1200 results about "Spectral density" patented technology

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention relates to the technical field of information transmission, in particular to a multi-band processing system for electronic communication, which comprises a signal separation module, a frequency deviation compensation module, an amplitude correction module, a phase correction module and a dynamic feedback adjustment module. According to the invention, a plurality of frequency bands are extracted to execute band-pass filtering to separate signal components, short-time power spectral density is calculated to describe energy distribution, normalization coefficient operation is carried out to enhance frequency domain balance, high-energy component screening is carried out to reduce noise interference, signal effectiveness is improved, and frequency offset compensation calculates a change rate based on adjacent phase data and eliminates abnormal fluctuation. A time domain coefficient is adjusted to correct a frequency offset error, amplitude correction depends on short-time Fourier transform to screen the maximum amplitude to optimize signal consistency, phase adjustment fits a group delay error curve to optimize compensation parameters, dynamic feedback optimizes a strategy according to a signal-to-noise ratio to enhance compensation precision, and signal transmission quality and stability are improved.

The invention relates to a method and a system for realizing channel characteristic simulation of a wireless channel model of an unmanned ship based on an unmanned aerial vehicle. The method comprises the following steps: constructing the wireless channel model of the unmanned ship by the unmanned aerial vehicle; the unmanned ship wireless channel model comprises the steps of generating a channel impulse response, generating an unmanned aerial vehicle random track, generating an unmanned ship random track, generating an evaporation waveguide model, generating an evaporation waveguide model, generating small-scale parameters and updating the small-scale parameters; calculating channel statistical characteristics; the channel statistical characteristics comprise time delay power spectrum density, a space-time correlation function, a stationary interval and root-mean-square time delay extension. The method can better correspond to a track in a real environment, and the randomness of the track is realized.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention relates to the technical field of gas extraction drilling machine vibration signal processing, in particular to a gas extraction drilling machine vibration signal self-adaptive noise reduction method based on multi-scale feature fusion. The method comprises the steps of collecting a vibration signal, converting the vibration signal into a two-dimensional waveform image, recognizing a drilling working condition area, constructing a multi-scale objective function to optimize VMD parameters, decomposing the signal and screening a dominant IMF component for reconstruction. Through multi-objective optimization and multi-feature fusion strategies of energy distribution and transient impact characteristics, the problems that in a traditional method, the noise reduction effect is poor, and the coal rock character recognition precision is insufficient are solved, the waveform similarity, the power spectrum density coincidence degree and the correlation coefficient are remarkably improved, and reliable guarantee is provided for coal mine safety production.

Systems, methods, and other embodiments associated with ML-based detection of amplification of vibration due to resonance are described. In one embodiment, a method includes recording vibrations of a reference asset while the reference asset is operated based on a test pattern that sweeps over a range of workload for the reference asset. Cross power spectral densities between the recorded vibrations and the test pattern are determined at intervals to identify resonance frequencies of the reference asset. Vibrations of a target asset are monitored at the resonance frequencies with a machine learning model trained to generate estimated values at the resonance frequencies that are consistent with the reference asset. Resonant vibration amplification is detected based on a dissimilarity between vibration values for the target asset at the resonance frequencies and the estimated values. And, an electronic alert that the target asset is undergoing the resonant vibration amplification is generated.

The invention discloses a bridge cable force real-time tracking method based on peak value automatic extraction, and aims to solve the problem that the manual extraction of real-time and batch power spectral density map peak values is tedious and difficult, and the method comprises the steps: firstly converting an acceleration signal collected by a sensor into frequency domain information through fast Fourier transform; then performing data preprocessing on the power spectral density map to ensure the peak value extraction accuracy; peak values in the power spectral density map are preliminarily extracted based on a data maximum value searching method, then the minimum saliency, the minimum height and the minimum spacing of the peak values are limited to eliminate false peaks, and resonance peaks in the power spectral density map and corresponding frequencies and frequency intervals of the resonance peaks are recognized; on the basis of a modal analysis method and a vibration string theory, cable force tracking is finally realized and is used for guiding subsequent bridge structure evaluation work.

