270 results about "Frequency domain decomposition" patented technology

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 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 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 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 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 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 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 a bridge structure modal parameter identification method, which is based on a bridge structure tiny vibration video acquired by a camera. VMD (variational mode decomposition), PBVM (phase-based video motion amplification), Gabor filtering-based phase extraction method GBP (Gabor filtering), an improved clustering algorithm ICA (independent clustering algorithm) and an integrated covariance-driven random subspace recognition (SSI-Cov) and FDD (frequency domain decomposition) algorithm are fused to construct a set of complete non-contact structural modal parameter recognition system. According to the method, effective decoupling of multiple vibration modes of a bridge structure, frequency domain feature extraction of small-amplitude vibration and intelligent and automatic modal parameter extraction are achieved under the conditions that the target frequency bandwidth does not need to be known in advance, environmental noise interference exists and no obvious feature target exists, and the method belongs to the technical field of bridge structure health monitoring.

The invention discloses an intelligent testing method and device for a circuit board, a program product and a medium, and relates to the technical field of circuit board automatic testing. The method comprises the following steps: acquiring response waveform data of a target circuit board; performing frequency domain decomposition processing on the response waveform data to obtain a frequency domain fingerprint spectrum; obtaining a modulation influence coefficient based on the frequency domain fingerprint spectrum; when any result exceeds a preset reasonable range, obtaining a reversible modulation result and an irreversible damage result based on the target frequency domain fingerprint spectrum corresponding to the target test item and each target modulation influence coefficient; for the reversible modulation result, obtaining a standard environment equivalent map based on the target modulation influence coefficient, and recalculating the result of the target test item based on the standard environment equivalent map; and for the irreversible damage result, extracting damage frequency domain characteristics from the target frequency domain fingerprint spectrum, and based on the damage frequency domain characteristics, obtaining a defect type of the target circuit board and corresponding defect position information. And the overall efficiency of circuit board testing is improved.

The invention belongs to the field of structural modal identification, and particularly relates to an automatic modal parameter identification method based on a multi-scale space, which comprises the following steps: collecting a vibration data sample from a target structure, the vibration data sample comprising an acceleration signal of the structure; selecting power spectrum calculation parameters including a window function and a fast Fourier transform (FFT) length; performing frequency spectrum estimation on the acceleration signal by using interpolation power spectrum estimation to obtain frequency spectrum data; analyzing the spectrum data by using a multi-scale space algorithm, and detecting a peak value in the spectrum data to obtain a peak value frequency; through a frequency domain decomposition method, according to the peak frequency, modal parameters of the structure are calculated, the modal parameters comprise the modal frequency and the damping ratio, interpolation power spectrum estimation and a multi-scale space algorithm are innovatively combined, automatic and accurate identification of the modal parameters is achieved, and the identification precision and efficiency are remarkably improved.

The invention discloses a robot joint hidden fault detection method and system based on artificial intelligence, and relates to the technical field of robot fault diagnosis, and the method comprises the steps: collecting a vibration signal data sequence of a robot joint; performing frequency domain decomposition on the vibration signal data sequence to obtain vibration components of a plurality of frequency bands; generating a vibration spectrum based on the vibration component, calculating an energy mean value of each frequency band in the vibration spectrum, and determining a characteristic frequency band; counting time points with abnormal energy in the characteristic frequency band, and analyzing probability distribution characteristics of a time interval sequence of the time points to obtain a fault characteristic probability; and according to the change trend of the fault feature probability, determining that the robot joint has a hidden fault risk and outputting fault risk early warning information. According to the method, the abnormal energy distribution rule of the vibration signal characteristic frequency band is analyzed, the robot joint hidden fault is early warned, the equipment fault can be effectively prevented, and the equipment operation reliability is improved.

