26 results about "Morlet wavelet" patented technology

The invention relates to the technical field of education big data analysis, in particular to a student information intelligent management system based on big data, comprising a data convergence module used for converging multi-source heterogeneous behavior data streams of target students in campus Internet of Things and interaction logs of the target students in a digital learning platform in real time to obtain an original time sequence data vector set; and the feature generation module is used for carrying out cross-modal time domain alignment and wavelet coherent transformation on the original time sequence data vector set so as to generate a multi-scale spatial-temporal feature map representing the individual learning and living states of the students. According to the invention, global network time protocol synchronization timestamp synchronization and Kalman filtering interpolation are introduced through the data aggregation module, and the feature generation module adopts remoley wavelet transform and wavelet coherence spectrum analysis, so that the phase locking relationship of online interaction and offline behaviors of students on a time-frequency domain can be quantified; therefore, the learning and living states of the students can be more comprehensively described.

The invention belongs to the field of building environment control, and provides a thermal comfort dynamic adaptive control method based on multi-modal wave analysis and AI driving. According to the method, environmental parameters, user physiological signals (HRV) and subjective thermal comfort data are synchronously collected in real time, time-frequency characteristics of the physiological signals are extracted through continuous Morlet wavelet transform, high-order dynamic characteristics are constructed, the thermal comfort state is predicted in combination with deep learning, a deep reinforcement learning strategy is optimized through an NSGA-II algorithm, a multi-target optimal strategy set is generated, and the optimal thermal comfort state is obtained. And finally, a multi-arm bandit model with Bayesian inference is utilized to realize strategy online adaptive updating. By applying the method, the thermal comfort prediction precision can be expected to be greater than or equal to 90%, the net energy conservation is greater than or equal to 32%, the limitation of the existing thermal comfort regulation and control method in a dynamic situation is effectively solved, and the dynamic balance of personalized thermal comfort and system energy conservation is realized.

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 water turbine governor fault modeling and parameter optimization method based on iterative learning control. The method comprises the following steps: S1, system dynamics modeling; s2, iterative learning parameter updating, wherein a water turbine governor fault diagnosis method based on iterative learning control realizes progressive identification of fault features through periodically correcting model parameters; s3, fault feature extraction: calculating a residual signal of an actual output and a model predicted value, performing time-frequency analysis on the residual signal by adopting improved Morlet wavelet transform, and extracting an energy entropy feature and a time domain statistical feature; s4, fault diagnosis and dynamic optimization: based on the extracted fault features, fault detection and classification are realized through a three-level linkage decision mechanism, a dynamic adjustment strategy is introduced to carry out online optimization on a diagnosis rule base, and fault modeling and parameter optimization closed loop are completed; according to the method, the problem of modeling misalignment of a traditional method under a nonlinear working condition is effectively solved.

The invention relates to a vegetation leaf dry matter content remote sensing high-precision inversion method based on wavelet analysis. The method comprises the following steps that S1, a leaf sample database is constructed; S11, a leaf actual measurement data set is constructed; s12, constructing a blade simulation data set; s13, data set division: dividing an actual measurement data set into an actual measurement training set and an actual measurement verification set according to wavelet features; s2, extracting dry matter feature information through wavelet transform; S2, performing multi-scale decomposition on the leaf spectral signal by using continuous wavelet transform; s22, adopting Morlet wavelet as a generating function to carry out continuous wavelet transform analysis on the original reflection spectrum of the blade; s23, carrying out correlation test on the obtained wavelet coefficient and an LMA value; s24, according to a set threshold value, wavelet coefficient characteristics sensitive to the dry matter content are screened out; and S3, constructing an LMA inversion model based on the wavelet coefficient characteristics and the spectral indexes. The method is high in inversion precision, strong in robustness and good in stability.

According to the CNN electromagnetic valve fault diagnosis method and device based on time-frequency analysis disclosed by the invention, the fault diagnosis performance under a complex working condition is improved by fusing time-frequency domain feature extraction and a double-attention mechanism. According to the method, current and voltage signals are used as input, a complex value convolution preprocessing layer containing STFT and Morlet wavelet transform is constructed, and corresponding complex value convolution kernel functions are deduced respectively; on the basis, a CBAM module is improved, and a double-time-frequency attention module fusing channel-space attention is constructed. And a dual-time-frequency attention module is combined with a one-dimensional convolutional neural network (CNN) to construct a dual-time-frequency attention network. And finally, carrying out validity verification on the pneumatic electromagnetic valve fault data set by using a double-time-frequency attention network. The method is suitable for a complex electromagnetic valve working environment, and the fault judgment accuracy is improved; according to the method, generalization and robustness are enhanced, and the modeling cost is reduced.

