36results about How to "Increase salience" patented technology

The invention relates to a flexible combined three-dimensional measurement method and device for the overall shape of a large aircraft, and the method comprises the steps: building a base station, and obtaining the spatial pose of a three-dimensional laser scanner; a three-dimensional laser scanner scans and collects local three-dimensional point cloud data of the large aircraft by using a three-dimensional measuring device; performing coarse registration on the acquired local three-dimensional point cloud data by using a coarse registration method based on base station positioning; voxel-based sampling is carried out on the local three-dimensional point cloud data after coarse registration, the overlapping degree between any two point clouds is calculated, and a maximum spanning tree is extracted according to the overlapping degree; a deep learning algorithm, namely a food grazing person, is put forward to carry out fine registration on the obtained maximum spanning tree; and the overlapping degree between any two point clouds is calculated again, a maximum spanning tree is constructed according to the overlapping degree, and refined registration is carried out by using a traditional algorithm ICP. According to the invention, the flexible and intelligent measurement of the data of the whole aircraft is realized, the overall appearance of the large aircraft is efficiently and accurately measured, and the precision requirement of the overall appearance measurement of the large aircraft is met.

The invention relates to the field of medical image processing and analysis, in particular to a cardiovascular image analysis system and method. The method comprises the steps of obtaining and preprocessing cardiovascular image data, converting the preprocessed cardiovascular image data into an initial feature space, optimizing cardiovascular image features in the initial feature space through recursion, and further constructing dynamic feature mapping to obtain a dynamic feature mapping value; adjusting the dynamic feature mapping value to obtain an optimized feature mapping value; a segmentation result is obtained by adopting block feature aggregation and a joint optimal iteration strategy, geometric features of the cardiovascular system are further extracted, and a vascular network graph is constructed; and detecting an abnormal region in a segmentation result, judging a lesion region, and finally generating diagnosis information. The problems that self-adaptive capturing capacity for dynamic features and local changes is lacked, analysis cannot be carried out in combination with a segmentation result in detection of a lesion area, and the segmentation result cannot comprehensively reflect the continuity of a cardiovascular structure are solved.

The invention discloses a multi-layer screen cover plate glass interlayer foreign matter detection device and detection method, and belongs to the field of image processing, and the detection method comprises the following steps: image acquisition; extracting a region of interest; feature enhancement; carrying out anisotropic Gaussian filtering; edge detection; foreign matter screening. According to the method, the ROI image is enhanced by applying frequency tuning saliency detection FT and Haar wavelet transform, and an attention mechanism is introduced to fuse features, so that the saliency of tiny and low-contrast defects is enhanced; the influence of noise is reduced by adopting anisotropic Gaussian filtering, and important edge information is reserved and interference is suppressed by adaptively adjusting parameters of a filter.

The invention provides an ethylene cracking furnace nitrogen oxide emission concentration prediction method and device and a medium, and belongs to the field of petrochemical engineering. The method comprises the following steps: collecting real-time operation data of the ethylene cracking furnace; the real-time operation data are preprocessed, emission concentration key parameters are determined, and cracking furnace thermal load composite variables are constructed; performing parameter optimization on the emission concentration key parameters and the composite variables based on an SEM structure equation model to obtain optimized parameters; and inputting the optimization parameters into a pre-constructed nitrogen oxide emission concentration prediction model for calculation to obtain prediction data. According to the method provided by the invention, the temperature factor variables influencing the emission concentration are combined into the composite variable, and the composite variable can reflect the influence of the fuel and the raw material on the nitrogen oxide, so that the significance of the influence of the temperature influence factors on the thermal nitrogen oxide is improved, and finally, the fitting degree of the model is favorably improved.

The invention belongs to the technical field of magnetism isolating plates, and particularly relates to a novel magnetism isolating plate which comprises a composite plate, the composite plate is composed of an outermost wear-resistant and corrosion-resistant protective layer, a middle high-permeability magnetism isolating material layer and an inner buffer layer in good contact with protected equipment, and the outermost wear-resistant and corrosion-resistant protective layer can be made of PTFE (polytetrafluoroethylene). The surface of the magnet vane is covered with the coating in a spraying or dipping mode, a uniform protective layer is formed, and the thickness of the uniform protective layer is usually 20 microns 300; and the middle layer of high-permeability magnetic isolation material layer is permalloy composed of 45 N i-Fe alloy containing 45% of nickel and 80 N i-Fe alloy containing 80% of nickel. According to the invention, the outermost layer is wear-resistant and corrosion-resistant, such as a PTFE or aluminum oxide ceramic layer, so that the interior is protected; the middle high-permeability magnetic isolation material layer effectively isolates a magnetic field; the inner buffer layer provides buffer, the buffer holes disperse stress and absorb energy and buffer, and the interval grooves enhance the fitting degree, so that stable operation of equipment in various complex environments is ensured jointly, and the service life of the magnet vane and the service life of the equipment are prolonged.

