58 results about "Complex wavelet transform" patented technology

The invention discloses a wavelet domain audio zero watermark algorithm based on dual-tree complex wavelet transform (DTCWT), variational mode decomposition (VMD) and discrete cosine transform (DCT). Firstly, encryption protection is carried out on a watermark image, then framing is carried out on an original audio signal, dual-tree complex wavelet transform (DTCWT) and variational mode decomposition (VMD) are carried out on each frame, a first IMF is taken to carry out DCT, and a feature vector is obtained. And finally, carrying out XOR operation on the polarity feature vector and the scrambled watermark to obtain a zero watermark key. Experiments show that the method can effectively resist conventional attacks, and especially has good robustness to noise attacks.

The invention discloses a vacuum arc-extinguishing chamber contact surface condition evaluation method and system, and belongs to the technical field of vacuum circuit breakers. According to the method, short-time Fourier transform noise reduction is improved by adopting an adaptive window length, signals are enhanced in combination with dual-tree complex wavelet transform, and data are dynamically normalized by improving K-means clustering; according to the constructed lightweight CRNN model, residual connection and an SE module are introduced into a convolution module, and a bidirectional quasi-recurrent neural network is adopted by a circulation module to capture time sequence features; the mixed loss function is combined with an AdamW optimizer, and the convergence stability is improved through L2 regularization; and dynamically evaluating the ablation degree of the contact based on a fuzzy logic algorithm. According to the method, through time-frequency feature fusion and lightweight network design, high-precision classification of direct-current pre-breakdown waveforms is realized, the problems of degradation, over-fitting and slow convergence of a traditional model are solved, and an intelligent solution is provided for vacuum arc-extinguishing chamber insulation performance evaluation.

The present disclosure relates to digital watermarking systems that may use visual masks to optimize watermark embedding in digital imagery. A visual mask provides guidance for adjusting digital watermark signal strength, enabling improved trade-offs between watermark imperceptibility and robustness. Multiple embodiments generate visual masks including: (1) a Perceptual Modeling Candidate approach using contrast masking and texture classification based on standard deviation mapping; (2) a wavelet-based approach using Dual-Tree Complex Wavelet Transform for translation-invariant frequency analysis; (3) artificial intelligence approaches employing convolutional neural networks trained to optimize embedding strength while minimizing perceptual distance metrics such as LPIPS; and (4) LPIPS threshold masking that determines optimal embedding strengths by testing multiple candidate strengths. Visual masks enable content-adaptive digital watermarking that places stronger signals in textured regions while maintaining imperceptibility in flat regions, improving visibility-robustness performance compared to uniform embedding approaches.

The invention relates to the technical field of image processing, and discloses a plastic package material defect detection method and system in a radiation environment based on machine vision. The method comprises the following steps: acquiring an anti-radiation image in a gamma-ray environment through a lead-tungsten alloy shielding high-radiation tolerance type image sensor, and performing adaptive contrast enhancement. Defect features including microcracks, bubbles, deformation and chromatic aberration are extracted by using dual-tree complex wavelet transform and an HSV color space; and inputting the multi-dimensional features into an AR-DCNN network to carry out multi-task learning, identifying defect types and carrying out scoring. Based on Weibull distribution and a random forest algorithm, the radiation-resistant life of the plastic package material is predicted, and a material degradation curve and safety threshold early warning are obtained. According to the invention, standardized processing of the radiation damage quantitative index is realized, and the prediction accuracy is effectively improved.

The invention belongs to the technical field of computer vision and computer graphics, and discloses a multi-view three-dimensional reconstruction method and device based on cross-domain feature fusion and a medium, and the method comprises the steps: carrying out the cross-feature-domain coding of an initial feature token generated by a multi-view image, according to the coding, characteristics are decomposed into low-frequency components and high-frequency components through dual-tree complex wavelet transform, amplitude modulation is carried out on the high-frequency components to enhance details, and space-frequency fusion characteristics are generated; variance embedding weighted combination is carried out on the multi-view space-frequency fusion features, the combination generates a weight by calculating the variance of each feature token, adaptive weighted clustering is carried out based on the weight, and fused multi-view features are generated; and performing three-source attention decoding on the fused features, and performing cross-domain attention calculation and up-sampling by taking static embedding as query, taking the space-frequency fused features as keys and taking the fused multi-view features as values, thereby finally generating a three-dimensional voxel reconstruction result of the target object.

