25 results about "Daubechies wavelet" patented technology

The invention discloses a DC fault arc identification method and device based on a wavelet transform algorithm, and the method comprises the steps: collecting a DC line current signal through a high-bandwidth current sensor, carrying out the digitalization of a signal conditioning circuit and a high-speed ADC, and sequentially carrying out the preprocessing of the elimination of baseline drift through differential filtering and the suppression of specific frequency interference through adaptive notch; performing 3-8-layer multi-scale decomposition on the pure signal by adopting a Daubechies wavelet basis, preferably selecting high-frequency detail coefficients from the first layer to the third layer, and creatively fusing a multi-scale energy entropy representing energy distribution randomness and a time domain kurtosis representing transient impact to form a composite characteristic quantity; a threshold value (threshold value = mean value + adaptive coefficient * standard deviation) is dynamically generated based on sliding time window historical data, the adaptive coefficient is dynamically adjusted according to the load type, the resistive load is 1.8-2.2, and the inductive / capacitive load is 2.5-3.5; and when the composite characteristic quantity exceeds a threshold value and the sample proportion is greater than or equal to 85% within the duration of 0.5-5ms, a tripping execution mechanism is triggered.

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

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

The invention relates to the technical field of deep learning and load identification, in particular to a deep learning load identification method and system based on bilateral filtering denoising and multi-wavelet feature fusion and a medium, and the method comprises the steps: firstly converting an acquired training data set into an image, and carrying out the preprocessing of bilateral filtering denoising; graying the de-noised image, extracting low-frequency and high-frequency components by using Haar wavelet transform, and extracting low-frequency approximation and high-frequency information in horizontal, vertical and diagonal directions by using Daubechies wavelet transform; pixel unification and normalization are carried out on the feature map, a training set and a test set are divided after category label integers are coded, and a convolutional neural network containing two branches is constructed to extract depth features and splice and fuse the depth features; and finally, extracting fusion features through a full connection layer, and taking sparse classification cross entropy as a loss function to train a CNN model in an off-line manner to obtain a load identification model. The method can improve the accuracy and stability of load identification, and is suitable for various electric equipment load identification scenes.

The invention discloses a multi-modal geologic feature fusion method and a multi-modal geologic feature fusion system based on a discrete wavelet transform (CLIP)-Stable Diffusion model. The method comprises the following steps: reconstructing multi-modal geological data through differential wavelet transformation, extracting a low-frequency trend from earthquake and deposition data by adopting a Daubechies wavelet, capturing high-frequency details from deposition numerical simulation and logging data by adopting a Coiflets wavelet, and vectorizing a geological text through an orthogonal basis matrix; a CLIP-Stable Diffusion fusion model is constructed, a text encoder is utilized to analyze geological semantic features, an image encoder is utilized to extract spatial features, and a U-net diffusion generator realizes cross-modal alignment under the guidance of text conditions through a cross attention mechanism; and adopting a loss threshold termination mechanism constrained by a geological law in diffusion training, and finally outputting fusion data through wavelet inverse transformation. According to the method, cross-scale correlation deficiency and semantic segmentation limitation of a traditional method are broken through, and reservoir modeling precision and exploration efficiency are remarkably improved.

The invention discloses an image small target detection method based on a fusion feature visual model, and relates to the technical field of computer visual detection. And the fusion feature visual detection model performs the following processing on an input image and outputs a detection result: adopting adaptive Daubechies wavelet transform to enhance high-frequency details, combining multi-scale feature fusion with a double attention mechanism to enhance small target feature expression, utilizing regression calculation to predict bounding box coordinates, and realizing category probability distribution prediction based on a Softmax function. A multi-task loss function and an Adam optimizer are adopted for end-to-end training, and the model performance is improved by balancing classification and regression loss. The method effectively solves the problems that small target features are deficient and are susceptible to background interference, significantly improves the detection precision while maintaining the calculation efficiency, and is especially suitable for unmanned aerial vehicle patrol and other practical application scenes.

