46 results about "Imaging problem" patented technology

The invention discloses a satellite-borne inverse synthetic aperture radar motion compensation imaging calibration integration method, equipment and a medium, and relates to the technical field of radar signal processing and radar imaging. The objective of the invention is to solve the problems of inaccurate motion compensation and difficult large-rotation-angle imaging in ISAR imaging. According to the method, a particle swarm optimization algorithm is adopted to optimize motion parameters, optimal motion parameters are obtained, and the motion parameters comprise relative translation motion parameters and relative rotation motion parameters of a target and a radar; and obtaining focusing and calibration imaging results based on the optimal motion parameters and echo data reflected by the target. The influence of the translation compensation effect on rotation parameter estimation is reduced, more accurate parameter estimation and better imaging focusing performance are realized, and the ISAR imaging problem under the condition of violent relative movement in the rendezvous scene is solved.

The invention relates to the technical field of image processing, in particular to an image dodging method, device and equipment and a medium, and the method comprises the steps: obtaining all microscopic images of a to-be-detected object, carrying out the shadow correction processing of all microscopic images, obtaining all target microscopic images, carrying out the splicing processing of all target microscopic images, and obtaining a target microscopic image; and obtaining a target fusion image with a preset target size, performing curve fitting processing on the target fusion image to obtain a target dodging image, and determining an image detection result of the to-be-detected object based on the target dodging image. Visibly, shadow correction processing, splicing processing and curve fitting processing are carried out on all the obtained microscopic images to obtain the target dodging image, and then the image detection result of the to-be-detected object is determined according to the target dodging image, so that the imaging problem caused by non-uniform illumination in the prior art is solved, and the detection accuracy of the to-be-detected object is improved. Therefore, uniform illumination of the finally obtained image is realized, shadow interference is eliminated, and the measurement precision of the image is effectively improved.

The invention is suitable for the technical field of radar signal processing, and provides a sparse-neighborhood joint constraint-based deep expansion ISAR super-resolution imaging method, which comprises the steps of firstly constructing an ISAR image degradation model, constructing an ISAR super-resolution imaging problem based on the ISAR degradation model and a compressed sensing theory, and solving the ISAR super-resolution imaging problem by using an ADMM (Amplitude Division Multiplexing). The process of solving the ISAR super-resolution imaging problem by the ADMM is expanded into a multi-stage neural network, and finally, the low-resolution ISAR echo is input into the trained neural network to obtain an ISAR super-resolution imaging result. According to the method, the sparsity constraint and the neighborhood amplitude constraint are combined, the corresponding signal model and the ADMM solving algorithm are deduced, the reconstruction capability of the algorithm on complex target details and the representation capability on structural information are effectively improved, and effective super-resolution imaging can be realized based on narrow-band short-aperture echoes.

The invention discloses a millimeter wave scattering aperture sparse three-dimensional imaging method and a millimeter wave scattering aperture sparse three-dimensional imaging system, and aims to realize high-resolution three-dimensional reconstruction through a sparse reconstruction algorithm aiming at a non-vision field target imaging problem under a rough scattering surface condition. The method comprises the following steps: firstly, improving the spectral resolution of an echo signal by using a time domain zero padding technology, accurately positioning a scattering aperture position based on spectrum peak estimation and completing distance compensation, converting a non-vision field echo into an equivalent vision field echo, and retaining complete information of a target; secondly, in combination with target space scattering point sparsity and scattering aperture sparse sampling characteristics, a virtual array and a measurement matrix are constructed, an imaging problem is converted into a sparse reconstruction task, and a target scattering coefficient is efficiently solved by adopting a generalized orthogonal matching pursuit algorithm; the dependence on a mirror reflection condition is broken through, and a non-vision target can be accurately reconstructed in a diffuse reflection scene; compared with an existing scattering aperture algorithm, the method has the advantage that the imaging resolution and the anti-noise capability are remarkably improved through double sparsity constraints. The method has important application value.