The invention provides a millimeter-wave radar personnel perception method based on a time-frequency domain and a deep CNN, and relates to the technical field of radar signal processing, and the method comprises the steps: carrying out the preprocessing, spectrogram conversion and enhancement of a millimeter-wave radar echo signal; spatial features are extracted by using depth separable convolution of a residual structure, time-frequency features are extracted by combining dual-tree complex wavelet transform and attention-enhanced cavity convolution, motion features are extracted through optical flow estimation and three-dimensional convolution, and the three features are adaptively fused; the method comprises the following steps: constructing a dynamic spatio-temporal reasoning network, obtaining key spatio-temporal features by using a recurrent neural tensor network, a non-local neural network and deformable convolution, extracting multi-scale features and performing adversarial feature alignment, and completing personnel target classification through a dynamic routing mechanism based on a capsule network. According to the method, clutter interference can be effectively suppressed, space, time frequency and motion information is fully utilized, accurate perception of personnel targets is realized, and the classification accuracy is improved.

The invention discloses a guided wave self-adaptive enhanced time reversal imaging method and system for vehicle structure damage and a computer program product, and belongs to the technical field of aircraft structure health monitoring. The envelope of the damage scattering signal is calculated through Shannon continuous complex wavelet transform and Hilbert transform, and m wave bands with the maximum peak value are selected as potential damage scattering signal wave bands; the damage factor of each potential wave band is calculated, the wave band with the maximum damage factor is a real damage scattering wave band, other signals are set to be zero, and therefore guided wave self-adaption enhancement is achieved, and the signal-to-noise ratio is increased; on the basis of extraction of a damage scattering wave band, a time reversal focusing imaging method is adopted, imaging results generated when all the sensors serve as excitation are superposed, and the point with the maximum pixel value is the damage position. The implementation process is accurate and efficient, and damage positioning can be achieved through the piezoelectric sensor monitoring network on the main load-bearing structure.

The invention discloses a vector binary condition Weibull distribution-based digital watermark detection method, which comprises the following steps of: combining non-sampling dual-tree complex wavelet transform with a fast and accurate Chebyshev Fourier moment to obtain a stable UDTCWT-FACHFMs amplitude domain, selecting an accurate moment in the amplitude domain as an embedding position of a watermark, and then, carrying out binary condition Weibull distribution on the basis of the vector binary condition Weibull distribution. Embedding the watermark into a UDTCWT-FACHFMs amplitude domain by using a multiplicative embedding method, then carrying out inverse transformation to obtain a watermark-containing image, and completing the embedding work of the watermark; according to the statistical property of the amplitude coefficient, selecting relatively appropriate binary condition Weber distribution to perform statistical modeling, further deducing the binary condition Weber distribution into a vector form, and performing parameter estimation by using a probability weighted moment method based on power density to obtain an accurate parameter estimator; and finally, in combination with a local optimal decision criterion, a new statistical watermark detector based on vector binary condition Weber distribution is developed, and the embedded watermark information can be accurately detected according to a threshold.

The invention relates to the technical field of traffic detection, and discloses a deep learning-based traffic flow situation monitoring method and system in snowfall weather, and the method comprises the steps: obtaining vehicle operation video data on a road in real time, and carrying out the data processing of the vehicle operation video data; constructing an improved even-number complex wavelet transform network model, and removing snow particles in the video image; removing snow grains in the video image through an image prior algorithm; vehicle detection is carried out, and vehicles in the video image are identified through a yov5 algorithm to obtain a vehicle identification result; and detecting the snow-removed video image through a detection line algorithm, and calculating to obtain a traffic flow parameter. The road operation state is evaluated through the traffic flow parameters, accurate perception of the traffic flow situation in the ice and snow weather is achieved, and the adaptability and practicability of the monitoring system in the complex environment are improved.

