333 results about "Wave transformation" patented technology

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention relates to the technical field of image processing, in particular to a road anti-corrosion effect detection and evaluation method based on an image processing technology, and the method comprises the steps: obtaining to-be-detected grayscale images corresponding to two different types of images on the surface of a road anti-corrosion layer, and carrying out the segmentation to obtain to-be-detected sub-images; respectively obtaining two difference images corresponding to the to-be-detected sub-image according to the row gray difference and the column gray difference of the gray difference image corresponding to the to-be-detected sub-image; obtaining the roughness degree of the to-be-detected sub-images, obtaining an optimal scale based on the roughness degree, decomposing the two to-be-detected grayscale images by using wavelet transform according to the optimal scale, and fusing according to decomposition results to obtain a fused feature image; and obtaining a defect region of the fused feature image, and obtaining a road quality detection result according to area features of the defect region. According to the invention, a more accurate road quality detection result can be obtained.

The invention discloses an intelligent primary and secondary fusion pole-mounted circuit breaker fault monitoring method, relates to the technical field of power equipment monitoring, and is used for solving the problem of insufficient real-time performance of fault monitoring under complex environment interference. According to the invention, electrical, mechanical vibration and environmental data are synchronously acquired through a multi-mode sensor array, and a timestamp synchronization mechanism is applied; performing multi-source interference classification on the original data, separating noise by using wavelet transform, and identifying interference types by clustering; kalman filtering adaptive compensation is applied based on an interference result, and environment feedback is introduced to ensure low delay; extracting multi-dimensional fault features, and forming a robust matrix through time-frequency analysis and principal component dimensionality reduction; inputting a deep learning model to carry out space-time modeling and rapid classification; a response mechanism is triggered to execute isolation or alarm, and closed-loop optimization is formed. The method effectively solves the problem of insufficient real-time performance under the interference of a complex environment, and improves the monitoring precision and the response speed.

The invention discloses a defect detection system and method for a glass bottle, and belongs to the field of industrial visual defect detection and analysis. The system comprises a GBDM module, the GBDM module is a defect detection module, and the GBDM module comprises a front attention enhancement structure, a rear attention enhancement structure and a defect detection structure, a multi-branch feature extraction structure is used for generating four feature branch diagrams; and the feature fusion structure is used for fusing the four feature branch diagrams. The GBDM module is embedded in the backbone network of the YOLOv8 network model, and multi-branch structures such as wavelet transform, Gabor direction filtering and specular reflection suppression are utilized, so that the feature extraction capability of multiple types of complex defects such as cracks, smudginess and damage on the surface of the glass bottle is enhanced, the generalization capability of the model in a small sample scene is remarkably improved, and the accuracy of the model is improved. The real-time requirement of an industrial scene is met, continuous self-optimization is achieved, and the method has high robustness, high accuracy and high application value.

The invention discloses a power load prediction method and system. The method comprises the following steps: acquiring historical power attribute data and constructing a time sequence; carrying out standardization processing on the sequence; performing multi-scale decomposition and reconstruction on the standardized sequence by using a wavelet transform convolution module, and extracting a reconstructed sequence fused with multi-scale features; and inputting the reconstructed sequence into an xLSTM-Informer hybrid network, capturing time sequence dependence characteristics through xLSTM, and outputting a load prediction result through introducing an Informer model of a probability sparse attention mechanism. According to the method, the problems of insufficient long sequence dependence capture, single multi-scale feature extraction and low calculation efficiency are effectively solved, and the precision and stability of long-time prediction are improved.

The invention discloses a distributed photovoltaic output prediction method and system in changeable weather, and the method comprises the steps: firstly obtaining historical photovoltaic station operation data, and carrying out the processing through a photovoltaic data preprocessing model: carrying out the feature engineering through timestamp feature extraction and multi-scale wavelet transformation, and combining a clustering algorithm with an anomaly detection algorithm, thereby achieving the prediction of the distributed photovoltaic output. Dividing and purifying the data into a plurality of weather type data sets; then, for each weather type data set, an independent photovoltaic output prediction model is trained, and the model is composed of an LSTM layer, a multi-head attention layer and a full connection layer; during prediction, the weather type attribution of the real-time data is firstly judged, and then the corresponding pre-training model is called to complete prediction. Weight calculation of multiple attention layers is dynamically guided by using frequency domain features extracted by wavelet transform, adaptive modeling of global features and local details is realized, and the accuracy and robustness of distributed photovoltaic output prediction are improved.

