park historical load data and external influence factors; dividing the park load into a plurality of typical scenes through a Kmeans clustering algorithm based on the similarity of load fluctuation characteristics; eEMD decomposition is carried out on the load sequence corresponding to each typical scene, Gaussian white noise is added to suppress modal aliasing, multi-frequency components are extracted and fused with external variables, and a prediction input data set is constructed; a prediction model is selected for different scenes, and adaptive adjustment and optimization are carried out through a Bayesian optimization algorithm; and carrying out dynamic weight fusion on the prediction result of each scene, dynamically adjusting the weight according to the historical load proportion of the scene, and outputting an overall load prediction value of the park.

The present invention provides a method for preprocessing vibration signals of a hydropower unit. The present invention proposes five strategies to improve the optimization accuracy of a dung beetle algorithm so that it has good robustness. The improved dung beetle algorithm MSIDBO jumps out of the local optimum through PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior integrating dynamic golden sine, sparrow alert mechanism and parameter adaptive adjustment to improve the accuracy of the optimization problem. With the minimum envelope entropy as the fitness function, MSIDBO is used to optimize parameters such as the white noise amplitude weight Nstd and the number of noise additions NE of ICEEMDAN, and the optimized ICEEMDAN is used to perform modal decomposition of the vibration signal of the hydropower unit. The MSIDBO algorithm has a faster convergence speed, higher solution accuracy and better applicability than the standard dung beetle algorithm. Through simulation signal analysis, it is concluded that the decomposition accuracy of the optimized ICEEMDAN is higher than that of the conventional decomposition algorithm, which improves the accuracy of the preprocessing of the vibration signal of the hydropower unit and provides a basis for the intelligent vibration monitoring and diagnosis of the hydropower unit.

The invention relates to the technical field of ground wire damage detection, and provides a ground wire damage detection method and system based on magnetostrictive guided waves, and the method comprises the following steps: generating a white noise signal through a magnetostrictive transducer to excite a ground wire, and collecting a guided wave response signal of the guided waves; identifying the guided wave response signal through a spectrum analysis model to obtain the inherent frequency characteristic of the ground wire, and obtaining the optimal excitation frequency according to the inherent frequency characteristic and the amplitude frequency mapping relation; adjusting the excitation frequency of the white noise signal according to the optimal excitation frequency to obtain an optimized guided wave signal; identifying the optimized guided wave signal through a defect detection and tension monitoring model to obtain a defect tension detection result; and based on the defect tension detection result, judging the damage level of the ground wire, and generating a detection report. According to the invention, defect detection and tension abnormity monitoring are carried out at the same time, the reliability of ground wire detection is improved, and an effective monitoring means is provided for safe operation of the ground wire.

The invention provides an unmanned aerial vehicle radio frequency signal identification method and a related device, and relates to the technical field of deep learning. Signal data are loaded from an unmanned aerial vehicle radio frequency signal data set, and the data set comprises different unmanned aerial vehicle types, detection distances and signal fragment labels; dividing the signal data into a plurality of sections of continuous sampling point sequences which are not overlapped with one another; adding Gaussian additive white noise with different powers into the signal data, and constructing a training data set comprising a plurality of signal-to-noise ratios; performing short-time Fourier transform on the signal data to generate a time-frequency graph; performing principal component analysis and dimension reduction on the time-frequency graph, and extracting key time-frequency features; performing hyper-parameter optimization on the deep neural network model by adopting a k-fold cross validation strategy; and the optimized deep neural network model is used to classify and identify the time-frequency graph. The technical effect of accurately identifying the radio frequency signal of the unmanned aerial vehicle under different signal-to-noise ratio conditions is realized.

The invention relates to the technical field of signal processing, and discloses a power equipment partial discharge signal denoising method and system, and the method comprises the steps: S1, generating an exponential decay type pulse signal simulating partial discharge, and superposing white noise and narrow-band interference into the exponential decay type pulse signal to generate a noisy signal; s2, decomposing the noisy signal into a plurality of intrinsic mode components through an ensemble empirical mode decomposition method, calculating a correlation coefficient and a kurtosis index of each intrinsic mode component, and identifying a noise component and a signal component based on the correlation coefficient and the kurtosis index; s3, wavelet threshold denoising is carried out on the noise components, and a wavelet threshold is dynamically adjusted for each noise component through a particle swarm optimization algorithm; and S4, reconstructing the de-noised noise component and the reserved signal component into a final de-noised signal, and evaluating the de-noising performance based on the signal-to-noise ratio, the root mean square error, the peak signal-to-noise ratio and the correlation coefficient.

