180 results about "Electrical imaging" patented technology

The invention discloses a multi-modal-based low-slow small target multi-dimensional detection method and system, and relates to the technical field of unmanned aerial vehicle detection. Omnibearing scanning is carried out on a target airspace through a radar, a suspicious target is determined through echo signal analysis, and initial position information of the suspicious target is acquired; adjusting the direction of the photoelectric turntable according to the initial position information, and capturing the suspicious target through a multi-band imaging technology to obtain a first target image set; preprocessing the first target image set to obtain a second target image set; and taking the second target image set as input of a pre-trained target detection model to obtain a target detection result. Through fusion of radar omnibearing scanning and multiband photoelectric imaging technologies, multi-mode and multi-dimensional detection of low-speed small targets is realized, and the problem that a single sensor is insufficient in recognition capability in a complex environment is effectively solved.

The invention discloses a reservoir effectiveness evaluation method and device based on acoustic logging data, and relates to the field of acoustic logging, and the method comprises the steps: respectively calculating stratum stoneley wave amplitude and dipole transverse wave spectrum energy for array acoustic logging data collected in an open hole well; based on the Stoneley wave amplitude and the dipole transverse wave spectrum energy, the effectiveness of the reservoir is analyzed by integrating electric imaging logging and other data; taking the Stoneley wave amplitude as an abscissa and the dipole transverse wave spectrum energy as an ordinate, establishing a reservoir effectiveness evaluation chart, and dividing the chart into a preset number of reservoir effectiveness evaluation intervals; and after array acoustic logging data corresponding to a to-be-evaluated well is processed, reservoir effectiveness evaluation is carried out according to the region where the stoneley wave amplitude and the dipole transverse wave spectrum energy are located in the chart. Therefore, the method can effectively improve the effectiveness evaluation precision of the tight reservoir and improve the evaluation efficiency, and solves the problems of complex evaluation process, long time consumption and poor universality of the conventional common reservoir evaluation technology.

The invention relates to a buried hill reservoir permeability evaluation method and system based on Stoneley wave time difference intelligent prediction, processing equipment and a storage medium. The method comprises the following steps: preprocessing core physical property, conventional logging and electric imaging logging data in a target area; array acoustic logging data are processed, and longitudinal wave, transverse wave and stoneley wave time difference of each well in the target area is extracted; establishing an intelligent stoneley wave time difference prediction model, and predicting the stoneley wave time difference of the interval with poor stoneley wave data quality of each well; determining the effective fracture development position and the fracture width of each well; carrying out a crack plate model experiment, and calculating the total permeability of the stratum at the effective crack development position according to the effective crack development position and the crack width of each well; inverting the total permeability of the buried hill reservoir; on the basis of the plunger sample core permeability and the total stratum permeability calculated at the effective fracture development position, permeability inversion results of the effective fracture non-development position and the effective fracture development position of each well are calibrated, and the method can be widely applied to the field of oil and gas exploration and development.

The invention provides a carbonate rock dominant productivity reservoir identification method. The carbonate rock dominant productivity reservoir identification method comprises the following steps: S1, acquiring element logging curve data, an electric imaging basic image, a conventional logging curve, core experiment data and gas logging data of a well in a research block; s2, by means of element logging curve data and conventional logging curve data, the mineral content of the region is obtained, and the lithofacies AL of the research region is determined; s3, a reservoir type parameter AE is given; solving an electric imaging porosity spectrum parameter by utilizing the electric imaging two-dimensional curve, and solving a porosity spectrum evaluation parameter AD; s4, analyzing the relationship between the conventional logging curve and the gas logging data, determining the boundary between the dominant gas production reservoir and the low-productivity reservoir, and constructing a productivity evaluation parameter AO; and S5, according to the calculated parameter values, respectively calculating evaluation standards and weights of different parameters, calculating the carbonate reservoir in the research area according to the standards and the weights to obtain a reservoir comprehensive quality index, and according to the reservoir comprehensive quality index, obtaining advantageous capacity reservoir distribution.

The application discloses a kind of active fault borehole stratum comparison methods combined with high-resolution logging data, it is related to stratum comparison technical field, comprising: first respectively obtaining test fault region and test bottom layer region;Real-time data acquisition is carried out based on electrical imaging logging, and differential analysis model is established;The differential analysis model is optimized, and the ground fault comparison model is obtained;Active fault borehole stratum comparison method is obtained based on the data in ground fault comparison model;The application is used to solve the problem of low precision and efficiency in the existing active fault borehole stratum comparison method.

