506 results about "Spatial frequency" patented technology

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 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 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 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 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 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 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 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.

The embodiment of the invention provides a beam coverage method and device of an antenna array, equipment and a storage medium. The method comprises the steps of determining an initial beam coverage area; determining a reference center position in the initial beam coverage area, and constructing a rectangular window according to the maximum spatial frequency deviation of the initial beam coverage area relative to the reference center position; constructing a rectangular window function; obtaining a space beam forming gain function and determining a first relation function between the beam forming weight vector and the beam forming vector; inverse Fourier transform is carried out according to the spatial beam forming gain function to obtain a beam forming weight vector function, and a second relation function is determined based on the rectangular window function and the beam forming weight vector function; and calculating a target beam forming vector of the antenna array in combination with the first relation function and the second relation function, and controlling the antenna array to perform signal coverage on the initial beam coverage area according to the target beam forming vector. In this way, the real-time performance of beam configuration of the antenna array can be improved.

The invention provides a cable water-blocking buffer layer ablation degree detection method. The method comprises the following steps: acquiring a visible light image and an infrared image of a water-blocking buffer layer of a cable; performing weighted fusion on the visible light image and the infrared image to obtain a target image; performing multi-scale decomposition on the target image to obtain a plurality of spatial frequency images corresponding to the target image, the multi-scale decomposition being decomposition of the image into different spatial frequencies; and under the condition that the spatial frequency corresponding to the at least one spatial frequency image is greater than a preset frequency threshold, determining the ablation of the water-blocking buffer layer of the cable, and determining the ablation degree of the water-blocking buffer layer of the cable according to the target image. According to the scheme, the problem that the ablation degree cannot be accurately evaluated by adopting single-dimensional data in the prior art is solved.

The invention discloses a fusion method and device of an ultraviolet image and a visible light image and terminal equipment, and belongs to the field of image processing. The method comprises the following steps: inputting an ultraviolet image and a visible light image of to-be-detected power equipment into a preset feature coupling network to extract features so as to obtain a first spatial frequency, a second spatial frequency and an information entropy; updating the feature coupling network according to a link strength parameter obtained by weighting the first spatial frequency and the information entropy; inputting the ultraviolet image and the visible light image into the updated feature coupling network to obtain a first ignition mapping graph and a second ignition mapping graph; obtaining a local activation difference according to the first ignition mapping graph and the second ignition mapping graph; and when the local activation difference is not greater than a preset gradient threshold, performing weighted fusion on a first pixel gray value corresponding to the ultraviolet image and a second pixel gray value corresponding to the visible light image according to the first spatial frequency and the second spatial frequency to obtain a fused image. According to the invention, the problem of poor fusion effect in the prior art can be solved.

The invention discloses an SAR (Synthetic Aperture Radar) image MTF (Modulation Transfer Function) quality evaluation method and system based on a natural scene edge, and the method comprises the steps: building a selection standard system for automatic edge screening of a natural scene SAR image, and determining a high-contrast linear edge according to the standard system; automatically detecting a high-contrast linear edge in the natural scene, and intercepting a region of interest for subsequent calculation based on the position of the linear edge; gray level distribution is extracted in the normal direction of the linear edge, and an edge spread function is constructed and used for describing gray level transition response of the imaging system in the edge area; carrying out normalization and smoothing processing on the edge spread function, and carrying out spatial domain first-order differential calculation to obtain a line spread function; and performing fast Fourier transform on the line spread function to obtain a modulation degree transfer function curve, and representing the detail transfer capability of the system under different spatial frequencies. According to the method, the imaging quality of the SAR image of the natural scene can be automatically, accurately and quantitatively evaluated under the condition that manual field arrangement is not needed.

The invention relates to the technical field of numerical control machining measurement and correction, and discloses a machining surface correction method for a numerical control milling center. The method comprises the steps of establishing an original point cloud of a processing surface and extracting a feature structure; analyzing each feature structure to form a structured description list; inputting the list into a geometric growth model, and generating a virtual feature structure set according to rule iterative evolution; extracting and rasterizing the contour and the height field, carrying out registration fusion with a reference plane equation, and carrying out variation calculation to obtain a smooth middle geometric surface; performing adaptive resampling on the intermediate surface to generate an intermediate point cloud; and finally, according to the spatial frequency distribution of a topological residual field formed by the difference between the original point cloud and the intermediate point cloud, adaptively determining a weight, and carrying out weighted mixing on the original point cloud and the intermediate point cloud to generate an optimized surface point cloud. According to the method, intelligent evolution of the machined surface features and frequency domain self-adaptive data fusion are achieved, and the correction machining precision and the surface forming quality are improved.