The invention relates to the technical field of automatic driving, in particular to a multi-modal three-dimensional target detection method, which comprises the following steps of: in a first stage, firstly acquiring neighbor depth information of point cloud to enhance image features, and then further enhancing image edges and reducing semantic confusion by utilizing wavelet transform; and then a cross attention mechanism is introduced to realize effective fusion of the enhanced image features and the point cloud, an initial region suggestion is obtained, and bounding box classification and prediction in the first stage are realized. And in the second stage, designing a double-attention module based on grid features to supplement more geometric detail information, acquiring more context information and position information by utilizing the initially suggested spatial features and channel features generated in the first stage, and then introducing a self-attention mechanism to dynamically distribute interaction weights of the grid features, so as to realize the self-attention interaction of the grid features. Rich context information is effectively captured, a local geometric structure is better coded, information loss is reduced, and the detection precision of the model is improved.

The invention discloses a gas leakage infrared detection algorithm for self-adaptive environmental noise suppression. The gas leakage infrared detection algorithm comprises the following steps: S1, preprocessing input data; s2, adaptive filtering is carried out; s3, carrying out multi-frame fusion, carrying out time-space domain analysis on continuous infrared image frames, separating high-frequency noise and low-frequency gas signals by utilizing wavelet transform, calculating a difference image of adjacent frames by combining an inter-frame difference method, enhancing gas diffusion morphological characteristics, distributing a weight for each frame according to a signal-to-noise ratio, and synthesizing a final image; and S4, post-treatment: concentration quantification and leakage area marking treatment are respectively carried out. According to the gas leakage infrared detection algorithm for self-adaptive environmental noise suppression disclosed by the invention, high sensitivity is kept through a self-adaptive environmental noise suppression algorithm, meanwhile, environmental noise interference is effectively eliminated, and the detection stability and accuracy in a complex scene are improved.

The invention relates to a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method. The method comprises the steps of integrating long-span bridge track vertical deviation three-dimensional historical data added with an environment temperature label; adopting empirical wavelet transform to construct an MRA component four-dimensional data set containing a temperature label and a wavelength label; after mileage dimension resampling, projecting data to an amplitude-wavelength space, drawing an accumulated amplitude growth curve, judging the type of the amplitude-wavelength curve, and determining a boundary wavelength of bridge deformation and track irregularity; projecting the data to an amplitude-temperature space, and judging an amplitude curve and temperature sensitivity; and in combination with the boundary wavelength and the temperature sensitivity, extracting a temperature sensitive component of track irregularity and a construction deviation component in actual bridge deformation. According to the method, synchronous identification and extraction of the temperature sensitive component in the track vertical deviation and the bridge construction deviation component can be realized, and a quantitative basis is provided for accurate maintenance and repair of a long-span railway bridge.

The invention provides a construction method for installing a large-span special-shaped beam curved template system, and belongs to the technical field of building construction. According to the construction method, accurate three-dimensional modeling is conducted on the shape of a special-shaped beam through a special-shaped curved surface parametric modeling algorithm, and material prefabrication is guided; eight oscillation excitation devices and twelve vibration monitoring devices are optimally arranged by using experimental design and a graph theory analysis method to form a dynamic monitoring network, vibration response data are analyzed in real time by using a wavelet transform monitoring algorithm, and the energy ratio of each frequency band is calculated as a quality evaluation index. A game optimization model is established, and according to different ranges of energy ratios, the support spacing is automatically adjusted, a back ridge fixing mode is combined or a supporting system is reinstalled, so that a comprehensive quality assurance system of accurate geometric modeling-dynamic state monitoring-intelligent parameter adjustment is constructed. The technical problem that the installation quality of a large-span special-shaped beam curved template system is difficult to guarantee is solved.