The invention discloses an electric energy quality data monitoring method, and relates to the technical field of electric power system monitoring and analysis, and the method comprises the following steps: S1, obtaining an original voltage and current waveform set; s2, performing time-frequency domain decomposition by using the waveform set; s3, performing feature fusion by using the feature component set; s4, performing time serialization by using the electric energy quality index set; and S5, performing multi-dimensional structured integration by using the time sequence index data. By setting a voltage and current characteristic component coupling mechanism, unified expression of voltage and current characteristics can be ensured on an original signal level, and fine-grained description of electric energy quality characteristics is realized in an index calculation link, so that the problems of data dimension separation and insufficient index precision in the prior art are effectively solved. The method not only improves the comprehensiveness and reliability of the electric energy quality monitoring result, but also lays a solid data foundation for subsequent time serialization and structuralization integration.

The invention discloses a three-mode saliency target detection method and system based on frequency domain decomposition and reconstruction. The method comprises the following steps: firstly, respectively preprocessing a training set and a test set in a three-mode saliency target detection data set; secondly, constructing a three-mode saliency target detection network based on frequency domain decomposition and reconstruction; and finally, sending the preprocessed training set image into a three-mode saliency target detection network for processing, outputting a prediction map consistent with the input image in size, completing target detection, and performing training and testing. According to the invention, through designing the interaction, fusion and enhancement network, the information complementation advantages of three modes of visible light, depth and thermal imaging are fully utilized, the synergistic interaction and global perception efficiency among multi-mode information are further enhanced, and accurate salient target detection is realized.

Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS) / distributed acoustic sensing (DAS) to monitor and provide real-time estimation of ice thickness on fiber optic communications facilities, and which integrate DSA data with a hybrid processing technique that combines frequency domain decomposition (FDD) and stochastic subspace identification (SSI). Aspects of our innovative systems and methods include: i) Hybrid Signal Processing Techniques; ii) Real-time, Continuous Ice Monitoring; iii) Enhanced Noise Robustness and Non-linear Dynamics Handling; and iv) Adaptability and Scalability.

The invention provides a TMD lockout control method for motion suppression of a semi-submersible platform, and the method comprises the steps: carrying out the frequency domain decomposition of a random wave predicted by a target sea condition, obtaining the energy distribution of each frequency component, obtaining the equivalent amplitude of each frequency component according to the energy distribution, considering the amplitude response of the platform under the action of waves of different frequencies, and achieving the motion suppression of the semi-submersible platform. The method comprises the following steps of: calculating a weight ratio of platform motion response caused by each frequency component under the action of random waves by integrating three types of information, namely amplitude, amplitude response and motion suppression effect, and finally carrying out weighted combination on the locking time corresponding to each frequency component according to the weight ratio, so as to obtain the motion suppression effect of the platform. And the optimal locking control time suitable for the current random wave is obtained. According to the method, a high-complexity non-causal control command does not need to be deduced, control parameters can be updated in real time according to short-term sea condition changes, pitching response of a platform is effectively reduced, and comprehensive optimization control over multi-frequency waves is achieved.

The invention discloses an X-ray image artifact definition automatic correction and enhancement method based on deep learning, and belongs to the technical field of image artifact correction, and the method specifically comprises the steps: obtaining an X-ray image, employing frequency domain decomposition and edge detection to generate an artifact image and a sharpness image, and extracting an imaging parameter set and an anatomical region label; establishing a parameter-driven artifact migration network, inputting an imaging parameter set and an artifact graph, learning an artifact vector field and phase prior, and forming reversible representation of an artifact source; constructing a double-branch decoder, dissecting branches to generate a structural skeleton diagram, and performing cross attention fusion to obtain a candidate correction field; according to the candidate correction field, geometric remapping and sub-band compensation are carried out on the image to obtain an intermediate correction result image, and an artifact vector field is sent to a feedback loop; and the consistency of the sharpness graph and the structural skeleton graph is taken as a loss item, artifact residual constraint is combined, a correction field and decoder parameters are optimized, and non-anatomical textures are suppressed.

