360 results about "Frequency domain decomposition" patented technology

The invention discloses a bolt looseness online monitoring method and system, and the method comprises the steps: collecting an original data set containing a vibration signal of bolt connection, temperature gradient data and structural stress distribution through a multi-mode sensor array, and carrying out the time-space alignment and frequency domain decomposition processing of the original data set, obtaining a multi-dimensional feature matrix of the bolt nodes; based on the multi-dimensional feature matrix, a graph neural network model is adopted to carry out bolt looseness probability calculation, and a real-time looseness probability value of each bolt is output; generating a risk level map based on time evolution according to the real-time loosening probability value; and the risk level map is mapped in real time through a three-dimensional visual interface, and when it is detected that the bolt loosening risk level exceeds a preset threshold value, an early warning message is generated and uploaded to an operation and maintenance platform, and closed-loop monitoring response is completed. According to the embodiment of the invention, accurate assessment, dynamic prediction and visual early warning of the loosening risk can be realized, and the intelligent level of structure safety monitoring is improved.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention relates to the technical field of circuit breakers, in particular to a circuit breaker fault diagnosis method and system for switching an alternating current filter in an extra-high voltage converter station, and the method comprises the following steps: S1, synchronously collecting multiple parameters: synchronously collecting vibration acceleration signals, opening and closing coil current waveforms, arc light intensity and environment temperature and humidity parameters; s2, time sequence feature extraction: performing time-frequency domain decomposition on the vibration acceleration signal to obtain a mechanical action time sequence spectrum; s3, operation mode classification: determining the operation mode of the circuit breaker; s4, three-dimensional fault matrix construction: generating a three-dimensional fault matrix including spatial distribution characteristics; s5, dynamic threshold analysis: generating a diagnosis threshold interval dynamically adjusted along with the environmental parameters; and S6, comprehensive diagnosis output: outputting a mechanical jam grade, a contact ablation degree and an insulation deterioration early warning signal. According to the invention, the mechanical, electromagnetic and insulation characteristics of the circuit breaker can be reflected more comprehensively, and reliable guarantee is provided for safe and stable operation of a high-voltage power grid.

The invention provides an intelligent grabbing control method and system based on tactile perception, and the method comprises the steps: obtaining a tactile feedback signal through a sensor array of a humanoid robot, carrying out the frequency domain decomposition of the signal through a Fourier transform method, and carrying out the analysis of the pressure and deformation data of each contact point on the surface of a to-be-grabbed object, calculating to obtain frequency characteristic distribution of the tactile feedback signal; if the initial estimation value of the object rigidity gradient exceeds a threshold value, performing finite element analysis on the object rigidity gradient, and combining a time domain processing result of the tactile feedback signal to obtain object rigidity gradient distribution; carrying out probability modeling on the deformation field by adopting a Bayesian inference method according to the gradient distribution of the rigidity of the object, and carrying out iterative optimization on a modeling result to obtain dynamic update parameters mapped by the deformation field; and regenerating an action sequence of the end effector of the humanoid robot based on the optimized strategy according to a trigger condition of grabbing failure detection, and determining a final grabbing control parameter.

The invention relates to the technical field of intelligent sensor application, and discloses a water meter flow real-time correction method and system based on an intelligent sensor, and the system comprises a flow signal collection module, a signal feature decomposition module, a frequency domain feature fusion module, an error compensation decision module and a correction instruction execution module. The method comprises the following steps: acquiring multi-dimensional vibration waveform data of a water meter pipeline through a multi-band sensor, performing time-frequency domain decomposition and energy spectrum feature extraction to obtain a local frequency domain feature tensor set, and performing dynamic noise suppression and band correlation optimization to obtain a local optimization feature tensor set; and performing global feature reconstruction based on frequency domain energy distribution to generate an error compensation decision feature map, and finally generating a real-time correction control signal. Through multi-dimensional signal acquisition and intelligent feature processing, the precision and dynamic adaptability of flow metering in a complex pipe network environment are improved, and the method is suitable for high-precision flow correction of an intelligent water meter and has the advantages of being high in anti-interference capacity, accurate in compensation and the like.

The invention provides a small target identification method and system based on YOLOv5, and the method comprises the steps: collecting a multi-scale image in a target range, and forming an original data set; performing image enhancement on the original data set through superpixel segmentation and adversarial enhancement operation to obtain an enhanced data set; performing multi-scale frequency domain aliasing enhancement on the enhanced data set through frequency domain decomposition and frequency band exchange operation to obtain a to-be-detected data set; improving the YOLOv5 model to obtain a small target recognition model; and performing small target identification on the to-be-detected data set through the small target identification model to obtain an identification result. According to the method, small target recognition is realized through the dynamic feature pyramid and the double-path detection head in combination with cross-level kernel sharing and a space-frequency double-domain attention mechanism, the problem of detail loss caused by a traditional static feature pyramid is solved, the recognition precision is improved, and the false detection rate is reduced.

