610 results about "Spatial frequency" patented technology

The invention discloses a panchromatic sharpening method based on multi-resolution panchromatic feature guidance, which comprises the following steps: firstly, designing a multi-resolution feature extraction network based on a spatial frequency Transform module, and respectively extracting multi-scale features from panchromatic and up-sampled multispectral images; secondly, in order to restrain and optimize the extracted detail features, a multi-head self-attention-based texture injection module is adopted to extract a dependency relationship between panchromatic and multispectral features, and more similar detail features are guided to be injected; thirdly, in order to solve information loss caused by multispectral image up-sampling, multi-level detail features are injected into a multispectral reconstruction network, the detail features are learned step by step and reconstructed to a panchromatic image size, and a spectrum fusion module based on adaptive channel attention is utilized to reconstruct a fused image in a high-quality mode; and finally, optimizing the model performance by combining reconstruction loss based on L1 norm, structural similarity constraint and transmission perception loss in training. According to the method, the spectral fidelity and the spatial resolution of the fused image are remarkably improved.

The invention provides an underwater image enhancement method combining high-frequency enhancement, space-frequency mixed feature extraction and a Mama enhancement fusion technology. The method aims at solving the problems of low contrast ratio, color distortion, detail loss and the like caused by absorption and scattering effects in the light propagation process of the underwater image, the image quality is improved, and the visual perception effect is improved. The method specifically comprises the following four main steps: firstly, acquiring and preprocessing initial data sets of different underwater scenes; secondly, extracting texture detail information of the image through a texture enhancement module; thirdly, further enhancing texture details by using a space-frequency mixed feature extraction module; and finally, in combination with the original image, the texture information and the spatial frequency domain enhancement result, realizing feature fusion of the image through a Mama enhancement fusion module, thereby effectively recovering details of the underwater image, correcting color deviation and improving the definition and visual effect of the image. According to the method, the enhancement performance of the underwater image in a complex environment can be improved, and powerful support is provided for applications such as underwater detection, target detection, object recognition and image segmentation.

The invention relates to an underwater image enhancement method based on frequency domain analysis and visual Mama, comprising the following steps: constructing a multistage wavelet transform network, the network being an encoder-decoder structure, the encoder comprising a space-frequency feature fusion module and discrete wavelet transform; the decoder comprises a space-frequency feature fusion module and inverse discrete wavelet transform; an underwater image is decomposed into a low-frequency part and a high-frequency part through discrete wavelet transform in an encoder, spatial feature extraction and frequency feature extraction are conducted on the low-frequency part and the high-frequency part through corresponding space-frequency feature fusion modules based on Mama and frequency domain convolution respectively, and splicing is conducted on the low-frequency part and the high-frequency part along a channel in the decoder according to the sequence of low frequency and high frequency. Reconstruction is carried out through inverse discrete wavelet transform; and inputting the underwater image data into the multistage wavelet transform network to obtain enhanced underwater image data. Compared with the prior art, the method has the advantages of improving the enhancement quality, reducing the complexity, quickly enhancing and the like.

The invention relates to the technical field of part size detection, and discloses a part size detection method based on machine vision. The method comprises the following steps: synchronously acquiring multi-angle original image data of a to-be-detected part by using a plurality of industrial cameras; preprocessing the original image data to obtain a de-noised and enhanced standard image set; performing three-dimensional point cloud reconstruction on the set to obtain spatial point cloud model data of the part; through a spatial frequency domain registration algorithm, registering the spatial point cloud model data with a preset part standard CAD model, and outputting a difference point cloud distribution diagram; extracting a key dimension feature vector in the image, inputting the key dimension feature vector into the trained optical feature fusion model, and generating a multi-dimensional dimension deviation quantitative index; and according to the index matching process compensation parameter set, outputting a size detection decision report. The method can comprehensively capture the part features, improves the detection precision, achieves the cooperation of detection and production technologies, and is suitable for the field of part size detection.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention discloses a visual identification method and system, and the method comprises the steps: obtaining a visible light image, infrared thermal imaging and depth point cloud data of a target scene, and generating a time-space consistent multi-modal heterogeneous feature tensor; inputting the multi-modal heterogeneous feature tensor into a spatial frequency sensing optimizer to generate a detail-enhanced optimized feature matrix; on the basis of the optimized feature matrix, adopting an adversarial generative network to synthesize a multi-scale shielding sample, and generating an identification feature vector with enhanced adversarial robustness; inputting the identification feature vector into a self-adaptive decision engine to generate an environment self-adaptive dynamic decision parameter; and constructing a multi-scale verification pyramid according to the dynamic decision parameters, fusing the confidence score of each level through a self-correction module, and outputting a final recognition result. According to the embodiment of the invention, high-robustness and high-accuracy visual identification can be realized.

