242 results about "Imaging interpretation" patented technology

The present invention relates to the technical field of intelligent remote-sensing-image interpretation, and specifically provides a texture-aware and boundary-aware target detection method and system for a weak and small target in a remote sensing image. The method comprises: inputting a remote sensing image into a feature extraction network to extract a basic feature, and then extracting a texture-aware feature; inputting the remote sensing image into a boundary map extraction module to extract a binary boundary map, and then extracting a boundary-aware feature; fusing the boundary-aware feature with the basic feature by means of a boundary-guided feature module, so as to obtain a boundary-guided feature; and decoupling the texture-aware feature and the boundary-guided feature and outputting same, inputting the texture-aware feature and the boundary-guided feature into a task-decoupling RCNN for double-branch decoupling prediction, so as to obtain a classification and positioning result, and completing texture-aware and boundary-aware target detection. By means of the method and system in the present invention, information that is hard for remote-sensing weak and small targets to show can be mined, thereby further improving the expression of the weak and small targets, and thus improving the detection performance regarding the weak and small targets.

The invention relates to the technical field of image processing, and provides an image artifact detection method and system based on a deep learning technology, and the method comprises the following steps: carrying out the preprocessing of an obtained to-be-processed image, and carrying out the feature extraction of the preprocessed image; position coding is carried out on the extracted feature map A, and the extracted feature map A is fused with the feature map A; performing multi-layer encoding and decoding on the fused feature map through an encoder and a decoder to obtain a feature map B containing an artifact target area; and generating an anchor frame of an artifact region for a target region in the feature map B, and dynamically adjusting the size and position of the anchor frame according to local information of the down-sampling rate, texture complexity and edge strength of the feature map B to obtain an artifact region detection result after the anchor frame is labeled. Automatic artifact detection and quantitative evaluation of the influence degree can be achieved, the accuracy and efficiency of medical image interpretation are improved, and more reliable data support is provided for clinical decision making.

The invention relates to a remote sensing image domain generalization semantic segmentation method based on multi-scale instance decoupling, which relates to the technical field of remote sensing image interpretation and comprises the steps of multi-scale instance coding, domain invariant instance decoupling and domain generalization semantic decoding. Image features in a segmented backbone network are used as input, and a multi-scale instance coding module effectively senses feature responses of cross-domain instances through deep convolution, multi-scale cooperation and multi-path sensing and is not affected by scale differences, spatial deformation and noise; designing a domain invariant instance decoupling module to decouple the cross-domain style from the image representation; and finally, the decoupled features are input into a domain generalization semantic decoding module so as to predict a segmented image on a target domain which is not seen, and the segmentation method can generalize various remote sensing images which are not seen so as to obtain better segmentation capability.

The invention relates to the technical field of wetland ecosystem soil carbon sequestration capability, in particular to a seasonal waterlogged wetland soil carbon estimation and dynamic monitoring method, which comprises the following steps of: selecting a sampling zone and a quadrat according to an equal-elevation water level gradient and a vegetation type; an optimal remote sensing estimation carbon model is determined through remote sensing image interpretation and screening of characteristic factors such as image waveband reflectivity, vegetation indexes, principal component characteristics and single-waveband textures, and soil organic carbon spatial distribution characteristics are analyzed through remote sensing inversion and GIS interpolation; constructing a Wetland-DNDC wetland carbon dynamic simulation model based on a DNDC model to predict wetland long-term carbon dynamic change and greenhouse gas emission; the invention provides a method for simulating carbon reserves and carbon dynamic monitoring based on field investigation and coupling a 3S technology with a Wetland-DNDC model. Compared with a traditional field observation method, the method has obvious technical and cost advantages, and can realize the technical advantages of spatial analysis and long-time sequence monitoring. The research method is suitable for a wetland ecosystem with seasonal hydrological characteristic changes.

The invention provides a modal information constraint-based multi-modal remote sensing image ground object classification method, which comprises the following steps of: firstly, adaptively learning a fusion weight of modal sharing and modal difference characteristics by adopting an MDF decoder, and adaptively controlling the contribution of modal sharing and modal proprietary information from a multi-scale angle so as to reserve the modal proprietary information; then, an MSIE decoder is adopted to guide modal difference features and multi-modal features to carry out a multi-modal auxiliary classification task so as to enhance optical and SAR proprietary information beneficial to classification; therefore, compared with other multi-mode remote sensing image ground feature classification methods, the method is higher in recognition precision, can effectively relieve the ground feature recognition performance bottleneck in a complex scene, and provides effective support for multi-mode remote sensing image interpretation.

