368 results about "Spatial encoding" patented technology

The present disclosure provides methods and assay systems for use in spatially encoded biological assays, including assays to determine a spatial pattern of abundance, expression, and / or activity of one or more biological targets across multiple sites in a sample. In particular, the biological targets comprise proteins, and the methods and assay systems do not depend on imaging techniques for the spatial information of the targets. The present disclosure provides methods and assay systems capable of high levels of multiplexing where reagents are provided to a biological sample in order to address tag the sites to which reagents are delivered; instrumentation capable of controlled delivery of reagents; and a decoding scheme providing a readout that is digital in nature.

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention relates to the technical field of people flow prediction, and discloses an urban people flow prediction method based on an urban point-of-interest spatio-temporal data set, which comprises the following steps: acquiring the urban point-of-interest spatio-temporal data set; performing multi-modal feature dynamic fusion by adopting a hierarchical perception attention fusion mechanism to obtain a point-of-interest feature vector; performing spherical spatial dependency modeling by adopting a regional adaptive spherical convolutional coding method to obtain a spatial coding vector; using a periodic mask generator to carry out business time constraint coding to obtain a time feature vector; and according to the interest point feature vector, the space coding vector and the time feature vector, space-time coupling features are extracted, regional perception time sequence modeling is performed on the space-time coupling features, and a people flow prediction value and crowding degree grade probability distribution are generated through multi-task sharing prediction. According to the urban people flow prediction method based on the urban interest point spatio-temporal data set, the urban people flow prediction performance is improved.

The invention discloses a time-space interaction vehicle trajectory prediction method based on speed perception, and the method comprises the steps: obtaining driving trajectory data of a target vehicle and surrounding vehicles in a perception range of the target vehicle, inputting the driving trajectory data into a pre-trained trajectory prediction model, and obtaining the trajectory data of the target vehicle in a prediction time period; the trajectory prediction model comprises an encoder module, a speed sensing interaction modeling module and a decoder module; in the encoder module, a spatial-temporal feature encoder is used for obtaining spatial-temporal feature codes based on the driving tracks of the target vehicle and other vehicles; the scene perception encoder is used for extracting a global spatial dependency relationship between vehicles to obtain a scene perception spatial code; the speed perception interaction modeling module is used for extracting space-time interaction features and scene interaction features of the target vehicle and surrounding vehicles by using a multi-head attention mechanism based on the space-time features and scene perception space codes, and further obtaining global interaction features; and the decoder module is used for obtaining a driving track of the target vehicle in the prediction time period based on the space-time interaction characteristics and the global interaction characteristics. According to the method, the vehicle-scene spatial dependency can be accurately captured, and the trajectory prediction accuracy is improved.

The invention discloses a city-level CIM scene streaming rendering method based on multi-modal fusion, and the method comprises the following steps: S1, collecting city data, and carrying out the preprocessing of the city data; s2, establishing a cross-modal entity alignment relationship, and constructing a city object structure body; s3, dividing a space by adopting a quadtree method, generating rendering sub-blocks and distributing a unique space code; s4, binding the rendering sub-blocks with the urban object structure body, constructing a multi-modal fusion network, and generating a multi-modal rendering unit set; s5, constructing an inter-frame rendering cost function, and scheduling and sequencing the multi-modal rendering unit set to form an inter-frame scheduling sequence; s6, loading the rendering units according to the inter-frame scheduling sequence to form a city-level three-dimensional view of continuous frames; and S7, updating the scheduling sequence, dynamically adjusting the loading sequence, and realizing stable and efficient streaming rendering of the city-level CIM scene. According to the method, the fusion efficiency and rendering performance of the urban large-scale three-dimensional data are improved, and the method has remarkable engineering applicability and popularization value.

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

The invention discloses a spatio-temporal data compression method and system based on lightweight processing, and relates to the technical field of image data processing, and the method comprises the steps: based on spatio-temporal data to be compressed, establishing standardized data entries, forming a window set through time axis segmentation, carrying out the periodic discrimination of windows according to the frequency domain energy distribution, and constructing spatio-temporal blocks, establishing an error control parameter table based on the global error budget; generating a time coding stream, performing integerization on the three-dimensional coordinates and the attribute values, generating a space coding stream through double difference and run length coding, performing edge folding lightweight and texture compression on the grid data, and generating a joint coding result; and based on a joint coding result, dividing the compressed data into a base layer and a plurality of enhancement layers, and establishing a relevance hierarchical storage structure and a block-level index table to generate the compressed data capable of being transmitted in a streaming manner. According to the method, collaborative compression can be carried out by utilizing spatial-temporal data internal relevance.