The invention discloses a signal identification method based on broadband spectrum detection, and the method comprises the steps: carrying out the power spectrum estimation and signal detection of broadband IQ data through an FPGA, obtaining a signal frequency point parameter, and configuring a DDC module to complete digital down conversion; performing power spectrum density estimation and noise floor estimation on the narrowband IQ signal on the upper computer; preliminary classification is carried out by calculating a ratio parameter C1 of a maximum value and a second maximum value of a frequency spectrum, and when C1 is greater than a threshold, AM and a single carrier signal are discriminated by combining a frequency spectrum characteristic parameter C2 and an amplitude spectrum characteristic parameter C3; when C1 is smaller than or equal to a threshold, signal detection is carried out by adopting a double-threshold algorithm, and merging and screening in a multi-signal scene are completed; classifying based on a normalized zero center instantaneous amplitude spectrum density maximum value parameter C4, judging FM, MSK and FSK signals when the C4 is less than a threshold, and judging BPSK, QPSK, 8PSK and 16QAM signals when the C4 is greater than or equal to the threshold; according to the method, a multi-parameter joint judgment strategy is adopted, and the accuracy and the real-time performance of signal recognition are remarkably improved.

The invention provides a model and data driven low-orbit navigation enhanced satellite clock error forecasting method and equipment, and the method comprises the steps: carrying out the preprocessing of original clock error data of a low-orbit satellite: converting clock error time domain data into frequency domain data through time-frequency conversion, and employing a quartile method to recognize and eliminate frequency domain outliers and corresponding time domain abnormal values; constructing a frequency domain energy model and extracting periodic terms, including performing power spectral density analysis on the preprocessed clock error frequency domain data, and adaptively extracting significant periodic components of the low earth orbit satellite clock error through a threshold value; establishing a polynomial low earth orbit satellite clock error forecasting model considering periodic term correction and forecasting a clock error sequence to obtain a corresponding fitting residual error sequence and a clock error forecasting value; and normalizing the fitting residual error sequence, inputting the fitting residual error sequence into the gating circulation unit neural network, training and forecasting by adopting a window sliding input mode, and outputting a residual error prediction result. According to the method, the interpretability of the forecasting result can be effectively improved while the low earth orbit satellite clock error forecasting precision and stability are improved.

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

The invention belongs to the field of electroencephalogram emotion recognition, and provides an emotion recognition method and system for a multi-scale convolutional network based on bidirectional attention, and the method comprises the steps: segmenting an electroencephalogram signal according to a time window, extracting differential entropy (DE) and power spectral density (PSD) features, introducing an electroencephalogram map to embed spatial information, and capturing a spatial dependency relationship in the signal. Then, the preprocessed features are input into an MSA-TCN model, and the model combines the attention in the bidirectional features, the attention between the bidirectional features, a multi-scale convolution network and a time sequence convolution network to effectively extract dynamic time features and cross-channel spatial features; the invention aims to overcome the limitation of the prior art in emotion recognition through an innovative bidirectional attention mechanism and a multi-scale convolutional network structure (MSA-TCN).

The invention discloses an intelligent-based abnormal carbon emission data automatic identification early warning system, which belongs to the technical field of intellectualization and comprises a data acquisition preprocessing module, a high-precision space-time analysis module, an intelligent analysis module, an automatic abnormal identification module, a carbon footprint tracing module, a self-adaptive adjustment module and an intelligent emission prediction module. A carbon emission source and time-space distribution characteristics are accurately positioned through spatial positioning and time sequence analysis of the high-precision time-space analysis module, a spatial distribution diagram is more detailed and accurate through the optimized sensor position and an interpolation algorithm, and time sequence analysis is more accurate through timestamp correction and power spectrum density analysis. The method effectively evaluates the periodic intensity of the signal, analyzes a hidden mode and an association rule in the data through the intelligent analysis module integrating the spatial-temporal characteristics and the related information of the data acquisition and preprocessing module, analyzes the spatial-temporal association between variables through the calculation of a clustering center and the association intensity, and facilitates the discovery of a potential abnormal mode.

The invention provides a long-span bridge pneumatic admittance identification method and device based on operation measured data, and relates to the technical field of bridge safety, and the method comprises the steps: building a generalized motion equation of a long-span bridge under the action of buffeting force; establishing a bridge finite element model, and updating the bridge finite element model through operation measured data to obtain an optimal bridge finite element model; calculating a fluctuating wind coherence function of the large-span bridge girder; obtaining a three-component coefficient, a three-component force and a generalized buffeting force of the long-span bridge at different wind attack angles based on the optimal bridge finite element model; performing frequency domain analysis on the generalized motion equation to obtain simulated power spectral density of each point of the large-span bridge girder; and based on the simulated power spectrum density, identifying pneumatic admittance parameters in a preset pneumatic admittance function, and completing pneumatic admittance identification of the long-span bridge. According to the method, the problem that the pneumatic admittance of the long-span bridge in the operation period cannot be identified by an existing method is solved.