The invention discloses an unmanned aerial vehicle target detection method based on a cross-spatial frequency domain and electronic equipment, and belongs to the technical field of image detection. According to the method, an unmanned aerial vehicle target detection network based on a cross-space frequency domain is constructed to carry out unmanned aerial vehicle target real-time detection; the unmanned aerial vehicle target detection network is composed of a cross-space frequency domain feature extraction network, a detail information hybrid encoder and a position relation decoder; inputting the unmanned aerial vehicle target image into a cross-space frequency domain feature extraction network, and extracting multi-scale frequency domain features of the unmanned aerial vehicle target image; performing multi-scale feature fusion and feature enhancement to obtain a multi-scale feature map; and the position relation decoder generates a target category and bounding box coordinates according to the multi-scale feature map in combination with the relative position relation features between the targets. According to the method, complementary information of a spatial domain and a frequency domain and potential spatial position relation characteristics between targets can be fully utilized, and the target detection precision and robustness of the unmanned aerial vehicle under a complex background are remarkably improved.

The invention provides a diffractive optical element design and manufacturing integration method based on gray level laser direct writing photoetching, which comprises the following steps that: a manufacturing simulator and a wave optical simulator are established, and the manufacturing simulator represents the spatial frequency response of an optical system through a low-pass filtering model; a mapping relation between exposure dose and a developing result is established by combining nonlinear response of photoresist, and the wave optical simulator predicts optical performance by utilizing output of the manufacturing simulator and combining a wave optical theory; by setting initial manufacturing parameters and performing iterative optimization, the surface appearance of the photoresist is dynamically adjusted to be close to the target optical performance, the optimization process is combined with an automatic differential technology, a gradient optimization algorithm and a block optimization strategy, and the calculation efficiency is improved; and the optimized parameters are used for processing gray laser direct writing equipment to manufacture the diffractive optical element. The problem of manufacturing deviation caused by the optical proximity effect in the prior art can be solved, and meanwhile the overall performance of the diffraction optical element is remarkably improved.

The invention provides a multi-modal crowd counting method and system, and the method comprises the steps: obtaining an image, and extracting the image features of the image, the image comprising a visible light modal image and a thermal infrared modal image; image features of the visible light modal image and the thermal infrared modal image are subjected to attention through spatial frequency in a spatial frequency guiding module layer by layer to generate a target attention map; fusing the image features of the visible light modal image and the thermal infrared modal image of each level and the target attention map through an adaptive dynamic fusion module to obtain each multi-modal fusion feature; according to the multi-modal crowd counting method, a prediction density map is generated from each multi-modal fusion feature through a multi-scale progressive fusion module, the multi-scale progressive fusion module is composed of cavity space pyramid pooling and Swin Transform Block, and through the modules designed in the steps, the problems that multi-modal crowd counting errors are large and counting precision is low can be effectively solved.

The invention provides a low-light large dynamic image enhancement method based on dynamic weight and pyramid fusion. The low-light large dynamic image enhancement method is suitable for complex illumination imaging in security monitoring. According to the method, the global weight and the detail weight of an image are obtained according to the global average brightness, the local brightness and the gradient of an image sequence, and the total weight is obtained through weighting. And the halo phenomenon easily occurring in the fusion process is reduced through guide filtering. And combining Gaussian pyramid and ratio pyramid decomposition and weighting to obtain a final fusion image. According to the method, the problems of detail loss and limited dynamic range in the low-light scene are effectively solved. Experiments show that the method is superior to the prior art in information entropy, average gradient, edge strength, spatial frequency and other key indexes. The technology has an all-time adaptive capability, is suitable for the fields of intelligent transportation, urban security and protection, low-illumination monitoring and the like, and has a wide market prospect.