The invention provides an electrostatic imaging method and an electrostatic imaging device, and the method comprises the steps: enabling each electric field sensitive element in an electrostatic sensor to sense a charge signal in each unit region of a detected object, so as to obtain a simulation charge signal of each unit; obtaining a digital charge signal value of each unit according to the analog charge signal of each unit, and storing the digital charge signal value to a corresponding storage area; obtaining a sensitive element surface electric field value of each electric field sensitive element according to each unit digital charge signal value, and storing the sensitive element surface electric field value in a corresponding storage area; and inputting the digital charge signal value of each unit or the surface electric field value of each sensitive element into the electric field intensity-position distribution table to obtain an electric field image of the measured object, and dividing unit gradient color blocks corresponding to the unit areas in the electric field image. According to the invention, the number of perceived charge signals can be increased, the charge conditions of a plurality of different unit areas in the detected area can be obtained, and after arrangement according to physical positions, the electrostatic gradient color block map of the detected object can be obtained more accurately.

The invention provides a natural fracture development group system identification method and system fusing well drilling and logging data and a terminal, and the method comprises the steps: determining a rock core homing picture according to a ground scanning gamma curve and an electric imaging logging gamma curve of a rock core; comparing the core homing picture with the imaging logging crack identification result picture to obtain an imaging logging crack identification chart; according to the imaging logging crack identification chart, determining crack types of the whole well, and classifying the crack types to obtain a crack development group system of the whole well; according to the logging drilling time and logging gas logging total hydrocarbon data corresponding to the whole well and the fracture development groups of the whole well, determining the logging drilling time and logging gas logging total hydrocarbon data corresponding to each fracture development group; according to the logging drilling time and logging gas logging total hydrocarbon data corresponding to each fracture development group, constructing a full-well-section fracture development group identification model; the model is used for predicting the fracture development group type of an unknown single well, and the development group identification precision is improved.

The invention relates to a crack segmentation deep learning method based on logging imaging. The method comprises the steps of data acquisition and preprocessing; optimizing a target detection algorithm, specifically including adding an MSF attention mechanism module, replacing a network loss function, replacing with a more lightweight network, and optimizing a pyramid pooling layer; and carrying out an ablation experiment, and screening out an electrical imaging fracture segmentation network model with optimal performance by combining comprehensive index analysis of experimental data. According to the invention, an attention mechanism module-MSF is designed, so that crack characteristics can be accurately represented and background interference can be effectively suppressed. By optimizing an original network structure, including improving a pyramid pooling network layer to integrate higher-scale features, replacing a more appropriate loss function and replacing the pyramid pooling network layer with a more lightweight skeleton network, the overall generalization ability and robustness of the model are remarkably improved through the optimization, and a model foundation is laid for a subsequent imaging crack segmentation task.

The invention discloses a photoelectric imaging system multi-component optimization method based on full-link model driving. The method comprises the following steps: determining a parameter value range of to-be-optimized sensor parameters of multiple components in a target photoelectric imaging system; the to-be-optimized sensor parameters comprise focal length, aperture, pixel size and integration time; based on a pre-constructed full-link simulation model of a target photoelectric imaging system and a target genetic algorithm, performing iterative optimization on the parameter value range of the to-be-optimized sensor parameters to obtain target parameter values of the to-be-optimized sensor parameters under aberration-free and stray-free conditions; and determining a target aberration range and a target stray irradiance range of the target photoelectronic imaging system based on the target parameter values of the optimized sensor parameters. According to the method, the synergistic effect and the mutual coupling restriction relation between different component parameters can be integrated in the optimization process, and the performance of the optimized target photoelectric imaging system is improved.

The invention discloses an electric imaging logging blank area intelligent restoration method based on graph adversarial transfer learning. The method comprises the steps that a first training data set is established according to public image data; constructing an initial image restoration model based on the graph neural network and the generative adversarial network, and training and testing the initial image restoration model by using the first training data set to obtain a pre-trained image restoration model; acquiring conductivity logging data, and establishing a second training data set according to the conductivity logging data; carrying out migration training on the pre-trained image restoration model by utilizing the second training data set to obtain an optimized image restoration model; and inputting a to-be-repaired electric imaging logging image into the optimized image repair model to complete the repair of the electric imaging logging image. The method can effectively improve the restoration precision of the image of the electric imaging logging blank area, fills up the image missing area caused by the instrument problem, and also has remarkable advantages in the aspects of processing speed and adaptability.