An image classification model, e.g., a neural network model, may be trained on a set of training medical imaging exams each including a training report and a training medical image. A model generation module or device may, for each of the training medical imaging exams: use finding item criteria to reorganize text of the training report into a list of finding items, each associated with text extracted from the training report text, use natural language processing to analyze the resultant text associated with each finding item to determine an associated classification of each finding item, store, in a training dataset, the training medical imaging exam, the associated finding items, the matching text, and the classifications resulting from the analysis of the matching text. The trained neural network learns to output, for other medical imaging exams, one or more finding items and an associated classification data.

The present disclosure discloses a method for suppressing the azimuth ambiguity of multi-channel SAR systems based on channel cancellation, which falls within the field of radar signal processing. The method includes: imaging echoed of the multi-channel SAR system based on a linear mapping reconstruction algorithm to obtain a reconstructed high-resolution wide-swath image; estimating positions of the azimuth ambiguity regions of the reconstructed high-resolution wide-swath image; extracting ambiguity images in the azimuth ambiguity regions based on channel cancellation and the refocusing algorithm; and suppressing a repetitive ambiguity image in imaging the echo of the multi-channel SAR system. The method weakens the influence of the original image on azimuth ambiguity extraction, improves the performance of ambiguity suppression, and solves the problem of additional repetitive ambiguity in multi-channel SAR systems by reconstructing the ambiguity signal, and improves the image quality of the multi-channel SAR system, which has great value for subsequent applications such as SAR image interpretation.

The invention relates to the technical field of image recognition, in particular to a remote sensing image segmentation method and system, a storage medium and equipment. Taking the preprocessed remote sensing image as a real sample, and obtaining a generated sample by using a conditional diffusion model; respectively converting the real sample and the generated sample into a set color space, calculating the color value of each channel pixel by pixel, determining the color distribution of the real sample, aligning the color distribution of the generated sample with the color distribution of the real sample through linear transformation, and obtaining the generated sample after color correction; and performing similarity screening on the generated samples after color correction, mixing the generated samples with real samples according to a set proportion, taking a formed mixed sample set as a training set of a semantic segmentation model, and performing training to realize remote sensing image segmentation. By combining the conditional diffusion model and color correction, the spectrum consistency of the generated samples is improved, the quality of the generated samples is optimized, and by means of mixed sample training, the requirement for manual annotation is reduced, and the precision of remote sensing image interpretation is improved.

The invention discloses a tunnel lining steel frame recognition method based on GAR-Net and related equipment, spatial domain enhancement is performed on geological radar data to improve the robustness of a neural network structure, a GAR-Net network model is constructed, the network model comprises a mileage recognition model, a lining thickness recognition model and a steel arch recognition model, and the mileage recognition model, the lining thickness recognition model and the steel arch recognition model are used for recognizing a tunnel lining steel frame. The lining thickness recognition model and the steel arch recognition model perform lining thickness recognition and steel arch recognition on the basis of the mileage recognition model respectively, whether a tunnel lining steel frame has defects or not can be judged according to the lining thickness and positioning information of the tunnel steel arch, and the defects of traditional radar image interpretation are overcome. The method can achieve the automatic recognition of the tunnel steel arch and lining thickness, and remarkably improves the precision and recognition efficiency of the automatic recognition of a geological radar image.

The invention discloses a causal reasoning-based satellite-ground collaborative remote sensing image interpretation method and device, relates to the technical field of remote sensing image processing, and mainly aims to solve the problem that the inference requirement of environmental causes in a remote sensing image cannot be met in the prior art. Comprising the following steps: a ground end obtains initial interpretation features obtained by performing initial interpretation on multi-modal remote sensing data by a satellite-borne end, and interprets the initial interpretation features based on a first interpretation model of which model training is completed to obtain core interpretation features; determining causal variables based on the core interpretation features, and constructing a causal variable graph based on the causal variables; determining a cause label and an interpretation result of the label area according to the causal variable graph, and obtaining a verification result corresponding to the cause label and the interpretation result; and based on the knowledge distillation and the verification result, determining learning core features of the first interpretation model for updating training, so that a second interpretation model in the satellite-borne end coordinates the learning core features fed back based on the ground end.

