135 results about "Domain transformation" patented technology

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention relates to the technical field of geotechnical mechanics and engineering, and particularly discloses a soft soil foundation settlement monitoring method and system based on multi-field multi-source information, and the method comprises the steps: collecting multi-source data of a target soft soil area; performing constitutive parameter inversion, data standardization and discrete Fourier transform frequency domain conversion on the data to obtain a standardized frequency domain multi-field multi-source data set; based on the data set, a soft soil constitutive parameter library and a soil mechanics physical equation, constructing an FD-PINN frequency domain physical information neural network, embedding the physical equation as a prior constraint, and training the model by adopting an alternating optimization strategy; inputting the real-time frequency domain data flow into the model, and outputting the current settlement amount, the settlement rate and the multi-physical field frequency domain distribution; and in combination with a pre-established large scale model test result, through IDFT inverse transformation, a time-space domain settlement field is reconstructed, multi-stage early warning is triggered, a targeted reinforcement scheme is recommended, the soft soil foundation settlement monitoring precision and the engineering practicability are remarkably improved, and the method is suitable for construction, operation and maintenance of infrastructures such as high-speed rails and highways.

An assessment apparatus and method for objective pain assessment are provided, wherein the apparatus includes a processor comprising a frequency-domain transformation module, a frequency-band segmentation module, a first assessment submodule, and a second assessment submodule. The frequency-domain transformation module generates a global time-frequency feature matrix, which is segmented by the frequency-band segmentation module in a frequency domain into five frequency bands associated with pain perception. The first assessment submodule extracts last time step features and an adjacency matrix from the time-frequency feature matrix and generates a first feature vector representing global association patterns among electrodes used for acquiring EEG signals. The second assessment submodule generates a second feature vector with local spatiotemporal dynamic features of EEG signals, concatenates it with the first feature vector to form a fused feature vector, computes its class probability distribution, normalizes it, and generates an objective pain quantification indicator corresponding to the EEG signals.

The invention discloses a motor working condition monitoring method with a high-speed data transmission capability, and relates to the field of motor operation working condition detection and information transmission. The method comprises the following steps of: synchronously acquiring multi-dimensional time sequence data of a motor under multiple working conditions, and performing data segmentation, standardization, fixed-step downsampling, frequency domain conversion and feature fusion to form a multi-dimensional feature vector; then building a BiLSTM-Attention deep learning model, extracting bidirectional time sequence dependence of the sequence, focusing key time step features, and training to obtain a working condition monitoring model; the monitoring reliability is guaranteed through a dual detection mechanism, and high-speed synchronous transmission of multi-sensor data is realized based on an EtherCAT communication protocol stack. According to the method, through collaborative modeling of multi-dimensional time-frequency feature fusion and a deep learning model, a high-speed synchronous transmission protocol stack and a dual detection mechanism are combined, motor working condition feature expression can be remarkably enhanced, and high-precision data transmission and reliable monitoring can be achieved.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides a quantum-enhanced multi-scale network intrusion detection method, which comprises the following steps: calculating a covariance matrix for an original traffic feature matrix, and obtaining a feature value and a feature vector through feature decomposition, mapping each sample xi to a quantum Hilbert space to generate an enhanced feature matrix, executing complex field transformation on the enhanced feature matrix to generate an entangled feature tensor, and realizing dynamic feature enhancement through a multi-head attention mechanism based on a quantum probability amplitude; performing space-time attention calculation and gating fusion on the feature tensor after dynamic feature enhancement to obtain a space-time fusion feature; converting the space-time fusion features into a time sequence form, extracting behavior features through a multi-scale convolution branch, and fusing the behavior features to obtain a three-dimensional feature tensor; and calculating a mean value of the three-dimensional feature tensor in a sequence dimension, generating a two-dimensional feature matrix, and performing classification prediction, uncertainty quantification and threat grading evaluation based on a classification network, an uncertainty network and a threat grading network.

The invention provides a multi-source environment interpretation method and device based on linear frequency modulation analysis, and belongs to the field of space-based remote sensing intelligent process.The method comprises the steps that a hyperspectral image and laser radar digital surface model data of a target area are obtained, local neighborhood windows with pixels as the centers are extracted respectively, and the local neighborhood windows are obtained; generating a spatial-spectral data cube and elevation data; performing fractional domain feature extraction on the spatial spectrum data cube and the elevation data in sequence, and obtaining reconstruction features based on linear frequency modulation basis function mapping and cross-modal fusion; and identifying and classifying the reconstructed features to obtain a target area ground feature classification result. According to the method, through cascade design of fractional domain transformation, frequency modulation modeling and cross-modal attention, multi-source distribution alignment, dynamic feature enhancement and efficient fusion and generalization are realized, and an interpretable and low-dependence solution is provided for multi-source cross-regional ground feature classification.