The invention relates to the technical field of defect detection of image recognition, and particularly discloses a mobile phone card holder processing defect detection method and system based on image recognition, and the method comprises the steps: collecting multiple frames of time sequence images, and carrying out the sub-pixel-level space-time registration, thereby eliminating the influence of mechanical vibration and displacement deviation; calculating an optical flow field, extracting a local strain tensor, an optical flow vorticity and a motion consistency entropy, and constructing a time-varying feature matrix; carrying out frequency domain decomposition and motion amplification on the characteristic matrix, and enhancing micro defect signal response; the dynamic time domain features and the static image features are fused, and a defect detection model based on deep learning is established; and finally generating a defect probability cloud picture, and outputting defect types and positions in combination with a dynamic threshold mechanism, so that high-precision and high-efficiency automatic detection of the small defects of the mobile phone card tray is realized, and the method has good stability and adaptability.

The invention relates to a frequency domain adaptive refining and feature decoupling fused target detection distillation method, which comprises the following steps: at a teacher network side, performing adaptive frequency domain decomposition on intermediate features through a learnable multi-wavelet basis selection network, and generating pure teacher de-noising features by using a cross-scale collaborative noise reduction module; on a student network side, generating a frequency domain response diagram by using the features, guiding the student features to be decoupled into geometric and semantic components, and dynamically guiding bidirectional fusion and attention enhancement of multi-scale features to obtain student fusion features; performing cross-scale refined alignment on the two, and training a student network based on the comprehensive distillation loss; and only a lightweight student network is reserved during final deployment. According to the method, the technical problems of feature robustness loss, insufficient multi-scale information fusion, low small target detection precision and the like of the existing knowledge distillation method in lightweight target detection model training can be solved.

The invention discloses a cardiovascular and cerebrovascular disease risk prediction method and system based on multiple modalities, and particularly relates to the technical field of medical prediction.The method comprises the steps that frequency domain decomposition is conducted on all modal signals of standardized time sequence data, and rhythm component time sequence signals corresponding to a preset physiological frequency band are extracted; performing Hilbert transform on the rhythm component time sequence signal to obtain an instantaneous phase of the rhythm component time sequence signal; based on the instantaneous phase difference between different modal rhythm components, stability quantification is carried out to obtain the phase synchronization intensity; constructing a multi-modal physiological rhythm synchronous dynamic network by taking the rhythm components as nodes and taking the phase synchronization intensity as a connecting edge weight; according to the method, through deep mining of multi-physiological-mode time sequence data in a continuous time window, phase synchronization dynamic changes among different-mode physiological rhythms are accurately captured, and the multi-mode physiological rhythm synchronous dynamic network construction and variation connected edge identification are combined; the normal fluctuation of the physiological system and the abnormal signal at the early stage of the disease can be effectively distinguished.

The invention discloses a cervical image classification method and system based on wavelet transform multi-modal fusion, and the method comprises the steps: 1) constructing a multi-branch feature coding network, and respectively processing a cervical OCT image, a colposcope normal saline image, an iodine coating image and an acetic acid white image; 2) introducing wavelet transform to perform frequency domain decomposition on the multi-modal image, obtaining high-frequency and low-frequency components and designing a cross-frequency interaction mechanism to more effectively capture the structure and detail information of the image, and extracting each modal feature through a ResNet-18 backbone network; 3) designing a feature fusion module, performing channel stacking on the three colposcope features, compressing the three colposcope features, and cascading the three colposcope features with the OCT features to form fusion features; (4) feature dimension reduction is achieved through double full connection layers, a weighted voting multi-mode decision classification strategy is adopted, a five-fold cross validation strategy is adopted, and a weighted cross entropy loss function is combined to solve the problem of data imbalance, and (5) finally Softmax classification is carried out.

The invention relates to the technical field of data analysis, and provides an incinerator combustion state analysis method and system based on feature extraction. The method comprises the steps that firstly, a flame monitoring data sequence in the incinerator operation process is collected, and the flame monitoring data sequence comprises flame image data flow and corresponding combustion parameter records; based on the data sequence, flame image imaging optimization data and a synchronous combustion parameter sequence are determined to reflect a combustion parameter change trend. And constructing a combustion characteristic feedback adjustment network, performing frequency domain decomposition on the optimized data and the parameter sequence, generating a flame contour characteristic matrix, and adjusting a characteristic extraction weight through a parameter feedback mechanism. And an energy efficiency ratio entropy index is calculated according to the flame contour feature matrix, a combustion state quantitative model is constructed in combination with the index, the combustion state of the incinerator is analyzed and recognized, and an analysis result representing the combustion uniformity and stability degree is obtained.

