525 results about "Spectral density" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a pulling and lifting cable frequency detection and cable force measurement method and system based on a millimeter wave radar, and belongs to the field of pulling and lifting cable force measurement. The method comprises the steps that vibration signals are collected through the millimeter wave radar to obtain complex data streams, time-frequency analysis is conducted on the complex data streams, and an interference model is established; the method comprises the following steps: reconstructing a noise-reduced vibration signal by adopting adaptive filtering, extracting candidate vibration frequencies through power spectral density estimation and spectral peak detection, calculating corresponding stability scores, and obtaining a first verification factor used for evaluating wind disturbance and a second verification factor used for evaluating vibration frequency deviation; according to the method, through multi-source sensing data fusion and self-adaptive signal processing, complex environment interference is effectively overcome, non-contact high-precision cable force measurement is achieved, and the method has the outstanding advantages of being high in anti-interference capacity, high in measurement reliability and the like.

The invention relates to the technical field of wireless communication and signal processing, in particular to a short-wave spectrum efficient sampling method and system based on compressed sensing, and the method comprises the steps: collecting full-band power spectrum density data through a low-speed scanning receiver; determining a strong interference frequency point set; generating a binary null observation sequence; loading the binary null observation sequence to an analog front-end frequency mixing circuit, performing analog domain orthogonal frequency mixing and integral processing on the input radio frequency signal, and outputting a voltage sampling value; resolving to obtain a signal sparse coefficient through a compressed sensing reconstruction algorithm; calculating a reconstruction residual error according to the voltage sampling value and the signal sparse coefficient; in response to the condition that the reconstruction residual error is greater than a preset error threshold, triggering a reacquisition process of the power spectral density data; outputting a signal sparse coefficient in response to the condition that the reconstruction residual error is smaller than or equal to a preset error threshold; according to the invention, the problems of limited dynamic range and weak signal submerging are solved, and the receiving sensitivity of weak signals in a strong interference environment is obviously improved.

The invention provides a frequency division joint communication method and device based on overlapped multiple beams, and relates to the technical field of satellite communication. Comprising the steps that frequency selection frames forwarded by a communication ground station through satellites are received, the frequency selection frames are all-one sequences after spectrum spreading, and the frequency selection frames of different beams are distinguished through different uplink frequency points; and capturing a frequency selection frame sent by the communication ground station in a frequency selection state, and determining all overlapped beams where the communication terminal is located according to a capturing result. And framing a signal to be transmitted to generate a service frame, and synchronously sending the service frame on the frequency points corresponding to all the determined overlapped beams through multi-beam joint modulation. A multi-beam joint transmission signal is transparently forwarded to a communication ground station through a satellite, so that after the communication ground station separates each beam signal and completes de-spreading operation to obtain symbol-level information, the signals are coherently combined through a cross entropy estimation algorithm, and effective data information is extracted. Multi-beam joint communication is realized, the power spectrum density of the transmitted signal is reduced, and the signal concealment is improved.

The present invention relates to techniques for detection of depression cues in EEG signals using power spectral density-based features for deep learning. In an embodiment, a system for detecting depression in a person may comprise an Electroencephalogram (EEG) electrode array attached to the person, the EEG electrode array comprising at least ten electrodes adapted to read EEG signals, an amplifier, a filter, and an Analog to Digital Convert to digitize the read EEG signals, and computing circuitry comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform: processing on the digitized read EEG signals to detect signal patterns indicative of depression in the person, and outputting a prediction indicating a likelihood of depression in the person based on the deep learning processing.

The invention provides an anti-noise interference frequency parameter estimation method for high-precision optical coherence ranging, which comprises the following steps: acquiring a spectral interference signal of an optical low-coherence interference system and a reference light spectral signal output only by a reference arm, and converting the spectral interference signal into interference signals with wave number space arranged at equal intervals, carrying out spectral density amplitude normalization to obtain a standard interference signal so as to eliminate interference of light source spectrum fluctuation and non-uniform sampling on frequency estimation; hanning window modulation is applied to a standard interference signal, spectrum leakage is suppressed, spectrum analysis is performed on the modulated signal, a peak value and an adjacent secondary peak value of a spectrum main lobe are extracted, a frequency correction amount is calculated based on a peak value and secondary peak value amplitude ratio relation derived by a Hanning window spectrum mode function, and an accurate frequency of the interference signal is obtained; and calculating the displacement of the object to be measured according to the product of the accurate frequency and the spectral resolution of the optical low-coherence interference system in combination with the refractive index of the light in the measurement medium.