The invention discloses an application method and system of an active light-emitting sign based on a road environment and traffic flow, and the method comprises the steps: collecting road environment data and traffic flow data in real time through employing a vehicle-mounted sensor and a roadside monitoring device, and the road environment data comprises the temperature and real-time illumination of an area where a road is located; transmitting the road environment data and the traffic flow data to a traffic control center; the traffic control center calculates the brightness, the color and the flicker frequency required by the LED lamp bead of the active light-emitting sign device in real time; transmitting the calculated brightness, color and flicker frequency to the active luminous sign equipment through wireless communication; the microcontroller of the active light-emitting sign device adjusts the LED lamp beads in real time according to the received brightness, color and flicker frequency. The method and the system can intelligently adapt to different weather, time and road conditions, are energy-saving and environment-friendly, are high in safety, are beneficial to traffic management optimization, and are high in expandability.

The invention relates to a hydrated borate micromolecule with a boron-nitrogen coordination bond, a derivative resin material and a preparation method of the hydrated borate micromolecule. The preparation method comprises the following steps: S1, synthesizing boron-nitrogen coordination micromolecules: dissolving and reacting boric acid, a derivative thereof and a polyfunctional group amine compound at room temperature through a solution reaction to obtain the boron-nitrogen coordination micromolecules; the borate micromolecule NCBW-X with intramolecular boron and nitrogen coordination is formed; s2, the NCBW-X micromolecule can be used for curing epoxy resin and synthesizing materials such as phenolic resin through end-NH2 and end-OH on two sides, the NCBW-X improves the mechanical property of each system through a rigid eight-membered ring, boron-nitrogen coordination of the NCBW-X can generate hydration, the environmental endurance is improved on the premise of retaining the original dynamic nature of boric acid ester of the NCBW-X, and the service life of the NCBW-X micromolecule is prolonged. Meanwhile, due to the dynamic reversibility of the NCBW-X, the NCBW-X is used for curing epoxy resin, and materials such as phenolic resin and the like are improved to a certain extent in the aspects of self-repairing and recycling.

The invention discloses a multivariable time series data prediction method based on depth sub-feature decomposition. The method comprises the following steps: firstly, recording and collecting time series data; and secondly, constructing a multivariable time series data prediction model, inputting the collected time series data, and carrying out characteristic decomposition to obtain a prediction result. And finally, training and testing the multivariable time series data prediction model. According to the method, the significance of the key time variable on the data level is improved, decoupling of complex mixed time sequence data is achieved, the time variable capturing difficulty is reduced, and the prediction precision and robustness of the model are improved.

The invention discloses a seismic data blind denoising method based on adaptive distribution estimation constraint. The method comprises the following steps: S1, constructing an initial noise distribution prediction network model and an initial generation diffusion denoising network model; s2, collecting seismic data with noise, and obtaining an ideal noise distribution prediction network model; s3, inputting a test data set in the noisy seismic data into the ideal noise distribution prediction network model for processing to obtain noise distribution estimation data; s4, processing the noise distribution estimation data through an adaptive noise distribution matching algorithm to obtain a matching time step; s5, inputting the test data set, the noise distribution estimation data and the matching time step into the initial generation diffusion denoising network model to obtain an ideal generation diffusion denoising network model; and S6, inputting the test data set into the ideal generative diffusion denoising network model to obtain the seismic data with the high signal-to-noise ratio.