The invention relates to the technical field of fatigue evaluation, and discloses an offshore wind turbine blade frequency domain fatigue evaluation method and system based on an MIMO nonlinear dynamic system, and the method comprises the steps: collecting the load data of an offshore wind turbine blade; preprocessing the load data; an MIMO nonlinear dynamic system is established; establishing a dynamic mapping relation between the preprocessed load data and the frequency domain response by using an MIMO nonlinear dynamic system; decomposing the time domain dynamic response data by using a frequency domain decomposition method, and quantifying the influence weight of each frequency band load on the blade stress based on a frequency domain energy distribution matrix to obtain a frequency domain energy distribution function of the total stress; and calculating the fatigue damage of the blade through a frequency domain fitting optimization method based on the frequency domain energy distribution function of the total stress. The method solves the problems that fatigue damage evaluation confidence is insufficient under the multi-load coupling effect and actual engineering requirements are difficult to meet in the prior art, and has the characteristic that the coupling effect of the wind load and the wave load can be accurately processed.

The invention discloses a running state monitoring method of an electric energy metering box, a storage medium and equipment, and relates to the technical field of intelligent power grid monitoring. The method comprises the following steps: performing multi-scale frequency domain decomposition on a multi-dimensional electrical signal to generate subsequences with different frequency characteristics; a dynamic window mechanism is adopted to adaptively segment signals and optimize a filtering strategy; extracting spatio-temporal joint features through multi-scale time sequence convolution and a physical constraint graph network; inputting the features into an attention prediction module integrated with a historical knowledge base for knowledge enhancement reasoning; and generating prediction uncertainty estimation based on Monte Carlo Dropout, calculating a composite abnormal index in combination with the distribution deviation degree, and triggering graded early warning. According to the method, accurate detection of transient abnormality and long-term trend is realized, the prediction accuracy is improved through spatio-temporal feature fusion and dynamic knowledge retrieval, the false alarm rate is reduced by using an adaptive threshold strategy, and a multi-dimensional and high-reliability state monitoring solution is provided for the electric energy metering box.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention discloses a power grid subsynchronous oscillation suppression method based on a neural network prediction model, and particularly relates to the technical field of power grid control. The method comprises the following steps: extracting a high-dimensional feature vector of power grid measurement data and performing frequency domain decomposition to obtain feature distribution information of different frequency components; utilizing a sparse coding algorithm to realize mode separation, and identifying a running state abnormal region set; constructing a dynamic coupling model to evaluate the interaction relationship between the oscillation mode and the operation state in the abnormal region and the time evolution characteristic; analyzing modal distribution characteristics through adaptive filtering, and calculating a local modal separation degree; based on oscillation evolution information and a modal structure evaluation result, power grid operation parameters and suppression device configuration are dynamically adjusted, and the recognition and response capability of a neural network to a potential dangerous subsynchronous oscillation mode is effectively improved.

The invention discloses a feature matching method based on differential wavelet transform and dynamic multi-scale feature fusion. The method comprises the following steps: constructing a feature matching model; the feature matching model comprises a backbone network, a key point detection network and a descriptor generation network; inputting a to-be-matched image to the backbone network, and generating a feature graph and a reliability graph; inputting a to-be-matched image to the key point detection network, generating a key point heat map, inputting the key point heat map, the reliability map and the feature map into the descriptor generation network to generate an enhanced descriptor of the to-be-matched image, and finally performing violent matching on the enhanced descriptor of the to-be-matched image; the differential wavelet transform down-sampling module in the backbone network performs frequency domain decomposition on features by using discrete wavelet transform, separates noise from key features, and effectively removes redundant information, enhances the key features and improves the matching accuracy of subsequent features in combination with differential operation of calculating the features and a neighborhood average pooling difference value.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source geological data processing method and system of a three-dimensional geological model.The method comprises the following steps that mountain landform and river valley images are obtained, gray frequency characteristics are extracted, a frequency energy gradient layer is constructed, a frequency continuous response area is screened to generate a structure boundary set, and a structure boundary set is constructed; the method comprises the following steps of: extracting a boundary normal vector by utilizing principal component analysis, identifying boundary sections with consistent directions, estimating a physical property parameter gradient direction, judging an included angle screening blocking region, generating a space attribute limiting layer, carrying out space alignment analysis on an overlapping region vector included angle, updating a boundary label, and generating an available attribute path structure set in three-dimensional geological modeling through a Dijkstra algorithm. According to the method, a conduction model is constructed through frequency domain decomposition and logarithmic transformation enhanced recognition, frequency window analysis noise reduction, principal component extraction vector analysis direction and center difference estimation, dynamic matching is promoted through alignment, a Dijkstra algorithm optimizes a path, and the geological model bedding characterization and conduction simulation precision is improved through cooperation of a multi-dimensional technology.