The invention relates to the technical field of aviation photoelectric imaging, in particular to a multi-load aviation photoelectric pod timing system control system which comprises a timing system analysis module, a synchronous control module, a communication control module and an information superposition module. The timing system analysis module generates an absolute moment, the synchronization control module provides a multi-source synchronization pulse signal, the communication control module realizes data interaction and generates additional information including the absolute moment, a pose and a system state, and the information superposition module synchronously superposes the additional information to image data. According to the technical scheme, automatic matching of the image and the telemetry data is achieved, the problems of poor synchronization precision and low efficiency caused by manual matching are solved, and the difficulty and workload of rear-end processing such as image splicing and radiation measurement are remarkably reduced.

The invention relates to a shale bedding structure accurate and efficient identification and classification method based on deep learning, and the method comprises the steps: calculating shale bedding structure pre-classification parameters according to a fast shear wave time difference curve, a slow shear wave time difference curve and a well logging curve having correlation with a shale bedding structure in target block well logging data; according to shale bedding structure pre-classification parameters, a segmented electric imaging logging diagram and a preliminary classification result are obtained; according to the preliminary classification result, the segmented electrical imaging logging graph is input into different stratification recognition network models, a shale stratification structure recognition result is obtained, the recognition result is analyzed through image morphology, a final classification result is obtained, and the stratification recognition network models are constructed based on a Swin Transform network model and obtained based on training of a training set. The invention provides a new division method for refined shale bedding structure types.

The application discloses a kind of magnetoacoustic electric imaging method, system, equipment, medium and product, it is related to magnetoacoustic electric imaging field, the method comprises: according to the number of array elements of annular array probe Randomly generate multiple sets of space delay coding;Space delay coding is set on the annular array probe, and the corresponding ultrasonic wave of space delay coding is emitted by annular array probe;The delay of each array element is different, and the ultrasonic wave waveform emitted by each array element is identical;While array element emits ultrasonic wave, the magnetoacoustic electric signal excited by space delay coding is collected by electrode, and the ultrasonic wave emission of each set of space delay coding corresponds to the collection of one magnetoacoustic electric signal;According to each set of space delay coding, corresponding acoustic vibration velocity field is simulated and generated;According to the acoustic vibration velocity field corresponding to all space delay coding, system matrix is determined;According to system matrix and magnetoacoustic electric signal, reconstruct conductivity image, realize magnetoacoustic electric imaging, and the application improves spatial resolution and improves imaging effect.

The present application relates to a method for identifying a type of a shale laminaset based on electrical imaging logging, includes loading and decoding electrical imaging data, and preprocessing the electrical imaging data to generate electrical imaging polar plate image data. An image center cluster is initialized by K-means++ algorithm, finding a new cluster center by iteration according to a mean value after calculating a Euclidean distance, and completing clustering after a threshold or a limit of times is reached. N clustered values are binarized to obtain an end-to-end connected matrix conforming to a principle of wellbore imaging, then a circularly connected component labeling algorithm is carried out, and sinusoidal bedding interface fitting is performed after extracting pixels of laminae by a bedding clustering algorithm. The laminae of a shale gas reservoir is then divided based on electrical imaging logging clustering response characteristic value ranges for different laminasets.

An oil-based mud electric imaging logging fracture opening degree calculation method comprises the steps of firstly determining response characteristics of oil-based mud electric imaging logging to various fractured stratums, then constructing a stratum multi-parameter calculation model, further constructing a fractured stratum multi-parameter image, then establishing a fracture recognition and image extraction processing method, and finally calculating the fracture opening degree of the oil-based mud electric imaging logging fracture opening degree. Finally, a fracture opening degree calculation method and model are established, the fracture opening degree can be calculated and evaluated based on electric imaging logging in the oil-based mud environment, stratum model evaluation and fracture stratum model evaluation are combined together, the respective functions of the stratum model evaluation and the fracture stratum model evaluation are highlighted, a mean value stratum model solves various necessary unknown parameters, and the evaluation accuracy is improved. A foundation is laid for crack opening degree calculation; in a homogeneous formation model and a fractured formation model, all possible multi-parameter combination measurement and calculation can be carried out, the application range is wide, and fracture parameter evaluation of various reservoirs is facilitated.