The invention relates to a cable insulation degradation degree evaluation method based on frequency domain characteristic analysis, and belongs to the field of cable insulation degradation detection.The cable insulation degradation degree evaluation method comprises the steps that cable operation environment data and laboratory accelerated aging data are collected, and a low-frequency impedance characteristic data set with water tree growth stage labels is generated; extracting a dielectric response spectrogram and analyzing a relaxation polarization peak value; when the peak value exceeds a threshold value, extracting harmonic component characteristics and loss factor distribution characteristics by using wavelet transform and principal component analysis; constructing a support vector regression model to predict the length, density and growth stage of the water tree; for middle and later water trees, microstructure evolution characteristic parameters are calculated, and insulation penetration time is predicted; the degradation probability is analyzed through Monte Carlo simulation, and a risk distribution curve is generated; and optimizing the cable replacement time by adopting dynamic planning, and outputting an evaluation report for quantifying the degradation degree and maintenance suggestions. According to the invention, accurate evaluation and prediction of cable insulation degradation are realized, and an important guarantee is provided for safe operation of a power system.

The invention discloses a method for detecting tea saponin in a tea extract, and relates to the technical field of chemical analysis and computer science, and the method comprises the following steps: eliminating high-frequency noise of tea extract data by using a Gaussian filtering algorithm, eliminating baseline drift by using a baseline correction algorithm, and generating a tea extract sample data stream; the method comprises the following steps: performing dynamic gradient separation through a chromatographic signal simulation algorithm on the basis of a tea extract sample data stream to generate a virtual chromatographic peak sequence, calculating a peak area through a sliding window integration algorithm on the basis of the virtual chromatographic peak sequence, and generating chromatographic signal data by using a wavelet transform threshold denoising algorithm, the chromatographic signal data are input into the deep convolutional neural model, the feature pyramid network is obtained through the multi-scale feature fusion algorithm, the feature information of the chromatographic peaks is extracted from different scales by constructing the deep convolutional neural model and combining the multi-scale feature fusion algorithm, and therefore the recognition precision of the feature peaks is improved.

The invention discloses a method for identifying interference signals of weld defects under different lifts-off conditions, which comprises the following steps of: S1, de-trending processing: carrying out de-trending processing on original detection signals; s2, Gaussian wavelet transform: carrying out Gaussian wavelet transform on the detrended signal; s3, optimal wavelet basis selection: by calculating correlation coefficients or energy ratios of different wavelet basis and defect signals, selecting the wavelet basis with the highest matching degree for reconstruction; s4, envelope processing: extracting a signal envelope based on Hilbert transform; s5, mean filtering: applying sliding window mean filtering to the envelope signal; and S6, threshold processing: setting a self-adaptive threshold screening signal, and retaining the feature points of which the amplitudes exceed the threshold. According to the method, de-trending and Gaussian wavelet transform are used for de-noising enhancement. According to the method, wavelet functions of different orders are constructed and matched with defects, secondary signal enhancement is carried out by selecting a filtering method, finally, threshold stripping interference is calculated, reliable defect identification is carried out, and technical support is provided for uneven welding seam quality monitoring.

The invention relates to the technical field of ultrasonic detection, in particular to a cable aluminum sheath defect grading detection method based on multi-frequency guided waves, which comprises the following steps: determining propagation characteristics of each guided wave mode according to a frequency dispersion curve, and determining excitation frequency for detecting a main mode based on the propagation characteristics; arranging and installing a transducer array on the cable aluminum sheath; according to the coarse screening main modal frequency, low-frequency guided waves are emitted, guided wave echo signals are collected, and low-frequency guided wave coarse screening is carried out to determine a suspected defect interval; and transmitting ultrasonic guided waves to the suspected defect interval according to the fine screening main modal frequency, accurately positioning the suspected defect interval, determining a comprehensive score through multi-feature weighting calculation, and determining a defect grade according to the comprehensive score. Low-frequency guided wave coarse screening is combined with wavelet transform and entropy verification, a suspected defect interval can be quickly locked, high-frequency fine screening extracts multi-dimensional defect features through variational mode decomposition, and accurate positioning of defect positions and scientific judgment of grades are achieved.