The invention provides a virtual power plant demand side response and load management method and system, and relates to the technical field of power system scheduling, and the method comprises the steps: obtaining a demand side response request and a historical response track of each distributed energy source; performing time-frequency domain decomposition on the historical response trajectory, extracting a transient component and a steady component, constructing a dynamic response model containing the current output power and the load deviation cumulant, and calculating load deviation distribution through Kalman filtering; a Markov decision process is constructed based on load deviation distribution, an optimal power distribution strategy is solved by adopting a value iteration algorithm, and a response execution scheme is generated; collecting a power change sequence after execution of each distributed energy source, converting the power change sequence into a load recovery demand vector, constructing a recovery capacity constraint cone based on the current available adjustment margin of the power grid, and solving an optimal projection point; and performing hierarchical division according to the optimal projection point to obtain a staged recovery sequence. According to the method, the accuracy of virtual power plant demand response and the coordination of load recovery are improved, and the power grid regulation pressure is reduced.

The invention discloses a small target detection system in a complex construction scene, which belongs to the technical field of target detection and is characterized in that a standardized image is acquired through an image acquisition and preprocessing module, and a target is detected by using an improved YOLO11 model. The improved YOLO11 model divides feature information into high and low frequency features through frequency domain decomposition and performs enhancement processing on the high and low frequency features; the edge and texture of the high frequency features are enhanced through expansion convolution and a channel attention mechanism, and semantic perception of the low frequency features is improved through multi-scale convolution and an adaptive gating mechanism. And the enhanced features are fused through an SE attention mechanism, so that the small target detection capability is improved. The system is also provided with a scale adaptive selection module which can dynamically adjust a detection strategy according to a target size to meet different detection requirements. According to the method, the problems of high false alarm and missing detection of small target detection in a complex construction scene are effectively solved, the detection precision and robustness are improved, and the method has important practical application value.

The invention discloses a variable-speed working condition rolling bearing fault diagnosis method based on improved wavelet packet transformation and envelope order tracking, and the method specifically comprises the steps: carrying out the equal-angle resampling of a vibration signal according to the phase of a rotating shaft, and carrying out the wavelet packet transformation of an angle domain vibration signal through an improved wavelet packet transformation algorithm, and finally, fault diagnosis is carried out according to the transformed envelope order spectrum of the wavelet packet coefficient of each node. According to the method, time domain signals are converted into angle domain signals through equal-angle resampling, frequency spectrum fuzziness caused by rotating speed changes is eliminated, and the angle domain signals are divided to different order bands through the excellent frequency domain decomposition capacity of an improved wavelet packet transformation algorithm so that sub-signals with the high signal-to-noise ratio can be obtained. And finally, solving the envelope spectrum of the sub-signal to further highlight the fault characteristics. According to the method, the problem of difficulty in fault diagnosis of the variable-speed working condition bearing is solved, meanwhile, the process of determining a high-frequency resonance frequency band by means of a kurtosis graph and extracting a high-frequency resonance signal through band-pass filtering is avoided, and the diagnosis method is simple, efficient and high in accuracy.

The invention relates to the technical field of cable manufacturing, in particular to a tension fluctuation suppression and pitch self-adaption method and system in the stranding process of multiple strands of wire harnesses. According to the method, real-time tension signals and equipment state parameters of each strand are obtained, tension fluctuation feature vectors are obtained through frequency domain decomposition, fluctuation source types and position mapping relations thereof are recognized accordingly, and a tension disturbance distribution diagram is generated. And generating a tension compensation instruction for each pay-off mechanism and a pitch adjustment instruction for the twisting main shaft based on the distribution diagram and the target equilibrium constraint, and performing adjustment. Feedback data is collected and used for updating processing parameters and optimizing an instruction generation strategy; according to the invention, accurate suppression of tension fluctuation and self-adaptive adjustment of the stranding pitch are realized, and the uniformity and stability of wire harness stranding are improved.

