835 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 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.

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 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 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.

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 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 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.

The invention discloses an epilepsy region positioning method and system based on brain power supply imaging and a dynamic brain network, and the method comprises the steps: obtaining T1 weighted magnetic resonance imaging data of a user, and constructing an individual three-dimensional head model through a boundary element method; acquiring scalp electroencephalogram data of a user, and preprocessing the scalp electroencephalogram data; based on an individual three-dimensional head model, performing inverse problem solving on the preprocessed scalp electroencephalogram data by using a standardized low-resolution brain power source imaging algorithm to obtain source current density signals of 68 brain regions; decomposing into six frequency bands, calculating the power spectrum density of each brain region and carrying out normalization processing, and screening effective frequency bands; based on the source current density signals of the 68 brain regions of the effective frequency band, information flow directions and intensities of different brain regions are calculated by adopting a directional transfer function method, a directional transfer function matrix of the effective frequency band is formed, and a directed brain network is constructed; and calculating a graph theory index and / or an epilepsy index of each brain region, carrying out maximum value normalization analysis, and determining an epilepsy region positioning result.

The invention discloses an encrypted communication system and method based on cognitive multi-carrier spread spectrum, and belongs to the field of covert communication. The encryption communication system comprises a transmitting device and a receiving device. The transmitting device comprises a data encryption module, a constellation encryption modulation module, a multi-carrier spread spectrum module, a frequency hopping module and a cognitive power distribution module. The receiving device comprises a band-pass filtering module, a de-hopping frequency module, a de-spreading module, a constellation decryption demodulation module, a data decryption module and a coherent combination module. The data encryption module is divided into an interleaving coding unit and a double-rule reversible cellular automaton encryption unit. The cognitive power distribution module continuously scans a wireless electromagnetic environment and carries out real-time spectrum analysis to give power spectrum density results sensed under all frequency hopping frequency sets. According to the invention, continuous detection and dynamic response to the spatial frequency spectrum state can be realized, and the communication concealment, the anti-interference performance and the resource utilization efficiency in a complex electromagnetic environment are remarkably improved by utilizing the frequency spectrum hole.

The invention provides an electromyographic signal fatigue detection method based on time window analysis, and the method comprises the steps: carrying out the preprocessing of collected electromyographic signals, and carrying out the self-adaptive time window segmentation based on a sliding overlapping mechanism; performing spectral analysis on each time window signal by using a Wilch method, and extracting frequency domain features such as median frequency, average power frequency and power spectral density ratio; a normalized composite fatigue index is constructed, and smoothing processing is carried out through multi-time scale moving average; finally, a self-adaptive multi-level threshold judgment mechanism is introduced, and fatigue state level recognition and dynamic updating are achieved. The fatigue evolution process can be continuously and quantitatively reflected, and the practicability and the intelligent level of electromyographic signal analysis under the scenes of training monitoring, man-machine work efficiency, rehabilitation evaluation and the like are improved.

The invention relates to the technical field of motor fault diagnosis, and discloses a multi-degree-of-freedom resonant excitation motor dynamic vibration test system and a test method, the system injects a specific harmonic current into a motor through closed-loop control and calculation and a high-bandwidth harmonic injection driver, so that the actual vibration of the tested motor tracks a preset target vibration profile in real time. In the testing process, the cross-spectral density of multi-point vibration signals is analyzed, and a transmission path and a collection area of vibration energy in a motor structure are calculated; and meanwhile, the electromagnetic impedance and the mechanical impedance of the motor are identified and decoupled on line by superposing a broadband disturbance signal in the driving current. In combination with energy flow analysis and an impedance decoupling result, the method can accurately position a vibration abnormal position, clearly causes whether a fault source is from mechanical structure characteristic change or electromagnetic part performance change from a physical level, and realizes deep and accurate diagnosis of the dynamic characteristics of the motor.

The invention relates to the technical field of signal reconstruction, in particular to a complex electromagnetic environment-oriented millimeter wave signal blind source separation and reconstruction system, which comprises a frequency spectrum trend division module, a path fading construction module, an initial cluster label generation module, a multi-solution path screening module and a fusion reconstruction execution module. According to the method, power spectral density sequence processing is carried out on millimeter wave frequency domain data, a multi-dimensional feature group is formed according to a path loss factor, an angle of arrival and the like, and an initial feature cluster is screened through an Euclidean distance, so that the accuracy of signal source classification is effectively improved; after the frequency domain response and the phase contour are continuously subjected to point comparison, path screening is completed by integrating a mean square error residual value, the path misjudgment probability is reduced, time domain resampling and phase frequency offset standardization are executed in fusion reconstruction, the consistency and fidelity of signal reconstruction are enhanced, and the reconstruction precision is improved. The whole process improves the separation accuracy and reconstruction precision of mixed signals in a complex electromagnetic environment.

