236 results about "Time spectrum" patented technology

The invention relates to an intelligent dimming method and device for a multispectral light source and a storage medium, and the method comprises the steps: obtaining the spectral parameters and environment illumination data of the multispectral light source, carrying out the analysis processing of the spectral parameters based on the environment illumination data, and obtaining the corresponding target spectral information; acquiring real-time output data of the multispectral light source, and performing association processing on the real-time output data and the target spectral information to obtain a corresponding real-time spectral group; acquiring illumination feedback data of the multispectral light source, and performing spectral prediction on the target spectral information to obtain corresponding spectral adjustment information; performing dimming planning analysis on the real-time spectrum group and the spectrum adjustment information to obtain a corresponding initial light source control scheme; performing spectrum identification on the illumination feedback data to obtain corresponding characteristic spectrum information; and performing dimming optimization on the initial light source control scheme according to the characteristic spectral response parameters to obtain a corresponding global dimming strategy. According to the invention, global optimization of the system can be realized through optimization and adjustment of the characteristic spectral response parameters.

The invention relates to the technical field of multispectral image processing and analysis, solves the technical problems that a traditional tree species classification model cannot fully mine seasonal change characteristics and cannot effectively construct a tree species space-time spectrum constraint relation, and particularly relates to a dominant tree species identification method based on time-space-spectrum multi-domain perception and guidance. Comprising the following steps: acquiring multi-temporal satellite-borne multispectral image data; performing parallel feature extraction on the multispectral image data through a double-flow network architecture; inputting the extracted double-flow features into a time-space-spectrum feature fusion module, and performing cross-domain fusion on the multi-temporal dynamic features through a hierarchical time sequence spectrum integration mechanism and a dynamic space-time excitation optimization mechanism to obtain space-time spectrum features; and outputting an identification result of the forest dominant tree species based on the fused time-space spectrum features. Through the effective feature extraction and integration strategy, the consumption of computing resources is reduced, the efficiency of processing the large-scale satellite-borne multispectral image data is improved, and the high-efficiency requirement in practical application is met.

The invention discloses a remote sensing cloud layer repairing method based on time-spectral domain fusion and time sequence self-attention enhancement, and relates to the technical field of remote sensing cloud layer image processing. The method comprises the steps of receiving a remote sensing missing image as a source image; the method comprises the following steps: constructing a generator, inputting a remote sensing missing image into the generator for preliminary processing, the generator comprising a feature extractor, a ConformerPlus module and a cloud detection module, and a new multi-head self-attention mechanism being embedded in the ConformerPlus module. According to the method, time-frequency spectrum domain information extraction and fusion can be carried out on data, so that the feature expression ability is enhanced, the edge information capturing ability of the model is improved, the model is guided to realize progressive cloud layer removal from outside to inside, the detail expression can be improved, noise interference can be suppressed, and the method is suitable for large-scale popularization and application. And the detail definition and the texture fidelity of the image are effectively improved.

The invention provides a time-varying structure vibration signal decomposition method based on experience multiple synchronous extraction, and the method comprises the steps: carrying out the frequency spectrum segmentation through searching a sudden change point of the internal and external energy of a characteristic frequency band in a signal time-frequency spectrum, so as to complete the time-varying signal decomposition: firstly, obtaining the time-frequency spectrum of a signal through the multiple synchronous extraction transformation; summing the time-frequency coefficients of all moments at each frequency position to obtain time-frequency total energy; searching an energy boundary by using an energy segmentation operator, and performing Fourier transform on the signal to obtain a signal frequency spectrum; then, combining a zero-phase filter and an energy boundary position, and extracting a frequency band corresponding to the single-mode signal; and finally, inverse Fourier transform is carried out on each section of frequency spectrum, a single-mode signal is reconstructed, and modal decomposition of the time-varying structure is realized.