The invention relates to the technical field of geophysical exploration, in particular to a polymorphic seismic exploration method and system, and the method comprises the steps: obtaining common midpoint discretized seismic collection data; based on a quantum state superposition theory, constructing wave function expression of a seismic wave field, and representing the seismic wave field as superposition of a reflection wave state and a scattering wave state; carrying out fusion processing on the acquired data under a weak constraint condition to obtain fused wave field data; carrying out wave state decomposition on the fused wave field data, and decomposing the fused wave field data into reflected wave field data and scattered wave field data; generating an underground interface structure image based on the reflected wave field data; the invention further provides a corresponding system, the system comprises a polymorphic acquisition subsystem, a quantum state wave field processing subsystem, a weak constraint fusion subsystem and a probability domain interpretation subsystem, the resolution limit of traditional reflected wave seismic exploration is broken through, and the probability distribution image of underground medium scattering characteristics is generated based on scattered wave field data. The problem of non-uniform and ultra-small-scale medium imaging is solved.

The application is suitable for the technical field of radar signal processing, and provides a deep unfolding ISAR super-resolution imaging method based on sparse-neighborhood joint constraint, comprising: firstly, constructing an ISAR image degradation model, constructing an ISAR super-resolution imaging problem based on the ISAR degradation model and the compressed sensing theory, using ADMM to solve the ISAR super-resolution imaging problem, unfolding the process of solving the ISAR super-resolution imaging problem by ADMM into a multi-level neural network, and finally inputting low-resolution ISAR echoes into the trained neural network to obtain an ISAR super-resolution imaging result. The application combines sparse constraint and neighborhood amplitude constraint, deduces a corresponding signal model and an ADMM solving algorithm, effectively improves the reconstruction ability of the algorithm to complex target details and the representation ability of the algorithm to structural information, and can realize effective super-resolution imaging based on narrowband short-aperture echoes.

The embodiment of the invention provides an AOI equipment defect detection method and system, and relates to the technical field of AOI equipment defect detection.The method comprises the steps that image collection is conducted on a circuit board in a first illumination mode, a first image is obtained, and a first type of imaging problem area is recognized according to the first image; performing image acquisition on the circuit board by adopting a second illumination mode to obtain a second image, and identifying a second type of imaging problem area according to the second image; according to the first type of imaging problem areas and the second type of imaging problem areas, an illumination adjustment map is generated, and the illumination adjustment map indicates spatial distribution of different imaging problem areas; according to the illumination adjustment map, customized illumination is applied to different imaging problem areas, and a composite illumination field is formed; and performing single image acquisition in the composite illumination field to obtain a complete image of the circuit board. According to the embodiment of the invention, the integrity and accuracy of image information can be improved, and the risk of missing detection and false alarm can be reduced.

The present application relates to the technical field of geophysical exploration, in particular to a multi-state seismic exploration method and system, the method comprising: obtaining common center point discretization seismic acquisition data; constructing a wave function expression of a seismic wave field based on quantum state superposition theory, representing the seismic wave field as a superposition of reflection wave states and scattering wave states; performing fusion processing on the acquisition data under weak constraint conditions to obtain fused wave field data; performing wave state decomposition on the fused wave field data to decompose it into reflection wave field data and scattering wave field data; generating an underground interface structure image based on the reflection wave field data; and generating a probability distribution image of the scattering characteristics of the underground medium based on the scattering wave field data, the present application also provides a corresponding system, comprising a multi-state acquisition subsystem, a quantum state wave field processing subsystem, a weak constraint fusion subsystem and a probability domain interpretation subsystem, which breaks through the resolution limit of traditional reflection wave seismic exploration and solves the imaging problem of non-uniform and very small scale media.

The invention discloses an industrial machine vision-oriented strong backlight environment imaging sharpening system and method. The method comprises the following steps: S10, performing optical coding imaging; s20, carrying out improved L-R iterative recovery and first-stage wavelet denoising; s30, carrying out secondary wavelet denoising and fine recovery; and S40, carrying out iterative convergence and output. According to the invention, a specific wavefront coding optical imaging module and an improved L-R image restoration algorithm module are integrated into a complete visual detection system for solving the problem of industrial strong backlight imaging. In the L-R iteration process, a staged denoising step based on wavelet transformation is introduced, and before each iteration, first-level high-threshold wavelet denoising is carried out on a residual error; and in a specific stage in the iteration process, carrying out secondary middle threshold wavelet denoising on the current estimation image.