An image classification model, e.g., a neural network model, may be trained on a set of training medical imaging exams each including a training report and a training medical image. A model generation module or device may, for each of the training medical imaging exams: use finding item criteria to reorganize text of the training report into a list of finding items, each associated with text extracted from the training report text, use natural language processing to analyze the resultant text associated with each finding item to determine an associated classification of each finding item, store, in a training dataset, the training medical imaging exam, the associated finding items, the matching text, and the classifications resulting from the analysis of the matching text. The trained neural network learns to output, for other medical imaging exams, one or more finding items and an associated classification data.

The invention discloses a remote sensing image multi-modal incremental panoramic perception method and system, and the method comprises the steps: constructing a remote sensing image multi-modal panoramic perception model based on incremental learning, and the model comprises a cross-modal general image encoder module, a cross-modal interaction knowledge distillation module, a cross-modal image encoder module, a cross-modal image encoder module and a cross-modal image encoder module; a task bias pseudo-mark generation module and a symmetric query interaction collaborative classification module; performing incremental training on the model; and performing multi-level and multi-mode continuous interpretation on the remote sensing image by using the trained model, and completing cross-mode multiple tasks of pixel-level classification, instance-level segmentation and image-level description of the remote sensing image. The method breaks through the limitation that an existing remote sensing image interpretation method only realizes predefined single-task interpretation, expands an interpretation product from a single form to a pixel-level, instance-level and image-level multi-mode increment interpretation mode, and can realize brain-like perception of the remote sensing image; the dependence of a deep learning model on data storage and labeling is reduced, the model training and maintenance cost is reduced, and the intelligent degree is improved.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The invention discloses a fine-grained representation remote sensing image interpretation method and system based on Shearlet priori knowledge. The method comprises the following steps: performing high-frequency sub-band information extraction on preprocessed remote sensing image data by adopting Shearlet transformation; a Laplacian pyramid filter and a shearlet filter are used for obtaining a high-frequency assembly and multidirectional high-frequency sub-bands obtained after the high-frequency assembly is split in the direction; sub-bands of the same level are adaptively fused through a space attention mechanism, and a detail-enhanced feature map is generated; carrying out multi-scale and multi-level weighted fusion on high-frequency component prior extracted through Shearlet transformation and a deep residual block obtained by a deep residual network; outputting an accurate remote sensing image analysis result by using the trained detail enhancement backbone and different detection heads of a plurality of tasks; through combination of Shearlet transformation and an advanced deep learning architecture, the accuracy and efficiency of remote sensing image interpretation are significantly improved, good model generalization ability and practicability are maintained, and it is predicted that remote sensing image processing will be promoted to develop towards a more intelligent and automatic direction.

The invention discloses a rain and sewage pipeline defect detection method and system based on distributed optical fibers. The method comprises the following steps: 1, laying sensing optical fibers in a pipeline; 2, calibrating an excitation position; 3, collecting and demodulating optical fiber vibration data; 4, performing defect identification and defect positioning on data processed by an intelligent algorithm; vibration signals are sent out through the excitation device, signal collection is carried out in combination with the distributed optical fiber sensing module, efficient recognition and accurate positioning of pipeline defects are achieved in cooperation with an intelligent detection algorithm, and hidden diseases which cannot be perceived by naked eyes can be recognized. By means of an intelligent defect recognition algorithm, signals collected by the optical fiber sensing module are deeply analyzed, the positions and the disease degrees of the defects in the pipeline can be automatically judged, dependence on manual image interpretation is avoided, and automation and intelligentization of pipeline defect detection are achieved. The system has the advantages of being high in adaptability, wide in coverage range, efficient, accurate, high in intelligent processing capacity, low in cost, high in efficiency and the like.