The invention relates to the technical field of cable quality detection, and discloses a cable quality detection supervision system. The invention aims to solve the problem of large eccentricity calculation deviation caused by incapability of accurately extracting the center of a conductor with an irregular cross section formed by twisting a plurality of wires in the prior art. According to the system, a three-dimensional coordinate system is established through a laser mark positioning module, a high-precision cross-section image is obtained through an ultrasonic discharge imaging module, an intelligent image processing module extracts multiple feature points to calculate an initial center, and an iterative registration module constructs an axial drift trajectory. The center decision module determines a real center and visual eccentricity distribution of the quality supervision platform in combination with a probability model and discharge verification, the center position of the irregular section conductor is accurately positioned through multi-module cooperation, the eccentricity detection precision is effectively improved, and cable quality full-process supervision is achieved.

The invention discloses a method for determining the development degree of a shale formation based on electric imaging data, and the method comprises the steps: 1, obtaining the electric imaging data of a shale well, and processing the electric imaging data to obtain dynamic imaging apparent conductivity data corresponding to each depth point; 2, abnormal data points are eliminated; 3, dividing the dynamic imaging apparent conductivity data of each depth point into data segments, and calculating a single-point arithmetic mean value of each data segment; 4, calculating a total average value of all depth points in any depth range; 5, setting an apparent conductivity fluctuation ratio, and determining the apparent conductivity fluctuation ratio according to the relationship between the single-point arithmetic mean value and the overall mean value of the two adjacent depth points; 6, calculating an apparent conductivity difference degree; 7, calculating a lamina development index; and 8, determining the lamina development degree. The method also has a better recognition effect on fine shale strata and strata with relatively poor definition. And the method is simple to operate, good in recognition effect, high in recognition speed, high in response degree, high in replicability, and good in universality and popularization value.

The application discloses a kind of acoustic-electric tomography image imaging algorithm based on two-point gradient iterative method, and the innovation is to improve the calculation efficiency by two-point gradient iterative method, and the accuracy of image reconstruction is improved by the selection of regularization term.The core of the application is to use the characteristics of acoustic-electric hybrid imaging, that is, when the boundary detection current is applied, the power density data is obtained by using ultrasonic disturbance, so as to obtain the internal data of biological tissue.At the same time, according to the different characteristics of different biological tissue conductivity, different regularization terms are introduced, two-point gradient iterative method is established, and high-resolution reconstruction of biological tissue conductivity is realized.Compared with the traditional acoustic-electric imaging method, the application reduces the calculation and storage cost of multiple observation currents, improves the imaging efficiency.And the iteration process is modified by using a special regularization term, so that the boundaries or geometric characteristics of different tissues can be accurately determined, and the imaging accuracy is improved.

The invention belongs to the technical field of laser damage measurement, and relates to a multi-angle damage real-time measurement system and method for a photoelectric imaging detector. The measuring system comprises a laser emitting unit, a first incident unit, a second incident unit, a detection light source, a measuring unit and a controller. The laser emission unit is used for emitting laser beams with adjustable energy and types, the laser beams irradiate the same preset area on the photoelectric imaging detector along a light path constructed by the first incidence unit and a light path constructed by the second incidence unit, and the energy of the laser beams is equal; the detection light source enables the photoelectric imaging detector to be controllably in an illumination working condition or a non-illumination working condition; the measuring unit is used for measuring a damage image output by the photoelectric imaging detector, laser spot energy distribution characteristics and a surface damage morphology image of the photoelectric imaging detector; and the controller receives the image output by the measuring unit and can send four control instructions. According to the invention, damage measurement under the action of laser multi-angle common irradiation can be realized.