The invention discloses a central air-conditioning system energy consumption prediction method based on a WTD hybrid algorithm, and relates to the technical field of central air-conditioning systems, and the method comprises the following steps: S1, carrying out the wavelet transform decomposition processing of original cold load data, carrying out the denoising through a soft threshold function, and reconstructing the data into a low-frequency trend component and a high-frequency detail component; s2, inputting the denoised data into a hybrid model comprising a Transform module and an LSTM (Long Short Term Memory) module, extracting global correlation characteristics of input variables by the Transform module through a multi-head self-attention mechanism, and enhancing time sequence information by using position coding; the LSTM module models a long-term dependency relationship of a time sequence through a gating mechanism of an input gate, a forgetting gate, and an output gate. According to the invention, through the improved wavelet transform decomposition technology, in combination with the Daubechies wavelet basis and the adaptive soft threshold function, accurate filtering of noise components and effective signal reconstruction are realized, and interference of non-stationarity on model input is reduced.

The invention discloses a perception method based on multi-semantic space attention and Daubechies wavelet double-branch Mama, and belongs to the field of hyperspectral image processing. The problem that in the existing Mama-based model feature extraction process, the capacity of capturing multi-scale local structures and direction sensing information is insufficient is solved. The method comprises the following steps: inputting a hyperspectral image; projecting the spectral vector to an embedding space through an embedding layer to obtain an embedding feature; inputting the embedded features into an encoder, wherein the encoder comprises an SMSAMama branch, a DWTMama branch and a self-adaptive feature fusion module; the SMSAMama branch is used for extracting spatial features; the DWTAMba branch is used for extracting spectral features; the adaptive feature fusion module performs weighted integration on the spatial features and the spectral features by using randomly initialized fusion weights; and inputting the integrated features into a segmentation head to generate a final perception result. The method is used in agricultural monitoring and urban planning fields.

The invention provides a top tension riser damage identification and positioning method based on wavelet packet transformation, and belongs to the technical field of ocean structure intelligent monitoring. Firstly, a vibration acceleration response signal of a preset node of the riser is collected and preprocessed; using a 6-order multi-Bezi wavelet as a mother wavelet, setting a four-layer wavelet packet decomposition layer number, and iteratively decomposing the preprocessed signal through a Mallat algorithm to obtain 16 frequency band component signals; secondly, calculating wavelet packet component energy of each node under 16 frequency bands by utilizing the sensitivity of the method to structural rigidity damage based on an arch bridge damage identification method based on the energy of the Singxiang wavelet packet; solving the wavelet packet energy curvature of each node through a variable quadratic difference value; and finally, the difference between the curvature and the curvature of the same node of the healthy riser is obtained to obtain an IWPECD index, the distribution rule of the index along the length of the riser is analyzed through five-point moving average smoothing, the node corresponding to the peak value is the damage position of the riser, and effective damage positioning of the jacking tension riser is achieved.

The invention relates to the technical field of diesel engine emission aftertreatment, and particularly discloses a diesel particulate filter internal carbon load distribution estimation method and system.The method comprises the steps that heat dissipation characteristic data of a diesel particulate filter (DPF) under the normal working condition is obtained, and a DPF heat dissipation model is established based on the heat dissipation characteristic data under the normal working condition; establishing a DPF regeneration temperature model according to the law of conservation of energy; extracting temperature change data caused by carbon combustion based on the DPF heat dissipation model and the DPF regeneration temperature model; according to the method, temperature change data is subjected to multi-scale analysis on the basis of db4 wavelet transformation of a Daubechies wavelet system, a carbon distribution estimation function is generated, carbon deposition distribution can be effectively analyzed through multi-scale analysis of wavelet transformation, then local hot spots in the DPF are captured, and the carbon distribution detection precision is improved; by identifying the carbon distribution in the DPF, the aging loss of materials caused by high-temperature hot spots is reduced, the replacement period of the DPF is prolonged, and the use cost is reduced.

The invention provides a pipeline leakage detection method, and relates to the field of pipeline leakage detection. The detection method comprises the following steps: acquiring a pressure signal data set of a pipeline; a Haar wavelet, a Daubechies wavelet and a Symlet wavelet are selected as primary functions of discrete wavelet transformation, discrete wavelet transformation is carried out on the pressure signal data set through the three wavelets, the discrete wavelet transformation scale of each wavelet is three levels, and a decomposition coefficient sequence of discrete wavelet transformation of the corresponding wavelet type is obtained; and respectively inputting decomposition coefficient sequences of the three discrete wavelet transformations into the self-attention mechanism model to obtain probabilities of three pipeline leaks, carrying out statistics on the probabilities of the three pipeline leaks, and obtaining a conclusion whether the pipeline leaks or not based on a statistical result. According to the method, for pipeline tiny leakage detection, pressure signals are analyzed through multiple wavelet transformations and a self-attention model, and whether the pipeline leaks or not can be accurately judged.