The invention discloses a multi-mode element learning remote sensing reconnaissance target identification method guided by a detection mechanism, and belongs to the field of computer vision and remote sensing image processing. The core of the method is as follows: multi-modal feature extraction and fusion guided by a detection mechanism: aiming at the electromagnetic scattering characteristic of an SAR image, the geometric texture characteristic of a visible light image and the thermal radiation characteristic of an infrared image, respectively designing a special feature extraction network, and introducing a cross-modal fusion module based on an attention mechanism, feature enhancement expression of physical information complementation is realized; according to the meta learning training normal form, an optimization-based meta learning device is constructed, and training is performed on a large number of multi-modal remote sensing task sets, so that the model obtains the capability of quickly learning new target categories from a small number of samples. According to the method, a physical detection mechanism and data-driven meta learning are combined, and the problems that a traditional method is low in recognition precision and poor in generalization ability in a complex reconnaissance scene with scarce samples, variable target types and difficulty in SAR image interpretation are effectively solved.

The invention relates to a remote sensing visual language large model training method and device based on unified reinforcement learning, and belongs to the technical field of artificial intelligence and remote sensing image processing, and the method comprises the steps: carrying out the preprocessing of an input remote sensing image and a text instruction, and extracting visual features and text features; performing modal alignment, and inputting the modal alignment result into a pre-trained large language model for supervised instruction fine adjustment to obtain a basic model; constructing a multi-dimensional deterministic unified reward module based on a truth value; and performing enhanced fine tuning on the basic model, calculating a reward value output in the group by using a unified reward module, and updating model parameters based on relative advantages to obtain an optimized remote sensing visual language large model. According to the method, a deterministic reward module is adopted, a value network is abandoned through group relative strategy optimization, the calculation cost is reduced, task-level indexes are directly optimized through a multi-dimensional unified reward function, and the precision and output normalization of remote sensing image interpretation are improved.

The invention discloses a large-model-driven high-resolution remote sensing image interpretation method, and belongs to the field of image processing and analysis. The method comprises the following steps: constructing a large-scale high-resolution remote sensing image data set; cleaning and labeling the data, and dividing a training set and a verification set; constructing a multi-modal remote sensing information intelligent extraction large model, and realizing extraction and collaboration of multi-modal remote sensing features; constructing a multi-modal remote sensing information fusion module to realize fusion of visible light coding features and thermal infrared coding features; and processing the fused multi-modal features based on an encoder of the current large language model to realize the interpretation of the high-resolution remote sensing image. By implementing the method provided by the invention, the interpretation accuracy of the high-resolution remote sensing image can be effectively improved.

The invention belongs to the crossing field of remote sensing image processing and machine learning technologies, and particularly relates to a high-resolution remote sensing image target integration machine learning interpretation method in a mine environment. Through the customized feature extraction method for the mine environment, information reflecting mine ground feature features can be accurately recognized and extracted, the method is combined with the optimized integrated machine learning model, the method better adapts to the particularity of the mine high-resolution remote sensing image, and the recognition precision of various targets in the mine environment is improved. According to the method, automation and intelligence of remote sensing image interpretation can be realized, manual participation is greatly reduced, interpretation time is shortened, dynamic changes of a mine environment can be quickly responded, remote sensing image interpretation efficiency is improved, and timeliness of mine management is improved. Mine ground feature priori knowledge is fully utilized for extraction and model optimization, so that the model can better adapt to image data of different mine environments and different time phases, and the applicability and stability of the model are effectively improved.

The invention provides a remote sensing image interpretation method based on large model reasoning and tool enhancement, which comprises the following steps: generating an initial reasoning node of a reasoning tree by using a large language model as an active decision maker based on system cue words and input remote sensing image features; based on the current inference tree state, the active decision maker executes inference and generates a new inference node, so that the inference tree is expanded; based on node scores, adopting a pruning strategy to eliminate reasoning paths with low scores; repeating the steps until a reasoning termination condition is met, and selecting a path with the highest accumulated score from the reasoning tree as an optimal reasoning path; and key information is extracted and organized into a structured intelligence report to be output. According to the method, a complete interpretation chain of'thinking-tool-observation 'is explicitly modeled through a tree reasoning mechanism, so that the generation logic of each intelligence conclusion is clear and visible, and the problems of trust crisis and insufficient interpretability caused by'black box' decision of a traditional deep learning model are solved.