The invention discloses a pipe gallery concrete lining vibration quality monitoring method and system, and relates to the technical field of pipe gallery concrete lining construction monitoring, and the method comprises the steps: arranging a three-way vibration sensor network at a lining reinforcing mesh key node according to a pipe gallery structure cross-section diagram; acquiring original data of the three-way vibration sensor in real time, and preprocessing the original data to generate standardized vibration characteristic data; based on power spectrum density analysis, establishing a correlation model of the vibration characteristic data and vibration energy so as to calculate the vibration energy value of each area in the vibration process; according to a concrete mix proportion and a vibrator frequency parameter, establishing an association relationship among vibration energy, compactness and a bubble rate so as to calculate the compactness and the bubble rate; and comparing the calculated values of the compactness and the bubble rate with respective set reference values, triggering a dynamic regulation and control mechanism according to a comparison result, and generating a vibration quality report. And the efficiency and reliability of pipe gallery concrete construction are effectively improved.

The invention relates to a red light therapeutic instrument real-time calibration method based on multi-modal data fusion, which comprises the following steps: firstly, collecting data such as optical power, target surface temperature, internal temperature, reflection spectrum and light source-target surface distance in a time window, and unifying time base alignment; performing anomaly elimination, de-noising and normalization on each modal data, and constructing a standardized data matrix; extracting multi-modal features through power spectral density, wavelet packet decomposition, exponential moving average and Kalman filtering, inputting the multi-modal features into a fusion network containing a modal special encoder and a cross-modal attention unit, and generating a red light treatment state vector; the driving current, the pulse width, the duty ratio, the emission angle and the aging compensation factor are solved in real time through constrained least square optimization and are issued to the driving control module, and the red light source is smoothly adjusted in a soft start mode; the system monitors the output power, calibration is completed if the output power meets a threshold value, and otherwise, the next iteration is started. According to the method, power closed-loop correction can be realized, and dose consistency and thermal safety are improved.

The invention provides an emotion analysis method based on a multi-modal comparative learning individual focusing model. The method comprises the following steps: collecting data; feature extraction is conducted on the preprocessed data, and electroencephalogram signal feature differential entropy and power spectrum density are obtained; constructing a multi-modal individual focusing comparison network architecture, and performing time-frequency domain feature learning by using an individual focusing network; performing feature fusion on the middle features extracted by the modules in each branch by using a multi-modal relation calculation fusion mechanism; calculating the difference loss from different individual features and the comparison loss from the data set by using the individual domain and sub-domain equilibrium comparison loss; and the comparison loss is added into the model training loss for training, the test set is sent to the trained network for prediction, and an emotion classification result is obtained. The method can effectively improve the discrimination capability of the emotion recognition model and the final classification accuracy.

The invention provides a voice enhancement and high-precision recognition method and system in a complex environment, and relates to the technical field of voice processing, and the method comprises the steps: collecting a time domain signal in an off-road parking sentry box environment for preprocessing, detecting a mute segment signal in a standard time domain signal for noise power spectral density estimation, and obtaining a noise power spectral density value; a reverberation parameter is obtained by combining voice onset information and noise spatial correlation estimation, prediction is performed by using a deep neural network model, voice masking is applied to microphone array signals to perform enhancement processing, adaptive feature extraction is performed on time domain enhanced voice signals, and a voice signal is obtained. And performing high-precision recognition on the voice adaptive feature sequence based on an acoustic model and a language model, and outputting a target recognition text. The technical problems of poor voice signal quality and low recognition accuracy in a complex noise environment in the prior art are solved. The technical effects of improving the voice signal quality and the recognition accuracy and realizing clear, accurate and real-time voice interaction are achieved.

The invention provides a water body motion state recognition method and device based on spectral moment characteristics and a readable storage medium thereof, and belongs to the technical field of hydrographic survey and intelligent analysis. According to the method, an acoustic Doppler flow meter is used for collecting a flow velocity sequence, the flow velocity sequence is converted into frequency domain power spectral density through short-time Fourier transform, multi-dimensional features including zero-order to fourth-order spectral moments, frequency band energy, statistics and spectrum features are extracted, a feature time heat map is constructed and input into a multi-scale residual convolutional neural network, and the multi-scale residual convolutional neural network is obtained. And identification of laminar flow, turbulent flow and backflow is realized. The method accurately depicts essential differences of three types of flow states through frequency domain features, retains weak signals in combination with multi-scale convolution kernel and residual connection, solves the problems that a traditional method is poor in interpretability and insensitive in distinguishing, and has the advantages of being high in recognition accuracy, high in robustness and suitable for multiple scenes such as rivers and dams.

The invention relates to the technical field of seismic data analysis, in particular to a seismic station ground motion noise quantification method and related equipment based on a GPU (Graphics Processing Unit), and the method comprises the steps: firstly obtaining waveform record data of a target seismic station in a set time period; performing sliding window segmentation processing on the waveform recording data in parallel by utilizing a GPU (Graphics Processing Unit) to obtain a plurality of waveform recording sub-segments; preprocessing the data of the plurality of waveform recording sub-segments in parallel through a GPU (Graphics Processing Unit); finally, the power spectrum density of the ground motion noise in the preprocessed sub-segments is calculated in parallel through the GPU; and carrying out parallel statistics on a power spectrum density probability density function result based on the power spectrum density by utilizing a GPU (Graphics Processing Unit) to complete the quantitative calculation of the earth motion noise. According to the method, the data processing efficiency is improved based on the parallel computing capability of the GPU, hardware computing resources are fully called, and the earthquake noise quantification of the seismic station record can be efficiently realized. The whole method is based on a CUDA programming model and a C language, cross-platform transplantation is easy, and the large-scale seismic network monitoring capability can be effectively improved.