The invention relates to the technical field of rolling bearing fault diagnosis, in particular to a rolling bearing fault diagnosis method based on data fusion and a neural network, and the method comprises the steps: S1, collecting vibration data of a plurality of sensors, superposing Gaussian white noise and pulse noise, and simulating a strong noise industrial environment; s2, extracting time domain and frequency domain features of vibration signals of each sensor, and constructing a preliminary feature matrix; s3, performing noise reduction processing and depth feature extraction on the feature matrix by using SDAE, fusing to generate a bearing data set, and dividing a training set and a test set; and S4, inputting the training set into an Adam optimization-based fractional order GRU model for training, and verifying the trained model by using the test set. According to the rolling bearing fault diagnosis method, the precision and robustness of rolling bearing fault diagnosis in a strong noise environment are remarkably improved by fusing the multi-source sensing data and the fractional order neural network model.

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention relates to the technical field of short-term power load prediction, in particular to a short-term power load prediction method combining improved empirical mode decomposition and a bidirectional long short-term memory network, which comprises the following steps: preprocessing historical load data; decomposing the load data into a plurality of intrinsic mode functions by adopting a complete adaptive noise integrated empirical mode decomposition algorithm, and adaptively adding Gaussian white noise to suppress a mode aliasing problem; constructing a prediction model based on a bidirectional long-short-term memory network and a self-attention mechanism for each component; extracting features by an input layer through a sliding window; the training layer adopts a bidirectional long short-term memory network to extract time sequence hidden features, and a key time point is focused through weighting of a self-attention mechanism; the output layer generates a component prediction value; and superposing all component prediction results, and reconstructing to obtain an accurate load prediction value of the to-be-predicted day. According to the method, the prediction precision and stability are effectively improved by improving signal decomposition and deep learning model fusion.

The invention relates to an audio control method and device, electronic equipment and a vehicle. The audio control method comprises the following steps: acquiring multi-modal data; performing feature analysis on the multi-modal data to obtain an analysis processing result, and constructing a user portrait based on the analysis processing result; generating audio white noise corresponding to the user portrait based on the user portrait; according to the embodiment of the invention, the multi-modal data can be acquired, the personalized characteristics of the user can be determined by analyzing the multi-modal data from multiple dimensions, the accuracy of user portrait construction is improved, on the basis, the demands of the user are deeply understood based on the user portrait, and the user experience is improved. According to the method, the audio white noise suitable for the user is generated, and audio control is performed through the audio white noise, so that the in-vehicle environment is improved to meet different requirements of the user, and the user experience is improved.

According to the method and device for predicting the blasting vibration waveform, the computer storage medium and the terminal, the random vibration prediction model constructed by modulating the white noise filtering model is used for testing each blast hole to obtain the single-hole waveform of the blast hole, the rock mass characteristics and the charging condition characteristics can be reflected, and the blasting vibration waveform prediction accuracy is improved. The accuracy of the underground blasting vibration prediction result is ensured, the single-hole blasting vibration waveform of related parameters determined according to the single-hole waveform is used as the superposition wavelet, the superposition calculation of the porous blasting vibration is completed, the method can be suitable for different types of porous blasting vibration waveforms, and under the condition that the applicability of the underground blasting vibration prediction result is ensured, the accuracy of the underground blasting vibration prediction result is ensured. A prediction model capable of reflecting porous blasting vibration waveforms such as rock mass characteristics and charging conditions is established, a tool is provided for urban underground space blasting construction, and technical support is provided for urban underground space construction.

The invention discloses a structural damage detection method based on a bridge digital twinning group, and the method comprises the steps: building a general damage detection model through a multi-dimensional digital twinning technology: building a reference bridge model through Abaqus, and generating a digital twinning group containing a single / multiple damage scene; training the ethnic group by improving a WGAN-GP architecture, outputting a marked damage image by a generator, introducing a multi-scale attention module into a discriminator, and constructing an ethnic group characteristic spectrum (EFM) matrix through characteristic distillation; then collecting feature parameters to train a CNN model, and fusing features through SENet channel attention; through Gaussian white noise verification and n-fold cross optimization, the F1 score of the model is greater than or equal to 0.89 in a 40% noise environment, and the average accuracy rate reaches 95% or above. According to the technology, the limitation of single structure detection is broken through, the model generalization ability is improved by 300% and the detection efficiency is improved by 50% through a dynamic twin-driven convolutional network self-evolution mechanism, and complex scenes with + / -20% size change and 0-100% damage degree can be accurately identified; and an efficient anti-interference solution is provided for health monitoring of structures such as bridges, high-rise buildings and the like.