The invention relates to the technical field of equipment defect diagnosis, and provides a voiceprint anomaly detection method and system for a power transformer, and the method comprises the steps: obtaining a risk value of a to-be-detected transformer through a reasoning network based on the input characteristics of the to-be-detected transformer, and judging that the to-be-detected transformer is abnormal when the risk value of the to-be-detected transformer exceeds a risk threshold value; wherein the reasoning network comprises a generation encoder, and a generation decoder and an abnormal score calculation network which are respectively connected with the generation encoder; the generation encoder processes the input characteristics of the transformer to be detected to obtain implicit vectors; the generation decoder processes the implicit vector to obtain a reconstruction feature; on the basis of the implicit vectors and the reconstruction features, reconstruction loss is obtained through calculation; the abnormal score calculation network processes the implicit vector to obtain an abnormal score; the risk value is the weighted sum of the reconstruction loss and the abnormal score. And the accuracy of voiceprint anomaly detection is effectively improved.

The invention relates to the technical field of molecular breeding, and discloses a molecular marker related to the LDL-C level of porcine serum, a detection reagent and a breeding method. The invention also discloses a primer group for identifying the molecular marker, and the primer group is used for amplifying the following SNP sites: SNP01, which are obtained on the basis of a pig reference genome Sscrofa11.1 and are positioned on 112bp, 008bp and 261bp of a third chromosome, and the genotype is A / G; sNP02 is located at 112bp, 008bp and 354 bp of a third chromosome, and the genotype of SNP02 is A / G; sNP03 is located at 112bp, 008bp and 583bp of the third chromosome, and the genotype of SNP03 is A / G. The primer group for identifying the molecular marker can quickly and effectively identify the LDL-C level of pig serum, and is used for assisting in breeding pig strains with low cholesterol or specific lipid metabolism characteristics.

The application discloses a multi-modal radio frequency authentication method based on a multi-scale signal representation, which comprises the following steps: firstly, pre-processing the original IQ signal of a target device received to obtain an instantaneous envelope signal; carrying out multi-scale decomposition and denoising processing on the instantaneous envelope signal to obtain a denoised envelope signal; constructing a multi-modal data set according to the original IQ signal and the denoised envelope signal; carrying out feature extraction and fusion on the multi-modal data set through a pre-trained target feature extraction network to obtain a fused feature representation; and carrying out classification on the fused feature representation through a pre-trained target classification network to output a classification result corresponding to the target device. The amplitude, frequency and time-frequency energy distribution information can be complementarily fused by constructing the multi-modal data set; the multi-modal features are extracted and fused through the target feature extraction network, so that the information loss is effectively avoided; and the target classification network is used for rapid classification, thereby reducing the calculation cost and ensuring the accuracy of identification.

The application discloses a smart community police management system and method based on Internet of Things security, and belongs to the technical field of smart communities, and solves the problem that existing system data processing cannot utilize edge computing nodes for low-delay real-time analysis, and cannot meet timely early warning requirements, the method comprising preprocessing of multi-source heterogeneous community monitoring information, analysis and processing of the community monitoring information based on edge computing, and generation of a risk propagation path in combination with front-end analysis results and heterogeneity characteristics; in the application, analysis and processing of the community monitoring information based on edge computing avoids the delay of centralized processing, can quickly trigger early warning in the early stage of a risk event, thereby gaining golden disposal time for risk event disposal, and a risk analysis model realizes a leap from single-node anomaly detection to composite risk quantification through multi-dimensional feature fusion and dynamic space-time reasoning, and realizes multi-dimensional feature fusion and dynamic reasoning capability of the risk event.

The invention belongs to the technical field of fertilizer preparation and tobacco planting, and particularly relates to a slow-release compound fertilizer and application thereof in tobacco planting. The slow-release compound fertilizer contains slow-release nitrogen and slow-release potassium and can meet the requirement for nitrogen and potassium absorption of tobacco in the whole development period, and then fertilization is conducted only once in the tobacco planting process, and the requirement for nutrients of tobacco growth and development can be met without topdressing; compared with conventional fertilization, under the condition that the application amount of the slow-release compound fertilizer is reduced by 10% or more than 10%, the slow-release compound fertilizer can still meet the growth and development of tobacco in the whole growth period, improve the lodging resistance of tobacco plants, promote the growth and development of the tobacco and improve the yield and quality of the tobacco, so that the effects of saving cost and increasing income are achieved; moreover, the slow-release compound fertilizer can improve the pH value of soil, so that the content of organic matters is kept stable, the characteristics of soil aggregates are remarkably improved, the physical structure of the soil is improved, the microbial diversity is improved, and the soil improvement effect is obvious.