The invention discloses an integrated learning-based unmanned aerial vehicle electrical appliance state evaluation and fault prediction method and device, and a medium. The method comprises the steps of collecting operation data; daubechies wavelet denoising is adopted, and transient fault features are reserved; searching an optimal LightGBM hyper-parameter in a hyper-parameter space by utilizing Bayesian optimization, training the model, and outputting an operation parameter predicted value at the next moment; and calculating the residual error of the predicted value and the real value, and judging abnormity and positioning a fault subsystem if the residual error exceeds the limit. The de-noising link is based on the thinnest-layer detail coefficient self-adaptive threshold shrinkage, and the Bayesian link adopts a Gaussian process-TPE acquisition function. According to the invention, the fault positioning accuracy can be effectively improved. The whole link does not need artificial features, online scheduling is supported to participate in embedded deployment, and the false alarm and operation and maintenance cost is remarkably reduced.

The invention provides an aerospace composite material damage visual feature extraction method. Firstly, integrated high-resolution nondestructive testing equipment is used for collecting multiple types of original data including ultrasonic waves, infrared thermal images, X-ray images and the like, and damage information is captured in multiple dimensions. And de-noising is carried out through a filtering algorithm based on Daubechies wavelet four-layer decomposition, and data is normalized, so that a foundation is built for subsequent processing. A CNN and LSTM hybrid network is used for preliminarily screening features, and an early stop method and a Dropout technology are used for preventing overfitting. And fusing the multi-modal preliminary damage features by means of a fusion algorithm based on a kernel method. Based on a genetic algorithm, features are optimized from multiple dimensions of damage identification degree, independence and redundancy. And finally, performing three-dimensional reconstruction by using a ray casting algorithm, and presenting the damage by different colors, transparencies and shapes in combination with an interaction function according to the spatial information of the key features, the damage type and strength, thereby realizing visual visualization and providing a reliable basis for the damage analysis and evaluation of the aerospace composite material.

The invention relates to an indwelling catheter blockage early warning method and system based on pressure waveform analysis, and relates to the technical field of monitoring early warning, and the method comprises the following steps: collecting a multi-source monitoring data set comprising original pressure waveform data, instantaneous flow velocity data and electrocardio synchronous signal data; performing four-layer multi-scale decomposition processing based on a Daubechies wavelet basis function on the original pressure waveform data to generate a multi-scale waveform component set; constructing a catheter downstream vascular bed fluid mechanical impedance model by combining the basic information of the patient, and deducing and generating a catheter downstream vascular bed impedance change index; dynamic baseline drift modeling is carried out through a sliding time window, and a self-adaptive catheter blockage risk reference threshold value is set; constructing a patient specific risk factor graph structure, performing time sequence prediction analysis on the feature sequence through a recurrent neural network, and generating a future risk evolution trend index; and a third-level early warning signal is generated. The method has the beneficial effect that the sensitivity and the specificity of early warning of catheter blockage are remarkably improved.

The invention provides a cylindrical surface error process tracing method based on multi-scale features and a neural network, and the method comprises the steps: carrying out the J-layer decomposition of an error signal through a multi-scale feature extraction technology, precisely separating the low-frequency machine tool spindle / clamping error, the medium-frequency tool wear / cutting vibration, and the high-frequency surface roughness error components, and carrying out the processing of the cylindrical surface error. And each frequency component characteristic is quantified through energy, statistics and shape characteristics, so that the technical bottleneck that a traditional method cannot distinguish error sources is solved, the error traceability accuracy is improved, the limitation that the traditional method can only obtain error values but cannot trace the source is overcome, error traceability is upgraded from qualitative judgment to quantitative analysis, and the error traceability efficiency is improved. The accurate basis is provided for targeted optimization of process parameters, meanwhile, the trial and error cost is reduced, the process development period is shortened, and a systematic solution is provided for efficient and high-quality manufacturing of precise cylindrical parts.