The invention discloses a continuous attention neural feedback training method and system based on a brain-computer interface, and relates to the technical field of neural feedback, and the method comprises the steps: collecting a multi-channel electroencephalogram signal of a user in visual task training in real time; extracting power spectral density characteristics of the multi-channel electroencephalogram signals in a beta frequency band, classifying the power spectral density characteristics by adopting a support vector machine algorithm, and outputting a judgment result of an alert or non-alert state; and according to a judgment result, dynamically adjusting an information fusion proportion alpha value in the visual task through a reward-punishment mechanism, updating image information feedback in the visual task in real time, and adjusting the attention state of the user through an image information feedback result. Neural feedback and a dynamic reward and punishment system are fused, real-time excitation feedback is obtained by autonomously adjusting electroencephalogram activity, the problem of insufficient training power caused by traditional static tasks or single positive feedback is solved, and the long-term training effect is enhanced.

The invention provides a power distribution network single-high-frequency resonance rapid tracking and adaptive suppression method, a multi-mode dynamic cooperative adaptive suppression system is established based on a PCCVF adaptive method, and the damping characteristics of a power distribution network are remodeled by injecting compensation current into a power distribution network system so as to realize broadband resonance suppression of the power distribution network. The compensation current injection method comprises the following steps: step 1, detecting resonant frequency deviation of a power distribution network through real-time FFT (Fast Fourier Transform); 2, performing dynamic phase angle correction based on a phase prediction residual error of real-time frequency deviation; step 3, constructing a layered impedance remodeling module, and ensuring stable power transmission; 4, establishing a frequency-variable impedance model of the power distribution network based on resonance energy spectral density analysis, dynamically optimizing parameters through fuzzy logic of a bell-shaped membership function, and feeding harmonic compensation current into the power distribution network by using a space vector pulse width modulation technology; according to the invention, single high-frequency resonance can be effectively suppressed, the response and tracking performance of the system is improved, and the spectrum analysis efficiency and bandwidth occupation are optimized.

The invention discloses a scraping surface morphology digital simulation method and system, and the method comprises the steps: 1, measuring a scraping surface, and obtaining the three-dimensional morphology data of the scraping surface; 2, calculating an overall surface parameter, a probability material ratio curve and an autocorrelation function of the three-dimensional shape data of the scraping surface; 3, obtaining three groups of surface height characteristic parameters according to the probability material ratio curve, generating a Gaussian surface by taking the parameters as input, and selectively superposing the Gaussian surface to obtain a surface with specified height characteristics; 4, calculating the power spectrum density of the scraping surface morphology through the autocorrelation function of the three-dimensional morphology data of the scraping surface; 5, generating a surface with a specified self-correlation function by taking the surface with the specified height feature and the power spectral density of the scraping surface morphology as input; step 6, reordering the surface with the specified height feature according to the sequence of the surface with the specified autocorrelation function so as to update the surface with the specified height feature; and 7, the step 5 and the step 6 are repeated until convergence is achieved, and the scraping surface with the specific height characteristic parameters and the specific space characteristic parameters is obtained. The method is suitable for digital simulation of scraping surfaces and multi-process surfaces.

The invention relates to the technical field of electric power information communication, and discloses a parameter configuration driving type HPLC communication unit cooperation system which optimizes the transmission efficiency and anti-interference performance of power line carrier communication through an intelligent matching mechanism of a dynamic parameter configuration table and HPLC communication conditions. Comprising an HPLC hardware module, a multi-dimensional parameter configuration table module, an intelligent matching algorithm module and a feedback control module, and supports the functions of working band dynamic switching, power spectral density adaptive adjustment and anti-interference parameter real-time optimization. The problems that in traditional HPLC communication, the frequency band is fixed, the anti-interference capacity is insufficient, and parameter adjustment depends on manual work are solved, the communication reliability and the data collection efficiency in the complex power grid environment are remarkably improved, and the method can be widely applied to scenes such as an intelligent power grid, electric energy metering and remote cost control.