In order to solve the problems of long design time, low efficiency, high calculation cost and low prediction accuracy caused by information loss in the prior art, a resonance super surface design method and device based on spectrum perception improvement are provided.The method comprises the following steps: designing a GLSAT forward prediction network based on spectrum perception improvement;training and optimizing the GLSAT forward prediction network; designing a DNN reverse design network; cascading the DNN reverse design network and the trained GLSAT forward prediction network to obtain a cascaded reverse design network; inputting the ideal spectrum pretreated by GSSG into the cascaded reverse design network, training and optimizing the DNN; and completing the design of the resonance super surface by using the optimized DNN reverse design network.The method has the characteristics of short time consumption, high efficiency and low calculation cost while improving the design prediction accuracy.

The invention relates to the technical field of microphone welding quality detection. The microphone welding direction detection method comprises the steps that acoustic feedback information of a microphone to be detected is obtained, the acoustic feedback information is processed, high-frequency interference and background noise are removed, filtered information is obtained, and the microphone welding direction is detected. Extracting frequency spectrum feature information in a preset frequency range from the filtered information, generating target frequency band information, performing phase calculation on the target frequency band information to obtain actual phase value information, inputting the actual phase value information into a welding direction judgment model, generating a feature difference value, and judging the welding direction based on the feature difference value. And determining the microphone with the wrong welding direction. The welding direction detection device has the effect of improving the reliability of welding direction detection.

The present invention relates to a method for detecting floating targets at sea using local energy accumulation of Doppler channels, and belongs to the technical field of radar signal processing and target detection. The present invention performs high-resolution power spectrum estimation on radar echo signals through MEM. Secondly, the Doppler frequency range of the buoy is calculated based on the roughly estimated radial velocity of the buoy, and the power spectrum density within the range is accumulated to form a characteristic value representing the overall energy of the target. Finally, the extracted energy features are detected through CA‑CFAR, and the detection results are post-processed in combination with regional connectivity analysis to further confirm the target area and eliminate false alarm signals. The present invention has higher accuracy in buoy target feature analysis and target detection under high sea conditions.

The present application relates to the field of construction engineering structure health monitoring, and particularly relates to a distributed optical fiber intelligent monitoring and early warning system and method for deep foundation pit construction, which comprises the following steps: obtaining an optical fiber strain sequence and differentially extracting a gradient sequence; performing multi-scale space-frequency decomposition to obtain a space-frequency energy matrix; extracting a slowly varying component to construct a background model, calculating a logarithmic domain difference to generate a residual saliency map; statistically quantifying multi-scale gradient direction consistency to quantify edge confidence, and adaptively adjusting a smoothing scale to output refined features; combining feature purity and spatial dispersion to generate a comprehensive atlas through weighted fusion; and constructing an energy function considering spatial adjacent consistency and performing global optimization to solve the problem. The present application effectively improves the weak anomaly detection and early warning accuracy by segmenting physically coherent abnormal areas to trigger early warning.

The invention provides a wind-noise-adaptive low-speed small target rapid detection method and system, and belongs to the technical field of target detection, and the method comprises the steps: firstly collecting the acoustic data of an unmanned plane, calling a bird acoustic data set, and constructing a high signal-to-noise ratio data set, a training set, a test set and a verification set; carrying out synchronous compression wavelet transform on the acoustic signals of the high signal-to-noise ratio data set, separating high and low frequency information, respectively extracting waveform features and spectrogram features, carrying out feature fusion, and optimizing feature fusion weight in the fusion process to obtain self-adaptive wind noise fusion features; calling a YOLO network, optimizing the structure of the YOLO network based on the fusion features, and constructing a feature extraction and target detection network model; and after training, testing and verification are carried out through the training set, the test set and the verification set, the model is deployed to a mobile terminal or a ground station to carry out low-slow-small target detection. According to the invention, through bimodal feature fusion and network optimization, the detection precision and timeliness of the low-speed small target under dynamic wind noise are improved.

The invention discloses a micro-seismic event pickup method based on deep reinforcement learning, and the method specifically comprises the steps: 1, obtaining micro-seismic signals, constructing a micro-seismic signal data set, and carrying out the preprocessing of each micro-seismic signal in the data set; step 2, carrying out preliminary identification and segmentation on the preprocessed signal by adopting an enhanced segmentation module, and identifying potential starting and ending points of a micro-seismic event; and step 3, based on the potential starting point and ending point of the micro-seismic event in the step 2, refining and confirming the exact moment of the micro-seismic event by adopting a deep refining reasoning module.