The invention provides a millimeter-wave radar personnel perception method based on a time-frequency domain and a deep CNN, and relates to the technical field of radar signal processing, and the method comprises the steps: carrying out the preprocessing, spectrogram conversion and enhancement of a millimeter-wave radar echo signal; spatial features are extracted by using depth separable convolution of a residual structure, time-frequency features are extracted by combining dual-tree complex wavelet transform and attention-enhanced cavity convolution, motion features are extracted through optical flow estimation and three-dimensional convolution, and the three features are adaptively fused; the method comprises the following steps: constructing a dynamic spatio-temporal reasoning network, obtaining key spatio-temporal features by using a recurrent neural tensor network, a non-local neural network and deformable convolution, extracting multi-scale features and performing adversarial feature alignment, and completing personnel target classification through a dynamic routing mechanism based on a capsule network. According to the method, clutter interference can be effectively suppressed, space, time frequency and motion information is fully utilized, accurate perception of personnel targets is realized, and the classification accuracy is improved.

The application provides a millimeter wave radar personnel sensing method based on a time-frequency domain and a deep CNN, relates to the technical field of radar signal processing, and comprises preprocessing, spectrum conversion and enhancement of a millimeter wave radar echo signal; spatial features are extracted by using a deep separable convolution with a residual structure, time-frequency features are extracted by combining a dual-tree complex wavelet transform and an attention-enhanced hollow convolution, and motion features are extracted by optical flow estimation and three-dimensional convolution, and the three are adaptively fused; a dynamic space-time reasoning network is constructed, key space-time features are obtained by using a recurrent neural tensor network, a non-local neural network and a deformable convolution, multi-scale features are extracted and aligned in an adversarial manner, and a dynamic routing mechanism based on a capsule network is used to complete personnel target classification. The application can effectively suppress clutter interference, fully utilize spatial, time-frequency and motion information, realize accurate sensing of personnel targets, and improve classification accuracy.

The invention provides a seabed node image processing method, system and device, and a storage medium. The method comprises the following steps: respectively carrying out one-dimensional dual-tree complex wavelet domain band-pass filtering processing on uplink and downlink wave imaging gathers in an original seabed node image one by one; performing two-dimensional dual-tree complex wavelet transform on the uplink and downlink wave imaging gathers subjected to the one-dimensional dual-tree complex wavelet domain band-pass filtering processing; performing matched filtering on the uplink and downlink wave imaging gathers subjected to the two-dimensional dual-tree complex wavelet transform; and performing two-dimensional dual-tree complex wavelet inverse transformation on the complex wavelet coefficients of the uplink and downlink wave imaging gathers after matched filtering processing to obtain a final seabed node image. According to the method, the quality of uplink and downlink wave imaging gathers is remarkably improved, the consistency of the uplink and downlink wave imaging gathers is improved, and the imaging definition is improved.

The present invention discloses a method and system for evaluating the surface condition of contacts of a vacuum interrupter, which belongs to the technical field of vacuum circuit breakers. The method uses adaptive window length to improve short-time Fourier transform for noise reduction, combines dual-tree complex wavelet transform to enhance signals, and dynamically normalizes data through improved K-means clustering; a lightweight CRNN model is constructed, the convolution module introduces residual connection and SE module, and the recurrent module uses a bidirectional quasi-recurrent neural network to capture time series features; the hybrid loss function is combined with the AdamW optimizer to improve the convergence stability through L2 regularization; and the degree of contact ablation is dynamically evaluated based on a fuzzy logic algorithm. The method achieves high-precision classification of DC pre-breakdown waveforms through time-frequency feature fusion and lightweight network design, solves the problems of traditional model degradation, overfitting and slow convergence, and provides an intelligent solution for the insulation performance evaluation of vacuum interrupters.