The invention belongs to the technical field of signal processing. According to the signal mode decomposition method and system for early fault diagnosis of the rotating machinery, vibration signals are decomposed through an MCKD algorithm, a characteristic mode set of the signals is obtained, fusion is carried out according to correlation coefficients of a cross-correlation spectrum of the modes and a mode envelope power spectrum density, and finally a characteristic mode representing a fault is extracted. According to the weak fault feature signal extraction method, the feature modes are extracted by using the fault period, the feature modes are fused on the aspects of time domain and frequency domain features, the sensitivity to complex noise is low, the weak fault feature signal extraction capability is high, the precision is high, and more efficient and accurate weak fault feature signal extraction is achieved.

The invention discloses an elderly psychological state assessment early warning method and system, and relates to the technical field of elderly psychological health monitoring, and the method comprises the steps: collecting electroencephalogram signals of an elderly assessment object in a daily fixed time period, calculating the power spectral density ratio of the electroencephalogram signals, and constructing a personal reference feature set of the elderly assessment object; obtaining a current electroencephalogram signal of the aged evaluation object, calculating a power spectral density ratio of the current electroencephalogram signal, comparing the power spectral density ratio with the personal reference feature set, and determining a short-term fluctuation amplitude and an accumulated deviation amount; and judging the abnormal degree of the psychological state according to the short-term fluctuation amplitude and the accumulated deviation, and generating a graded early warning signal containing a risk grade and a timestamp. According to the invention, the detection accuracy and early warning timeliness of the abnormal psychological state of the elderly are improved, and a further solution is provided for psychological health management of the elderly.

The invention provides a method for testing flow-induced noise of a pipeline. The method comprises the following steps: S1, determining measurement positions at an upstream flow channel and a downstream flow channel of a measured element, carrying out normalization processing on measured signals, and calculating the pressure power spectrum density of the inner wall surface of a pipe; s2, calculating a wall surface pressure fluctuation time domain cross correlation coefficient, and analyzing correlation characteristics between wall surface pressure pulsation; s3, spectrum intrinsic orthogonal decomposition is carried out on the pressure pulsation field, and space-time decoupling is achieved through time domain-frequency domain conversion; s4, extracting a dominant flow-induced noise mode, constructing an empirical frequency-wave number spectrum, and decomposing fluctuating energy distribution into contributions of different frequency and wave number combinations; s5, analyzing the decoupled flow-induced noise characteristic result, and identifying the amplitude-frequency characteristic of the flow-induced noise; according to the invention, real-time measurement of pressure pulsation in the pipeline can be realized, decoupling analysis of flow field pulsation and sound pressure pulsation can be carried out on the measured signal, and characteristics and a generation mechanism of pipeline flow-induced noise can be identified.

The invention discloses a modal parameter identification method based on a long short-term memory neural network, belongs to the field of artificial intelligence, system identification and structural health monitoring, and aims to improve the precision of modal parameter identification by using the neural network. The method comprises the following steps: firstly, acquiring response data of a structure model after excitation, preprocessing the response data to obtain a sample set, and dividing the sample set into a training set and a test set; then, a neural network based on long and short term memory is constructed, wherein the neural network is mainly composed of an encoder (MLP), an LSTM feature extractor, a decoder (MLP) and a linear reconstructor; training the neural network by using the training set data until the loss function converges, identifying by using the test set data, and finally obtaining the natural frequency of the structure through the power spectral density analysis of the obtained data. According to the method, the modal frequency identification precision is remarkably improved, and an efficient solution is provided for intelligent health monitoring of a complex structure.

This disclosure provides systems, methods, and apparatuses for wireless communication. An example apparatus selects a resource unit (RU) for a physical (PHY) layer convergence protocol (PLCP) protocol data unit (PPDU). The selected RU includes a set of contiguous tones spanning a bandwidth. The apparatus maps the set of contiguous tones to a set of non-contiguous tones distributed across the frequency spectrum, and transmits the PPDU over the set of non-contiguous tones. Another example apparatus selects an RU of a group of RUs that collectively span a frequency spectrum, and formats a PPDU based on a first frequency bandwidth wider than the selected RU's bandwidth. The apparatus parses the contiguous tones of the selected RU to a set of non-contiguous tones spanning a unique segment of a second frequency bandwidth wider than the first frequency bandwidth, and schedules a transmission of the PPDU over the set of non-contiguous tones.