The invention discloses a method for selecting and triggering gamma instant radiation pulse events, and belongs to the technical field of nuclear radiation detection. The method comprises the following steps: carrying out normalization preprocessing on an original gamma pulse radiation time spectrum; s time-frequency transformation is carried out on the preprocessed data, and time-frequency features are extracted; optimizing parameters of the frequency threshold and the amplitude threshold after S time-frequency transformation by adopting a deep reinforcement learning model to obtain an optimal frequency threshold and an optimal amplitude threshold; performing filtering processing on the signal of the S time-frequency conversion result, and separating noise from an effective signal; and searching a pulse radiation event for the filtered signal by applying a morphology-based peak searching method, and positioning the position and the characteristic of a real gamma instant radiation pulse event. According to the method, time-frequency analysis is carried out through S time-frequency transformation, the problem that the frequency of an analysis window cannot be adjusted through short-time window Fourier transformation is solved, the method has better frequency resolution and phase retention characteristics, and gamma pulse signal characteristics can be represented more accurately.

The invention discloses a scanning high-resolution double-optical-comb spectrum system based on Raman gain modulation. The method is suitable for real-time spectrum detection with a wide spectrum range and high spectral resolution. The system comprises a single-frequency laser and driver, an optical coupler, a mode-locked laser, a rare earth doped amplifier, a wavelength division multiplexer, a Raman gain fiber, a sample to be tested, a photoelectric detector, a data processor and a wavemeter. The single-frequency laser couples part of light to the wavemeter, and the remaining light is divided into two paths to be coupled with the two amplified mode-locked lasers to enter the Raman gain optical fiber for Raman gain modulation and is converted into high-mutual-dryness double optical combs. The double combs pass through a to-be-measured sample in a combined manner, and interference signals are measured by the photoelectric detector and received by the data processor. And meanwhile, the processor sends a tuning signal to the single-frequency laser driver, so that the double combs scan in a wide spectrum range to obtain high-resolution spectrum information. Electrical feedback locking is not needed, and high-precision spectrum acquisition with megahertz resolution in a terahertz range can be completed within a few minutes.

The invention discloses a time sequence classification method, system and device based on multi-dimensional and multi-scale spatio-temporal spectrum characteristics, and the method comprises the steps: obtaining remote sensing time sequence data, carrying out the enhancement of time information based on a time impact factor, and carrying out the normalization processing to obtain an enhanced time characteristic; enhancing the spatial information to obtain enhanced spatial features; zooming the dimension of the spectral information to a preset dimension based on linear transformation to obtain an enhanced spectral feature; constructing a local feature extraction module to obtain multi-dimensional and multi-scale space-time spectrum features corresponding to the enhanced time features, the enhanced space features and the enhanced spectrum features; and processing the coding features to obtain space-time spectrum comprehensive features, and adjusting channel dimensions of the space-time spectrum comprehensive features to enable classification categories and numbers to be matched with each other. A space-time spectrum residual converter network is provided, space-time spectrum information is enhanced through three independent channels, and features are extracted through a local feature extraction module and a global feature extraction module.

The invention discloses a power grid panoramic waveform fault identification and traceability method and system of a space-time spectrum S network. The method comprises the following steps: calculating a plurality of characteristic values according to panoramic waveform data, and splicing the characteristic values to obtain a fusion characteristic value; the time domain signal with the fused feature value is input into a trained space-time spectrum S network to obtain a fault type and a fault position, the space-time spectrum S network comprises a multi-stage feature extraction module, a full connection layer and a multi-task learning assembly, and the feature extraction module comprises a time-frequency spectrum module, a convolution layer and a pooling layer which are connected in sequence; the time-frequency spectrum module comprises a convolutional layer, a global feature extraction branch, a local time-frequency feature extraction branch and a feature fusion module, and features extracted by the feature extraction module are sent to the multi-task learning component through a full connection layer to obtain the fault type and the fault position of the power grid. The invention aims to improve the accuracy and positioning capability of fault monitoring of the novel power system and solve the problem that novel faults of a power grid in the novel power system are difficult to identify and trace.