The invention relates to the technical field of spectral analysis, in particular to an intelligent spectral pesticide residue detection system which comprises a spectral data acquisition module, a spectral feature analysis module, a pesticide spectral feature classification module, a spectral matching weight optimization module and a pesticide residue concentration analysis module. In the invention, by collecting spectral signals of a pesticide sample in different wavelength ranges and combining time, space and frequency dimension information, the comprehensiveness of data collection is ensured, spectral data is analyzed in multiple dimensions, mutation features are extracted, associated frequency bands are identified, angle deviation is eliminated, and the accuracy and reliability of spectral features are improved; classification is performed according to spectral distribution differences and change rates, detection resolution is improved, spectrum matching rules are optimized, high-contribution wavebands are screened, detection accuracy is improved, concentration normalization analysis is combined, key feature contribution values are adjusted, misjudgment risks are reduced, optical paths are optimized, optical loss is reduced, signal acquisition stability is improved, and adaptability of a detection system is enhanced.

The invention relates to the technical field of data analysis and processing, in particular to an identity data analysis method based on a spatial frequency sensing fusion network, and the method specifically comprises the following steps: collecting identity data to be detected, and marking real identity information; preprocessing the collected identity data, and dividing the collected identity data into a training set and a test set in proportion; constructing a spatial frequency sensing multi-scale network for real-time deep identity analysis, and training the network; inputting the identity data in the test set into the trained network for forgery detection and prediction, and outputting a probability score that each identity is true or false so as to obtain an identity analysis result. By constructing a lightweight deep neural network structure which combines space and frequency feature perception and has a multi-scale fusion capability, the method can be used for efficiently detecting identity information in actual scenes such as video conferences and social media.

The invention discloses an electroencephalogram fatigue recognition method based on multi-scale convolution and a double attention mechanism, and belongs to the technical field of electroencephalogram signal analysis and fatigue detection. The method comprises the following steps: carrying out preprocessing and data integration on multi-channel electroencephalogram signals, extracting sub-band energy by adopting wavelet packet decomposition, and mapping the sub-band energy to four frequency bands of delta, theta, alpha and beta; constructing a data matrix of channel * frequency band * time step, and inputting the data matrix into a multi-scale convolutional neural network to extract time domain features; a space attention mechanism is introduced, and key channel response is enhanced; time sequence characteristics are modeled through an LSTM network, a frequency attention mechanism is added, and a fatigue related frequency band is highlighted. According to the method provided by the invention, the modeling capability of time dependence and spatial frequency band correlation of the electroencephalogram signals is remarkably enhanced, and deep mining of fatigue state characteristics is realized. The model can realize efficient and accurate fatigue recognition without priori knowledge, and is suitable for an electroencephalogram intelligent monitoring scene in a complex environment.

The invention relates to the technical field of wind power generation, in particular to a fan blade defect detection method and system based on a target detection model, and the method specifically comprises the steps: obtaining image data of a to-be-detected blade, and generating a fan blade defect image through employing an improved HP-VAE-GAN, so as to achieve the extension of a training set; in combination with a multi-exposure image fusion method based on a spatial frequency integration framework (MEF-SFI), the data quality is optimized; performing lightweight improvement on a YOLOV11 target detection model, and inputting the marked data set into the trained model to obtain a final fan blade state detection result; according to the method, the edge computing equipment is combined, the problems of large data acquisition amount, low transmission speed and the like are solved, and the accuracy, convenience and real-time performance of blade detection are improved on the whole.

The invention provides a power cable test data identification method and system, and the method comprises the steps: obtaining an electrical signal sequence of a power cable under a plurality of preset test condition combinations, carrying out the space-frequency joint feature decomposition of the electrical signal sequence, and constructing a three-dimensional time-frequency data body comprising a space position, a time sequence and a frequency feature; constructing a dynamic phase space on the basis, reconstructing a state trajectory of each physical segment, calculating a fractal dimension spectrum and a nonlinear divergence coefficient set of the trajectory, and generating a defect evolution characteristic tensor; inputting the defect into a spatio-temporal chaotic mapping network, aligning characteristic evolution paths of different physical segments, and constructing a defect propagation spatio-temporal atlas; and analyzing the fault development trend of each physical segment in the time dimension and the defect diffusion path of each physical segment in the space dimension, and outputting an identification result containing a defect type evolution probability sequence and a three-dimensional position offset field. According to the invention, a more accurate and comprehensive basis is provided for state evaluation and maintenance of the power cable.