The application discloses a kind of synthetic aperture assisted multi-channel radar forward-looking learning imaging method, first, the echo data of the region to be imaged is acquired, the data obtained is compressed in distance direction pulse, then the echo data after pulse compression is imaged using the synthetic aperture assisted depth unfolding iterative shrinkage threshold algorithm ISTA network, i.e. in real aperture dimension, the imaging problem is converted into the estimation problem of target scattering coefficient, the target function is solved using the depth unfolding ISTA network, then the azimuth focusing is carried out using back projection BP algorithm in synthetic aperture dimension, then the synthetic aperture dimension result and the ISTA network result are fused to obtain the left-right unambiguous super-resolution imaging result.The method of the application overcomes the problem of left-right ambiguity and low resolution of single-base radar forward-looking imaging, realizes multi-channel radar forward-looking high-resolution imaging, and through depth learning, the parameter setting step is saved, the efficiency is improved, and the problem of signal-to-noise ratio sensitivity is overcome.

This paper discloses a method for indoor Wi-Fi signal imaging based on an improved VAE-MoE architecture. The method involves path planning and data acquisition, data preprocessing, signal propagation simulation, linearized physical model construction, deep learning model training, and image generation. By deploying two sets of Wi-Fi signal devices and indoor cameras, the method uses path planning and RSSI data collected from Wi-Fi signals. Furthermore, the method combines physical modeling with deep learning techniques for phased optimization, significantly improving data imaging accuracy. This method addresses the challenge of indoor imaging in data-limited environments and demonstrates high technical innovation and practical application value.

The invention discloses a welding turbid environment vision sharpening system and method based on circularly polarized light imaging. In the system, an active circularly polarized lighting module comprises a laser, a beam expander, a linear polarizer and a first quarter-wave plate which are connected in sequence; the polarized image acquisition module comprises a second quarter-wave plate, an analysis mirror and a camera which are connected in sequence; the active circular polarization illumination module is located at the position of a welding workpiece, and the polarization image acquisition module is located at the position of a welding gun; and the image processing and control unit controls the on-off of the active circular polarization illumination module and the image acquisition of the polarization image acquisition module, and performs image recovery. According to the invention, the lighting source is innovatively replaced by the circularly polarized light from the traditional linearly polarized light, and a set of self-adaptive image recovery algorithm is combined, so that the imaging problem of the vision of the welding robot in a turbid and high-reflection environment is successfully solved, and reliable visual perception guarantee is provided for realizing high-precision and intelligent welding operation.

The utility model discloses a dustproof motor, which comprises a motor body and an electrostatic adsorption mechanism arranged on the motor body and used for adsorbing small particles in the motor body. The electrostatic adsorption mechanism comprises a conductive unit and a dust sticking unit covering the surface of the conductive unit. According to the utility model, through the synergistic effect of active electrostatic adsorption and passive dust-sticking fixation, the limitation of the traditional motor dust-proof technology is broken through, the POG imaging problem caused by the fact that small particles fall on a BG is avoided, and the imaging quality and the reliability of the camera module are remarkably improved.

The invention relates to the technical field of 3D printing, in particular to a 3D printing process monitoring system and method based on a high-speed two-dimensional swing mirror. According to the invention, the high-speed two-dimensional oscillating mirror is arranged in front of the object space light path of the imaging lens group, so that rapid optical scanning of the working surface is realized; the controller firstly divides a working face into a target dot matrix, and then calculates the diopter dot matrix of the liquid zoom lens according to the current printing layer number and the object distance of each point position; in the actual detection process, the controller drives the high-speed two-dimensional swing mirror to sequentially point to each target point position, and meanwhile, the controller controls the liquid zoom lens to adjust the focal length in real time according to a preset diopter dot matrix, so that the industrial camera can obtain a clear image at each point position; not only is the problem of high-precision imaging in a large-view range solved, but also adverse effects caused by mechanical vibration are avoided through a pure optical scanning mode; the whole detection process is more efficient, the system structure is simple, and the detection precision and reliability are improved.

The invention discloses a radar super-resolution foresight imaging method and system, and the method comprises the steps: constructing a weight vector and a manifold matrix of distance and azimuth dimensions according to a waveform parameter and an array structure of a radar, and further constructing a distance spread function and an azimuth spread function; on the basis, target features are extracted through a mean shift theory, and a constructed distance spread function and a constructed azimuth spread function are used when a mean vector is calculated, so that a diffusion mode of a target in a radar image is better met, an image with smaller full width at half maximum can be obtained, and super-resolution imaging is realized. According to the method, the super-resolution imaging problem is considered innovatively in combination with the characteristics of the radar from the perspective of an image domain. Compared with a conventional super-resolution method based on signal processing, the method is more universal, is not liable to be limited by factors such as waveforms and array structures, does not have a strict performance upper limit, and can further improve the resolution on the basis of a conventional super-resolution imaging result.