The invention relates to a semiconductor material phonon average free time spectrum reconstruction method and device and a storage medium. The method comprises the steps that S1, response temperature rise changes of a sample under excitation of alternating current heat flows with different heating frequencies omega H are obtained through experimental testing; s2, according to the first-order spherical harmonic function, approximately solving a Boltzmann transport equation to obtain a diffusion type equation, and in combination with a correction slip boundary condition, obtaining an analytical expression of theoretical temperature change of the sample, the theoretical temperature change of the sample is related to the amplitude and frequency of alternating current heat flow excitation, the size of a heater, the average free time spectrum of intrinsic phonons of the sample and the phonon penetration coefficient between the heater and the sample; and S3, after the experimental conditions are substituted, fitting the response temperature rise change and the theoretical temperature change measured by the experiment to obtain the average free time spectrum of the intrinsic phonons of the sample and the phonon penetration coefficient between the heater and the sample to complete the reconstruction of the average free time spectrum of the phonons. Compared with the prior art, the method has the advantages of high speed, high accuracy and the like.

The embodiment of the invention provides a space instant gamma radiation detection device and monitoring method, and relates to the technical field of gamma radiation monitoring, the device comprises a device body and a signal acquisition and processing system; the device body comprises a shell, and a scintillator array assembly and an anti-coincidence scintillator assembly which are fixed in the shell; the center of the anticoincidence scintillator assembly is provided with a cuboid groove, and the scintillator array assembly is located in the cuboid groove. Wherein the scintillator array assembly and the anticoincidence scintillator assembly are both connected with the signal acquisition and processing system. According to the technical scheme, the problem of instant gamma radiation time spectrum measurement in a space complex radiation background environment is solved, the signal-to-noise ratio and the time performance of gamma radiation time spectrum measurement are improved on the basis of ensuring lightweight design, and the accumulation probability is reduced while the sensitivity upper limit is expanded.

The invention relates to a power distribution network fault detection method based on generalized S transformation containing variable factors, and belongs to the technical field of power distribution network fault detection. Data such as a time-frequency spectrum and transient energy are obtained through generalized S transformation containing variable factors, and three line selection criteria of a time-frequency spectrum comprehensive similarity coefficient, a transient energy peak value and a transient energy relative entropy are constructed; and multi-criterion fusion is realized in combination with a D-S evidence theory, so that a line selection result of a single criterion is subjected to comprehensive analysis of multi-criterion fusion, and a more accurate line selection result is obtained. According to the method, accurate line selection can be carried out in different fault distances, fault lines, fault initial angles, fault grounding resistances and noise environments, and the method has good adaptability, sensitivity and anti-interference performance. According to the method, the calculated transient energy characteristics are converted into a reliability distribution function to construct an evidence combination, and different fault characteristic quantities are utilized to measure the credibility to construct a line selection criterion, so that mutual complementation and mutual fusion are realized, and a more accurate line selection result is obtained.

The invention relates to a time-synchronous compression extraction transformation method for a time-frequency spectrum, and belongs to the technical field of oil and gas field exploration. The method first performs conventional time-frequency transformation on seismic data to calculate a time-frequency spectrum and a group delay operator, then constructs a time-synchronous compression extraction operator through group delay and a Gaussian window, and finally performs synchronous compression extraction transformation on the time-frequency spectrum according to the synchronous compression extraction operator, thereby obtaining an amplitude spectrum with a certain width in the time direction after the compression extraction transformation, and the amplitude spectrum of each frequency has the same time resolution. Compared with the amplitude spectrum before the compression extraction transformation, the amplitude spectrum has improved resolution in the time direction, thereby improving the accuracy of time-frequency analysis. Compared with the amplitude spectrum obtained by the conventional time-synchronous compression method, the amplitude spectrum of each frequency has the same time resolution, and the reliability of comparative analysis of amplitude spectrums of multiple frequencies is improved.