The invention relates to the technical field of automatic driving, in particular to a multi-view-angle perception-driven automatic driving test scene twin reconstruction method, which comprises the following steps of: S1, acquiring and preprocessing multi-view-angle image perception data to form a multi-view-angle image sequence under a unified coordinate system; s2, spatial coding and modeling based on an orthogonal three-plane structure; s3, on the basis of a view perception-space interaction encoder of a Transform architecture, completing space coding processing represented by an orthogonal three-plane structure; s4, structure and semantics combined twinborn reconstruction decoding is carried out; and S5, generating a simulation compatible format and deploying a platform. According to the method, the terrain, facilities, traffic elements and other contents of a real road scene can be automatically restored, the storage and calculation cost is remarkably reduced while the spatial information integrity is ensured, various simulation platform support formats can be flexibly output, the method serves for closed-loop testing of automatic driving, and the reconstruction efficiency and the system suitability are improved.

The invention discloses an underwater image enhancement method based on dual-path feature decoupling and gating fusion. The method comprises the following steps: firstly, constructing a training data set; then, an encoder-bottleneck layer-decoder is used as a trunk, and a dual-path encoding and decoding mechanism is adopted to construct an underwater image enhancement model; the encoder is used for extracting direction encoding features and space encoding features; the bottleneck layer is used for extracting global and local features and fusing and outputting bottleneck features; the decoder is used for decoding and multi-scale refinement so as to reconstruct and obtain an underwater enhanced image; then training is carried out to obtain a trained underwater image enhancement model; and finally, the device is deployed to acquire the underwater image in real time for underwater image enhancement. According to the method, independent modeling is carried out for anisotropic scattering / edge attenuation and spatial non-uniform atomization / local brightness imbalance, and mutual interference of different degradation causes in the same feature space can be reduced.

The present disclosure is directed to a traffic signal apparatus communication system and methods of communicating traffic information to vehicles using same. The traffic signal apparatus communication system includes a traffic signal apparatus for providing a message to a vehicle. The apparatus includes at least one spatially encoded marker, and the vehicle is configured to receive returns of a radar signal from the spatially-encoded marker. At least one controller of the vehicle is configured to determine the message encoded by the spatially-encoded marker based on the returns and to control the vehicle based on the message. The message may include a value indicating a time to a transition of a new state of the traffic signal apparatus, where the new state includes emission of light from one of a first light source, a second light source, or a third light source of the traffic signal apparatus.

The invention discloses a tooth CBCT image super-resolution reconstruction method based on a potential diffusion model, and relates to an image processing and generating technology in the field of computer vision. The in-vivo medical image is trained based on hidden space coding and probability diffusion, so that the network has a better super-resolution effect on the real in-vivo tooth CBCT image.

The invention discloses an end-to-end polarization hyperspectral image classification method and system, belongs to the technical field of deep learning and optical imaging, and solves the technical problems of low reconstruction process speed, limited precision, low information utilization rate in a classification process and weak feature extraction capability in the prior art. The method comprises the following steps: carrying out target shooting based on a snapshot type space coding hyperspectral polarization imaging system, and carrying out system coding on a shot image to obtain two-dimensional aliasing data; reconstructing the two-dimensional aliasing data into a polarization hyperspectral data cube by using the trained reconstruction network; training the classification network based on the polarization hyperspectral data cube to obtain a trained classification network; and performing joint fine tuning on the trained reconstruction network and the trained classification network to obtain a polarization hyperspectral image classification model, and performing polarization hyperspectral image classification by using the polarization hyperspectral image classification model. The method is used for realizing high-quality reconstruction and accurate classification of the polarization hyperspectral image.

In various examples, three-dimensional multi-camera perception systems and applications is described herein. Systems and methods are disclosed herein that process image data generated using multiple cameras located throughout an environment in order to directly determine three-dimensional (3D) information associated with objects located within the environment. For instance, the image data may be processed using one or more feature extractors (e.g., one or more backbones) to determine multi-view image features associated with images represented by the image data. These multi-view image features, along with calibration data associated with the cameras, may then be processed using one or more spatio-temporal transformers (e.g., one or more spatial encoders, one or more temporal encoders, etc.) in order to determine 3D locations of objects within the environment.