The invention discloses a gravimeter active vibration isolation control method, and belongs to the technical field of gravimeter active vibration isolation, and the control method comprises the steps: S1, presetting a three-axis acceleration sensor and a gravimeter bearing platform, and detecting a space vibration signal of the gravimeter bearing platform in real time through the three-axis acceleration sensor; s2, the signal processing unit performs frequency domain decomposition on the space vibration signal; s3, establishing a feedforward control unit, a feedback control unit and a synthesis unit; s4, a multi-degree-of-freedom actuator is preset, and reverse compensation force is applied in the X-axis direction, the Y-axis direction and the Z-axis direction through the multi-degree-of-freedom actuator according to the composite control signal; and S5, the preset state monitoring unit samples the vibration suppression rates in three continuous sampling periods. The high and low frequency vibration components are dynamically divided according to the frequency-band energy accumulation proportion, the vibration interference model is established in combination with the high and low frequency vibration components, and compensation failure caused by model mismatch under complex working conditions is avoided.

The invention provides an advertisement pushing method and system based on multi-modal data fusion. The method comprises the following steps: determining a user staying event of each position according to intensity peak value distribution of a user foot heat radiation signal of each position in continuous N time windows; performing frequency domain decomposition on the user foot heat radiation signals at the plurality of positions so as to extract a people flow density component and a staying time length component which are synchronous with a timestamp of a user staying event; calculating a discrete difference degree between the people flow density component and the staying duration component by adopting a variable coefficient method; and according to the discrete difference degree, adjusting a content playing queue of an advertisement screen in the offline commercial place, so that the playing content is matched with the commodity category corresponding to the shelf vertex. According to the technical scheme provided by the invention, the user staying event can be intelligently identified, the content of the advertisement screen is dynamically matched, precise commodity marketing is realized, and a consumption scene is adapted.

The invention provides an infrared image frequency division enhancement method based on a diffusion model, and the method comprises the steps: firstly carrying out the multi-scale frequency domain decomposition of an infrared image through discrete wavelet transform, and dividing the image into a low-frequency component and a high-frequency component; in the low-frequency component processing process, a diffusion equation is constructed in combination with a thermal radiation physical model, and discretization solution is performed by using a finite difference method. The high-frequency component is converted from a space domain to a frequency domain through Fourier transform, energy distribution of the high-frequency component is analyzed, and a diffusion probability function is adjusted. In the fusion stage, according to the signal-to-noise ratio of the local area of the image, the fusion weight of the low-frequency component and the high-frequency component is dynamically adjusted, the processing results of all the local areas are integrated in a weighted fusion mode, and finally the high-quality enhanced infrared image is generated. The method has excellent performance in improving image contrast, definition and detail performance, is suitable for the fields of security monitoring, military reconnaissance, industrial detection and the like, and can effectively improve monitoring, analysis and detection effects.

The invention relates to a helicopter rotor crack fault identification method and device based on video semantic segmentation. The method comprises the steps that video collection and denoising and stability enhancement processing are carried out through an unmanned aerial vehicle; through designing a global-local feature interaction double-branch network, parallel computing and bidirectional fusion are carried out on context global feature extraction branches and detail mining branches, so that rotor wing crack feature extraction is realized; designing a hierarchical crack feature reconstruction decoder to realize cross-scale fusion and spatial precise alignment of crack features; and designing a multi-frequency-domain boundary sensing enhanced training head, and realizing multi-scale feature extraction and dynamic weight distribution of the crack edge through the synergistic effect of a multi-stage wavelet frequency domain decomposition module and a self-adaptive boundary weighting supervision module. Through the innovative modules, high-precision and robust crack detection is realized aiming at the problems of view limitation, global-local feature fusion difficulty, detail loss caused by down-sampling, edge blur, class imbalance and the like in helicopter rotor crack detection.