The invention belongs to the technical field of mineral calculation and reservoir evaluation, and particularly discloses an electric imaging logging mineral component quantitative characterization method based on porosity spectrum correction, which comprises the following steps: preprocessing original electric imaging logging data, and generating a high-density pixel image; performing probability density statistical analysis on the pixel value of each depth point in the high-density pixel image to obtain a pixel waveform spectrum; calculating the apparent porosity of each pixel point in the high-density pixel image to generate a pore spectrum; performing pore fluid influence correction on the original electrical imaging data based on the pore spectrum, performing image enhancement to generate a pure mineral pixel waveform spectrum, and performing equalization processing to obtain an imaging mineral probability map; according to mineral content data obtained through core XRD total rock analysis, an imaging mineral spectrum is subjected to quantitative calibration, and distribution cut-off threshold values and pixel point value ranges corresponding to different minerals are recognized and deduced. The method can effectively solve the calculation error caused by the influence of the traditional imaging mineral calculation fluid on mineral calculation.

The invention relates to the technical field of aviation photoelectric imaging, in particular to an aviation photoelectric pod load interface control device and method.The aviation photoelectric pod load interface control device comprises a main controller, a communication control module and a receiving and transmitting switching module, and the main controller is connected with a plurality of subsystem assemblies and a man-machine interaction system; a communication interface of each subsystem assembly is connected with a corresponding input / output interface in the communication control module, a communication interface of a man-machine interaction system is connected with a corresponding input / output interface in the communication control module, and a normal communication control task is completed; and in the maintenance mode, the communication interface of the maintained subsystem assembly is switched to the external interface. According to the invention, the communication interface of the maintained subsystem assembly is switched to the external interface, so that the disadvantage caused by disassembly due to maintenance is avoided, and the maintenance efficiency is improved.

This application discloses a design method for an optoelectronic imaging system with laser protection and privacy protection functions, relating to the field of optoelectronic imaging. The method includes: determining an optimized point spread function based on preset point spread distribution characteristics, target energy divergence, and point spread radius using an optical-algorithm joint optimization framework; determining an initial phase distribution based on imaging parameters using the Gerchberg-Saxton algorithm; adjusting the initial phase distribution using a stochastic gradient descent algorithm, guided by the difference in energy divergence of the point spread function, to determine the optimized phase distribution corresponding to the optimized point spread function; and using the optimized phase distribution to load the phase modulation component of the optoelectronic imaging system. This application can achieve high-quality optoelectronic imaging with laser protection and privacy protection capabilities.

The invention discloses a shale deposition structure automatic identification method and device, and relates to the field of shale oil exploration and development, and the method comprises the steps: obtaining an image of a rock core and an electric imaging logging image corresponding to the rock core; processing and identifying a bedding boundary in the electrical imaging logging image; determining the number of stratification in the core image; determining a corresponding relation between the number of bedding boundaries in the electrical imaging logging image and the number of bedding in the image of the rock core; based on the corresponding relation and the bedding boundary of the electrical imaging logging image of the non-coring stratum, the actual bedding number of the non-coring stratum is determined, and then the sedimentary structure of the non-coring stratum is determined. After the corresponding relation of the bedding boundary between the actual image of the stratum core and the electric imaging logging image corresponding to the rock is determined, the sedimentary structure of the shale stratum can be automatically determined according to the electric imaging logging image, and the actual core does not need to be sampled and manual identification does not need to be carried out. And the automatic identification of the sedimentary structure of the shale stratum can be realized by adopting electric imaging logging.

The invention discloses a three-dimensional electrocardiogram imaging method and device based on a dynamic atrium model and a medium, and relates to the technical field of electrocardiogram imaging, and the method comprises the steps that atrium multi-temporal image data and physiological state related data are collected, and the physiological state related data at least comprise respiratory signals and body position change data; based on the atrium multi-temporal image data, constructing an initial dynamic atrium model in combination with an adaptive grid optimization technology; performing form and position optimization on the initial dynamic atrium model through a self-adjusting geometric optimization algorithm by using the physiological state related data; generating an electrical activity transfer matrix based on the optimized dynamic atrial model, wherein the electrical activity transfer matrix is used for describing transfer characteristics of atrial electrical activity; based on the electrical activity transfer matrix and the body surface potential signal, three-dimensional electrocardiogram imaging is achieved through electrocardio imaging inverse problem solving. According to the invention, the solution precision of the ECGI inverse problem can be improved.

The present application relates to a kind of microfracture width calculation method and system based on electric imaging plate model, it includes: based on plate formation model, the fracture width of plate formation, angle, length and mud resistivity, matrix resistivity electric signal response are obtained;From the electric signal response result, the response law of fracture parameter of electric imaging is extracted, and the fracture width representation formula of the fracture angle of the fracture position based on resistivity disturbance rate and electric imaging extraction is obtained according to the response law of fracture parameter;After electric imaging instrument calibration, the final resistivity fracture width calculation result is obtained.The present application can solve the problem that the calculation error of fracture of 100 microns level is larger using traditional fracture width calculation formula, and can be widely applied in the field of petroleum geological exploration.