The invention discloses a color three-dimensional image zero-watermark processing method based on eight-element matrix factorization, which comprises the following steps of: firstly, cutting square invariant feature region images at the centers of left and right viewpoints of a three-dimensional image, and carrying out non-subsampled dual-tree complex wavelet transform on the invariant feature region images in different channels; averaging the four low-frequency sub-bands obtained by each channel image to obtain a low-frequency mean sub-band, then performing block scrambling on the low-frequency mean sub-band from each channel, selecting a part of sub-blocks to construct an octuple matrix, and performing real number field octuple singular value decomposition on the octuple matrix; obtaining a maximum singular value matrix and then binarizing the maximum singular value matrix to obtain a robust binary feature matrix; and finally, carrying out XOR on the binary watermark which is scrambled and encrypted by the four-wing chaotic system and the cyclic code and the robust binary feature matrix to obtain a zero-watermark image. Before watermark authentication, a KAZE matching algorithm is used to correct left and right viewpoints of a to-be-authenticated stereo image. The method has relatively high robustness on common non-geometric attacks and geometric attacks.

The present invention discloses a method for actually measuring the attenuation coefficient of broadband laser ultrasonic Lamb waves in metal thin plates, belonging to the field of nondestructive testing. Data acquisition stage: Use a nanosecond laser to set a point source excitation on the surface of an aluminum alloy thin plate, and then use a scanning laser Doppler vibrometer (SLDV) to measure the wave field and collect linear wave field data. Data processing stage: Use the time-frequency analysis of continuous complex wavelet transform to obtain the time-frequency spectrum of the one-dimensional wave field signal. Determine the effective analysis range, including the time upper limit and the frequency upper limit. Extract the amplitude data represented by the intercept line. Attenuation characteristic analysis stage: Extract the peaks of the amplitude data at multiple distance points to obtain discrete points of the amplitude change with distance at a specific frequency. The present invention provides a method for actually measuring the attenuation characteristics of broadband laser ultrasonic Lamb waves. The solution of the attenuation coefficient provides an important theoretical reference for the calculation of the effective detection range and the selection of the filtering frequency of damage scattered waves.

The application discloses a virtual viewpoint image quality evaluation method and system based on double-tree complex wavelet transform. The method comprises the following steps: using an algorithm to perform feature matching on a reference image; performing multi-scale double-tree complex wavelet transform on the matched reference image and a virtual viewpoint image for multiple times to obtain a first wavelet subband set corresponding to the matched reference image and a second wavelet subband set corresponding to the virtual viewpoint image; calculating gradient amplitude similarity between third-level wavelet subbands of the first wavelet subband set and the second wavelet subband set, and using the gradient amplitude similarity to calculate a texture distortion score of the virtual viewpoint image; calculating structural similarity between fifth-level wavelet subbands of the first wavelet subband set and the second wavelet subband set, and using the structural similarity to calculate a structure distortion score of the virtual viewpoint image; and calculating a quality score of the virtual viewpoint image according to the texture distortion score and the structure distortion score. The application can accurately evaluate the quality of the virtual viewpoint image.

The invention discloses a common transformer side single-phase intelligent electric meter electric quantity acquisition accuracy improving system, and relates to the technical field of electric variable measurement. According to the system, sampling rate adaptive switching is realized through the working condition modal identification module and the dynamic sampling control module, full-band power accurate metering is carried out by using complex wavelet transform, online calibration and optimization are carried out by means of the parameter self-learning module, the accuracy and adaptability of an electric meter in a complex power grid environment are comprehensively improved, and the accuracy and adaptability of the electric meter in the complex power grid environment are improved. The problems that the fixed sampling rate of a traditional intelligent electric meter is not matched with the dynamic working condition, the harmonic and inter-harmonic power metering is lacked, and the working condition self-adaptive capacity is lacked are effectively solved, the electric meter achieves self-adaptive balance of precision and efficiency in a complex power grid environment, full-band accurate metering of fundamental wave, harmonic wave and inter-harmonic power is completed, and the electric energy utilization rate is improved. And the metering accuracy and stability of the electric meter in long-term operation are ensured through a self-learning mechanism.