The invention provides an integrated circuit chip electromagnetic interference intelligent suppression and shielding method and system, and relates to the technical field of integrated circuits, and the method comprises the steps: collecting chip electromagnetic interference data, and carrying out the time-frequency domain decomposition and feature extraction, so as to obtain an initial electromagnetic interference feature vector; calculating an electromagnetic interference threat index based on state space mapping and action space search, and further evaluating the electromagnetic interference situation and determining the minimum shielding strength; carrying out optimization design on the electromagnetic shielding structure by adopting a gradient-guided genetic algorithm to obtain an optimal shielding structure parameter; real-time electromagnetic interference data are adjusted according to the parameters, the suppression ratio is calculated, and iterative design is carried out by combining the suppression ratio optimization genetic algorithm. According to the invention, intelligent evaluation and dynamic suppression of electromagnetic interference are realized, and the accuracy and adaptability of shielding structure design are improved.

The invention provides an infrared small target detection method based on frequency domain decomposition, and aims at solving the problem that a traditional spatial domain method is insufficient in detection performance under the conditions of complex background, low contrast and noise interference. Aiming at the problem that an infrared small target generally has relatively small spatial distribution and high-frequency characteristics, an input image is decomposed into high-frequency and low-frequency components through a self-adaptive frequency domain decomposition technology, frequency domain information perception and a spatial information aggregation module are combined, and frequency domain characteristics are weighted and extracted by adopting a sparse attention mechanism; the contributions of different frequency components are dynamically adjusted. Through efficient feature fusion, the expression of target information is optimized, and especially in a low-contrast and complex background environment, the detection precision and robustness are improved. An experimental verification result on an NUAA data set shows that compared with an existing mainstream method, the method has remarkable advantages on multiple evaluation indexes, and the effectiveness and superiority of the method in infrared small target detection are verified.

The invention relates to the technical field of power systems, in particular to a power system broadband oscillation disturbance source grading positioning method and system, and the method comprises the steps: calculating the compression ratio of a measurement signal according to the sampling resolution of an original measurement signal and a real-time transmission interval, and obtaining an initial compression coefficient; performing frequency domain decomposition on the original measurement signal to obtain frequency spectrum characteristic data, and compressing the original measurement signal based on the frequency spectrum characteristic data and the initial compression coefficient to obtain a measurement compression signal; embedding a time sequence position code in the measurement compression signal to generate time sequence input data; analyzing the time sequence input data by using a multi-head attention mechanism to obtain an attention weighted signal; and generating a reconstruction signal according to the attention weighted signal, and obtaining a broadband oscillation disturbance source grading positioning result based on the reconstruction signal and the disturbance propagation path. According to the method, rapid and accurate grading and positioning of the broadband oscillation disturbance source are realized through technologies of self-adaptive compression, broadband oscillation characteristic reservation, topology propagation path analysis and the like.

The invention relates to the technical field of hydraulically-driven industrial control, and particularly discloses a self-adaptive control method and system for a hydraulically-driven guniting manipulator, which are characterized in that a pressure fluctuation signal, a flow change signal and a mechanical arm displacement signal of a hydraulic system are collected in real time, and a frequency domain decomposition processing technology is combined to realize self-adaptive control of the hydraulically-driven guniting manipulator. Separating the composite signal into a low-frequency characteristic signal reflecting load change and a high-frequency characteristic signal reflecting pressure impact; for a low-frequency signal, a dynamic weight adjustment strategy is adopted to generate a load compensation control quantity; for high-frequency signals, pressure oscillation is suppressed through differential order adjustable phase compensation processing; and finally, inputting the two paths of control characteristic values into a driving control model of the guniting manipulator, and generating an adjusting instruction of the hydraulic actuating mechanism.

The invention discloses a pavement disease image adaptive enhancement and restoration processing method, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a pavement disease image, and carrying out the frequency domain transformation; recognizing a pavement material type based on the frequency domain features and determining a material sensing parameter set; performing multi-scale frequency domain decomposition according to the material perception parameter set, and performing adaptive enhancement on a low-frequency illumination component and a high-frequency detail component; carrying out disease feature detection and generating a disease perception weight map, and carrying out key enhancement on a disease area; according to the method, the self-adaptive enhancement model for road surface material perception is established, and targeted image enhancement processing is realized.

The invention discloses a lightning arrester on-line monitoring method and device, and relates to the field of intelligent monitoring, and the method comprises the steps: firstly collecting original current and voltage waveform data, carrying out the frequency domain decomposition operation, then introducing an intelligent prediction mechanism, carrying out the intelligent prediction of a phase error based on the fundamental voltage phase, temperature change and harmonic characteristics, and finally, carrying out the calculation of the phase error. And performing phase angle correction operation and preliminary calculation of resistive current on the fundamental current and voltage by using the corrected phase information, and introducing a historical data smoothing algorithm to perform noise reduction optimization on a result. And finally, comparing the analyzed and processed resistive current with a preset threshold value, and judging whether an alarm prompt is generated or not. Therefore, real-time evaluation and abnormal alarm of the operation state of the lightning arrester are realized, so that a powerful guarantee is provided for safe operation of a power system.