The invention relates to the field of drilling ship manufacturing, in particular to a data acquisition method and system of a driver for oil exploitation. Six-degree-of-freedom motion data of a ship motion reference unit, pressure data of a hydraulic cylinder of a marine riser tensioner, hook load data of a drill string hung by a top driving system, torque data and radial acceleration data are synchronously collected, and collected multiple paths of signals are aggregated to form synchronous multi-source vibration data flow. According to the method, multiple types of vibration, load and pressure signals are synchronously collected and aggregated and segmented under the unified time reference in the operation process of the drilling ship, so that the consistency of multi-source data on the time scale is ensured, and the real dynamic coupling relation can be reflected by subsequent calculation; in the signal processing stage, key acceleration and pressure fluctuation information are extracted, and the actual working state of the marine riser system is represented through dynamic response characteristics constructed by the peak position, the peak amplitude and the power spectral density.

The invention discloses a river water level detection method, and relates to the technical field of computer processing, and the method comprises the following steps: S01, presetting a distributed pressure sensor array in each target collection region to obtain underwater pressure data k and real-time water flow data v of a plurality of target collection regions of a river to be detected; s02, establishing a dynamic pressure water level model based on the water flow direction pressure conduction quantity estimation of the plurality of target acquisition areas in combination with real-time water flow velocity and wave period parameters; s03, performing signal attenuation compensation on the underwater pressure data according to the dynamic pressure water level model to obtain initial water level data; s04, multi-source data fusion calibration is carried out on the preliminary water level data, a river water level detection result is generated, and the result comprises a wave height frequency spectrum and a water level change curve after peak elimination correction; and S05, generating a wave energy spectral density map according to the calibrated water level data, performing real-time comparison according to the wave energy spectral density map and a preset threshold value, and performing result return according to a comparison structure. The small-wave-height short-period wave measurement precision and the water level detection reliability are remarkably improved, the intelligent flood prevention early warning and dynamic adjusting capacity is achieved, and it is guaranteed that data collection is accurate and consistent.

The invention belongs to the technical field related to energy storage safety, and particularly relates to a simulation method, a simulation device and a simulation system for container shipping vibration. A sea wave spectrum model and a cargo ship model are obtained, and a time domain signal is obtained according to the sea wave spectrum model; defining the time domain signal as fluid excitation, and obtaining a first response signal of a preset acquisition point of the cargo ship model according to the fluid excitation; acquiring a frequency domain sequence according to the first response signal, and calculating and acquiring power spectral density according to the frequency domain sequence; obtaining a container model, and calculating and obtaining a second response signal of the container model according to the power spectral density; whether the container of the current cargo ship meets the safety requirement or not is judged according to the second response signal. Safety simulation is carried out on the energy storage container body during marine transportation, so that mechanical damage caused by design problems during marine transportation of the designed energy storage container is avoided.

The invention relates to the technical field of data processing, discloses a deep tunnel blasting control instruction generation method and system based on cross-scale data, and aims to solve the problems of uncontrollable rock mass damage, high support failure risk and parameter optimization lag in the existing method. According to the scheme, the method mainly comprises the steps that rock mass fracture density, blasting vibration velocity, supporting structure strain and blasting thermal disturbance temperature rise are monitored in real time, and a rock mass fracture vibration frequency band, a supporting structure resonance frequency band and power spectrum density of signals corresponding to the supporting structure strain are determined; calculating a rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, a stripping index and support time-varying stiffness; determining a blasting parameter set based on a genetic algorithm, and predicting a predicted rock mass damage index at a future moment; the blasting parameters in the blasting parameter set are corrected, corresponding engineering measures are determined, and a blasting control instruction is generated. According to the invention, the accuracy of the control instruction, the safety of tunnel construction and the timeliness of control response are improved.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The invention provides an emotion recognition method based on electroencephalogram feature fusion and a double-stage attention mechanism, and the method comprises the following steps: A, electroencephalogram signal processing: carrying out the preprocessing of an electroencephalogram signal; and B, double-stage attention feature fusion: in each selected frequency band, adopting a double-stage attention mechanism to fuse the electroencephalogram features, and generating fusion features for emotion classification. And C, double-branch feature extraction: performing double-branch 3D convolution processing on the fused features, extracting multi-scale space-spectral time features, and splicing the multi-scale space-spectral time features along a channel dimension to form uniform features. And D, classification and output: inputting the unified features into a classifier, and generating an emotion category prediction result through a flattening layer and a full connection layer. According to the method, the difference entropy, the power spectrum density and the difference entropy asymmetry feature are fused through unified three-dimensional feature representation, a double-stage attention mechanism is introduced, and high-accuracy emotion recognition is achieved.