The present invention discloses a method, apparatus and storage medium for detecting metal surface defects. The method includes: obtaining a defect synthesis image according to a normal sample image; performing dual-tree complex wavelet transform on the defect synthesis image to transform the image features from the pixel domain to the wavelet domain, obtaining a low-frequency component and high-frequency components of multiple scales; modifying the obtained low-frequency component and high-frequency components, and performing inverse dual-tree complex wavelet transform to obtain a reconstructed image; training an image prediction model by using the defect synthesis image and the reconstructed image; obtaining an image to be detected, inputting the image to be detected into the trained image prediction model, and outputting a detection result. The present invention does not need to collect real defect samples, and only needs normal samples to have good defect detection and positioning capabilities, and can be widely used for automatic online detection of metal surfaces with a certain metallic luster. The present invention can be widely applied to the technical field of metal surface defect detection.

The present application discloses a method for structural shock location based on adaptive time-reversal focusing imaging and image fusion, the method comprising extracting multiple narrowband wave signals of different frequencies of the shock stress wave signal through complex Morlet wavelet transform; setting the initial parameters of the algorithm, for each of the narrowband wave signals, obtaining the adaptive time-reversal focusing imaging result images of all narrowband wave signals based on the set initial parameters of the algorithm, and then calculating the image focusing index of each imaging result image, setting the focusing index threshold of the imaging result image according to the actual shock location imaging accuracy requirements, multiplying and fusion all the imaging result images above the focusing index threshold, and obtaining the grid point coordinates corresponding to the maximum pixel value of the fused image as the predicted shock position. The shock location accuracy of the present application can reach a relatively high level. The present application also discloses a corresponding system.

The invention discloses a high-speed magnetic suspension track settlement monitoring method based on wavelet transform, which comprises the following steps of: extracting global phase information of a power curve in an existing high-speed magnetic suspension millimeter wave communication system in a train running process, extracting a local phase of a signal by adopting complex Morlet wavelet transform, and calculating the settlement of the high-speed magnetic suspension track; and a global mapping relation between the phase difference and the orbit settlement amount is established, space-frequency domain analysis is performed on signals through wavelet transform, and signal phase difference information is extracted in combination with analysis of a wavelet coefficient envelope and a ridge line, so that high-precision sensing and monitoring of the orbit settlement state and the evolution trend thereof are realized. According to the method, the bottlenecks of an existing track monitoring method in the aspects of complex operation, poor real-time performance, high cost and the like are broken through, the problems that a modern monitoring technology is poor in settlement adaptability, expensive in equipment and the like are solved, additional hardware equipment is not needed, the system construction, operation and maintenance cost is reduced, high-precision, low-cost and real-time monitoring is achieved, and the method is suitable for popularization and application. And a new technical path is provided for dynamic tracking of track settlement and deformation.

The invention provides a PET-CT (positron emission tomography-computed tomography) multi-modal image fusion method, system and device and a medium, and belongs to the field of image processing. And processing and normalizing the CT image and the PET image, and performing spatial alignment on the normalized images. Inputting the aligned image into a multi-modal image fusion model, extracting local and global features of the CT image and the PET image by a position convolution module, and respectively extracting low-frequency information of the aligned PET image and high-frequency information of the aligned CT image through even complex wavelet transform and Gaussian-Laplacian operator decomposition; respectively inputting the low-frequency information of the PET image and the high-frequency information of the CT image into a convolution pooling module CPB for processing to obtain PET low-frequency features and CT high-frequency features; and inputting the local and global features, the PET low-frequency features and the CT high-frequency features into a local gradient energy fusion LGE module for fusion, and outputting a PET-CT fusion image after step-by-step processing. And through effective fusion of high and low frequency information, the contrast and details of a focus area are enhanced, so that the method is more comprehensive in medical diagnosis application.