The application discloses a kind of based on gateway machine's intelligent auxiliary control monitoring system of substation, including data acquisition module, protocol self-adapting adjustment module, abnormality detection module, fault early warning module and intelligent operation control module.The application relates to the technical field of intelligent management of substation, specifically refers to a kind of based on gateway machine's intelligent auxiliary control monitoring system of substation, the present scheme utilizes self-supervised contrast learning and second-order Markov chain automatically extracts and predicts protocol features, combines confidence and delay decision, realizes no label fast switching, high reliable communication;Fusion sliding window transfer entropy and kernel PCA reconstruction residual, unsupervised detection and positioning electrical, network and environmental anomaly;With the help of multi-scale Morlet wavelet entropy and DBSCAN rare cluster identification, unknown fault precursor spontaneous early warning is realized;Based on real-time apparent power autoregressive prediction and mixed integer convex optimization, peak suppression, load smoothing and temperature constraint are considered, and scheduling strategy is optimized.

The double-teacher sleep staging feature migration method based on knowledge distillation and domain adaptation belongs to the field of signal processing and pattern recognition. First, the sleep electroencephalogram and electrooculogram signals are preprocessed to obtain a plurality of multi-modal sleep signal data samples. Next, each channel contained in each sample of the source domain and target domain data is sequentially extracted using Morlet wavelet transform of different resolutions to obtain time-frequency features, and then input into the source domain teacher and target domain teacher for pre-training. In the training and optimization of the student, the two teachers with frozen feature extractors are introduced for guidance to constrain the student to learn the source domain and target domain general features and the domain-specific features of the target domain. Experiments prove that the model proposed in the application fully utilizes the features of the data for feature migration, and good results can be obtained when the amount of target domain data is small, which can effectively solve the problem that the existing automatic sleep staging method has a decreased accuracy when facing new data sets.

The invention belongs to the technical field of intelligent safety guardrails, and particularly relates to a multifunctional sensing and local control intelligent safety guardrail system, which comprises a data acquisition module, a wavelet transformation module and an early warning module, and is used for performing Morlet continuous wavelet transformation on a vertical real-time depth map and a vertical reference depth map output by the data acquisition module to obtain a vertical real-time depth map and a vertical reference depth map; respectively generating a plurality of phase spectrums of the vertical real-time depth map and the vertical reference depth map; obtaining a continuous phase difference distribution map for the phase difference between the two complex phase spectrums; the linear relation between the phase difference and the displacement is used for calculating the horizontal displacement, the Morlet wavelet transform is used for directly calculating the point cloud displacement through the frequency domain phase difference, the process that an ICP algorithm is needed for multiple iterations in traditional electric cloud calculation is avoided, the geometrical characteristics of the infrastructure are converted into the physical scale of wavelet analysis, time domain localization analysis is achieved, and the calculation efficiency is improved. Displacement detection is converted into a phase matching problem of a signal and a wavelet, and the precision is improved while the speed is increased.

This invention discloses a method for extracting moiré fringe phase information based on Morlet wavelet transform, comprising: converting a target moiré fringe image into a grayscale image; filtering the grayscale image to obtain a filtered binarized image; performing mathematical morphological denoising on the binarized image to obtain a processed image, wherein the mathematical morphological denoising includes erosion and dilation; convolving each column of the processed image using modulated Morlet wavelets and calculating the amplitude and phase of the wavelet coefficients; determining wavelet ridges based on the amplitude of the wavelet coefficients and extracting the phase of the scale coefficients and time coefficients corresponding to the wavelet ridge positions; and obtaining complete moiré fringe phase information based on the phases of all extracted columns. This method is highly accurate and easy to implement, making it suitable for further refractive index reconstruction and key parameter measurement of the measured flow field.

The application relates to the technical field of structural damage positioning, and discloses a damage positioning method based on an acoustic emission array, which comprises the following steps: firstly, the acoustic emission signal is subjected to adaptive inverse filtering and delay compensation through a virtual time reversal mirror technology; secondly, a narrowband single-frequency signal is extracted based on Morlet wavelet transformation to inhibit frequency dispersion and multi-modal interference; and finally, a long-short-time kurtosis ratio enhancement operator is adopted to realize high-precision adaptive pickup of the arrival time in a strong noise environment. Through multi-stage cooperative processing, the application can effectively overcome multi-modal effects, frequency dispersion and noise interference, realize high-precision, high-robustness and high-real-time acoustic source positioning in a complex industrial scene with sparse sensor arrangement, non-uniform medium and variable noise, and significantly improve the detection capability for hidden defects and the system adaptability.