The invention discloses a multi-frame reconstruction multispectral imaging system based on dynamic space-time joint coding. The system comprises a filtering window module used for limiting the spectral range of imaging; the spatial code modulation module is used for providing spectral codes in a spatial dimension; the time coding modulation module is used for carrying out inter-frame modulation on the spectrum codes provided by the space coding modulation module; the optical imaging module is used for imaging the light field modulated by the time coding modulation module on the color acquisition module; the color acquisition module is used for carrying out further spectral coding on the light field to obtain a measurement image; a calculation reconstruction module; the method is used for recovering a high-dimensional multispectral image data cube by using a measurement image. The method has the advantages of being light in weight, low in cost, high in underqualitative relieving capacity, efficient and stable in reconstruction method, high in adaptability and practicability and the like.

The invention provides a medical magnetic resonance image reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining sampling coding and under-sampling k space data; processing the under-sampled k space data through inverse Fourier transform, and converting the k space data into an image domain to obtain under-sampled image data with blurring or artifacts; carrying out preliminary restoration on the undersampled image data based on sampling coding, further applying data consistency operation on the image to obtain a rough image, and taking the rough image as an image condition vector after underspace coding; acquiring a text serving as a cue word, inputting the text into a text encoder, and encoding the text into a high-dimensional semantic embedding vector by the text encoder; and inputting the high-dimensional semantic embedding vector and the image condition vector into a reconstruction model based on the correction flow to predict a full-sampling MRI image, and generating a final reconstructed MRI image.

The invention discloses a three-dimensional radar echo reflectivity variational auto-encoder pre-training method, which belongs to the technical field of meteorological radar data analysis, and comprises the following steps: extracting advanced features from three-dimensional high-resolution radar data through a space encoder based on an attention mechanism; mapping the advanced features into determined probability distribution parameters using a probabilistic encoder; sampling according to the distribution parameters by using a re-parameterization technique to obtain continuous potential variables; mapping the potential variables back to a pixel space by using a probability decoder based on an attention mechanism to complete data reconstruction; and finally, constructing a mixed loss function consisting of a mean square error and KL divergence by utilizing a reconstruction result and a distribution parameter to train the model. According to the invention, by optimizing the network architecture and introducing the attention mechanism, high-quality reconstruction of high-resolution three-dimensional radar data is realized while extremely low video memory requirements and low parameter quantity are ensured, and the practical value of data synthesis and enhancement is remarkably improved.

The invention discloses an overhead power line icing thickness detection method and system based on a structure perception framework, and the method comprises the steps: firstly obtaining point cloud data of a power transmission line, preprocessing the point cloud data, and inputting the preprocessed point cloud data into a structure perception semantic segmentation network; the network adopts a four-level encoder-decoder architecture, an encoder extracts multi-scale features through local space encoding, cross-scale space attention fusion and local aggregation operation, and a decoder realizes feature fusion in combination with a jumper connection mechanism and outputs a lead semantic segmentation result; extracting a conductor point set based on a segmentation result and carrying out Euclidean clustering strand splitting; performing biplane projection and polynomial fitting on each strand of wire to construct a three-dimensional center line; and finally calculating the distance from the point to the center line, selecting a peripheral point set to calculate the wire envelope radius, and combining the radius of the bare wire to obtain the icing thickness. Measurement deviation caused by point cloud shielding, wire bending and asymmetric icing is effectively solved, millimeter-level precision non-contact ice thickness detection in a complex environment is achieved, and reliable technical support is provided for power grid disaster prevention.

The invention discloses a lightweight SDN (Software Defined Network) attack detection method based on multi-scale iterative attention, relates to the technical field of network security, and solves the problem that an SDN attack detection method based on deep learning in the prior art is insufficient in feature selection static state and spatial modeling and gives consideration to both lightweight and high precision. The method is based on a feature contribution degree evaluation mechanism, the most critical features for attack discrimination are screened out in real time, redundant information is eliminated, and the calculation burden is reduced. Moreover, the attack feature map is generated through normalization, time window overlapping slicing and multi-channel space coding, so that the perception capability of a complex attack mode is improved. Besides, a multi-scale iteration attention mechanism is embedded in a lightweight network architecture, key features are highlighted and redundant information is suppressed through multi-granularity convolution extraction and iteration weight fusion, and both lightweight and high-precision detection are realized, so that the method is suitable for real-time network environment and edge device deployment.