The application discloses a motor output state evaluation method and system, comprising collecting the stator current signal of a target motor, and performing zero-phase band-pass filtering pretreatment on the stator current signal of the target motor to obtain a pretreated current signal. The application firstly retains the phase characteristics of the original signal through zero-phase band-pass filtering pretreatment, then accurately extracts an instantaneous frequency signal through complex wavelet transform, constructs a frequency-adaptive comb notch filter set relying on the motor working specification, filters out the operation signal related to the normal operation of the motor in the instantaneous frequency signal, so as to capture the residual frequency fluctuation caused by the dynamic balance fault, and then obtains an enhanced imbalance confidence index by combining the instantaneous spectrum kurtosis and the fractal dimension calculation, thereby improving the accuracy and reliability of the dynamic balance fault evaluation, and more accurately identifying the early dynamic balance abnormality of the motor, providing a reliable basis for the preventive maintenance of the motor, and effectively guaranteeing the operation safety of the transmission system.

The application discloses an infrared image non-uniformity prediction and correction method based on wavelet transform, which comprises the following steps: adopting double-density dual-tree complex wavelet transform to perform multi-scale decomposition on an input infrared image; in a high-frequency subband, detecting a blind element position based on a local variance statistical method; in a low-frequency subband, constructing an autoregressive model, predicting non-uniformity of a background region, and performing accurate processing on a prediction result; combining the prediction results of the high-frequency subband and the low-frequency subband to generate a pre-correction coefficient; and adopting the pre-correction coefficient to perform correction processing on an original infrared image to obtain a corrected image. Through the combination of an adaptive fusion strategy, space-time consistency constraint and dynamic gain and bias updating technology, the problems of non-uniformity noise, balance between details and background, device drift compensation and consistency in a dynamic scene in infrared image processing are solved, and the accuracy and stability of infrared image processing are significantly improved.

The invention discloses a multi-modal image marine target individual identification method, system and device based on wavelet decomposition, a medium and a product, and relates to the field of marine target identification, and the method comprises the steps: obtaining a marine target image; the marine target image comprises an infrared image and a visible light image; constructing and training an individual recognition model; the individual identification model comprises a dual-tree complex wavelet transform decomposition module, a noise processing module, a feature fusion module, a feature coding module and a classification module which are connected in sequence; and inputting the maritime target image into the trained individual identification model to obtain the identity of the maritime target, and completing the individual identification of the maritime target. According to the invention, the robustness and accuracy of marine target individual identification can be improved.

The present invention relates to a ground-penetrating radar signal denoising method that combines two-dimensional VMD with DT-CWT. The method first performs one-dimensional VMD on the original two-dimensional ground-penetrating radar noisy data in the time dimension, removes the intrinsic modal components corresponding to the noise according to corresponding judgment criteria, and then reconstructs the data. The reconstructed data is then subjected to one-dimensional VMD in the spatial dimension, and the intrinsic modal components corresponding to the noise are removed and then reconstructed. The reconstructed data after the two-dimensional VMD denoising process is subjected to a discrete dual-tree complex wavelet transform, and a corresponding threshold formula is selected for threshold denoising. The data is then reconstructed by inverse dual-tree complex wavelet transform to obtain the denoised data. This method takes into account the noise distribution of the data in the spatial dimension, performs two-dimensional VMD denoising on the original noisy data, and effectively improves the data signal-to-noise ratio. At the same time, to further improve the data signal-to-noise ratio, a dual-tree complex wavelet transform that can achieve precise reconstruction is used to perform secondary denoising on the data.

The invention relates to the technical field of wind turbine generator monitoring, and discloses a fault diagnosis method and device for a fan gear box, electronic equipment and a storage medium, and the method comprises the steps: obtaining a vibration signal of the fan gear box; calculating a power spectrum of the vibration signal; determining a first type of fault characteristic frequency and a second type of fault characteristic frequency respectively according to a first region and a second region in the power spectrum; performing multi-scale complex wavelet transform on the vibration signal and demodulating envelope to obtain a multi-scale complex wavelet envelope spectrum; analyzing the multi-scale complex wavelet envelope spectrum to obtain a third type of fault characteristic frequency; and performing fault diagnosis on the fan gearbox according to at least one of the first type of fault characteristic frequency, the second type of fault characteristic frequency and the third type of fault characteristic frequency to obtain a fault diagnosis result. Through the above mode, the vibration signals are analyzed in different modes, so that the vibration signals can be comprehensively analyzed, and a more complete fault characteristic frequency is obtained.