The invention relates to a dual-domain aligned non-paired hyperspectrum and SAR image space-time spectrum fusion method, which comprises the following steps: carrying out up-sampling on a hyperspectrum and carrying out feature extraction, and carrying out Gaussian blur and feature extraction on an SAR image; enabling the radiation distribution of the SAR features to approach the radiation distribution features of the hyperspectral image; adjusting the spatial geometric information of the SAR features; extracting change information between image features, and predicting a hyperspectral image with low spatial resolution through information injection; and fusing the hyperspectral image with the low spatial resolution and the aligned original SAR image to obtain a final hyperspectral image with the high spatial resolution. The method has the beneficial effects that the fusion precision of the hyperspectrum and the SAR image space-time spectrum is improved, and reliable support is provided for subsequent application.

The invention relates to a spectrum sensing system and method for a discrete shared frequency band, and belongs to the technical field of wireless communication. The system comprises a base station, an edge computing device and a plurality of sensing terminals, the plurality of sensing terminals collect spectrum data, perform spectrum sensing and send sensing results to the base station, and the base station forwards the sensing results to the edge computing device for fusion processing of the plurality of sensing results. And the edge computing device fuses the plurality of sensing results, predicts the spectrum occupation condition of a plurality of subsequent time steps through a pre-trained binary prediction model, and sends a spectrum prediction result to the base station, and the base station screens idle spectrum frequency points according to the spectrum prediction result to communicate with the sensing terminal. According to the method, discrete shared frequency band sensing is carried out based on the system, and the method can adapt to the conditions that the signal coverage range of discrete frequency bands is wide, an unknown communication system exists and real-time spectrum analysis is needed.

The invention discloses a magnesium oxide grinding monitoring method and system, and particularly relates to the technical field of grinding monitoring, and the method comprises the following steps: firstly, collecting a reference spectrum generated by light reflection from the surface of a reference substrate with a target thickness; in the grinding process, the reflection spectrum of the surface of the magnesium oxide grinding base material is captured from multiple angles through a plurality of optical sensors; then, calculating a picture dust influence coefficient of each optical sensor according to the K value and the dust space-time concentration distribution; determining an angle correction factor and a picture dust influence coefficient, and comprehensively distributing a reflection spectrum weight coefficient obtained by each optical sensor; fusing and outputting the reflection spectrums acquired by the optical sensors according to the weight coefficients of the reflection spectrums acquired by the optical sensors; finally, matching the real-time spectrum with a historical spectrum library; comparing the target thickness of each region, and determining a rate deviation; the grinding pressure of different areas is dynamically adjusted through a pressure control system; and the grinding end point time of all the areas tends to be consistent.

The invention provides a high-precision and high-time-resolution terahertz wave band tau radiation measuring device. The high-precision and high-time-resolution terahertz wave band tau radiation measuring device comprises an antenna module, a calibration assembly, a local oscillator signal source module, a refrigeration module, a superconducting receiver module, a data acquisition module and a system control module. Measurement of atmospheric opacity and atmospheric radiation temperature calibration in any direction of a station site are realized through the antenna module and the calibration assembly. The superconducting receiver module adopts a superconducting SIS mixer, the detection sensitivity is improved by one order of magnitude compared with that of a normal-temperature Schottky mixing receiver, and high-precision and high-time-resolution atmospheric opacity measurement can be realized by combining the narrow-band frequency gating and real-time spectrum data quick reading characteristics of a high-spectrum-resolution real-time FFT spectrum analyzer of the data acquisition module. And meanwhile, a real-time FFT frequency spectrograph of the data acquisition module of the device has high-frequency-resolution frequency spectrum processing capability, so that the device can be used as a spectral line receiver with high sensitivity and high frequency spectrum resolution, and terahertz astronomical molecule spectral line observation with high frequency spectrum resolution can be realized.

The present invention comprises a method for jointly processing data from the nuclear magnetic resonant (NMR) response of oil and rock samples in in natura condition, and its main product is the delimitation of the oil saturation (So) present in the rock samples, i.e., the determination of the range of admissible values of the volumetric fraction of the porous volume of the rock occupied by such fluid. The method requires a small volume of oil, originating from or of a composition similar to the oil present in the rock being tested, since it is based on both the measurement of the NMR response of the rock sample and the response of the fluid sample. These two sets of data feed a statistical inference scheme that (1) determines a range of admissible values for the So of the sample, delimited by the minimum and maximum oil saturation values compatible with the two sets of data presented; (2) produces, for each of these extreme values of admissible So, a decomposition of the rock relaxation time spectrum into two complementary spectra, the first highlighting the signatures relating to oil and the second comprising the distribution of the rock relaxometric signatures that cannot correspond to such fluid under the prescribed saturation condition.