The invention provides a component intelligent warehouse inventory accurate management system based on the Internet of Things, and relates to the technical field of automatic control, and the method comprises the steps: generating an associated data message through data fusion calculation according to environment parameters and physical attribute characteristic data; based on the associated data, when the environment parameters trigger a preset low-temperature condition, generating a first regulation and control instruction for adjusting the working mode of the RFID reader-writer so as to obtain a real-time working state frame returned by the reader-writer; and according to a real-time working state frame returned by the reader-writer and a material box physical attribute feature database, the initial storage position information of the target material box is converted into a unique space code, and a three-dimensional grid unit area to which the target material box belongs is determined according to the space code. According to the invention, precise and intelligent management of component warehouse inventory is realized.

The invention discloses a multi-parameter dynamic coding method for tactile feedback. The method comprises the following steps: arranging a multi-channel electrode array in a tactile sensing area, and establishing a corresponding relation between each channel of a multi-channel array electrode and a spatial position in the tactile sensing area through spatial coding; calibrating the minimum value and the maximum value of various types of electrode stimulation parameters reflecting the touch perception degree to obtain a dual-threshold calibration result; performing intensity coding according to the double-threshold calibration result; constructing a type code, wherein the type code reflects a corresponding relation between the frequency interval and the touch induction type; and generating a parameter dynamic mapping model based on the intensity code and the type code, wherein the parameter dynamic mapping model reflects a corresponding relation between an interaction characteristic between the touch sensing area and a target and an electrode stimulation parameter combination sensed by the touch sensing area. The method can be widely applied to man-machine interaction scenes needing tactile feedback.

The invention relates to an on-chip dynamic control device for the wavefront of a terahertz metasurface transmission wave or surface wave, and belongs to the technical field of electromagnetic wave wavefront control devices. Since the traditional phase gradient metasurface fails to dynamically control the wavefront of a transmission wave or surface wave, the present invention combines an integrated photosensitive material metasurface with a spatially coded light source to realize on-chip dynamic control of the wavefront of a terahertz transmission wave or surface wave. The device deposits a dielectric layer on a metal reflective bottom plate, and the top layer of the dielectric layer is divided into two regions: an excitation region and an intrinsic region. The excitation region is composed of a metal ring array with multiple openings uniformly distributed and photosensitive materials deposited at the openings. By controlling the intensity of the pump light source irradiating the photosensitive material, a 2-bit coding unit is formed. The excitation region is constructed according to a preset coding sequence. When a circularly polarized wave is incident on the surface of the excitation region, a transmission wave or surface wave can be generated. By changing the coding sequence, the phase of the transmission wave or surface wave is reconstructed, and the wavefront of the transmission wave or surface wave is dynamically controlled, thereby realizing an on-chip dynamic control device for the wavefront of a transmission wave or surface wave.

The invention discloses an accelerator architecture design method and electronic equipment, relates to the technical field of computers, and performs dynamic fractal space coding on a design parameter matrix of an accelerator to obtain an analog coding vector. And performing meta-learning dynamic weight entropy search on the analog coding vector by using a historical task data set to determine a new design scheme. And when the difference between the sensitivity parameters in the new design scheme and the sensitivity parameters in the neighbor set meets a difference condition, performing multi-objective optimization simulation on the new design scheme. And when the difference does not meet the difference condition, multiplexing the simulation result of the neighbor set. And performing iterative search according to Bayesian optimization, and determining a final accelerator architecture design scheme in the selected design scheme and the simulation result thereof. Dimension reduction is performed on a high-dimensional discrete space, and a search direction is guided by means of an efficient meta-learning dynamic weight entropy acquisition function. And rapid exploration of an accelerator architecture design space under multi-objective optimization is realized.

The invention provides a single-pixel calculation hyperspectral imaging system and method, and the technical scheme of the invention comprises a spatial light modulator which is used for carrying out the spatial coding of a light beam from an imaging scene; and the reconfigurable spectrum detector is used for detecting the light beams after space coding. The reconfigurable spectrum detector is provided with a group of spectral response functions which are tunable along with external bias voltage, broadband and correlative, and the reconfigurable spectrum detector forms a core sensing unit of the micro-computing spectrometer. A barrel signal sequence is obtained by synchronously collecting light intensity integral values under different space coding patterns and different bias voltages. And finally, in a data processing module, a spectral data cube of the target scene is jointly solved from the bucket signal sequence according to the spatial coding matrix and the spectral response function set by utilizing a calculation reconstruction network. According to the invention, two computational imaging technologies are creatively fused, the structure is very compact, and real-time and wide-spectrum spectral imaging can be realized at a low sampling rate.