The invention discloses a method for identifying blade surge characteristics based on blade tip vibration velocity, and relates to the technical field of non-contact measurement of rotating machinery rotor blades. According to the method, the vibration speed and the vibration displacement of the blade tip are monitored according to the two blade tip timing sensors installed in a small angle difference mode, the theoretical arrival time of the blade does not need to be considered when the vibration speed and the vibration displacement of the blade tip are calculated, and the calculation efficiency and the calculation accuracy are greatly improved; in combination with the time-frequency enhancement characteristic of a synchronous compression wavelet transform method, the dominant subsynchronous frequency of the blade is tracked and identified through the blade tip vibration speed, and meanwhile, a frequency ridge line is extracted for surge frequency identification and a surge interval is positioned; and finally, the vibration intensity during blade surge is judged according to a proposed intensity quantized value calculation method based on the blade tip vibration speed. The method for efficiently calculating the blade tip vibration parameters is provided, the surge frequency can be quickly recognized, the surge interval can be positioned, and the surge intensity can be quantitatively evaluated.

The application relates to the technical field of target recognition, and specifically provides a small target recognition method for an unmanned aerial vehicle based on wavelet decomposition and a motion vector, which comprises the following steps: performing three-level wavelet decomposition on a small target image to obtain a third-layer low-frequency information region and a plurality of high-frequency detail information regions; performing image enhancement processing on the plurality of high-frequency detail information regions; performing image reconstruction on the plurality of high-frequency detail information regions after image enhancement and the third-layer low-frequency information region to obtain a reconstructed image; inputting the reconstructed image into a YOLOv5 improved model to recognize small targets in the reconstructed image; and performing false detection judgment on the recognized small targets. The application can effectively improve the recognition precision of small targets in a complex background by using wavelet transformation to enhance image detail information, and can further improve the precision of a target recognition algorithm by removing false detection caused by part of environmental noise through false detection judgment.

The invention relates to the technical field of power grid monitoring, in particular to a fault monitoring method, system and equipment for a small power generation and distribution system. According to the method, fluctuation characteristics of monitoring signals are analyzed, fluctuation time periods are determined, fluctuation trends are obtained through the lengths of the fluctuation time periods and voltage instability, and short-term fluctuation time periods and long-term fluctuation time periods are obtained through classification. And analyzing each time point one by one, and further screening out a superposed short-term fluctuation time period in the long-term fluctuation time periods through the voltage value difference between the continuous time points. And for the superposed short-term fluctuation time period, the continuation range is adjusted through the fluctuation trend characteristics, and a monitoring signal with accurate characteristic information is obtained. According to the method, the superposed short-term fluctuation time period is effectively extracted, wavelet transform is used for reconstruction, the monitoring signal with accurate feature information is obtained, the power grid monitoring effect can be improved, and subsequent power control over the micro-grid is facilitated.

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

The invention provides a high-voltage circuit breaker abnormity identification method based on a vibration signal envelope line. The method comprises the following steps: acquiring an operation vibration signal of a high-voltage circuit breaker, generating vibration data by a conditional GAN, filtering the vibration signal by wavelet transform, extracting the envelope line of the vibration signal and associating an operation state label to form a sample library; an AM-CNN-based high-voltage circuit breaker abnormity identification model is constructed, and sample library data is used for training so as to predict the operation state of the circuit breaker on line; and perfecting the sample library according to the prediction state and updating the circuit breaker abnormity identification model. According to the method, the fault state of the high-voltage circuit breaker is diagnosed by automatically extracting the fault features of the vibration signals, the error state and the unknown fault state are judged through self-learning so as to continuously improve the abnormal recognition precision of the circuit breaker, power transformation operation and maintenance personnel are assisted to make decisions, and the operation state recognition efficiency of the high-voltage circuit breaker is improved.