The invention discloses a thin film spectrum door lock management system and method based on data analysis, and relates to the technical field of data analysis, and the method comprises the following steps: 1, collecting same-batch intact thin film spectrum data, disturbance spectrum samples and corresponding environment parameters; step 2, carrying out denoising smoothing, baseline correction, derivative operation and normalization processing on the spectral data; step 3, extracting spectrum key features to construct vectors, combining the vectors, and decomposing the vectors into a substrate and a coefficient matrix by using a non-negative matrix; step 4, respectively calculating intact and disturbance thresholds, and constructing a regression model by using environmental parameters and coefficient offset; and 5, collecting real-time spectrum and environmental parameters, carrying out projection solution on a coefficient vector after preprocessing, and dynamically adjusting a judgment reference and carrying out decision making. The method can effectively improve the situation that in the prior art, thin film spectrum door lock management mainly depends on a fixed threshold value and is difficult to adapt to disturbance and environmental changes.

The invention relates to a signal control system for intelligent industrial equipment and a control and adjustment method thereof. The method comprises the following steps: dynamically distributing a signal transmission priority according to a key operation state of the equipment; generating a dynamic global interference map of the factory environment through spectrum sensing; global interference map updating is realized through decentralized data fusion between devices; and adaptively adjusting the signal frequency band according to the transmission priority of the signal and the global interference map. According to the method, dynamic priority distribution, real-time spectrum sensing, decentralized cooperative control and adaptive frequency band adjustment technologies are fused, the problems of signal delay, interference conflict, too high energy consumption and the like in a traditional industrial communication system are solved, and the communication real-time performance, reliability and energy efficiency in a complex industrial environment are improved.

The invention provides a self-supervised machine abnormal sound detection method based on a dual-path WaveNet, and the method comprises the steps: 1, carrying out the modeling of frequency information of each time frame of an input sound signal in a frequency domain path through employing the WaveNet, and extracting a frequency domain feature; 2, the time domain path takes the output # imgabs0 # of the frequency domain path as the input, and modeling is carried out on the time dimension after transposition is carried out on the time domain path; 3, performing further feature fusion on the output # imgabs1 # of the frequency domain path and the time domain path through a two-dimensional convolutional layer to generate a one-dimensional embedded vector; 4, adopting a self-supervision strategy, and utilizing metadata to construct a classification task so as to train a model; and 5, in a detection stage, the trained model judges whether the model is normal or not according to the abnormal score # imgabs2 of the model test sample. According to the dual-path design, comprehensive modeling of the time-frequency spectrogram is achieved, local details of time and frequency dimensions are reserved, frequency domain harmonic information and time domain dynamic characteristics can be effectively captured, and the problem that complex characteristic modeling in the time-frequency spectrogram is insufficient is solved.

The invention discloses a multi-channel integrated radio frequency system based on double-threshold interference detection and dynamic spectrum aggregation. A radio frequency front-end unit is used for realizing broadband spectrum access and performing analog-to-digital conversion; the digital channel processing unit is used for realizing channelized segmentation through up-conversion and down-conversion, carrying out band elimination filtering to shield unavailable channels and outputting filtering parameters to support dynamic channel recombination; the digital signal parallel processing unit performs parallel access by using the divided channels; the spectrum sensing unit is used for carrying out double-threshold channel interference detection and real-time spectrum analysis according to the parallel access signals; and the frequency spectrum decision-making unit is used for judging the channel state and fusion detection judgment according to the interference detection result, judging the availability of a specific channel in the data link network and carrying out cooperative dynamic frequency spectrum aggregation. According to the method, a consistent spectrum access decision is realized by adopting a spectrum monitoring, sensing convergence and fusion decision mechanism, and consistent understanding of an available frequency set and high-reliability network domain dynamic spectrum aggregation are guaranteed.