This invention discloses a ghost wave suppression method for OBS data based on horizontally extended complex wavelet transform. The method transforms land and water detection data into the complex wavelet domain using horizontally extended dual-tree complex wavelet transform. Then, the envelope energy ratio and phase ratio of the land and water detection data are calculated in different frequency bands of the complex wavelet domain to obtain the land detection amplitude and phase correction operators for each frequency band. These operators are applied to the land detection data to obtain calibrated land detection data. Subsequently, the land and water detection data are subtracted to obtain downlink wave data. Simultaneously, the above data undergoes an inverse complex wavelet transform to obtain a time-domain ghost wave model. Finally, the original land detection data and the ghost wave model are subtracted using least-squares matched subtraction to obtain the final uplink wave data. This invention, through fine calibration in the horizontally extended complex wavelet domain, can more effectively eliminate amplitude and phase differences in land and water detection data, thereby achieving higher-precision uplink and downlink wave field separation and ghost wave suppression.

The application discloses a system for improving the accuracy of power collection of a single-phase intelligent electric meter on the public power supply side, and relates to the technical field of electric variable measurement. The system realizes adaptive switching of a sampling rate through a working condition mode identification module and a dynamic sampling control module, performs accurate power measurement of a full frequency band by using complex wavelet transform, and is online calibrated and optimized with the help of a parameter self-learning module, thereby comprehensively improving the accuracy and adaptability of the electric meter in a complex power grid environment, effectively solving the problems of mismatching of a fixed sampling rate of a traditional intelligent electric meter with dynamic working conditions, missing of harmonic and interharmonic power measurement, and lack of working condition adaptability, enabling the electric meter to realize adaptive balance of precision and efficiency in a complex power grid environment, completing full-band accurate measurement of fundamental wave, harmonic and interharmonic power, and guaranteeing the measurement accuracy and stability of the electric meter in long-term operation through a self-learning mechanism.

The application belongs to the technical field of digital watermark embedding and relates to an adaptive digital watermark embedding method, device and readable storage medium. An image is subjected to double-tree complex wavelet transform decomposition to obtain a low-frequency approximation subband and a high-frequency complex coefficient subband. The subband variance, subband entropy and gradient energy of each high-frequency complex coefficient subband are calculated to obtain the joint score of each high-frequency complex coefficient subband. The amplitude graph of the high-frequency complex coefficient subband with the highest joint score is subjected to non-overlapping block division to obtain a plurality of candidate blocks. The block variance, block gradient intensity and gradient direction consistency of each candidate block are calculated to obtain the block score of each candidate block. The embedding block set is screened based on the block score, the embedding strength of each embedding block is calculated, the amplitude of all pixel positions in each embedding block is subjected to multiplicative embedding, the complex coefficient of the target embedding subband corresponding to each embedding block is reconstructed, and the watermark image is reconstructed based on the reconstructed target embedding subband, the low-frequency approximation subband and the high-frequency complex coefficient subband.

The application belongs to the technical field of computer vision and computer graphics, and discloses a multi-view three-dimensional reconstruction method based on cross-domain feature fusion, equipment and medium, the method comprises the following steps: cross-feature domain coding is carried out on the initial feature token generated by multi-angle images, the coding decomposes the feature into low-frequency and high-frequency components through dual-tree complex wavelet transform, and the amplitude modulation is carried out on the high-frequency component to enhance the details, and the space-frequency fusion feature is generated; the space-frequency fusion features of the multi-angle are weighted and merged through variance embedding, the weight is generated by calculating the variance of each feature token, and adaptive weighted clustering is carried out based on the weight to generate the fused multi-angle feature; three-source attention decoding is carried out on the fused feature, the static embedding is taken as the query, the space-frequency fusion feature is taken as the key, and the fused multi-angle feature is taken as the value, cross-domain attention calculation and up-sampling are carried out, and finally the three-dimensional voxel reconstruction result of the target object is generated.