The present application provides a fault diagnosis method and system based on wavelet graph convolution. The fault diagnosis method based on wavelet graph convolution includes: extracting multi-scale time-frequency features of a signal based on Dobesi wavelets and obtaining wavelet components at multiple scales; performing time-frequency analysis on each wavelet component at each scale and obtaining corresponding wavelet coefficients and time-frequency domain information; constructing a multi-scale graph convolution network combined with a similarity attention mechanism and extracting multi-scale time-frequency graph features; fusing multi-scale time-frequency graph features and obtaining a multi-scale fusion feature; and obtaining a label for a composite fault. This fault diagnosis method based on wavelet graph convolution can solve the technical problem that the fault impacts generated by different fault sources may have complex nonlinear and strong coupling relationships, resulting in low accuracy and speed of fault diagnosis in existing gearbox fault diagnosis methods.

The invention relates to the technical field of environment and climate science, and discloses a seasonal infectious disease diffusion risk prediction system based on big data, and the system comprises the steps: collecting multi-source heterogeneous data, and fusing the data into a multi-dimensional feature library; performing multi-scale wavelet transform decomposition by adopting a Daubechies wavelet basis function to form a layered feature representation library; carrying out fractal geometric constraint scale self-similarity mapping on the low-frequency seasonal components by adopting a Mandelbrot set variant, and generating a mapped seasonal vector; embedding infectious disease dynamic constraints, and optimizing parameters through a projection gradient method and a mixed loss function; multi-quantile uncertainty distribution is generated through Monte Carlo sampling, and multi-scale risk assessment is carried out; and according to the harmonic oscillation phase and fractal feedback, dynamically adjusting an early warning threshold and a resource scheduling strategy, and outputting a risk early warning report. According to the invention, cross-seasonal risk prediction is realized through a multi-scale wavelet harmonic coupling framework.

The application discloses a structure impact positioning method based on multiple wavelet decomposition and time reversal, which comprises receiving impact stress wave signals of each sensor on a monitoring area in a Cartesian coordinate system, selecting multiple different Morlet wavelet base functions according to the scale of the Morlet wavelet base function, decomposing the impact stress wave signals by the selected multiple different Morlet wavelet base functions, reconstructing the signals, performing normalization processing, assuming the similarity of all time reversal signal waveforms of the assumed impact source position by using a cosine similarity formula, and multiplying to obtain the pixel value of the assumed impact source position; calculating the pixel values of multiple assumed impact source positions in the monitoring area, and positioning the impact source position according to the pixel value. The method disclosed by the application adopts multiple Morlet wavelet base functions to decompose and reconstruct the impact stress wave signals, weakens the influence of interference signals such as boundary reflection and noise on impact positioning, and improves the positioning precision.

The present application relates to a kind of based on pressure waveform analysis's indwelling catheter blockage early warning method and system, it is related to monitoring early warning technical field, including acquisition contains original pressure waveform data, instantaneous flow rate data and electrocardiogram synchronous signal data multisource monitoring data set;Original pressure waveform data is executed based on Daubechies wavelet base function four-layer multiscale decomposition processing, generates multiscale waveform component set;Combining patient basic information constructs catheter downstream vascular bed fluid mechanics impedance model, deduces and generates catheter downstream vascular bed impedance change index;Through sliding time window, dynamic baseline drift modeling is carried out, and adaptive catheter blockage risk benchmark threshold is set;Construct patient-specific risk factor graph structure, through recurrent neural network, time series prediction analysis is carried out to feature sequence, generates future risk evolution trend index;Generate three-level early warning signal.The present application has beneficial effect for significantly improving the sensitivity and specificity of catheter blockage early warning.

The application discloses a three-phase low-voltage line single-phase grounding fault monitoring and protection method and system in the field of power system relay protection and electrical monitoring, and the application takes the bus zero sequence current amplitude over-limit as the starting criterion of the system, obtains the transient current and voltage at the bus and at the outlet of each line, selects the fault line by the Daubechies wavelet modulus extreme value comparison method of the transient current traveling wave, further calculates the Hermitian wavelet energy of each phase current of the fault line to select the fault phase, triggers the bypass grounding switch to act by the microprocessor, and makes the fault current discharge to the ground through the current-limiting resistor without affecting the operation of the power grid line, thereby protecting the operation stability of the system. The protection method provided by the application has the advantages of fast detection speed, low cost, simple device, higher reliability and better safety compared with the existing single-phase grounding protection device.