The invention discloses a method for improving the axial resolution of photoacoustic microscopic imaging obtained based on a single-pixel frequency domain. The method comprises the following steps: 1) creating a Fourier substrate pattern with the spatial frequency of k in the axial direction; 2) respectively irradiating four Fourier substrate patterns with the spatial frequency of k and the initial phases of # imgabs0 #, # imgabs1 #, # imgabs2 # and # imgabs3 # to an imaging object, carrying out photoacoustic microscopic imaging, and acquiring data by using an ultrasonic transducer to obtain a response value of the ultrasonic transducer; 3) according to the obtained response value of the ultrasonic transducer, adopting a four-step phase shift method to obtain a Fourier coefficient corresponding to the spatial frequency k; and 4) gradually increasing the spatial frequency of the Fourier substrate pattern to the cut-off frequency of the objective lens, further obtaining Fourier coefficients corresponding to all spatial frequencies, obtaining a complete Fourier spectrum, and performing inverse Fourier transform to obtain a photoacoustic microscopic image after the axial resolution is increased. The method disclosed by the invention is expected to promote wider application of photoacoustic microscopic imaging in living cell and tissue imaging.

A scanning imaging method includes splitting an optical beam into a reference beam and a scanning beam, generating an interference map, and processing the interference map to produce a reconstructed image of a sample. Generating the interference map includes, for each of a plurality of sections of the sample, generating a respective interference-map element of the interference map by: (i) illuminating the section with the scanning beam to generate a plurality of scattered beams, each scattered beam corresponding to a respective spatial frequency of the scanning beam, (ii) attenuating the reference beam, (iii) generating a plurality of interference signals at least in part by interferometrically combining the plurality of scattered beams and the attenuated reference beam while modulating a phase difference between the reference beam and the plurality of scattered beams, and (iv) detecting the plurality of interference signals to yield the respective interference-map element.

The invention discloses a panchromatic sharpening image fusion method and device based on a double-domain flexible converter. The method comprises the following steps: performing multilayer space-frequency joint attention operation on a low-resolution multispectral image and a high-resolution panchromatic image to generate a feature tensor; performing double-domain feature alignment operation, aligning structural semantic features based on an attention mechanism, fusing amplitude and phase frequency spectrum information through Fourier transform, and matching modal distribution differences by using instance normalization; multi-level fusion is carried out, and splicing, convolution and space-frequency joint attention operation are carried out on each level in sequence; carrying out residual error reconstruction on the fusion features; and adding the reconstructed features and the up-sampled low-resolution multispectral image element by element, and outputting a high-resolution multispectral image. According to the method, image structure distortion and detail blurring can be remarkably reduced, the image definition and the edge reduction capability are improved, the fusion consistency and the physical authenticity are improved, and the detail expressive force of the fused image is enhanced.

The invention provides a high-precision stainless steel weld defect recognition device based on a multi-round cross-modal attention mechanism. The high-precision stainless steel weld defect recognition device comprises an ultrasonic signal acquisition module, a data preprocessing module, a cross-modal defect recognition module and an online result display module, the ultrasonic signal acquisition module is used for synchronously acquiring an A scanning original waveform and workpiece position information corresponding to the A scanning original waveform; the data preprocessing module is used for preprocessing the A scanning original waveform, mapping a time sequence signal into a Gramb angle field two-dimensional image, and generating complementary time sequence image bimodal data; the cross-modal defect identification module sends the bimodal data into a deep learning network, extracts time sequence and spatial frequency features through a feature extraction neural network, reinforces correlation between the time sequence and spatial frequency features by means of multi-round cross-modal multi-head attention iteration interaction, and combines channel attention and focus loss strategies at the same time; and outputting a stable defect classification result under the condition that the sample categories are unbalanced.