The invention discloses an electric imaging logging crack online identification method and system driven by graph representation learning, and relates to the field of geological resource exploration and mining, and the method comprises the steps: segmenting obtained real-time electric imaging logging crack data into a plurality of sequence electric imaging fragment graphs; performing binarization processing and de-noising processing on each sequence of electric imaging fragment images to obtain binarization fragment images; determining an original path diagram according to the binarization fragment diagram; according to engineering-geology priori knowledge, determining spatial features and morphological features of each node in the original path diagram and embedding corresponding nodes to obtain a reconstructed knowledge path diagram, projecting the reconstructed knowledge path diagram to a high-dimensional feature space, and calculating to obtain high-dimensional spatial distances of different segments; connecting the fragments of which the distances are lower than a threshold value to obtain a recombined meta-crack knowledge graph; and inputting the recombined element crack knowledge graph into an intelligent crack identification model driven by graph representation learning to obtain a crack type. According to the invention, on-site electric imaging-based incomplete crack intelligent on-line identification can be realized.

To provide a manufacturing method of a toner for electrostatic charge image development, which can suppress the generation of fogging.SOLUTION: A manufacturing method of a toner for electrostatic charge image development includes the following steps 1 to 3 in this order: a step 1 of aggregating resin particles containing an amorphous polyester resin A in an aqueous medium so as to acquire an aggregated particle 1; a step 2 of aggregating resin particles which contain an amorphous polyester resin B and a styrene resin with an acid value of 60 mgKOH / g or more relative to the acquired aggregated particle 1, so as to acquire an aggregated particle 2; and a step 3 of fusing the acquired aggregated particle 2 so as to acquire a fused particle.SELECTED DRAWING: None

This application relates to the field of optoelectronic imaging technology, specifically to an image sensor, imaging device, and imaging method. The image sensor includes a pixel array, which comprises multiple functional units arranged in an array. Each functional unit includes at least two pixel sub-units. Each pixel sub-unit includes at least two polarization pixels and at least one spectral pixel. The spectral pixel includes a first photosensitive pixel layer and a narrowband filter layer. In each functional unit, the narrowband filter layer of the spectral pixel corresponding to the at least two pixel sub-units has a different center wavelength. This application enables efficient acquisition of polarization and spectral information through a single exposure and effectively reduces device size and manufacturing costs.

An electrostatic charge image developing toner set contains a white toner that contains white toner particles including a binder resin, a white colorant, and a release agent, and a non-white color toner that contains color toner particles including a binder resin, a color colorant, and a release agent, in which, in observation of cross sections of the white toner particles and the color toner particles, in a case where a proportion of an area of a domain of a release agent present in a surface layer region from a surface of the white toner particles to a depth of 1 μm or less with respect to an area of all domains of the release agent present in a cross section of the white toner particles is represented by WW, and a case where a proportion of an area of a domain of a release agent present in a surface layer region from a surface of the color toner particles to a depth of 1 μm or less with respect to an area of all domains of the release agent present in a cross section of the color toner particles is represented by WC, a relationship of WC<WW is satisfied.

A large-aperture reflective zoom and laser communication integrated camera is disclosed, relating to the fields of optoelectronic imaging and laser communication technology. It addresses the technological gap where liquid zoom lenses cannot be applied to large-aperture RC systems, and the problem of relying on additional components for integrated visible light imaging and laser communication, making it difficult to balance lightweight design and high performance. The camera consists only of a primary mirror, a liquid zoom lens array, a grating film, a visible light detector, and an infrared detector. The front of the liquid zoom lens integrates a multi-layer liquid zoom lens array, with each layer having a different focal length. Independent drive circuits electrically control and adjust each lens layer to eliminate aberrations and adjust the focal length for optical systems of different apertures. The grating film on the back exhibits high reflectivity to visible light and high transmittance to 1550nm laser light, enabling visible light to form an RC structure imaging optical path, while the laser light is transmitted to complete communication. This invention can be widely applied in aerospace, aviation, and ground observation scenarios.