The invention relates to the technical field of remote sensing image intelligent processing, in particular to a multi-scale progressive surface temperature fusion downscaling method and device, and the method comprises the steps: preprocessing a surface temperature image in a multi-scale image library; constructing a multi-scale training data set containing the surface temperature and at least one auxiliary parameter; constructing a surface temperature downscaling network based on multi-parameter fusion, and constructing a domain transformation network based on heterogeneous high-frequency information guidance; training a surface temperature downscaling network and a domain transformation network based on a progressive dual-network joint training strategy; finely adjusting the downscaling model meeting a preset low-medium resolution condition; and performing low-medium-high progressive downscaling on the low-resolution surface temperature image to be processed to generate a multi-scale progressive surface temperature meeting a target high-resolution condition. The method fully considers the problem of descending scale difference of different resolutions, achieves the maintenance of the physical characteristics of the surface thermal field, and improves the processing efficiency of large-area remote sensing data.

The invention provides an image compression artifact removal method based on a frequency domain hybrid expert network, and relates to the field of image processing. The method comprises the following steps: acquiring image data with compression artifacts; extracting shallow image features of the image data through an independent convolutional layer; extracting long-distance dependent image features of the shallow image features through a spatial attention mechanism; based on long-distance dependent image features, inputting the long-distance dependent image features into a frequency domain transformation expert network, and determining an optimal frequency domain transformation expert through parallel processing of multiple frequency domain transformation experts and an expert selection mechanism; processing the long-distance dependent image features through an optimal frequency domain transformation expert to obtain enhanced image features; performing image reconstruction on the enhanced image features to obtain a reconstructed image; the reconstructed image is an image after the compression artifacts are removed. The method is used in an image compression artifact removal process, and solves the technical problem that multiple types of complex compression artifacts are difficult to effectively remove in the prior art.

The invention relates to the field of seismic data reconstruction, in particular to a seismic data low-rank reconstruction method fusing double-domain transformation, and the method comprises the steps: obtaining original incomplete seismic data, and initializing to-be-reconstructed seismic data and other model variables and parameters under an alternating direction multiplier method frame; performing fractional order gradient transformation on seismic data to be reconstructed along different directions, and mapping to obtain fractional order gradient domain data; performing space-time Hankel tensor expansion transformation on each piece of fractional order gradient domain data, and converting the data into a space-time Hankel matrix; performing low-rank constraint on each space-time Hankel matrix by adopting a Schatten norm to obtain a seismic data low-rank reconstruction model fused with double-domain transformation; and carrying out iterative solution on the model by using an ADMM framework until the model is stable, and obtaining a final seismic data reconstruction result. The method provided by the invention can effectively reconstruct the seismic data with the missing trace, and has a reconstruction effect with strong robustness and relatively good precision for incomplete seismic data with different missing degrees.

The invention discloses a multivariate time sequence prediction method based on dynamic complex spectrum filtering, which comprises the following steps: acquiring multivariate time sequence data of a target scene, determining a predicted target variable and a window length, and constructing a training set, a verification set and a test set based on the data; constructing a DCSFNet prediction model, wherein the model sequentially comprises a reversible instance normalization module, a frequency domain conversion module, a dynamic complex spectrum filtering module, a time domain projection module and an inverse reversible instance normalization module; and realizing multivariate time sequence prediction by using the trained DCSFNet model. According to the method, the frequency domain features are adaptively processed through the dynamic complex spectrum filtering module, and reversible normalization and time domain mapping are combined, so that the problems that an existing method is poor in adaptability to diversified time sequence data, frequency domain information is insufficient in utilization, and prediction precision and calculation efficiency are difficult to consider at the same time are effectively solved; and multivariate time sequence prediction with high precision, high efficiency and cross-scene adaptability is realized.