The present application relates to the field of communication signal processing, in particular to a kind of air defense disaster early warning alarm real-time control system based on Beidou third-generation communication, system uses Daubechies wavelet family to carry out 5 layers discrete wavelet decomposition, and total satellite signal delay is decomposed into different scale coefficients. By accurately controlling the boundary scale, the system can effectively separate ionospheric error and tropospheric error. By inserting zero values between filter coefficients, information loss caused by downsampling operations is avoided. The system also adaptively selects the optimal wavelet basis function according to different atmospheric error characteristics. It maintains stable correction effect under extreme weather conditions and builds an efficient and practical air defense disaster real-time early warning control method.

A kind of finger vein and finger knuckle print dual-mode identification method based on canonical correlation analysis, comprising: Daubechies wavelet pre-processing is carried out to the finger vein image and finger knuckle print image for training, to obtain the low-frequency image of finger vein image and finger knuckle print image respectively, K-L transform is used to extract features from two groups of low-frequency images respectively, then correlation criterion function between two groups of feature matrices is established, two sets of canonical projection vectors are obtained according to criterion function, finally the canonical correlation characteristics are fused by given feature fusion strategy and applied in classification identification.The beneficial effects of the present application are: the present application obtains the feature information of two modes by dimension reduction, reduces the recognition calculation amount, improves the recognition accuracy, introduces more class information into the criterion function more fully, not only reaches the purpose of information fusion, but also effectively eliminates the redundant information between features, and makes the classification performance greatly improved.

The invention belongs to the field of numerical control machining and manufacturing of complex thin-walled parts, and provides a thin-walled part milling state evaluation method and system based on sliding window-wavelet transform. The method comprises the following steps: collecting a milling force and an acceleration signal, denoising and normalizing, segmenting by adopting a sliding window, carrying out wavelet transform on each data block, carrying out multilayer decomposition by selecting a Daubechies wavelet basis function, identifying a sensitive frequency band related to flutter and tool wear based on an energy threshold, and reconstructing and generating a two-dimensional time-frequency spectrogram through a soft threshold; a convolutional neural network is utilized to learn features and establish a recognition model, and real-time evaluation and early warning of steady-state machining, flutter, cutter failure, cutting overload and other states are realized. According to the method, a flutter mechanism and time-frequency analysis are fused, the recognition sensitivity and accuracy of non-stable and transient states are remarkably improved, the method is suitable for intelligent manufacturing of complex thin-wall parts such as aero-engines, and effective support is provided for machining quality control.

The application provides a pipeline leakage detection method, and relates to the field of pipeline leakage detection. The detection method comprises the following steps: acquiring a pressure signal data set of a pipeline; selecting Haar wavelet, Daubechies wavelet and Symlet wavelet as the base functions of discrete wavelet transform, and performing discrete wavelet transform on the pressure signal data set through the three kinds of wavelets respectively, the scale of the discrete wavelet transform of each wavelet is three, so as to obtain the decomposition coefficient sequence of the corresponding wavelet type discrete wavelet transform; inputting the decomposition coefficient sequences of the three kinds of discrete wavelet transforms into a self-attention mechanism model respectively, obtaining three kinds of pipeline leakage probabilities, and statistically analyzing the three kinds of pipeline leakage probabilities to obtain a conclusion of whether the pipeline leaks or not. The method can accurately determine whether the pipeline leaks or not through the analysis of the pressure signal by multiple wavelet transforms and a self-attention model for the detection of slight pipeline leakage.

This invention relates to the fields of building engineering technology and intelligent monitoring technology, specifically disclosing a BIM-based building construction data management system. The system includes a data acquisition module, a soil stability analysis module, a settlement trend identification module, a foundation condition judgment module, and a risk linkage control module. The system deploys high-precision soil negative pressure sensors and micro-settlement monitoring instruments to collect foundation-related data in real time. It then uses methods such as sliding window mean processing, Daubechies wavelet transform, and empirical mode decomposition to extract soil negative pressure change characteristic values ​​and cumulative settlement trend characteristic values. Furthermore, it integrates these two types of features into a comprehensive feature vector, inputs it into a support vector regression model for foundation risk level assessment, and combines it with the BIM platform to achieve construction progress linkage and hierarchical early warning control.