The invention relates to an intelligent hypnosis method and system based on a brain-computer interface and a storage medium, and belongs to the technical field of human-computer interaction and artificial intelligence. The method comprises the following steps: acquiring an EEG signal of a user in real time through EEG acquisition equipment, and extracting a brain wave segment power value as state input through preprocessing; a deep reinforcement learning model (such as DRQN) selects music type actions (such as classical music and white noise) based on an epsilon-greedy strategy; calculating a reward value (maximizing delta wave increment and inhibiting beta wave) according to the electroencephalogram state change after playing, and optimizing model parameters by adopting Q-Learning; and dynamically adjusting the strategy through iterative interaction until the user reaches a preset sleep target. The system comprises an electroencephalogram acquisition module, a preprocessing module, a reinforcement learning module and a music control module, and realizes closed-loop regulation and control. The method has the advantages of high personalization, high hypnosis efficiency (induction to sleep in 1-7 minutes), flexible adaptation to different users and self-evolution optimization capability, and effectively improves the sleep induction effect.

The invention provides a rolling bearing fault diagnosis method based on deep learning, and relates to the technical field of mechanical fault diagnosis. According to the method, global optimization is carried out on a parameter combination in an ICEEMDAN decomposition process through a CPO optimization algorithm, the optimal amplitude of white noise and the number of times of adding the white noise are determined, an intrinsic mode function is screened and denoised in combination with a correlation coefficient, reconstruction of a vibration signal of a rolling bearing is completed, and the vibration signal of the rolling bearing is reconstructed. The problem of mode aliasing is solved while the reliability and the stability of a vibration signal of the rolling bearing are improved; a K nearest neighbor method is used to construct a graph model, an edge connection relation between nodes can be effectively established, so that a foundation is laid for subsequent deep analysis and processing of vibration signals based on a graph structure, a graph attention neural network is input, topological structure information of the nodes and edges is fully utilized, extracted node features are classified and identified, and the accuracy of vibration analysis is improved. Therefore, fault diagnosis of the rolling bearing is realized.

The invention provides a power load prediction method and system based on adaptive modal decomposition, and the method comprises the steps: obtaining an original load sequence and a multi-modal data set based on time sequence distribution, and the multi-modal data set comprises meteorological data, equipment state data, and economic and social activity data; copying the original load sequence into a plurality of copies, and adding adaptive white noise to each original load sequence to obtain a plurality of noise-added sequences; performing modal decomposition, component screening and modal reconstruction on each noise adding sequence, and performing superposition to obtain a de-noising load sequence; performing feature screening based on causal driving on the de-noising load sequence and the multi-modal data set to obtain a feature vector; and inputting the feature vector into a preset load prediction model, so that the load prediction model outputs a corresponding load prediction result based on an attention mechanism and a preset physical rule constraint, and the accuracy of power load prediction in a complex scene is improved.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention relates to a switch cabinet partial discharge signal positioning method based on a time difference positioning method, and belongs to the technical field of electrical engineering and signal processing. Aiming at the problem that the ultrasonic partial discharge signal has white noise, an improved wavelet threshold algorithm is adopted to construct a threshold function, and a continuous self-adaptive wavelet threshold denoising algorithm is adopted to carry out denoising processing on the partial discharge signal, so that the problems of excessive denoising and incomplete denoising existing in a traditional denoising algorithm are solved, effective separation of the signal and the noise is realized, and the denoising efficiency is improved. And the performance of the denoising algorithm is improved. In order to obtain a time delay value between each path of signals, a generalized cross-correlation time delay estimation algorithm based on PHAT-SCOT joint weighting is used for substituting a time delay estimation value into a switch cabinet partial discharge positioning equation set, and an optimal equation is obtained. According to the partial discharge optimal solution algorithm based on DE-PSO, the optimization equation is solved, the spatial coordinate position generating the partial discharge signal is positioned, the defects of parameter setting and threshold selection of the optimization algorithm are overcome, and the positioning precision of the partial discharge signal is improved.