The application relates to the field of image processing and computer vision, and discloses a low-light image enhancement method based on the combination of Retinex and wavelet transform, aiming to improve the visual quality and detail definition of images in low-light environments. The method effectively handles noise, uneven illumination and detail blur in low-light images by combining Retinex theory, wavelet transform and deep learning technology. Through the illumination estimator and the degradation restorer, the method can generate enhanced illumination information and repair various forms of degradation, including noise, artifacts, underexposure / overexposure and color distortion. In addition, the method also uses a gating mechanism and the self-attention mechanism of the Transformer to selectively fuse features, so as to retain edge and detail information and promote global enhancement and restoration. Experimental results show that the method is superior to existing advanced methods on multiple datasets.

The invention provides a ship detection-oriented multi-source remote sensing image fusion method, a ship detection-oriented multi-source remote sensing image fusion medium and a ship detection-oriented multi-source remote sensing image fusion system, and belongs to the technical field of remote sensing images. Constructing an electromagnetic scattering constraint model based on physical optical approximation and a geometric diffraction theory to calculate a predicted value of the backscattering cross section of the target, introducing a fusion cost function, generating a fusion weight matrix by adopting an adaptive feature weighting model, and performing multi-scale fusion on the image through hypercomplex wavelet transform; and iteratively optimizing the fusion result by using an information diffusion heat conduction equation, and performing physical consistency correction to output an enhanced fusion image, thereby solving the technical problem of poor physical consistency of the fusion result caused by lack of physical model constraint when the synthetic aperture radar image and the multispectral image are fused.

The invention discloses a soil moisture dynamic monitoring system and method based on the Internet of Things, and relates to the technical field of Internet-of-Things monitoring, and the method comprises the steps: collecting soil moisture data in real time, obtaining phase features through Hilbert transform, obtaining phase differences between measurement points, and carrying out soil state evaluation; decomposition is carried out through wavelet transform, periodic feature extraction is carried out by using a time-frequency conjoint analysis method, and abnormal measurement point positioning information is obtained; on the basis of abnormal measurement point positioning information, isolating abnormal measurement points by using a Deckstra algorithm and selecting a shortest path to obtain a moisture monitoring network structure; and dynamically adjusting a wavelet transform decomposition parameter and a periodic feature extraction parameter based on a moisture monitoring network structure, and outputting a soil moisture dynamic monitoring report. According to the method, the shortest path is dynamically selected by using the Deckstra algorithm, and the standby communication channel is constructed through the redundant path, so that the data transmission stability is improved.

The invention belongs to the technical field of nuclear equipment monitoring, and aims to solve the problems of spatial resolution reduction caused by a multi-source acoustic signal aliasing effect in a complex industrial environment and feature extraction difficulty caused by industrial noise and fault sound source spectrum overlapping. According to the method, spatial decoupling is carried out, sparse Bayesian learning and minimum variance undistorted response beam forming joint optimization is carried out, a sound source orientation is preliminarily estimated, synchronous extrusion wavelet transform and NMF joint time-frequency analysis is carried out, and a spatial anomaly acoustic diagnosis result is obtained. Sound source transient characteristics and energy evolution laws are extracted through time-frequency conjoint analysis, a multi-dimensional characteristic space is constructed, the number of fault types is determined for fault type classification, and three-dimensional coordinate mapping and error feedback are displayed; the system comprises an airspace decoupling module, a time-frequency decomposition module, a feature fusion module and a dynamic imaging module. According to the invention, the sound source positioning precision can be realized, and the resolution limitation of traditional beam forming is broken through.

The invention provides a water quality prediction network, a water quality prediction model training method and device, and a water quality prediction method and device. The water quality prediction network comprises a time sequence decomposition module used for obtaining three components; fusing the three components to obtain a reconstructed input sequence; the time-frequency fusion module comprises a dynamic convolution sub-module, a wavelet transform sub-module and a mutation detection sub-module, the dynamic convolution sub-module is used for extracting short-term fluctuations and modes of a reconstructed input sequence, and the wavelet transform sub-module is used for extracting a main variable sequence from the reconstructed input sequence and extracting a multiband energy spectrum of the main variable sequence; the mutation detection sub-module is used for identifying sudden change points in the reconstructed input sequence; the channel fusion module comprises a multi-head attention sub-module, and the multi-head attention sub-module is used for fusing short-term fluctuations and modes, multiband energy spectrums and sudden change points by adopting a cross-channel multi-head attention mechanism to obtain a fusion feature sequence; the time sequence prediction module is used for obtaining predicted water quality time sequence data.