The invention discloses a landslide identification method, system, equipment and medium, and relates to the technical field of disaster detection, and the method comprises the steps: obtaining remote sensing image data and digital elevation data of a target slope section, and taking the data as a landslide data set; the method comprises the following steps: segmenting image data in a landslide data set into tokens with n * n pixels, inputting the token into a neural network model based on an encoder and decoder structure, and processing the token by an encoder and a decoder to obtain a token of a reconstructed image and reconstruction loss between the token and an original image token, and obtaining an optimal neural network model by taking the minimized reconstruction loss as a target, inputting remote sensing image data and digital elevation data of a real-time target slope section into the optimal neural network model for image interpretation, and obtaining a landslide map in the target slope section. According to the method, image interpretation is carried out by optimizing the neural network model, high precision is displayed through the image interpretation result of the region, and the application potential of the method in the field of disaster prevention and reduction is further verified.

The invention provides a remote sensing image interpretation method based on deep learning. The method comprises the following steps: S1, preprocessing a remote sensing image; s2, constructing a deep learning model, and training the deep learning model by using the remote sensing image data sample; s3, interpreting the preprocessed remote sensing image by using the trained deep learning model and outputting a result; and S4, evaluating the output result. The remote sensing image intelligent interpretation method can effectively solve the problems of complex space-time heterogeneity of the remote sensing image and scarcity and imbalance of interpretation samples, thereby promoting application of the remote sensing image intelligent interpretation method in large-range and complex scenes.

The invention discloses a wetland extraction method based on a neural network. The wetland extraction method comprises the following steps: step 1, acquiring data; the acquisition time interval of the optical remote sensing image data and the acquisition time interval of the radar remote sensing image data have an overlapping part, the overlapping duration is between 30 days and 90 days, and the overlapping duration is comprehensively determined according to the wetland vegetation growth cycle and the hydrological change cycle; the multi-source feature information of the wetland in the key period is reflected by the data; preprocessing operation is carried out on collected original remote sensing image data, and the error rate of radiation correction is strictly controlled within + / -3%. Compared with a traditional artificial remote sensing image interpretation wetland extraction mode, the neural network-based wetland extraction method has the advantages that the processing efficiency is greatly improved, large-scale multi-source remote sensing image data can be quickly processed, and the time and labor cost required by wetland information extraction is reduced.

The invention provides a linear motion constraint-based tail-section trajectory parameter surface-surface intersection measurement method, which comprises the following steps of: shooting and recording tail-section flight images of a flight target flying to a target from different angles by using at least two high-frame cameras in a staring measurement mode; retrieving the same-frame picture image of the flight target and the target according to the tail-section flight image, and playing back and outputting an encountering-section image in a real-time SDI video stream mode; reading multiple paths of playback SDI video streams, and performing real-time image interpretation by adopting a target detection algorithm to obtain interpretation data of a flight target; and carrying out filtering on the interpretation data by adopting a three-point random resampling method, carrying out angle synthesis to obtain target interpretation point data, and carrying out calculation on the target interpretation point data by adopting a surface-surface intersection algorithm based on linear motion constraint to obtain a tail-section flight path parameter of the flight target. According to the method, the tail section track parameters can be rapidly and accurately measured, equipment time synchronization is not needed, and the application range is wide.

The invention relates to the technical field of ecological monitoring, in particular to a resident-free island resource monitoring method based on a spatial three-dimensional model, and the method comprises the following steps: S1, building a remote sensing image interpretation information base; s2, based on the remote sensing image interpretation information base, generating an island space three-dimensional model containing island land terrain and shoreline distribution; s3, generating a total element three-dimensional dynamic map; s4, the interpretation result and the total element three-dimensional dynamic map in the S3 are superposed, and a reef area comprehensive distribution map is generated; s5, calculating the island resource development intensity and the ecological risk level, and outputting an island ecological assessment report; and S6, generating an island resource monitoring report. According to the invention, unmanned aerial vehicle aerial survey, GNSS measurement, depth sounding technology, image interpretation and ecological assessment are combined, a high-precision total-factor three-dimensional dynamic monitoring system is constructed, accurate analysis and visual expression of the current island resource utilization situation are realized, and scientific decision support is provided for island management and ecological protection.