The invention discloses a direction of arrival estimation method based on a residual network. A space encoder and an improved residual network cascade module are included. According to the system, complex signals received by an antenna array element are used as input, a covariance matrix is extracted, and a multi-channel characteristic pattern is constructed. Direction-of-arrival coarse estimation is carried out through space encoder partition, and further direction-of-arrival fine estimation is carried out through an improved residual network. Through reasonable combination of the deep neural network and the residual network, high estimation precision and operation speed can still be maintained in complex scenes of low signal-to-noise ratio, small signal source angle interval, multipath interference and the like. Compared with the traditional direction of arrival estimation method, the method provided by the invention has a remarkable advantage in the direction of arrival estimation precision under the complex test conditions of low signal-to-noise ratio and the like. Meanwhile, on the premise that high precision is kept, the method has the higher operation speed, and good real-time performance and engineering application prospects are embodied.

The invention provides a space-time fusion coding method and a data relation generation method and system, and is applied to the technical field of physical world-oriented multi-source heterogeneous data organization management. Space information of a target data object is mapped to a geographic space subdivision grid coding system to generate a space code; the method comprises the following steps: standardizing time information according to preset time granularity (such as year, month, day and hour), mapping the time information to a layered time coding system to generate a time code, and fusing a space code, the time code and object identification information according to a preset rule to generate a unique space-time fusion code; and finally, the space-time fusion codes and attribute information are bound to form standardized data records, data objects of different sources and different formats are mapped to a unified space-time coding system, data islands are broken, and a data foundation is laid for constructing a unified space-time relation network.

The invention relates to the technical field of visible light communication, and discloses an intelligent lamp data transmission system based on visible light communication. The system comprises an optical signal processing module, a transmission management module and a terminal control module. The optical signal processing module collects and processes the visible light signal and outputs a light intensity space coding signal; after the transmission management module receives the signal, the signal is decoded by the signal analysis unit to obtain an original data stream signal and a channel interference characteristic signal, the channel optimization unit processes the original data stream signal and the channel interference characteristic signal to obtain an optimal modulation parameter signal, and the transmission strategy unit outputs a dynamic bandwidth allocation signal; in the terminal control module, an adaptive modulation execution unit adjusts the driving parameters of the light emitter according to the optimal modulation parameter signal, a lamp array control unit adjusts the working state of a lamp light-emitting unit and collects a real-time link state signal, and a link monitoring module outputs a communication quality index. The system is suitable for an intelligent illumination and data transmission fusion scene.

The invention relates to the technical field of deep learning, and discloses a deep learning prediction method and system for multi-source space-time lattice point data, and the method comprises the steps: obtaining live lattice point data, forecast lattice point data and static lattice point data, and carrying out the preprocessing, thereby obtaining uniform lattice point feature data; and generating missing mask lattice point data, generating confidence coefficient lattice point data according to observation coverage information, interpolation distance information, a time-space consistency test result and a forecast aging attenuation rule, and forming model input lattice point data. And constructing a training sample and generating forecast aging coding data. And constructing and training a deep learning prediction model, wherein the model comprises a space coding network, a time coding network and a forecast aging segmentation prediction network. In the training stage, confidence coefficient is used for weighting regression loss. In the reasoning stage, a release prediction result is obtained. According to the method, the learning stability of the multi-source grid point data under the missing measurement and filling conditions is improved, and the reliability of continuous value prediction and grading alarm probability under different prediction time periods is improved.

The invention provides a text guidance-based generative reverse face recognition method. The method comprises the following steps of: obtaining an initial latent vector and a fine tuning generator by using a pre-trained editing encoder and an original face image; taking the initial latent vector as an initial vector during gradient updating, and starting to circularly update until a final latent space code is obtained; and inputting the subsurface space code into the fine-tuned generator, and taking the generated image as a protected image corresponding to the original face image. The total loss function comprises an adversarial loss for explicitly promoting diversity and a loss function of a visual effect; the fine-tuned image generated by the generator is closer to an auxiliary image randomly selected from the auxiliary data set so as to realize protection of dynamic tracking of face recognition; meanwhile, it is ensured that the image generated by the fine-adjusted generator follows the specification of target text prompt and keeps the visual similarity with the original face image. The method has excellent performance in the aspect of preventing the face image of the user from being identified by the dynamic FR strategy.