Some embodiments disclose tetraphenylpyridine derivatives for use in electrochromic materials, including compounds represented by the following general formula or salts thereof: Wherein, X is any one of F, Cl, and Br, and Y is ethyl or n-hexyl; electrochromic materials are prepared using compounds represented by the general formula; the obtained electrochromic materials include the compound represented by the general formula, ferrocene, poly(vinylidene fluoride-hexafluoropropylene), and an ionic liquid, with a molar ratio of 4:1:15:60; the ionic liquid is 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imine or 1-ethyl-3-methylimidazolium tetrafluoroborate. The introduction of heavy atoms F, Cl, and Br into the tetraphenylpyridine structure effectively increases the light absorption capacity and enhances the significance of the color change. The thin-layer structure of this tetraphenylpyridine derivative exhibits excellent light transmittance and provides structural stability and durability of the electrochromic material during the electrochromic process.

The invention discloses a thermal imaging-based constructional engineering quality nondestructive testing system and method, and belongs to the technical field of constructional engineering quality detection.The system comprises a multispectral thermogram fusion module, a physical constraint deep learning module, a temperature time sequence analysis module and a defect depth estimation module, the system enhances defect features through multi-band infrared thermal image adaptive fusion, carries out defect identification based on a thermal conduction physical equation constraint deep learning network, evaluates the material aging degree through temperature time sequence analysis, and estimates the defect depth based on a thermal diffusion theory. All the modules form a closed-loop cooperation mechanism through confidence feedback and depth prior feedback, continuous optimization of detection performance is achieved, the system can detect hidden defects 15 cm below the surface of a building structure, the detection accuracy reaches 91.3%, and an efficient tool is provided for building engineering quality management.

The invention provides a preparation method of an InSb-based multilayer film based on a LiNbO3 substrate and an application of the InSb-based multilayer film. According to the preparation method of the multilayer film, LiNbO3 is used as a substrate, and a buffer layer In (y) Al (1-y) Sb, an active layer InSb and a protective layer SiOx are sequentially deposited on the substrate in a magnetron sputtering mode. The buffer layer In (y) Al (1-y) Sb can effectively relieve stress generated by lattice mismatch and prevent impurity diffusion; the active layer InSb is a key layer for interaction between the surface acoustic wave and carriers in a semiconductor; the protection layer SiOx effectively prevents the performance failure of the active layer InSb caused by annealing. The subsequent annealing treatment further improves the crystal quality of the multilayer film, reduces the internal stress, and removes residual gas and impurities, thereby achieving the purpose of optimizing the electrical properties of the multilayer film.

The invention discloses a phase model construction method based on a U-Net network, and belongs to the field of phase image reconstruction methods. In an existing phase unwrapping method, a space phase unwrapping technology is relatively high in calculation efficiency, but has no robustness to severe noise conditions, and an algorithm with relatively high anti-noise capability is large in calculation amount, so that the speed is low. The invention discloses a construction method of a phase model based on a U-Net network. The construction method comprises the following steps: generating a data set; preprocessing the generated data, and determining the size and parameters of a data set; constructing a phase unwrapping model based on a U-net network; constructing a loss function and an optimization method in phase unwrapping; when the model is trained, the learning rate is set for training, so that the convergence speed of the model is higher, and the model effect is better; and introducing a weight attenuation strategy to reduce learned model parameter values. According to the method, the U-net model is optimized and improved, so that the operation and detection time is shortened. And the accuracy, timeliness and significance of phase unwrapping are improved.

The invention relates to a display method and device, electronic equipment and a storage medium, and the method comprises the steps: statically displaying a first medium card in a display interface when multimedia data of a target type is played, and the first medium card comprises first interaction prompt information, the first interaction prompt information comprises a first dynamic effect of a posture adjustment operation; when the multimedia data is played to a first target playing node, hiding the first medium card, and dynamically displaying a second medium card in a display interface, the dynamic display of the second medium card comprising displaying a second dynamic effect for adjusting the posture of the second medium card; and displaying an associated page corresponding to the multimedia data in the display interface in response to a posture adjustment operation for the terminal equipment. By the adoption of the method and device, the page skipping modes can be enriched, and the page skipping efficiency is improved.