The invention discloses an optical remote sensing satellite high and low frequency relative radiation error correction method and system, and the method comprises the steps: S1, carrying out the systematic high frequency radiation error calibration of a remote sensing image, so as to eliminate stripe noise; s2, performing frequency domain decomposition on the image after high-frequency correction, and separating high and low frequency components; s3, calculating a global statistical moment of the low-frequency component as a correction reference; s4, calculating a local statistical moment of the low-frequency component column by column; s5, calculating an independent correction coefficient for each column according to the global and local statistical moments; s6, correcting the low-frequency component by using the correction coefficient; and S7, recombining the corrected low-frequency component with the original high-frequency component to generate a final image. The method is suitable for improving the quality of the remote sensing image with multiple composite radiation errors, high-frequency stripes and low-frequency brightness gradient can be effectively eliminated, original texture details of ground features are reserved to the maximum extent in the correction process, and the radiation fidelity is guaranteed.

The invention discloses a multi-degraded image recovery method based on frequency domain decomposition, and aims to solve the problems that a single model is difficult to deal with various image degradation and recovery processes of different frequency domains are mutually coupled in the prior art. According to the method, a degraded image is decomposed into a high-frequency space and a low-frequency space through fast Fourier transform, and a double-branch network architecture is adopted for targeted processing: for the high-frequency part, a high-frequency feature adaptive processing module HFPM is designed, and detail texture features are effectively extracted and interference is suppressed through feature enhancement and cross-layer fusion technologies; and for the low-frequency part, constructing a low-frequency feature conversion enhancement module LTEM, and capturing global context information by using cyclic convolution to improve the integrity of the structure contour. According to the method, decoupling processing of frequency domain features is realized, and the image restoration performance of the model in various degradation scenes such as rain removal, noise removal and defogging is remarkably improved through the synergistic effect of high-frequency detail enhancement and low-frequency structure optimization. Experimental results show that the method has excellent recovery effect and robustness when a plurality of image degradation tasks are processed at the same time, and can be effectively applied to visual tasks such as traffic accidents with high image quality requirements.

The invention provides a reactor impedance characteristic automatic measurement and analysis method and system, and relates to the technical field of reactors, and the method comprises the steps: collecting voltage and current signals, calculating original impedance frequency characteristic data, carrying out the frequency domain decomposition of the data, carrying out the processing of a neural network compensation model, and building an impedance frequency characteristic curve; real-time monitoring data are continuously collected and compared with theoretical features, fault feature vectors are constructed, fault judgment criteria are established through the Euclidean distance and the Mahalanobis distance, automatic detection and accurate positioning of reactor faults are achieved, and the operation reliability of a power system is effectively improved.

The invention relates to the technical field of resolution improvement, in particular to a video definition improvement method and system.According to the video definition improvement method and system, through window accumulation of adjacent frame gray difference and coordinate difference, minimum displacement is selected, mapping is updated, boundary neighborhood weighted correction is carried out, cross-frame dislocation is reduced, and motion area connection is guaranteed; capturing a three-frame gray scale sequence to calculate a difference value, writing a replacement value according to a monotone and amplitude condition, inputting the replacement value and an alignment frame difference into Fourier transform, decomposing amplitude and phase distribution in a residual field in a frequency domain, and performing amplitude and phase consistency elimination on an abnormal block during frame-by-frame superposition to obtain an abnormal block; row difference and column difference absolute values of neighborhood blocks in the residual iteration texture matrix are accumulated into energy, the energy and a center difference are combined with a threshold value to output a mark, and a texture region and an edge region are distinguished; the brightness of the low-frequency position is updated according to the weight mean value, the maximum difference and the direction difference are combined for the high-frequency position, the edge gradient is extracted through the convolutional neural network, a correction value is generated, edge steps and fuzziness are suppressed, and high-frequency details are continuously kept.

The invention relates to the technical field of mine safety, in particular to a rock burst prediction method based on dynamic frequency domain decomposition, relative event coding and dynamic sparse attention. According to the method, data decomposition of Fourier transform, adaptive frequency domain noise reduction, relative event coding and a dynamic sparse attention mechanism are combined, key features in micro-seismic data can be effectively extracted, and the accuracy and robustness of rock burst prediction are improved. The method is excellent in performance in rock burst prediction, the problems of noise interference, error accumulation and the like can be effectively solved, and reliable technical support is provided for accurate early warning of rock burst.