The invention belongs to the field of sound source localization, and particularly relates to an FPGA accelerated sound source localization algorithm implementation method and system. The method comprises the following steps: acquiring an audio signal through a microphone array with more than two channels to obtain a time domain digital signal corresponding to the audio signal; after the digital signal is converted into a frequency domain signal through the FPGA, the frequency domain signal is stored in a memory outside the FPGA in parallel through a parallel interface of a protocol with parallel transmission capability; the capacity of the memory is greater than that of storage resources in the FPGA chip; acquiring the stored frequency domain signal from the memory by using the protocol through the FPGA, calculating the cross-power spectral density of each microphone pair in the microphone array in parallel according to the frequency domain signal, and storing the calculated cross-power spectral density in the memory in parallel through a parallel interface of the protocol; through the FPGA, the cross-power spectral density is obtained from the memory, and in combination with the obtained pre-stored TDOA value, the response intensity of each microphone pair in the microphone array is calculated in parallel for realizing sound source localization.

A method of predicting a seizure occurrence or a surgical outcome includes monitoring a patient using an electroencephalography (EEG) system, including recording EEG data that indicates a seizure, and extracting a peri-ictal segment of the EEG data that includes a pre-ictal period immediately preceding the seizure, or a post-ictal period immediately following the seizure. The method also includes processing the peri-ictal segment, including generating an electrode-wise power spectral density (PSD) feature across a frequency band defined by at least one of a delta frequency range, a theta frequency range, an alpha frequency range, a beta frequency range, and a gamma frequency range. The method also includes inputting the PSD feature into a model, wherein the model predicts a seizure occurrence or a surgical outcome of the patient.

A reconfigurable optical add / drop multiplexer using a wavelength selective switch (WSS) component to multiplex wavelength channels into a wavelength division multiplexed (WDM) signal. When a given channel is dropped, an amplified spontaneous emission (ASE) injection signal is multiplexed as a ghost channel into the WDM signal. The ASE injection channel can mitigate polarization hole burning and can provide a fuller power spectrum density. However, the ASE injection channel also defines a monitoring window. As an optical channel monitor (OCM) monitors the WDM signal, the OCM can detect, within the monitoring window, any underlying characteristic of the given wavelength channel. In this instance in response to the detected characteristic, the WSS component switches from multiplexing the ghost channel into the WDM signal to multiplexing the given wavelength channel into the WDM signal.

The invention discloses an emotion recognition method based on multi-modal signal fusion, and relates to the technical field of emotion recognition, and the method comprises the steps: obtaining an electroencephalogram signal and a peripheral physiological signal to form a multi-modal signal, and carrying out the preprocessing of down-sampling, baseline correction and band-pass filtering on the multi-modal signal; based on the preprocessed signal, extracting a difference entropy feature and a power spectrum density feature; mapping the extracted features into a standardized space grid to generate a feature tensor with a uniform structure; performing adaptive weighting on the mapped feature tensor by using a frequency band fusion attention mechanism, and generating a frequency band weight through global average pooling and a full connection layer; inputting the weighted features into a full connection layer of the parameterized hypermatrix, and performing feature compression and modeling through matrix decomposition and reconstruction; a multi-task learning framework is adopted, classification results of emotion titer and awakening degree are output at the same time based on compressed features, and multi-task collaboration is optimized through a shared feature layer and a dynamic loss weight.