The invention discloses a wavelet noise reduction and gradient feature extraction method for buried pipeline welding seam positioning, and relates to the technical field of nondestructive testing. The method comprises: acquiring an original magnetic field signal of a pipeline area through a magnetic field acquisition device; wavelet transformation is carried out on the signal, wavelet reconstruction is carried out after a noise coefficient is suppressed through threshold processing, and a denoised signal is obtained; finally, gradient feature extraction is conducted, specifically, the gradient of the denoised signal is calculated and standardized, extreme points in the standardized gradient are recognized by setting a threshold value to serve as welding seam feature points, and therefore accurate positioning of the welding seam position is achieved. According to the method, non-periodic noise is effectively filtered through wavelet transformation, gradient analysis is combined to strengthen and extract the magnetic field abrupt change features of the welding seam, the problem that a traditional frequency domain method is poor in adaptability in buried pipeline welding seam positioning is solved, and the method has the advantages of being good in filtering effect, accurate in feature extraction and high in robustness.

The invention discloses a road early warning method, system and device based on an unmanned aerial vehicle and a storage medium, and the method comprises the steps: obtaining the position data of a road and the take-off parameters of the unmanned aerial vehicle, the position data of the road comprises the latitude and longitude data and direction data of a lane, and the take-off parameters of the unmanned aerial vehicle comprise a take-off reference point and a take-off height; the unmanned aerial vehicle carries a task load; generating a route of the unmanned aerial vehicle according to a preset early warning scheme, the position data of the road and the take-off parameters of the unmanned aerial vehicle; and sending the route of the unmanned aerial vehicle to the unmanned aerial vehicle, so that the unmanned aerial vehicle flies according to the route of the unmanned aerial vehicle and completes the warning task after receiving the take-off instruction. The embodiment of the invention can effectively prevent and reduce traffic accidents in the road-occupying construction area, improves the safety of road-occupying construction, and can be widely applied to the technical field of road maintenance.

The invention relates to the technical field of ultrasonic equipment, in particular to a lung ultrasonic pleural line and B line state detection method and system in a noise environment. The method comprises the following steps: constructing a lung ultrasonic network; collecting lung ultrasound video data, and performing classification processing on the lung ultrasound video data to obtain a training set; s2, training a lung ultrasonic network by using the training set obtained in S2 to obtain a trained lung ultrasonic detection model; according to the method, noise reduction processing is carried out on the original ultrasonic image through the image preprocessing and frequency domain enhancement technology, the significance of key features is enhanced, and effective information of original data is reserved; according to the method, the neural network model adapting to the dense noise environment is designed, effective feature representation can be automatically extracted from the complex noise background, and accurate detection of the pleura line and the B line state is achieved.

The invention discloses a video monitoring scene intelligent analysis and event classification system based on semantic segmentation. The system comprises a data preprocessing module, a preliminary classification module, a weight generation module and a correction and detection module. The data preprocessing module is used for acquiring and preprocessing original video data of a video monitoring scene, and extracting scene semantic features of the preprocessed video data through a semantic segmentation model; and the preliminary classification module is used for inputting the scene semantic features into a classifier for performing preliminary event classification to obtain an initial classification result. The invention relates to the technical field of video monitoring and intelligent analysis. According to the video monitoring scene intelligent analysis and event classification system based on semantic segmentation, the coupling degree between modules of the system is low, the semantic category number, the classifier structure and the updating strategy can be freely configured according to different monitoring tasks, and the system is suitable for various application scenes such as traffic flow analysis, personnel gathering monitoring and industrial operation anomaly detection.

This invention relates to a method for improving the crystal quality of large-size thick-film silicon epitaxial wafers. By comprehensively designing parameters such as the radial temperature gradient difference of the silicon substrate placement area, the silicon epitaxial layer growth temperature, the evacuation time before silicon epitaxial layer growth, the silicon epitaxial layer growth rate, the flatness of the graphite substrate, and the rotation speed of the graphite substrate during silicon epitaxial layer growth, the crystal quality of large-size thick-film silicon epitaxial wafers is improved with simple process and batch reproducibility. It avoids problems such as edge chipping, cracking, and fragmentation of silicon epitaxial wafers. It can be applied to the industrial mass production of large-size thick-film silicon epitaxial wafers with diameters of 150~200mm and silicon epitaxial layer thicknesses greater than 100μm.