This invention discloses a brain-computer interface (BCI) teaching demonstration system and control method, belonging to the field of BCI and artificial intelligence education technology. The system includes: a multi-channel EEG headband for synchronously acquiring EEG signals and head posture data; an edge AI hub with a built-in NPU accelerator for dynamically gating and adaptively filtering EEG signals based on head posture data, generating a two-dimensional time-frequency graph through complex Morlet wavelet transform, and using a lightweight RepEEG-Net neural network with structural reparameterization and quantization processing to analyze user intentions in real time, while simultaneously generating visualized teaching data; and a microcontroller execution chassis equipped with a real-time operating system (RTOS) for controlling the actions of the execution mechanism through multi-priority task scheduling. This invention achieves low-latency, highly interference-resistant brain-controlled demonstrations by deploying lightweight intelligent algorithms at the edge, and visualizes the algorithm processing process, significantly improving the real-time performance, robustness, and intuitiveness of the teaching demonstration.

This application provides a method and system for early screening of Alzheimer's disease based on electroencephalogram (EEG) signals, comprising: acquiring the resting-state EEG signals of the subject as raw EEG signals, and performing data preprocessing on the raw EEG signals; extracting the average power value of the preprocessed signals based on the multi-band Morlet wavelet transform algorithm, constructing a functional connectivity matrix based on the extracted average power value and performing multi-dimensional feature fusion, and using the resulting multi-dimensional functional connectivity tensor as the core feature; inputting the multi-dimensional functional connectivity tensor into a pre-constructed deep separable residual convolutional network to perform end-to-end feature learning and decision classification on the core features, and using the network output as the early screening result for Alzheimer's disease. This application covers three core links: automatic preprocessing, data analysis, and classification detection, which work together to achieve efficient and convenient early screening for Alzheimer's disease.

A transformer short-circuit impedance three-phase synchronous testing method and system, comprising: synchronously collecting different signals on the high-voltage side of the transformer; dynamically adjusting the center frequency and bandwidth parameters of the Morlet wavelet base function based on the real-time frequency; respectively performing wavelet decomposition on the different signals based on the adjusted Morlet wavelet base function to obtain the complex wavelet coefficient matrix corresponding to the different signals; purifying the complex wavelet coefficient matrix of each signal; selecting an effective calculation interval based on the maximum ridge line area of the modulus matrix of the purified different signals, and solving the optimal estimation value of the short-circuit impedance through the least square method according to the elements of the purified complex wavelet coefficient matrix of the different signals in the effective calculation interval; and correcting the calculated short-circuit impedance value through temperature and frequency. The present application ensures the strict alignment of three-phase data and effectively supports the fault diagnosis based on three-phase unbalance degree.

The invention discloses a transformer iron core damping measurement method and system, and the method comprises the steps: placing a transformer iron core on a smooth plane, and enabling the environment noise to be smaller than a preset value; an acceleration sensor is adsorbed on the surface of the transformer iron core; an iron core is tested through excitation of a vibration exciter, vibration signals of the acceleration sensor are collected, and the modal inherent frequency corresponding to the damping ratio is determined; the method comprises the following steps: calculating a Moley wavelet based on a modal inherent frequency, introducing a wavelet time broadening coefficient and a length coefficient to control the length of the Moley wavelet, converting a vibration signal by using the Moley wavelet, calculating an amplitude ratio, and finally solving a damping coefficient by using a relational expression between the amplitude ratio and the damping coefficient; the method has the advantage that the damping coefficient of the transformer iron core is accurately obtained.

The invention provides a data transmission analysis method, device and equipment based on multi-scale condition mutual information, and relates to the technical field of data mining. The method comprises the following steps: performing Morlet wavelet decomposition on an original sequence to obtain wavelet coefficients under a plurality of time scales, and constructing a candidate lag variable set corresponding to the time scale based on each wavelet coefficient under each time scale; screening a key lag component in each candidate lag variable set by adopting a conditional mutual information progressive strategy to obtain an optimal embedding vector corresponding to each time scale; and for each time scale, estimating conditional probability density and marginal probability density based on the corresponding optimal embedding vector, calculating conditional mutual information, and substituting the conditional mutual information into a corresponding variable embedding data transmission intensity calculation formula to obtain data transmission intensity and direction between the first original sequence and the second original sequence. According to the method, multi-scale, self-adaptive and high-precision depiction of the dynamic influence relationship among the cross-stage engineering data can be realized.