The invention discloses a heart sound feature extraction, decomposition, reconstruction and quantification method which is characterized by comprising the following steps: S1, collecting heart sound signals; s2, carrying out noise reduction pretreatment; s3, carrying out wavelet decomposition and reconstruction on the heart sound signals; s4, calculating signal power spectral density and normalizing an energy spectrum; s5, calculating a signal energy entropy value; and S6, analyzing the correlation between the energy entropy and the defect size. According to the method, visualization of the heart sound digitized signals is achieved through multiple steps, the heart sound digitized signal decomposition and reconstruction method is provided, the waveform distribution condition of the recombined heart sound is visualized, the accuracy is high, and the reliability is high.

The invention discloses a forehead electroencephalogram rhythm-driven sleep state staging method based on multi-dimensional features, and belongs to the field of electroencephalogram signal processing in the field of digital signal processing, and the method comprises the following steps: step 1, preparing a sleep physiological signal data set; 2, preprocessing the forehead electroencephalogram signals; 3, rhythm wave extraction is conducted on the forehead electroencephalogram signals; 4, time domain feature values, feature values based on power spectral density and feature values based on multi-scale entropy of the forehead electroencephalogram signals and the electrooculogram signals are calculated respectively; step 5, sending the characteristic values calculated in the step 4 into an SVM (Support Vector Machine) for automatic staging, and finally optimizing a characteristic value group to obtain an automatically predicted sleep staging result; the method has high accuracy, can provide visual feedback for the user, and helps the user to know the sleep quality and state of the user so as to improve the sleep habit of the user.

The invention discloses a ship equipment vibration test method and system based on frequency domain kurtosis mapping and non-Gaussian signal generation. The ship equipment vibration test method comprises the following steps: collecting an actual ship impact vibration signal and extracting a target power spectral density curve; the method comprises the following steps: identifying an impact energy concentration frequency band based on spectrum energy distribution, carrying out frequency band division by adopting an elliptical filter bank with a second-order section structure, and calculating an envelope kurtosis value of each frequency band to establish a kurtosis-frequency mapping model; after the initial Gaussian driving signal is generated, a non-Gaussian driving signal is generated through a phase randomization and amplitude dynamic scaling algorithm based on a mapping model; a multi-target optimization controller based on Pareto leading edge search is adopted, a power spectral density error and a kurtosis error are taken as optimization targets, synchronous optimization is carried out through fast non-dominated sorting and an adaptive hybrid variation strategy, and a driving signal is output. According to the invention, the broadband and non-Gaussian impact vibration environment of the ship equipment in the actual severe sea condition can be reproduced in a high-fidelity manner, and the authenticity of the test and the fault mode coverage capability are remarkably improved.

The invention discloses a high-order random wave representation method for a non-Gaussian wind field, and the method comprises the following steps: S1, deducing a frequency-wave number spectrum of a random wave based on a target power spectrum density and a theoretical coherence function model gamma (xi, omega); s2, defining frequency-wave number three spectrums according to the frequency-wave number spectrums, and obtaining a biased three-coherence function of high-order random waves; s3, calculating a pure frequency-wave number spectrum through a partial three-coherence function; s4, performing Fourier-Stetius integral expansion on the random waves, decomposing an orthogonal increment process, and establishing a new orthogonal increment process according to a pure frequency-wave number spectrum and a pure frequency-wave number three-spectrum; and S5, substituting the established orthogonal increment into the Fourier-Stetius integral expansion of the random wave in the step S4 to obtain the high-order spectrum representation of the non-Gaussian random wave. The invention further discloses a non-Gaussian wind field simulation method and system and a storage medium.

The invention relates to the technical field of sensor measurement, in particular to an intelligent water area virtual electronic fence based on acousto-optic-electric fusion. The system specifically comprises a water area sensor fusion network, a water area interface fluctuation feature extraction unit, an intrusion probability prediction unit and a self-adaptive alarm response unit. The water area sensor fusion network is used for capturing a sound wave signal, an optical signal and an electromagnetic signal in a target water area, and performing dynamic attenuation correction according to physical characteristics of water to obtain a fusion sensing result; the water area interface fluctuation feature extraction unit is used for extracting fluctuation energy spectrum density related to water disturbance at each frequency; the intrusion probability prediction unit is used for calculating the current anomaly metric and predicting the intrusion probability of each position in the target water area; and the adaptive alarm response unit is used for calculating the current alarm intensity of each position and executing an early warning strategy. According to the invention, high-probability intrusion threats can be rapidly identified and amplified.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.