The invention discloses an ISAR image ship coarseness identification method and system based on scattering feature estimation, and relates to the technical field of target identification, and the method comprises the steps: constructing and training a target identification model; the target recognition model comprises a small grazing angle image feature enhancement module based on scattering characteristic estimation, a feature extraction fusion module based on motion attitude decoupling and a full connection module; the small grazing angle image feature enhancement module comprises a scattering center parameter estimation module, a complex waveform feature extraction module and a physical data feature fusion module; the feature extraction fusion module comprises a spatial frequency domain attitude mode representation module, an attitude adaptive deformation convolution module and a frequency domain space double-attention fusion module; obtaining an ISAR image of a to-be-identified marine ship; and inputting a to-be-identified marine ship ISAR image into the trained target identification model for ship type identification. According to the invention, the identification precision of the ship type under the conditions of small grazing angle and complex motion imaging is improved.

The invention relates to the field of data storage, in particular to a multi-layer optical storage data reading method and system for dynamic inter-layer crosstalk suppression. Comprising the following steps that chromatography interference signals of a target data layer in a multi-layer optical storage medium are collected through a low-coherence interferometer, spatial frequency characteristics of adjacent data layers are synchronously obtained, and the spatial frequency characteristics comprise power spectral density, frequency band width and phase distribution parameters. According to the invention, through frequency domain convolution quantization of coherent coupling coefficients and deep network weight generation, dynamic analysis of transient coherent crosstalk intensity is realized for the first time. The real-time aperture regulation (0.45-0.85 NA continuous change) of the variable aperture diaphragm is utilized to match the frequency band suppression priority, and the band limit constraint of a traditional fixed filter is broken through; and a multi-channel interference reference light path and a second-order differential compensation algorithm are combined, so that the interlayer spacing error is compressed to a submicron order, and the phase drift caused by the medium refractive index gradient is effectively inhibited.

The utility model provides an optical path difference regulation structure lens, and relates to the field of optical lenses, the lens comprises a lens body and a lens part, the lens body comprises a convex surface and a concave surface opposite to each other; the lens part is adjacent to the concave surface of the lens body, the lens part comprises a micro-lens array, and the micro-lens array comprises a plurality of micro-lenses which are sequentially arranged to form an ultra-microstructure area; the micro lens is of a nanoscale diffraction structure and has the effective haze not exceeding 15% so as to reduce the spatial frequency and the imaging degree of incident light in the ultra-microstructure area. According to the invention, multiple functions of defocus optimization design, contrast control, spatial frequency modulation and the like are integrated; the effective defocusing amount of the ultrastructure is larger, and the ultra-low disturbance amount is achieved; and the haze of the micro lens is controlled at a nano level, so that a high-fidelity optical effect can be provided.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

The invention discloses an ultrasonic fingerprint identification method, a chip, a system, electronic equipment and a storage medium, and the method comprises the steps: controlling an ultrasonic piezoelectric transducer to work based on first configuration information, and obtaining one or more first fingerprint images; performing spatial frequency identification on the one or more first fingerprint images, and determining a first spatial frequency corresponding to the one or more first fingerprint images; when one or more first fingerprint images meet the adaptive condition, determining second configuration information based on the first spatial frequency and the first configuration information; and updating the first configuration information by adopting the second configuration information. According to the method, the first configuration information is dynamically adjusted based on the first spatial frequency corresponding to the first fingerprint image, so that the adjusted second configuration information can effectively guarantee the overall performance of the ultrasonic fingerprint system, the quality of the fingerprint image acquired subsequently is improved, the recognition effect is improved, and the universality of the system is improved.

The invention discloses a micro-plastic detection model construction method, a detection method, equipment and a medium. The method comprises the following steps: constructing a micro-plastic detection model; the feature extraction module is used for extracting feature maps of various different scales from an input image, performing feature fusion and feature enhancement on the feature maps of various different scales, and then identifying micro-plastic information from the feature maps and outputting the micro-plastic information; the model comprises an MFM module, an FS-C3k2 module and a GSConvE module; the MFM module is used for carrying out feature fusion on feature maps of different scales; the FS-C3k2 module is used for dividing an input feature map into two channels, one channel is spliced with the other channel after space-frequency feature extraction and feature enhancement, and a first enhanced feature map is obtained; the GSConvE module is used for performing deep feature enhancement on the first enhanced feature map; and taking model loss function minimization as a target training model. According to the invention, the precision and accuracy of micro-plastic detection are improved.

The application relates to a kind of diffractive optical element beam shaping methods based on spatial frequency filtering, obtain incident light beam, based on the homogenization light beam of preset, obtain corresponding geometric analytical phase distribution;Adjust the homogenization light beam to rectangular flat-top light beam, the phase correction processing of obtained geometric analytical phase;Based on spatial frequency filtering method processing the geometric analytical phase after phase correction, get the light field regulation and control distribution requirement after processing, the light field regulation and control distribution requirement after processing has phase and amplitude regulation and control requirement simultaneously;Design diffractive optical element, make it realize the light field regulation and control distribution requirement required, and utilize the diffractive optical element to realize the incident light beam shaping as the rectangular flat-top light beam.The application can realize the light beam homogenization effect of high uniformity, edge sharp and high diffraction efficiency, and obtain wide application prospect in laser welding, photoetching and biomedical and other aspects.