The invention relates to the field of remote sensing image detection and identification, and discloses a cross-space-frequency domain multi-modal remote sensing small target detection method, which designs a bimodal feature level fusion target detection architecture, extracts visible light and infrared channel features in a distributed manner, and proposes a cross-domain gating self-attention fusion module to perform interaction and fusion of bimodal features. And finally, fusing the multi-scale features and inputting the fused multi-scale features into a detection head to obtain a multi-modal target detection result. Wherein the cross-domain gating self-attention fusion module explores global context information of an image from the perspective of a frequency domain, and introduces a polarization self-attention mechanism to establish a long-range dependency relationship for space-frequency domain differential features, so that redundant information is filtered, and self-adaptive fusion of multi-modal complementary features is enhanced. According to the model, the precision and robustness of remote sensing small target detection are improved, meanwhile, the calculation amount is extremely low, the model is suitable for satellite-borne and airborne end mobile deployment, and the image processing precision and the response speed are improved.

This application provides a method and system for estimating DOA in a limited area based on atomic norm minimization. The method includes: limiting the DOA range of the incoming wave and converting the limited DOA range into a spatial frequency range; designing a corresponding set of atoms and a corresponding atomic norm minimization problem based on the limited spatial frequency range, designing a corresponding mapping operator, and converting the atomic norm minimization problem into an SDP problem based on the mapping operator; solving the SDP problem to obtain an estimate of the spatial frequency; and converting the obtained spatial frequency estimate into an estimated DOA value. The advantages of this application are: compared with grid-based algorithms, DOA estimation is more accurate because it is not affected by grid mismatch; compared with the original CA-ANM algorithm, the method of this application is consistent with its performance in terms of accuracy, but with significantly reduced computation time.

The invention relates to the technical field of image restoration, in particular to a spatial frequency hybrid restoration method for JPEG (Joint Photographic Experts Group) compressed blurred image restoration, which is characterized in that a spatial frequency hybrid restoration network is constructed for compressed blurred image restoration and comprises three coding blocks and three decoding blocks, each of the coding block and the decoding block is composed of a plurality of spatial frequency mixing blocks; the spatial frequency mixing block comprises a spatial frequency double-branch mixing structure and an ISS block; the SFHRN provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

The invention discloses a real haze image defogging method based on a haze degradation model. The method comprises the following steps: constructing a haze degradation model fusing multiple scattering effects and multiple image degradation factors; using the haze degradation model to construct a training data set including the clear image and the corresponding pseudo haze image; constructing a defogging network for a real haze scene; training the defogging network by adopting the training data set until a preset loss function is converged; and inputting a to-be-defogged image into the trained defogging network to obtain a defogging result. According to the haze degradation model constructed by the invention, the difference between a synthetic domain and a real domain is effectively relieved; a designed space-frequency hybrid module improves the adaptability of the model to complex degradation characteristics; the prior-guided feed-forward network fully excavates and fuses dark channel prior information, and the sensing and modeling capability of the model to the haze area is effectively enhanced.

The present invention discloses a privacy protection device and method based on physical light field modulation, which belongs to the field of optical devices and data security technology. In view of the problem that the existing privacy protection scheme relies on software algorithms, resulting in the risk of retaining original data, the device selectively attenuates the spatial frequency of the incident light field through an optical modulation unit, and implements irreversible destruction of high-frequency sensitive features (such as facial texture), while retaining low-frequency contour features (such as human body posture) to maintain machine vision functions. Users directly perceive that their privacy is protected through the optical phenomena of the physical appearance of the modulator (atomized surface, diffraction spot) without relying on electronic signal verification. This solution can effectively resist GAN data reconstruction attacks, is compatible with mainstream image sensors, and is suitable for scenarios such as smart security and medical image acquisition that require both privacy and machine vision.

The invention provides a system, a method, a storage medium and a magnetic resonance imaging (MRI) system for deep learning technology for magnetic resonance image reconstruction. The magnetic resonance imaging (MRI) system includes: a magnetic system including: a B0 magnet configured to provide a B0 field for the MRI system; a gradient coil configured to provide a gradient field for the MRI system; and at least one RF coil configured to detect a magnetic resonance (MR) signal; and a controller configured to: control the magnetic system to acquire MR spatial frequency data using non-Cartesian sampling; and generating an MR image from the acquired MR spatial frequency data using a neural network model, the neural network model comprising one or more neural network blocks, the one or more neural network blocks comprising a first neural network block, where the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transform.