The invention relates to a cable partial discharge defect identification method based on a support vector machine, and belongs to the technical field of electrics. The problems that an existing method is low in recognition rate and prone to interference are solved. The method comprises the following steps: firstly, carrying out interference suppression on an acquired partial discharge high-frequency signal, wherein the interference suppression comprises narrowband interference suppression by subspace reconstruction and frequency slice wavelet transform white noise suppression based on ensemble empirical mode decomposition; then, carrying out support vector multi-classification by utilizing an optimized support vector data description algorithm, and constructing a plurality of classifiers; and finally, calculating the generalized distance from a test sample to each classifier, and making a decision, thereby achieving the recognition of the partial discharge defect. According to the method, the partial discharge defect recognition rate is effectively improved, the anti-interference capability is high, and a reliable technical means is provided for power cable insulation state detection.

The invention discloses a gravitational field inversion method considering unsteady noise of a satellite gravity observation value. The gravitational field inversion method comprises the following steps: A, acquiring original observation data of a gravity satellite; b, setting observation value noise as white noise, and constructing a random model considering non-stationary noise; c, performing parameter estimation to obtain a post-test residual error of an observation value; d, estimating the noise auto-covariance and precision factor matrix of the observation value based on the post-test residual error of the observation value; e, repeating the steps C and D for iteration to realize refinement of the gravitational field parameter and observation value random model; and F, evaluating the time-varying gravity field model in the spectral domain. According to the invention, the defects in the prior art can be improved, and the precision and stability of gravitational field inversion are improved.

The invention discloses a baby cry recognition method for white noise equipment, and relates to the field of audio processing and intelligent acoustic recognition, and the method comprises the steps: collecting a pure reference signal, a first-path signal and a second-path signal, carrying out the synchronization and preprocessing, and obtaining a mixed audio signal based on the first-path signal; a residual signal is calculated through an adaptive echo cancellation algorithm; calculating an acoustic masking parameter based on the residual signal, the pure reference signal, and the ambient noise estimate; constructing a three-level recognition processing path including lightweight feature detection, registration voiceprint comparison and multi-modal information fusion; based on the numerical range of the acoustic masking parameter, selecting an identification processing path, and determining a crying event of the target infant from the residual signal; and triggering a corresponding grading alarm based on a crying event confirmation result. A three-level identification path is scheduled through acoustic masking parameters, and accurate and low-power-consumption baby crying monitoring under strong interference is realized by fusing multi-modal information.

The invention provides an earth rotation parameter determination method and system based on a GNSS data stream, and the method comprises the steps: achieving the purpose of increasing the estimation of earth rotation parameters in a process of achieving precise orbit determination through employing square root information filtering, regarding the acceleration values of polar shift and UT1 change as white noise in time updating, and forming a state equation; in the measurement updating process, a UT1 value in an ultra-fast ERP forecasting product provided by the IGS is added to serve as virtual observation to form a virtual observation equation, so that UT1 parameters and orbit parameters are decoupled and can be estimated, stable estimation of real-time earth rotation parameters is achieved, the precision is superior to that of earth rotation parameter forecasting products provided by the IGS and IERS, and the accuracy of the earth rotation parameter forecasting products provided by the IGS and the IERS is improved. And meanwhile, a precise track product with higher precision is obtained, and the real-time high-precision ERP application requirement can be better met.

The invention relates to the technical field of deep sleep management, in particular to a deep sleep staging management method and system based on electroencephalogram feedback. Comprising the steps that electroencephalogram feedback of a target user is collected, an actual deep sleep waveform is extracted and judged with an expected spectral domain of an ideal state transfer stage, and an unreached stage is locked; in an unreached stage, constructing a signal mode prediction model, and determining an optimal guide window stage by combining with intervention entry point migration analysis; performing wavelet basis decomposition to obtain an excellent wave band and a clutter band of the optimal guide window period; when the signal-to-noise ratio is high, crosstalk coupling analysis is executed, and white noise is generated by using the rhythm coding library for guide management; when the signal-to-noise ratio is low, light environment and temperature response regulation is planned, and deep sleep is managed in combination with a color coding chain and a signal excitation result. According to the method and the system, guide management of different decisions can be executed in the stage of entering the deep sleep of the target user, and the deep sleep quality, stability and continuity are improved.