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; performing an encode on a frame of the video by the content delivery service that converts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features, by a machine learning model of the content delivery service, for an input, at a first resolution, of the first pixel values and the first residual of the block; generating a second set of features, by the machine learning model of the content delivery service, for an input, at a second lower resolution, of second pixel values and a second residual of the block; upsampling the second set of features to the first resolution to generate an upsampled second set of features; generating a modified version of the frame based on the first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

The invention discloses an aggregation scheduling method and device for adjustable resources, and belongs to the field of power scheduling, and the method comprises the steps: constructing a generalized energy storage model of each adjustable resource; clustering all adjustable resources according to the battery energy dissipation rate and the charging and discharging efficiency to obtain a plurality of polymers; solving a target translation factor of each adjustable resource in each polymer in each time section by taking the maximum space of the translated resource feasible region as a target and combining the transformation constraint of the feasible region; constructing a polymer feasible region constraint according to the generalized energy storage model and the target translation factor; the maximum new energy consumption is taken as a target, the polymer feasible region constraint and the power balance constraint are combined, the response power of each polymer is generated, and day-ahead scheduling is carried out according to the response power of each polymer. By implementing the method and the device, the demand response potential of the adjustable resources can be fully played, so that the problem that the response feasible interval of each resource cannot be accurately reflected when the adjustable resources are aggregated in the prior art is solved.

The invention discloses an unsupervised image conversion imaging method based on the Schrodinger bridge theory, and the method comprises the following steps: 1, constructing an image domain-domain conversion frame based on the Schrodinger bridge theory, simulating a smooth evolution path from a source domain image to a target domain image through a time condition generator, and forming antagonism training in combination with a discriminator; 2, integrating a saliency content guide constraint, a global feature consistency constraint and a contrast learning constraint in the conversion framework so as to enhance semantic content retention and detail generation capability; 3, establishing a composite loss function, and carrying out weighted combination on Schrodinger bridge path loss, adversarial loss and each auxiliary constraint loss for guiding model optimization; and 4, training a time condition generator and a discriminator through an optimization algorithm, and carrying out stable and high-fidelity target domain conversion on the source domain image by utilizing the generator after training is completed. According to the method, the stability of model training and the quality and fidelity of the generated image are remarkably improved.

The invention provides a dynamic adaptation method and system for a multi-mode power supply compatible charging pile system, and the method comprises the steps: building a multi-protocol physical layer compatible connection between a charging pile and a power supply module through a multi-communication interface layer, and receiving a data frame inputted by the charging pile; analyzing a baud rate, a check bit and an instruction set of the data frame, and identifying a charging pile protocol type and a power module protocol type; querying a matching mapping relationship between the charging pile protocol type and the power supply module protocol type in the dynamic adaptation matrix; if the matching mapping relation exists, data field conversion is executed, and a target protocol frame is generated; and if the matching mapping relation does not exist, starting a machine learning process, creating a temporary mapping rule, and effectively solving the problems of poor compatibility and high manual configuration cost of the charging pile and the power supply module in a traditional protocol mode through the modes of protocol analysis, intelligent matching and dynamic learning.

The invention relates to a frequency domain modeling method, device and equipment for a serial DR-MMC port and a medium, and the method comprises the steps: injecting current disturbance at the serial DR-MMC port, carrying out the measurement equivalence of a response quantity at a grid-connected point according to a preset grid-connected interface equivalent parameter set, and obtaining low-voltage side current small signals of a DR and an MMC; according to the low-voltage side current small signal, performing dual frequency domain transformation on a DR time domain switching function, establishing a DR switching function frequency domain model, and further deriving a DR AC side small signal relationship according to the switching function frequency domain model; constructing a DR frequency domain sub-model, an MMC frequency domain sub-model and a direct current frequency domain sub-model based on the relationship between the low-voltage side current small signal and the alternating current side small signal and in combination with device alternating current and direct current constraints, MMC loop control constraints and direct current network equivalent constraints; and assembling the DR frequency domain sub-model, the MMC frequency domain sub-model and the DC frequency domain sub-model at the grid connection point to obtain a frequency domain model of the AC port. The method has the effect of improving the characterization accuracy of the frequency domain characteristic of the grid-connected point port in the broadband range.

In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. A title transfer network element is provided to transfer digital assets via a blockchain. The system may use domain transformations and difference engines.

The invention discloses a low-frequency attribute model acquisition method, device and equipment and a readable storage medium, and belongs to the technical field of seismic inversion. The method comprises the following steps: acquiring a three-dimensional geological structure model of a target area, a first attribute value of each sampling point included in an observation well located in the target area and a second attribute value of each seismic node included in a seismic body corresponding to the target area; generating a first sequence small layer model according to the three-dimensional geological structure model, each sampling point and each seismic node; domain transformation is carried out on the first sequence small-layer model to obtain a second sequence small-layer model under the sedimentary domain; according to a first attribute value and a second attribute value of a target node in nodes included in each layer in the second sequence small-layer model, obtaining a layer model corresponding to each layer in the second sequence small-layer model; and generating a low-frequency attribute model according to the second attribute value of each node included in the second layer sequence small-layer model and the layer model corresponding to the layer where each node is located. And the precision of the low-frequency attribute model is improved.