The invention belongs to the technical field of electromagnetic spectrum management, and particularly relates to an intelligent planning method for an electromagnetic spectrum time sequence. Comprising the following steps: collecting real-time spectrum use data through a sensor network or wireless communication equipment; carrying out preprocessing operation on the collected data; constructing an electromagnetic spectrum time sequence intelligent planning model based on the preprocessed data; formulating a dynamic spectrum allocation strategy according to a prediction result of the intelligent planning model; and the allocation strategy is continuously optimized and adjusted in the spectrum allocation process. According to the algorithm provided by the invention, efficient and reasonable time sequence planning is carried out on electromagnetic spectrum resources in an intelligent mode, so that the increasing wireless communication requirements are met, and the utilization rate of the spectrum resources and the overall performance of a communication system are improved.

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) land subsidence periodicity and trend feature extraction method considering spatial heterogeneity. According to the method, long-time-sequence synthetic aperture radar image data is acquired, high-precision monitoring is carried out on land subsidence by using a PS-InSAR technology, on the basis, dimension reduction processing is carried out on deformation information by using principal component analysis (PCA), a combinatorial algorithm integrating spatio-temporal spectral clustering and blind source signal separation ICA is provided, different types of subsidence characteristics are efficiently identified, and the accuracy of land subsidence monitoring is improved. According to the method, the periodic characteristics, the tendency characteristics and the randomness characteristics of different types of land subsidence are extracted, the land subsidence can be analyzed more comprehensively, meticulously and accurately, a more reliable and scientific basis is provided for subsidence monitoring, risk assessment and prevention and treatment decision, and the method has a wide application prospect.

The invention provides a stratigraphic macroscopic capture cross section calculation method and system based on a sliding time window. The method comprises the following steps: acquiring a time spectrum, and initializing thermal neutron decay life tau; generating a time window on the time spectrum according to the thermal neutron decay life tau; calculating a counting ratio of the time window, and judging whether the counting ratio meets a preset condition or not; if the counting ratio does not meet the preset condition, modifying the thermal neutron attenuation life tau, sliding the time window, and recalculating the counting ratio; and if the counting ratio meets the preset condition, calculating the stratum macroscopic capture cross section Sigma according to the counting ratio. By means of the method, the position and time of the time window can be adjusted in a self-adaptive mode, and then the calculation precision of Sigma is improved.

The invention discloses a thin interbed lithologic structure identification method based on deep learning, and the method comprises the steps: carrying out the manual collection and processing, and obtaining seismic data; performing well-seismic calibration and depth-time conversion to obtain a lithologic structure and seismic data of a thin interbed section at a well point; performing time-frequency analysis and sliding a time window to obtain a time-frequency spectrum image in a local time window of a seismic channel beside a well, and taking the image as a deep learning network input sample; obtaining a thin interbed lithologic structure in a local time window, and classifying and coding the lithologic structure as a sample label; finishing thin interbed lithologic structure identification network model training to obtain optimal network parameters; traversing all seismic traces by using the optimal network parameters to obtain lithology classification results of the thin interbed in all time windows; and processing results in the overlapped time windows, and determining final spatial distribution of the thin interbed lithologic structure. Compared with the prior art, the method has the advantages that logging and seismic data are combined, seismic resolution can be broken through, spatial continuous distribution of the thin interbed lithologic structure is obtained, wavelet information is not needed, the recognition precision is high, and popularization is convenient.

The invention provides a data link wireless communication timing synchronization method, which comprises the following steps of: adopting a plurality of groups of PN sequences as the basis of synchronous timing, detecting TOD after PN serial numbers when an effective synchronization symbol number is detected, carrying out CRC (cyclic redundancy check) on information contents, adopting RS (Reed-Solomon) coding and decoding, and then carrying out 8-time spectrum spreading; and the demodulation end determines an effective timing position according to serial numbers of different hops by taking the verified correct position as a basis for synchronizing the timing time point. The method has the beneficial effects that the synchronization signal-to-noise ratio can be reduced to be lower than 0db, and meanwhile, the false alarm probability and the missing alarm probability are greatly reduced, so that the frame synchronization performance is improved; and the anti-interference capability of the time domain and the frequency domain is enhanced.