The invention discloses a large-range landslide mass displacement early warning method based on GNSS and radar data fusion, and the method comprises the steps: obtaining GNSS monitoring sequence data in a landslide region based on the position information of a reference station and a GNSS monitoring station in a GNSS monitoring system, and further obtaining the displacement related information of the landslide region; collecting data through a satellite InSAR, and generating deformation field data of a landslide area by comparing radar images at different time points; performing space-time alignment on the obtained data, and performing two types of data fusion S by adopting an adaptive Kalman filtering method after alignment; decomposing time series data of the fused landslide mass displacement by using wavelet transform, and extracting multi-scale features of a time domain and a frequency domain; the proposed features are combined with historical data of landslide mass displacement, a landslide mass displacement trend prediction model using an LSTM method added with a memory decline factor is input, and future trend prediction of landslide displacement is carried out; setting a multi-level threshold value, judging the change trend of the landslide mass displacement according to the displacement rate, the acceleration and the prediction result, and generating a multi-level early warning signal.

The invention relates to a hole bottom sediment thickness adaptive direction measurement method for irregular hole shapes. The method comprises the following steps: acquiring hole wall images and vertical distance data in multiple directions; image preprocessing is carried out, hole wall feature points are extracted, three-dimensional coordinates are converted, a dynamic three-dimensional model is fitted, deformation grades are divided, and an inclination angle is determined; preferentially distributing detection points in a serious deformation area, calculating an optimal angle, monitoring by a gyroscope, correcting by an electromagnetic damper, adjusting by a servo motor, and ensuring that an included angle between a probe axis and a hole bottom tangent line is compliant; transmitting multi-frequency ultrasonic waves according to deformation grades, analyzing reflected signals through wavelet transformation, extracting sediment layering characteristics, and calculating the distance from a probe to a layering surface through speed correction; the reference distance is calculated based on the reference coordinates, the sediment total thickness and the dense layer thickness are finally obtained, and the measurement method which is adaptive to hole wall deformation, eliminates interference, dynamically adjusts detection parameters and is accurate in reference positioning can meet the high-precision requirement of engineering for sediment thickness and compactness quantification.

The invention discloses a smart watch arrhythmia early warning method and system based on deep learning. The method comprises the steps that firstly, PPG signals are collected through a photoelectric volume pulse wave sensor, and denoising and standardization processing are conducted through an adaptive filtering algorithm; then, the heart beat position is recognized through a wavelet transform peak value detection algorithm, and a heart rate variability feature sequence is obtained; converting the one-dimensional heart rate variability feature sequence into a two-dimensional heart rhythm image by using an improved Gramian angular field transformation algorithm; and training an arrhythmia classification model by adopting a ResNet-50 convolutional neural network to realize automatic identification of different types of arrhythmia. And finally, continuous monitoring and timely alarming are realized through a sliding window technology and an early warning mechanism. The key technical problems of low detection precision, poor real-time performance, high equipment cost, poor user experience and the like in the existing arrhythmia detection technology are effectively solved, and an innovative solution is provided for heart rhythm health monitoring of the intelligent wearable equipment.

The invention belongs to the technical field of charging pile load scheduling, and provides a power distribution network measurement data fusion driven ultrafast charging load flexible regulation and control method and system, and the method comprises the steps: firstly processing multi-source data through a multi-source measurement data fusion module by employing a self-adaptive weighted fusion and Kalman filtering algorithm, and forming a high-fidelity data pedestal; the load is predicted through a charging load accurate prediction module in combination with wavelet transform, an LSTM-GNN model and a variational mode decomposition algorithm; then, a dynamic regulation and control strategy generation module generates a regulation and control strategy through marginal cost pricing and deep reinforcement learning according to prediction; the source-load collaborative optimization module integrates related systems and optimizes scheduling by means of a particle swarm optimization algorithm; the real-time monitoring and feedback module monitors and corrects the strategy in real time; and the security protection module ensures data and system security. During use, the above module processes are operated in sequence, and flexible regulation and control of the ultrafast charging load are realized.