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the super-resolution enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; downsampling a source frame of the video to generate a frame; performing an encode on a the frame of the video by the content delivery service that coverts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features at the first resolution, by a machine learning model of the content delivery service, for a first input at the first resolution, of the first pixel values and the first residual of the block; upsampling the first set of features to a target resolution to generate an upsampled first set of features; generating a second set of features at a second lower resolution than the first resolution, by the machine learning model of the content delivery service, for a second input based on the first pixel values and the first residual of the block; upsampling the second set of features to the first target resolution to generate an upsampled second set of features; generating a modified version of the frame based on the upsampled first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

This invention discloses a multivariate time series forecasting method based on prediction domain transformation and dual-path fusion. A time series forecasting model, PDT, is proposed. It utilizes data-adaptive prediction domain transformation to project the time series onto the energy-concentrated optimal latent space for prediction. A dual-path architecture is designed to simultaneously achieve efficient extrapolation of dominant linear trends and capture complex nonlinear dynamics. A masked channel dependency strategy and a lightweight linear encoder are used to adaptively fusion and filter inter-variable dependencies and deeply extract intra-channel features, respectively. The proposed time series forecasting model significantly solves the problem of insufficient nonlinear expressive power of traditional linear models and enhances noise resistance and computational efficiency in high-dimensional multivariate scenarios, comprehensively improving the prediction accuracy and robustness of the model in multivariate time series forecasting tasks.

Quality improvement processing to which a learning result is applied can be performed appropriately even if the signal characteristics of an input signal are different from the signal characteristics of student data at the time of learning. A quality improvement processing unit applies quality improvement processing to an input signal to obtain an output signal using a learning result obtained using a signal of a first domain having first signal characteristics as student data. A domain conversion unit converts the input signal to a signal of the first domain and sends the same to a quality improvement processing unit when the input signal is a signal of a second domain having second signal characteristics different from the first signal characteristics.

The invention relates to the technical field of communication, in particular to a communication method for inhibiting an overhigh peak-to-average ratio in an orthogonal time-frequency-space system, which comprises the following steps of: 1, converting a signal from a delay-Doppler domain convenient for channel characterization to a time-frequency domain convenient for modulation implementation; step 2, performing preliminary suppression on the peak-to-average ratio by adopting a T-SLM method, generating a plurality of groups of candidate signals, and selecting one group with the peak-to-average ratio lower than a preset threshold or with the minimum peak-to-average ratio; step 3, carrying out Hisenberg transformation on the selected signal to obtain a time domain signal; carrying out iterative amplitude limiting filtering processing, adding a cyclic prefix, and then sending; step 4, removing the cyclic prefix by a receiving end, and recovering to a time delay-Doppler domain through Wigner transform and Sextile Fourier transform; performing preliminary detection by adopting a message passing algorithm to obtain a hard decision value and a log-likelihood ratio; and selecting a high-reliability observation value based on a log-likelihood ratio, and constructing a reliability selection matrix.

The application discloses a radiation source identification method and device based on signal structure characteristics, equipment and medium, belongs to the technical field of electromagnetic signal analysis, the method is aimed at different system burst signal to complete characteristic word matching, and selects the signal segment based on the matching result, carries out domain transformation processing, generates the corresponding domain transformation result; extract the best sampling point of the signal, and complete the structure characteristic extraction based on the sparse feature difference processing mode; according to the structure characteristic, the individual identification of radiation source is completed. The application can effectively distinguish the communication radiation source individual by extracting the signal structure characteristics, analyzing and comparing the common points of the structure characteristics between the same radiation source individuals and the differences of the structure characteristics between different radiation source individuals, which is a key supplement to the existing radiation source individual identification method.

The present application relates to the technical field of communications, and discloses a channel state information (CSI) data processing method and apparatus, a terminal, a network side device, a medium, and a product. The method in embodiments of the present application comprises: a target node acquires first information, wherein the first information comprises at least one of the following information associated with a target artificial intelligence (AI) unit: information used for grouping CSI data; information for performing domain transformation on the CSI data; and information for combining the CSI data, and the target AI unit comprises at least one of the following: a first AI unit for acquiring target CSI by a terminal; a second AI unit for acquiring the reconstructed CSI by a network side device; a reference AI unit for acquiring the target CSI or the reconstructed CSI by the terminal or the network side device; a third AI unit used by the terminal, the network side device or a test device during testing; and a fifth AI unit for matching a fourth AI unit by the terminal, the network side device or the test device during testing.