A training method, prediction method and device for an aftershock time-frequency spectrum prediction model, the method comprising: constructing an initial prediction model, the initial prediction model comprising an encoder and a decoder connected in sequence; acquiring a training data set and a test data set, wherein each piece of data comprises a time-frequency spectrum corresponding to a main earthquake acceleration time history, a main earthquake seismological parameter characteristic matrix and an aftershock seismological parameter characteristic matrix; training the decoder to obtain a trained decoder; training the encoder by using the training data set, the test data set and the trained decoder to obtain a trained prediction model; the prediction model is used for obtaining an aftershock time-frequency spectrum corresponding to the main shock based on the main and aftershock seismological parameters and the time-frequency spectrum corresponding to the main shock acceleration time history, and the aftershock time-frequency spectrum is used for obtaining an aftershock acceleration time history after phase recovery. According to the method, the decoder is trained first and then the decoder is trained, so that the model training difficulty can be reduced, the training efficiency can be improved, machine jamming can be avoided, and the computer performance can be improved.

The present invention discloses a method for effectively disassembling a nuclear magnetic resonance relaxation time spectrum, comprising the following steps: S1, constructing a probability density function of pores and caves according to the distribution characteristics of pores and caves in a core nuclear magnetic resonance T2 spectrum; S2, setting the sum of the nuclear magnetic spectrum areas to 1, randomly generating n data points within the interval of pore distribution, and dividing all the data points into multiple data sets by screening peak values; S3, calculating a likelihood function; S4, calculating a priori probability that each data point belongs to a pore or a cave; S5, updating various parameters in an iterative method using the priori probability; and S6, calculating a new logarithmic likelihood based on the updated parameters, and updating the expected E in the iterative method. p and E v , expected E p and E v Repeat steps S2 to S5 until the convergence condition is met and the iteration is terminated. The present invention combines nuclear magnetic resonance testing with an iterative method to decompose the nuclear magnetic resonance spectrum, thereby improving the accuracy of the spectral shape distribution.

The present invention discloses a remote sensing cloud repair method based on time-spectrum domain fusion and temporal self-attention enhancement, and relates to the technical field of remote sensing cloud image processing. The present invention includes: receiving a remote sensing missing image as a source image; constructing a generator, and inputting the remote sensing missing image into the generator for preliminary processing, wherein the generator includes a feature extractor, a ConformerPlus module, and a cloud detection module, and a new multi-head self-attention mechanism is embedded in the ConformerPlus module. The present invention can extract and fuse the time-spectrum domain information of the data, thereby enhancing the expression ability of the features, and improving the model's ability to capture edge information, thereby guiding the model to achieve progressive cloud removal from the outside to the inside, while also improving detail performance and suppressing noise interference, effectively improving the detail clarity and texture fidelity of the image.

The application provides an audio signal alignment method and device, a medium and a terminal, and relates to the technical field of audio processing. The method comprises the following steps: calculating a short-time frequency domain representation of an audio signal, wherein the audio signal comprises a first audio signal and a second audio signal; calculating a frequency response of a time-varying linear prediction error filter according to the short-time frequency domain representation of the audio signal, and calculating a short-time spectrum of a linear prediction error; extracting a low-frequency coefficient from the short-time spectrum and calculating a short-time spectrum of a corresponding linear prediction error envelope; calculating a short-time cross-power spectrum of the linear prediction error envelope corresponding to the audio signal according to the short-time spectrum of the linear prediction error envelope; calculating a frame-based time delay between the two audio signals according to the short-time cross-power spectrum of the linear prediction error envelope; and performing a variable-speed constant-pitch processing on the first audio signal or the second audio signal according to the calculation result of the frame-based time delay, so as to obtain an aligned audio signal. The present scheme can improve the accuracy of time delay estimation.