The invention discloses a land coverage classification method based on inference segmentation, and relates to the technical field of remote sensing image inference segmentation and deep learning. Comprising the steps of training sample set establishment, multi-scale feature extraction submodule design, cross-modal feature fusion module design, land coverage classification model structure design based on reasoning segmentation, land coverage classification model training based on reasoning segmentation, model performance evaluation and index analysis. According to the method, effective combination of remote sensing image features and semantic information is realized, and the differentiated cognitive ability for different ground object targets is improved. Meanwhile, a remote sensing image land cover intelligent classification technology with practical value is obtained, the professional technical threshold is remarkably reduced, the intelligence and universality of remote sensing image interpretation are realized, and a convenient remote sensing information acquisition method is provided for users in various fields.

The invention provides a graph structure guided remote sensing image interpretation method and system, a terminal and a medium, and the method comprises the steps: carrying out the interpretation through employing a visual language model based on an obtained remote sensing image and expert priori knowledge; the model training process comprises the steps of obtaining a graph structure based on a vector graph corresponding to a remote sensing image, and rapidly synthesizing a large number of samples for training by randomly modifying the graph structure; model training is carried out based on the synthesized training remote sensing images and the corresponding training graph structures and training vector diagrams, graph structure features obtained by the training graph structures are adopted as training priori knowledge, a large number of image features obtained by the training remote sensing images are interpreted, and feedback optimization is carried out with the training vector diagrams as labels. The visual language model adopted by the invention can obtain the spatial information and semantic information of the remote sensing image, avoids inaccurate interpretation caused by spatial relationship deficiency, and has a better interpretation effect.

The invention relates to an image interpretation method and device based on a visual language model. The method comprises the following steps: determining a sample reasoning instruction according to a visual label of a sample image and a target knowledge graph; generating image interpretation information of the sample image based on the sample image and the sample reasoning instruction through a question and answer engine; training a to-be-trained visual language model based on the sample image, the sample reasoning instruction and image interpretation information of the sample image, and determining a target visual language model; and determining image interpretation information of the target image according to the target image and the target reasoning instruction through the target visual language model. According to the scheme, the data format of the model training data set is unified, the data set construction efficiency is improved, the labor cost is saved, and meanwhile, the trained visual language model can be subjected to deep knowledge reasoning.

The invention discloses an unmanned aerial vehicle autonomous path planning and high-precision surveying and mapping integrated system, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The system comprises a terrain modeling module, a task configuration and scheduling module, a path planning module, an unmanned aerial vehicle flight control module, a surveying and mapping sensing module, a data caching and transmission module and a ground control and result processing module. The terrain modeling module constructs three-dimensional terrain modeling through remote sensing image interpretation and a digital elevation model; the path planning module fuses simulated annealing, an adaptive genetic algorithm and a large neighborhood search algorithm to realize route static optimization and multi-aircraft cooperative path generation; the flight control module autonomously completes flight according to the path instruction; the data caching module provides breakpoint resume and edge relay support; and the ground platform completes image processing, splicing modeling and result exporting. According to the invention, intelligent route planning and high-precision and multi-source surveying and mapping data acquisition under complex terrains are realized, and the surveying and mapping automation level and the data accuracy are remarkably improved.

The invention provides a calcification section coronary artery risk prediction method and device based on chest CT (Computed Tomography) plain scanning. The method comprises the following steps: firstly, acquiring and screening chest CT (Computed Tomography) plain-scan image data; secondly, performing calcification section coronary artery labeling on the image data; thirdly, performing CCTA image interpretation and grouping on the marked areas; and finally, collecting a CT image and a classification label model corresponding to the CT image, using a ResTransNet network to extract features from the calcification section coronary artery label, training the ResTransNet model, and predicting a risk state. According to the method, the deep learning method is applied to the coronary artery of the calcification section of chest CT flat scanning, the plaque type and the blood vessel stenosis degree are accurately predicted, and effective information is provided for subclinical coronary atherosclerosis risk screening and prevention; constructing plaque types and coronary artery stenosis degrees gt; and the prediction device has great clinical significance when being used for screening the risk of subclinical coronary atherosclerosis.