The present disclosure relates to a controller that optimizes read thresholds of a memory device using domain conversion. For decoded data of each read operation, the controller determines an asymmetry ratio (AR) and a number of unsatisfied checks (USC), the AR representing a ratio of a number of first binary values to a number of second binary values in the decoded data. The controller determines a Z-axis such that AR values of a threshold set are arranged in a set order along the Z-axis. The controller determines an optimal read threshold set using coordinate values on the Z-axis that correspond to set AR values and set USC values.

The application discloses a semantic communication method and system based on interleaved frequency division multiplexing, belongs to the field of semantic communication, and is applied to a semantic communication system. The method comprises the following steps: a sending end extracts semantic information through a semantic encoder and transmits the semantic information after modulation through interleaved frequency division multiplexing; a receiving end reconstructs a semantic signal based on a received signal through a cross-domain iterative detector and completes a target task through a semantic decoder. The cross-domain iterative detector performs linear detection in a time domain by utilizing channel sparsity, performs nonlinear detection in a characteristic domain based on a fractional model, and realizes cross-domain transformation through an interleaved frequency division multiplexing modulation matrix with universal characteristics. The application can realize training of an encoder / decoder and decoupling of a channel through interleaved frequency division multiplexing modulation, the channel has strong universality, and the compatibility with an existing digital communication system is good. Since the type of the encoder / decoder is not limited, the whole system can perform multiple tasks, and the task universality is good.

Methods and devices are provided for rectifying a forward mapping coding bit length issue introduced by LMCS. In one method, a plurality of prediction samples, in a mapped domain, of luma component of a CU that is coded by a CIIP mode under LMCS framework is obtained, a plurality of residual samples, in the mapped domain, of the luma component of the CU is obtained, the plurality of prediction samples in the mapped domain is added to the plurality of residual samples in the mapped domain, resulting in a plurality of reconstructed samples, in the mapped domain, of the luma component, and the plurality of reconstructed samples of the luma component is converted from the mapped domain into an original domain based on a pre-defined plurality of inverse mapping scaling factors.

The application discloses a hardware accelerator and an acceleration method based on a full homomorphic encryption isomorphism mapping, and the accelerator comprises a polynomial storage area, a Tag and Sel reloading unit, a read address calculation unit, a permutation network and a cache unit. The method comprises the following steps: according to the architecture parallelism configured during compilation and the maximum polynomial degree to be supported, after compilation, the isomorphism mapping of the polynomial can be completed according to the actual polynomial degree and the isomorphism parameter of the input. The embodiment of the application can reduce the domain conversion overhead and the overhead of the bit inversion arrangement coefficient, and reduce the calculation burden of the homomorphic rotation. The application can be widely applied to the field of homomorphic encryption technology.

The embodiment of the invention relates to the field of oil-gas exploration electrical method data processing, and discloses a self-adaptive electromagnetic signal curve denoising fitting method and system, and the method comprises the steps: carrying out the reconstruction processing of an original data pair (xi, yi, i = 1,..., N), and the data reconstruction processing comprises the data translation, the number domain conversion, and the data compression and expansion processing; performing iterative fitting on the reconstructed data by using polynomials of different times, and judging according to a fitting difference to obtain an optimal fitting polynomial; and performing data anti-reconstruction on the optimal fitting polynomial, and performing regression to an original data form. According to the embodiment of the invention, the data multi-reconstruction technology is utilized, the morbidity of curve fitting is eliminated, and any electromagnetic signal curve can be effectively fitted.

The invention discloses a ship structure crack positioning and feature reconstruction method and system. The method comprises the steps that a sensor array is arranged in a to-be-detected area, guided wave signals are emitted and collected, domain transformation, mode screening and matched frequency window filtering are conducted on original signals, and enhanced signal data are obtained; carrying out multi-mode feature extraction on the enhanced signal, constructing a multi-dimensional feature vector containing mode features and a coupling relation thereof, and realizing initial crack positioning by combining sensor position information; time inversion and wave field back propagation are further carried out on the enhanced signal, an energy field is constructed, accurate crack positioning is realized according to an energy field extreme value, and crack direction and size information is extracted; according to the method, the signal-to-noise ratio, the positioning precision and the stability and quality of time reversal imaging can be remarkably improved.