The application discloses a radar super-resolution forward-looking imaging method and system, constructs a weight vector and a manifold matrix in the distance and azimuth dimensions according to the waveform parameters and the array structure of the radar, and further constructs a range spread function and an azimuth spread function.On this basis, target features are extracted through mean shift theory, and the constructed range spread function and azimuth spread function are used when calculating a mean vector, so that the diffusion mode of the target in the radar image is more in line with the diffusion mode, an image with a smaller full width at half maximum can be obtained, and super-resolution imaging is realized.The application innovatively considers the super-resolution imaging problem from the perspective of the image domain and in combination with the characteristics of the radar.Compared with the previous super-resolution method based on signal processing, the application is more universal, is not easily limited by factors such as the waveform and the array structure, and has no strict performance upper limit, so that the resolution can be further improved on the basis of the existing super-resolution imaging result.

The invention belongs to the technical field of computer vision and image processing, discloses a nighttime high-quality imaging method for an electronic rearview mirror, and aims to solve the problem that daytime and nighttime imaging needs to be processed at the same time and respectively construct learning samples and solve light source masks for daytime and nighttime images according to a brightness threshold. And a two-dimensional U-net structure is used as a model basis, and attention and a multi-scale mechanism are added to construct a learning model, so that better CMS image high-quality imaging is realized.

The utility model discloses a multi-point dynamic adjustment type tunnel light source which comprises a working frame, a workpiece positioning unit, a light source assembly and a visual assembly are fixedly arranged on the working frame, and the light source assembly and the visual assembly are located at the positions corresponding to the workpiece positioning unit. The height, the angle and the distance of the two reflecting shells can be adjusted relative to the workpiece, so that the irradiation angle of a light source is adjusted according to different detection requirements and object shapes, the optimal illumination effect is achieved, the visual imaging problem of the metal high-reflection rotary body is solved, and the detection accuracy is improved. According to the invention, a plurality of camera modules can be simultaneously used and X-axis moving working scenes can be handled, and light can be subjected to set reflection and heat dissipation in the reflection cavity, so that a uniform diffuse illumination effect is realized, shadows and light reflection are reduced, a uniform and stable illumination environment is provided for a machine vision system, image information of an object can be accurately acquired, and the visual effect of the machine vision system is improved. And powerful guarantee is provided for machine vision detection.

The present invention discloses a method for cutting photovoltaic module images based on mask generation, and the method steps are: constructing an image data set; image preprocessing; generating a mask based on UNet; generating a binary image; separating candidate cell units; generating cell units; obtaining a coordinate frame containing any number of cell units through a set frame, and then extracting the corresponding cell unit image from the original photovoltaic module image according to the coordinate frame. The beneficial effects of the present invention are as follows: the present invention effectively improves the cutting accuracy of the cell units, alleviates the problems of poor robustness and low generalization of the method caused by imaging problems; can effectively improve the generalization ability of the model; the cell unit separation method can effectively solve the problem of inaccurate and incomplete cell unit segmentation due to poor image quality, and improve the cutting accuracy; it can be applied not only to full-cell, half-cell, and shingled photovoltaic modules, but also to any image with similar image features.

The invention relates to an adaptive radar sparse imaging method based on a near-field physical model, and the method is constructed at least based on the following scheme: imaging modeling: at least constructing an imaging problem of a specific distance unit into a linear inverse problem; designing a physical decoupling network architecture, and at least designing a learnable induction strategy and an adaptive threshold; offline training: updating learnable parameters at least according to the obtained total loss to complete model training; and performing online reasoning, and outputting a target image at least based on forward propagation. The problems of model mismatch and imaging failure caused by the fact that an existing deep learning model LISTA cannot adapt to the change of a physical observation model along with the distance in near-field or high-resolution radar application are at least solved.

The present application relates to the technical field of image processing, and particularly relates to a kind of image light uniformity method, device, equipment and medium, the method comprises: obtaining all microscopic images of the object to be measured, all microscopic images are carried out shadow correction processing, obtain all target microscopic images, all target microscopic images are carried out splicing processing, obtain the target fusion image of preset target size, target fusion image is carried out curve fitting processing, obtain target light uniformity image, based on target light uniformity image, determine the image detection result of the object to be measured. Visible, all microscopic images obtained are carried out shadow correction processing, splicing processing and curve fitting processing to obtain target light uniformity image, and then according to target light uniformity image, determine the image detection result of the object to be measured, so as to solve the imaging problem caused by uneven illumination in the prior art, so that the finally obtained image realizes uniform illumination, eliminates shadow interference, effectively improves the measurement accuracy of image.