This invention discloses a method and device for early warning of energy consumption in air conditioning refrigeration systems based on genetic algorithms. It aims to solve the problems of traditional energy consumption prediction models, such as single feature set, weak nonlinear modeling ability, reliance on experience for parameter tuning, and susceptibility to local optima. The method first integrates operating parameters such as indoor and outdoor temperature difference, humidity, compressor performance, and refrigerant charge to construct input features that comprehensively reflect the system state. Second, it designs a wavelet neural network with Morlet wavelet function as the activation function and introduces a genetic algorithm to globally optimize network weights, scaling factors, and translation factors, automatically searching for the optimal parameter combination to improve model convergence speed and prediction accuracy. Third, it dynamically sets energy consumption thresholds based on historical data, compares the prediction results in real time, and triggers an early warning when the threshold is exceeded. This method ensures high accuracy while possessing good generalization ability and engineering applicability, providing effective support for intelligent operation and maintenance and energy-saving management.

The invention discloses an LPG sales volume prediction method and device based on multi-source features and an adaptive network, and aims to solve the problems that the LPG terminal sales volume is affected by multi-source data, the time sequence features are complex and the general model adaptability is poor. Basic sales volume, user behaviors, external influences and operation response levels are covered; time sequence data time-frequency domain enhancement is realized through Morlet wavelet transform, and capture of an LPG service period is enhanced in combination with a service awareness position coding enhancement model; secondly, designing a multi-channel convolution-attention-cycle hybrid network MCA-RNet, and processing local fluctuations, periodic trends and external factors in parallel; and finally, outputting a prediction result through business rule verification and a dynamic optimization mechanism. According to the method, high-precision prediction of the LPG sales volume is realized, and reliable support is provided for filling operation plans, inventory optimization and dynamic pricing.

The invention discloses a vibration signal fault diagnosis method and device based on visual identification. The method comprises the following steps: acquiring an original vibration signal; preprocessing the original vibration signal to generate an RGB picture; carrying out model training by taking the RGB picture as the input of a neural network model; and performing fault diagnosis by using the trained model. On the basis of a traditional Morlet wavelet, the amplitude attenuation rate of a primary function is changed by increasing a waveform adjustment factor, and then the primary function with adjustable waveform characteristics is constructed. The method comprises the following steps: acquiring a signal RGB image, learning feature mapping from the signal RGB image by using a continuous convolutional layer, calculating a new feature mapping as a weighted average of the original feature mapping by adopting an attention mechanism, and in this way, learning that a model emphasizes an important region of the RGB image and suppresses a region weakly related to classification so as to more accurately capture abnormal fluctuation.

This invention discloses a damage localization method for elastomeric layered carbon fiber composite materials based on elliptic probability fusion, comprising: acquiring Lamb wave signals from multiple sensing paths using different sensors as excitation sources; performing continuous wavelet transform using complex Morlet wavelets to extract the measured flight time; introducing a layered wave velocity correction model to optimize the elliptic trajectory method and calculating the theoretical flight time and damage probability; introducing a dynamic minor axis optimization model to optimize the probability weighting method and calculating the damage probability; generating a prior distribution using the results calculated by the elliptic trajectory method and the probability weighting method; constructing a likelihood function based on the difference between the measured flight time and the theoretical flight time; obtaining the posterior distribution of the damage location by combining the prior distribution and the likelihood function using Bayes' theorem; sampling the posterior distribution using an adaptive MCMC algorithm and generating a probability cloud map of the damage location; and outputting the final imaging result of the damage location for damage localization.

The invention discloses a rice transplanter operation row accurate identification and self-adaptive tracking control method and system, and belongs to the technical field of agricultural machinery intellectualization. The method comprises the following steps: accurately identifying seedling rows through an improved YOLOv8n visual algorithm, wherein a P2 small target detection layer is added, a C2f module is reconstructed by adopting RFAConv, and an SPPF module is replaced by utilizing a Focal Modulation module; performing seedling row positioning point clustering and navigation line extraction based on a detection result, and converting image coordinates into world coordinates through a BP neural network model; and performing path tracking by adopting fuzzy prediction function control based on feedback linearization, performing accurate linearization on a non-linear motion model of the rice transplanter through feedback linearization, and dynamically adjusting a weighting coefficient by utilizing prediction function control taking Morlet wavelet as a primary function and a fuzzy controller. According to the method, the problems of difficulty in seedling row identification and poor path tracking precision in a complex paddy field environment are effectively solved, and the automation level and the operation quality of the rice transplanter are remarkably improved.