The invention discloses a white light interference phase unwrapping method, device and equipment and a medium, and relates to the technical field of interference light measurement. The method comprises the following steps: acquiring a plurality of white light interference images of a white light interference vertical measurement method; calculating light intensity difference images of the adjacent white light interference images, and calculating a plurality of difference images by adopting a centroid method to obtain a rough contour surface of the sample piece; calculating the original light intensity data by adopting a spatial frequency domain method to obtain ununwrapped phase data; obtaining a phase jump position based on the ununwrapped phase data, and segmenting the difference data of the rough contour surface and the ununwrapped phase data according to the jump position; and the phase is corrected by comparing adjacent segments, so that phase unwrapping is realized. The phase unwrapping method for correcting the phase jump region of the phase data on the basis of the rough contour surface has the advantages of being simple in algorithm implementation, high in precision, high in processing speed and good in robustness.

The invention relates to the technical field of spectrum detection, and discloses a quality control method for traditional Chinese medicine capsules, which comprises the following steps: continuously scanning by using a micro light spot probe during capsule movement to obtain a spatial spectrum response sequence; performing adjacent micro-area differential operation on the sequence, and filtering a shell background and retaining a particle scattering signal by utilizing a microstructure difference between a capsule shell continuous film and a powder content discrete accumulation; according to the method, a traditional path of a shell standard model is established, through a spatial frequency domain decoupling mechanism, reference errors caused by batch drifting of shell physical attributes are avoided, and the detection sensitivity of trace component fluctuation and foreign matter mixing is improved.

The invention relates to the technical field of welding seam defect detection, and discloses a welding seam defect detection method, device and equipment based on three-dimensional holographic scanning. Coherent light is used for irradiating a welding seam to be detected, a reference state hologram sequence and a loading state hologram sequence are generated, a phase difference field is calculated, and the deformation gradient is analyzed; marking a first abnormal region and extracting a deformation concentration ratio and a spatial distribution mode, calculating a dominant spatial frequency through space-frequency domain joint analysis, fusing abnormal features, marking a second abnormal region and extracting amplitude distribution and spatial continuity parameters, and performing defect characterization analysis by combining information of the first and second abnormal regions to obtain weld defect detection information. The method has the advantages of high precision, non-contact and real-time monitoring, the accuracy and efficiency of weld defect detection can be effectively improved, and the problem that weld detection is not accurate enough in the prior art is solved.

The invention relates to a lens element (10), in particular for a spectacle lens, a contact lens or an intraocular lens, intended to be worn by a wearer, comprising:-at least a first zone (12) having a dioptric region having a front face and a rear face, the dioptric region having a refractive power based on a prescription of the wearer's eyes; -at least one microstructured second zone (13) outside said first zone (12) and configured for myopia or hyperopia control, said microstructured second zone (13) being characterized by a measurable surface spatial power spectral density PSDmeasure (f) such that, within a spatial frequency range between 0.3 mm-1 and 10 mm-1, formula (I), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula (II), formula PSDnom is a nominal function defined by a Hann window, and f is spatial frequency. # imgabs0 #

The invention discloses a method and a system for identifying an anti-counterfeit label. The method comprises the following steps: acquiring spectral response characteristics of the anti-counterfeit label to be identified through a multiband optical sensor; extracting a dynamic coding area and a base material characteristic area in the spectral response characteristics, wherein the dynamic coding area comprises topological distribution information of a programmable encryption particle array; processing the topological distribution information of the dynamic coding region through a feature compression algorithm to generate a first verification factor, and extracting a spatial frequency feature of the base material feature region as a second verification factor; and uploading the first verification factor and the second verification factor to a cloud verification platform through a secure communication protocol, wherein the cloud verification platform is internally provided with a dynamic spectral feature library and a physical feature association model. According to the invention, through a double-engine architecture of programmable dynamic target and environment adaptive verification, the problems of static replicability and environment vulnerability of physical anti-counterfeiting are solved, and collaborative rise of anti-counterfeiting strength and use reliability is realized.