The invention provides a broadband radar echo denoising method and device based on a U-shaped self-attention model, and relates to the technical field of radar signal processing. Comprising the following steps: acquiring high-resolution range profile echo data; gaussian white noise is added to the high-resolution range profile echo data to obtain a training data set; a range profile denoising network based on a U-shaped self-attention model is constructed, the range profile denoising network comprises a plurality of encoders, and each encoder comprises a first target multi-head self-attention layer added with a range profile supporting area mask and a wavelet denoising down-sampling layer; training the range profile denoising network by using the training data set to obtain a trained range profile denoising network; and inputting the noisy high-resolution range profile echo data into the trained range profile denoising network to denoise the noisy high-resolution range profile echo data, and outputting the denoised high-resolution range profile echo data. Therefore, the detail information of the range profile is reserved, and the recognition rate of the subsequent recognition network is improved.

The short-term photovoltaic power prediction method based on multi-strategy optimization and data decomposition comprises the following steps: collecting meteorological data and historical power data of a target photovoltaic power station region to form a data set; using ICEEMDAN to decompose the historical power data to obtain an intrinsic mode component; inputting the intrinsic mode component group into an improved WLSSVM (Weighted Least Square Support Vector Machine) for prediction training; performing multi-strategy improvement on the snow melting algorithm to obtain an MISAO algorithm; in the training process, the white noise amplitude weight Nstd and the noise addition number NE of the ICEEMDAN are optimized through the MISAO, and the parameters lambda and delta of the WLSSVM are optimized through the MISAO; and after the prediction training is finished, the prediction result of the intrinsic mode component obtained through prediction is superposed to obtain a photovoltaic power prediction value. The device for realizing the method comprises a data acquisition module, a clustering module, a sequence decomposition module and a photovoltaic power prediction model construction and verification module. According to the method, multiple strategies are fused in the MISAO algorithm, the population diversity is increased, the search space is expanded, local optimum is avoided, the prediction efficiency is improved, and the generalization performance and stability of the model are enhanced.

The transformer substation connection fitting damage identification method provided by the invention comprises the following steps: emitting white noise to a transformer substation connection fitting to obtain a vibration signal of the transformer substation connection fitting; an optimal influence coefficient of the preset harmonic sine structural element is determined by adopting a starfish optimization strategy, an optimal harmonic sine structural element is obtained, and the starfish optimization strategy is that after influence parameters of the preset harmonic sine structural element are initialized, an objective function corresponding to the vibration signal is adopted to perform optimal influence parameter solution search; and carrying out morphological gradient combination product operation on the vibration signal by adopting the optimal harmonic sine structural element, and then carrying out spectral analysis to obtain a damage identification result of the transformer substation connection fitting. Therefore, accurate identification of the damage of the connection fitting is realized.

The invention belongs to the technical field of partial discharge detection, particularly relates to a partial discharge signal denoising method based on SVDTQWT, and aims to effectively remove periodic narrow-band interference and white noise in partial discharge signals. Comprising the following steps: performing Fourier transform on a noisy partial discharge signal to obtain a frequency spectrum, determining the number of periodic narrowband interferences through singular value decomposition, constructing a Hankel matrix to eliminate the periodic narrowband interferences, and obtaining a preliminary de-noised signal; and decomposing the preliminarily denoised signal by adopting adjustable quality factor wavelet transform to obtain a plurality of sub-bands. And dividing the plurality of sub-bands into high-frequency sub-bands and low-frequency sub-bands through sample entropy. Wherein the sample entropy indicates measurement of the complexity of the time series. And de-noising the high-frequency sub-band by using a group sparse total variation de-noising algorithm, de-noising the low-frequency sub-band by using an improved wavelet threshold de-noising algorithm, and reconstructing by using adjustable quality factor wavelet transform to obtain a pure partial discharge signal.

The invention relates to an image restoration method based on adaptive color correction, back scattering light optimization and scene transmittance improvement, which comprises the following steps: calculating a total value and an information retention factor of each color channel, and selecting a reference channel; calculating an adjustment coefficient; compensating a low pixel value; determining whether the maximum color loss is greater than a loss threshold; dividing the color correction image into a structure component and a texture component; estimating a scene transmittance on the structure component; estimating backscattered light on the structure component; and obtaining a clear image according to the enhanced structure component and the enhanced texture component. According to the method, adaptive color correction is realized, contrast enhancement is optimized, robustness is realized, and more detailed information is provided while the natural appearance of the whole image is kept; the maximum saturation constraint is adopted for scene transmission rate estimation, block artifacts can be avoided under ideal conditions, the edge of a significant object is enhanced, white noise and misjudgment of the object are eliminated, and a more accurate result can be generated.