The application relates to the technical field of cable fault diagnosis, and discloses a distribution network cable fault diagnosis method, device, equipment and storage medium. The method comprises the following steps: obtaining a target current signal of a target distribution network cable to be diagnosed; initializing a frequency scaling factor and a center frequency of a Morlet wavelet base function based on a target sampling frequency, and constructing a frequency scaling kernel function; performing one-dimensional convolution operation on the target current signal based on the frequency scaling kernel function, and generating a fault feature map; processing the fault feature map to obtain a fusion feature map; inputting the fusion feature map into a target connection linear layer for processing, and determining a fault type; determining a fault point position based on a time difference of a fault harmonic peak value of adjacent collection points and a current wave propagation speed; determining a fault target investigation path based on the fault type, the fault point position and a distribution network topological structure, and investigating and determining a target fault. The scheme optimizes a fault diagnosis network structure, and improves fault positioning precision and fault recognition accuracy.

The invention discloses a de-noising optimization method based on a wavelet basis and a dynamic adaptive threshold, and the method comprises the following steps: S1, obtaining a DAS-Morlet wavelet basis function based on a DAS signal in combination with a Morlet wavelet basis; s2, after a dynamic threshold decision model is constructed based on channel statistics, dynamic threshold processing is carried out on the DAS-Morlet wavelet signals; and S3, carrying out multi-scale fusion on the DAS-Morlet wavelet signal after threshold processing by adopting a progressive residual model, and outputting a reconstructed DAS signal. According to the method, the Morlet wavelet basis combined with the DAS features is adopted to form the DAS-Morlet wavelet signal, the DAS-Morlet wavelet signal is subjected to dynamic threshold processing based on the decision model of the dynamic threshold of the channel statistics, and meanwhile, the progressive residual model is introduced, so that the accuracy of the DAS signal after reconstruction is improved.

This invention discloses a groundwater level simulation method based on the hysteresis response characteristics of a multi-aquifer system, belonging to the field of groundwater level simulation technology. The method includes the following steps: Step 1, hysteresis response feature extraction; Step 2, model input data preparation; Step 3, multi-head self-attention mechanism feature identification; Step 4, gated cyclic unit time-series simulation; Step 5, result output. This invention uses cross-wavelet transform with Morlet wavelets as the basis function to quantitatively calculate the hysteresis time between unconfined aquifers and precipitation, and between confined aquifers and overlying aquifers. This hysteresis feature is integrated into the model input and training process, enabling the model to realistically reflect the recharge hysteresis relationship of "precipitation-unconfined water" in a multi-aquifer system. This fills the gap in existing machine learning models lacking physical mechanism support and effectively improves the physical interpretability of the model.

The invention discloses a drought and flood sudden change event identification and intensity quantification method based on multi-source data fusion, and the method comprises the steps: comprehensively employing a Mann-Kendall trend analysis method, an R / S analysis method, an M-K sudden change detection method, a sliding T detection method, a Morlet wavelet analysis method, an inverse distance interpolation method and other data analysis methods and models through the multi-source data fusion; the spatial-temporal change characteristics of drought and flood sudden turning are systematically and accurately analyzed. The method can comprehensively and accurately analyze the spatial and temporal change characteristics of drought and flood sudden change, reliably predicts the future trend, provides an important scientific basis for regional water resource management and agricultural disaster prevention and reduction, and provides technical support for drought and flood disaster response in related fields.

The invention relates to a Morlet wavelet decomposition-based field mill electric field sensor signal post-processing optimization method. The method comprises the following steps of 1, acquiring a periodically changing induction current signal by adopting a field mill type sensor; step 2, completing preliminary extraction of signals through a signal conditioning circuit; 3, performing subsequent signal processing by adopting a wavelet decomposition algorithm; 4, in the multi-period data superposition processing process, the sampling results of the multiple periods are superposed in the corresponding scale space so as to enhance the time-frequency characteristics of the signals; step 5, carrying out denoising processing on the superposed signals by adopting a soft threshold method; and 6, performing signal reconstruction on the processed detail wavelet function and approximate wavelet function through inverse transformation. The invention provides a field grinding electric field sensor signal conditioning optimization algorithm based on Morlet wavelet decomposition, and aims to solve the technical problems that weak signals in an existing field grinding type electric field measurement system are easily interfered, low-frequency noise is difficult to effectively suppress, and parallel connection of amplifiers affects bandwidth and response speed.