According to the three-wavelength and four-angle polarization sensitive holographic imaging system provided by the invention, the imaging system has wavelength and polarization resolution capability by using a mode of increasing information channels and synchronously acquiring; distortion phase and scattering noise, caused by medium refractive index disturbance and smoke multiple scattering in a severe imaging environment, of a target light field are analyzed, and the complex and severe environment imaging problem similar to an energetic particle combustion field can be well solved. Furthermore, four-angle polarization is introduced to increase an independent channel, so that the anti-interference capability of holographic imaging is improved, and the information storage capacity of the system is improved by four times on the premise that the physical size does not need to be increased. The problem of extinction and speckle interference caused by high particle concentration number is solved by means of the thought of synchronous interpretation of multi-dimensional light field information, and polarization characteristic parameters capable of distinguishing particles from background areas are extracted from polarization channels by means of the advantage of more stable transmission of polarization characteristics of light so as to obtain three-dimensional position information of the particles.

The present application relates to the technical field of image processing, in particular to training of an image enhancement model, an image enhancement method and an electronic device, the method comprising: obtaining a sample image group, the sample image group comprising a sample image collected by a binocular camera and a reference image, the sample image being an RGB image and an NIR image collected by the binocular camera after near-infrared light compensation under a first illumination, the reference image being an RGB image collected by the binocular camera under a second illumination, the first illumination being lower than the second illumination; inputting the RGB image and the NIR image into an image enhancement model to perform field-of-view alignment and enhancement reconstruction processing on the RGB image and the NIR image by using the image enhancement model to obtain an image enhancement prediction image; and performing loss calculation based on the image enhancement prediction image and the reference image to update parameters of the image enhancement model and determine a target image enhancement model. The method solves the imaging problem in a low-illumination environment.

The invention relates to the technical field of intelligent visual inspection, and discloses a real-time glue dispensing and overflowing monitoring method and system based on intelligent vision, and the method comprises the steps: collecting image data in a glue dispensing process in real time, and carrying out the regulation and intelligent sampling through a self-adaptive light source; carrying out segmentation and feature extraction on the colloid region; analyzing time sequence change characteristics and a multi-point space association relationship in a dispensing process, and dynamically identifying and predicting excessive glue in advance; a glue overflow detection result is converted into an equipment control instruction, and preventive intervention is carried out through advanced control; local real-time processing and cloud deep analysis are carried out; constructing a digital twin model in the dispensing process, and performing quality prediction and process optimization to obtain a quality prediction strategy and a decision support scheme; according to the invention, a self-adaptive light source regulation and control and reflection interference elimination technology is adopted to solve the problem of strong reflection metal surface imaging.

The application provides a signal-to-noise ratio robust ISAR imaging method and device based on an HSNet. The method comprises the following steps: obtaining ISAR echo data to be imaged; performing motion compensation and range-Doppler imaging processing on the ISAR echo data to be imaged to obtain a range-Doppler image of original echo; inputting the range-Doppler image of the original echo into a pre-trained HSNet network to output parameter information of a reconstructed ISAR image; obtaining an ISAR imaging result based on the parameter information of the reconstructed ISAR image; and setting a preset loss function of the pre-trained HSNet network as a negative variational lower bound. In the application, the parameter information of the reconstructed ISAR image is obtained through the pre-trained HSNet network, so that the calculation complexity of the existing ISAR imaging problem is reduced; in addition, by setting the preset loss function as the negative variational lower bound, the imaging quality is improved, and the network interpretability is greatly improved.

The present invention discloses a scanning radar super-resolution method under unknown broadening of the antenna pattern. First, through echo modeling, the azimuth echo is constructed as the convolution of the target scattering coefficient and the antenna pattern. Then, a broadening correction matrix is introduced into the echo convolution model to characterize the error of the antenna pattern function. Finally, an alternating iteration strategy is adopted to update the broadening correction matrix and the target scattering coefficient until convergence. All range cells in the echo are traversed, and the azimuth super-resolution imaging result is output. The method of the present invention introduces a broadening correction error matrix into the azimuth echo convolution model and continuously updates the target and the error matrix by means of alternating iteration, so as to achieve robust imaging under error conditions. Compared with the existing super-resolution methods, it can achieve robust super-resolution imaging when there is unknown broadening in the antenna pattern function, has stronger applicability, and solves the problem of super-resolution imaging when the antenna pattern function undergoes unknown broadening.