The invention discloses a voltage circuit breaker AC open circuit control method and device, and relates to the technical field of open circuit control, and the method comprises the following steps: obtaining a target waveform sequence of a target AC circuit, and obtaining a fundamental component and a higher harmonic component through frequency domain decomposition, so as to extract current waveform features; constructing a zero-crossing prediction model based on current waveform characteristics, dynamically correcting the prediction model in combination with higher harmonic components and load asymmetry characteristics, and generating a preliminary breaking moment candidate interval; determining a final on-off moment by combining the instantaneous power direction reversal point and an arc maintenance energy threshold value; and triggering the circuit breaker to act at the final breaking moment, and dynamically adjusting the contact separation speed based on the residual arc energy until the arc energy is attenuated to a safety value. According to the method, the zero-crossing prediction model is constructed by extracting the current waveform characteristics, the contact separation strategy is dynamically corrected, and the problems of arc extension and equipment damage of a traditional circuit breaker under the condition of harmonic distortion or sudden disturbance are solved.

The invention provides a new energy station power climbing risk assessment and scheduling optimization method, which comprises the following steps: acquiring historical power data and real-time monitoring data from a plurality of new energy stations, and extracting a first power climbing feature matrix by adopting a time-frequency domain decomposition algorithm; analyzing power climbing coupling strength among different stations according to the first power climbing characteristic matrix, and generating a first coupling effect distribution diagram; multi-dimensional meteorological parameters are obtained, a power climbing prediction model is constructed by combining the first coupling effect distribution diagram, and a first extreme weather prediction result is generated; according to the first extreme weather prediction result, a sliding time window technology is adopted to evaluate a power climbing risk, and a first early warning signal is generated; according to the first early warning signal, obtaining power grid topology structure data, analyzing key nodes, and generating a first power grid topology constraint matrix; and generating a first new energy consumption scheduling scheme according to the first new energy output distribution scheme in combination with the capabilities of an energy storage system and a conventional unit.

The invention discloses an ultrasonic image boundary perception segmentation method, system and device, and relates to the field of image processing, and the method comprises the steps: obtaining an ultrasonic image, and sequentially processing the ultrasonic image through a block embedding module and an image coding module to obtain spatial domain features; performing frequency domain feature extraction processing on the spatial domain feature by using a preset multi-scale frequency extraction strategy to obtain a fine-grained high-frequency feature, a coarse-grained high-frequency feature and a low-frequency feature, and obtaining a frequency fusion feature in combination with a preset frequency alignment strategy; a preset frequency guide boundary refining strategy is combined to determine refining features; and according to the refined features and a preset boundary guiding decoding strategy, determining a segmentation mask representing a result of segmenting the target region under the ultrasonic image from the background region. A low-frequency structure and multi-scale high-frequency boundary details are explicitly separated through frequency domain decomposition, an ultrasonic image boundary sensing segmentation scheme based on frequency guidance is provided, the boundary sensing ability is high, and the cross-domain generalization ability is high.

The invention relates to the technical field of medical detection and monitoring, in particular to a neurology patient sleep disorder monitoring method, which comprises the following steps: S1, collecting and preprocessing body signals of a patient; s2, constructing a cross-modal association graph, and forming a heterogeneous graph structure with a time sequence weight and a modal label; s3, performing graph convolution and sparse attention operation on the cross-modal association graph, and executing node screening and edge weight updating; s4, time domain and frequency domain decomposition is carried out, and alignment is carried out based on time and frequency indexes; s5, performing segmentation and boundary calibration on the feature sequence, dividing sleep stages, and indexing and storing cross-modal fragments; s6, retrieving abnormal stage fragments, constructing abnormal feature sub-graphs and executing structure clustering operation; and S7, mapping the sleep disorder category labels and the cross-modal features to generate associated features and output results. According to the method, the cross-modal heterogeneous graph is constructed and the time-frequency features are fused, so that the recognition precision and the analysis depth of the sleep disorder of the patient in the neurology department are improved.