The invention discloses an atmospheric turbulence phase screen generation method based on layered physical constraints, and belongs to the crossing field of optical communication and artificial intelligence. According to the method, a diffusion probability model is combined with layered atmospheric physical constraints, independent physical constraint loss functions are established for a boundary layer, a troposphere and a stratosphere of the atmosphere, and it is ensured that a generated phase screen conforms to turbulent flow statistical characteristics of all layers through power spectrum density constraints and structure function constraints; enhancing feature learning of each layer of phase screen by adopting a physically guided attention mechanism; and designing a time-dependent constraint weight strategy, and adaptively adjusting the physical constraint strength in the diffusion denoising process. The method solves the technical problems that the vertical layering difference is ignored, the physical consistency is insufficient, and the generation quality depends on a large amount of labeled data in the existing method, is particularly suitable for high-fidelity atmospheric turbulence modeling of a satellite laser communication link, and can also be widely applied to the fields of free space optical communication system simulation, adaptive optical system design and the like.

The invention discloses a broadband spectrum compression detection method based on digital twinning and signal noise, and relates to the technical field of mobile communication, and the method comprises the following steps: S1, inputting historical spectrum data, geographic position information and electromagnetic environment parameters into a digital twinning model based on an improved FEDform model, and obtaining a digital twinning model; outputting predicted power spectral density and signal support set probability distribution; s2, generating an optimized observation matrix and a digital compression measurement vector; s3, outputting a whitening measurement value; s4, innovative components are extracted; s5, outputting reconstructed broadband spectrum data; s6, outputting a binary support set matrix containing frequency point occupancy state information; and S7, outputting the updated digital twin model. According to the invention, the limitations of dependence on high-speed sampling hardware, poor anti-noise capability and early warning delay in a traditional broadband spectrum detection method are overcome, and an efficient and accurate solution is provided.

The invention discloses an overcharge load and multi-element energy storage equipment suitability analysis method, device and equipment, and relates to the field of energy storage, and the method comprises the following steps: obtaining the real-time data of an overcharge load, extracting a random pulse component from the overcharge load, carrying out the integration, and generating a load quantification parameter; dC bus ripples are used as a non-intrusive excitation source, a dynamic impedance spectrum is analyzed and solved through cross-power spectral density, a self-adaptive physical characteristic reconstruction model is constructed based on the dynamic impedance spectrum, an energy storage system is reconstructed, and capacity reconstruction parameters are output; establishing a cross-dimensional mapping mechanism of physical loss and economic cost, mapping the dynamic impedance spectrum into life marginal cost, calculating expected service earnings in combination with load quantization parameters, and constructing a profit and loss game objective function; and optimizing the objective function of the profit and loss game by taking the capability reconstruction parameter as a hard constraint, and generating an adaptability analysis report. The method is used for solving the problem that the optimal balance of life and income cannot be realized due to the fact that the physical state sensing of the battery is lagged in the overcharge transient state.

The invention discloses an emotional state recognition method, system and device, and relates to the technical field of biomedical signal processing and artificial intelligence emotional computation.The method comprises the steps that electroencephalogram signals of a to-be-detected individual are collected; extracting power spectral density (PSD) and differential entropy (DE) features of the electroencephalogram signals on each frequency band according to a preset frequency; performing structured combination on the PSD and DE features corresponding to each frequency band, and constructing a dual-channel spectrum feature matrix; inputting the dual-channel spectrum feature matrix into a pre-trained shared encoder, extracting emotional feature representation of the individual, and obtaining an emotional state recognition result according to the emotional feature representation; according to the method, the depression emotion state of a new individual can be evaluated without participation of target domain data, the robustness and recognition precision of the model under cross-individual and low-labeling conditions are effectively improved, and the method has relatively high popularization and application potential.

The invention relates to the technical field of blank defect detection, in particular to a large-scale shaft forging blank defect identification method based on ultrasonic flaw detection data, and the method comprises the following steps: obtaining an ultrasonic A scanning signal of a large-scale shaft forging blank; extracting a back scattering signal from the ultrasonic A scanning signal; the back scattering signals are preprocessed; calculating the power spectrum density of the back scattering signal; obtaining characteristic parameters related to depth based on the power spectrum density; calculating to obtain a dynamic attenuation coefficient changing along with the depth based on the characteristic parameters; a compensation factor is obtained through exponential operation; compensating the original echo peak amplitude of the defect detected in the A scanning signal by using the compensation factor to obtain a defect signal amplitude; and comparing the amplitude of the defect signal with a threshold value of a standard DAC curve under a corresponding depth, and judging whether a defect exists or not. According to the invention, the accuracy and reliability of defect identification are improved.