The application discloses a kind of acoustic three-dimensional imaging method and image sensor of resonant holographic sensing, applied to acoustic imaging field, the imaging method utilizes acoustic lens focusing reception target object reflected ultrasonic echo, forms imaging sound field and is imaged in acoustic image sensor surface, the acoustic image sensor is by Helmholtz type microcavity metasurface, RS type laminated MUT unit array and special integrated circuit sequentially laminated and constitutes, and it is arranged in pixelization array, imaging sound field enters corresponding internal microcavity through each surface through hole of metasurface, and it is carried out resonant interference with the reference sound field radiated by the bottom RS type laminated diaphragm, forms holographic sound field and reacts on RS type laminated diaphragm, generates holographic resonance signal, after the signal is peak detection, denoising processing, addressing readout and phase reconstruction, the three-dimensional image of target object ultrasonic echo is formed.The present application can overcome the existing acoustic three-dimensional imaging technology faces the imaging problem of large field of view, high resolution and fast real-time.

The invention discloses a non-vision-field high-resolution scattering imaging method based on speckle fingerprints, and the method comprises the steps: shooting a series of speckle images based on a built non-vision-field imaging optical system; any two speckle images are selected from the speckle images for frequency domain filtering processing, an envelope is obtained from low-frequency information, and spatial normalized speckles are obtained from high-frequency information; according to the obtained envelopes, calculating the cross-correlation of the envelopes corresponding to the two speckles to obtain corresponding target displacement information, and calculating the corresponding position of the autocorrelation value on the target autocorrelation; combining the obtained autocorrelation value and the corresponding position thereof into a pixel on target autocorrelation; and through loop iteration, constructing a complete target self-correlation image containing all pixels, and reconstructing a target image O through a phase retrieval algorithm. According to the method, high-resolution imaging and tracking of the moving target in the non-vision-field mode can be realized, prior knowledge and invasive operation are not needed, the accuracy of image recovery is improved, and a new effective means is provided for solving the non-vision-field imaging problem.

The present application belongs to the field of space optical remote sensing, and provides a long-distance wavefront correction Fourier stack imaging system to improve the resolution and quality of long-distance imaging and solve the imaging problem of traditional technology under the influence of atmospheric turbulence and optical distortion. The system is composed of an illumination subsystem and an imaging subsystem, wherein the illumination subsystem generates a light source with a specific polarization state, and the imaging subsystem includes Hartmann-Shack sensors, an anamorphic lens, a beam splitter, a linear polarizer and other components, which fuse and reconstruct the image into a high-resolution image through Fourier stack imaging technology. At the same time, the Hartmann-Shack sensor monitors the wavefront distortion in real time, and the anamorphic lens corrects according to the feedback information to ensure the imaging quality. The present application combines the distortion wavefront correction module with the Fourier stack imaging technology, effectively overcomes the atmospheric disturbance and system distortion in long-distance imaging, and improves the adaptability and stability of the imaging system in complex environments.

The invention discloses a transfer orbit design method in on-orbit service of a synchronous orbit satellite. The problem of how to effectively image a high-orbit target satellite with low fuel consumption is solved. Belongs to the technical field of space. Comprising the following steps: determining total transfer time according to different initial states of a service satellite and a target satellite; constructing a transfer orbit mathematical model based on the total transfer time to obtain the running time of the service satellite on the initial orbit, the transfer orbit and the approaching orbit in the transfer process and the speed increment for completing the Horman transfer; taking a relative position vector included angle and an illumination angle of two stars at the end of intersection as an objective function, taking total transfer time as a constraint condition, taking the operation time of a service star in an initial orbit and an approaching orbit as an optimization variable, optimizing the objective function by using a genetic algorithm based on an HPOP model, and correcting parameters of a transfer orbit mathematical model to obtain a transfer orbit mathematical model; obtaining a transfer orbit mathematical optimization model; according to the method, effective imaging of the high-orbit target with low burnup is realized through the mathematical optimization model of the transfer orbit.