484 results about "Image database" patented technology

The invention discloses a brain tumor area automatic segmentation method and system based on deep learning, and belongs to the technical field of image segmentation, and the method comprises the following steps: obtaining a disclosed brain tumor segmentation data set, and constructing a multi-modal brain tumor medical image database; performing data preprocessing on the multi-mode brain tumor medical image database; dividing the preprocessed multi-modal brain tumor medical image database into a training set, a verification set and a test set in proportion; a deep learning model LG-FPN-TransBTS based on the improved TransBTS is designed, and the deep learning model LG-FPN-TransBTS is designed; and training, evaluating and applying the model. The system comprises a data preprocessing module, a data set division and secondary processing module, a deep learning model construction module, a model training and optimization module and a model evaluation and application module. According to the method, the precision and robustness of brain tumor image segmentation are remarkably improved.

The invention discloses an airborne infrared-visible double light combined building outer wall hollowing identification and positioning method. The method comprises the following steps: designing flight parameters of an unmanned aerial vehicle; designing a calibration checkerboard; shooting an infrared thermal image and a visible light image of the checkerboard calibration target, recording the images as dual-light images for calibration, and calibrating and calculating internal and external parameters and a distortion coefficient of a camera; collecting images of all external facades of the target building; carrying out distortion removal processing on the dual-light image to form a dual-light image database for detection; the registration between the dual-light images synchronously shot by the unmanned aerial vehicle is realized; realizing registration between the building facade view and the visible light image; determining a correct hollowing area identification result, and marking the infrared thermal image corresponding to the correct hollowing area identification result as a hollowing infrared thermal image; calculating a pixel coordinate range of the hollowing area; and accurate positioning of the hollowing area is realized. According to the method, the visible light image serves as a medium, registration of the infrared thermal image and the macroscopic 3D model is achieved, the problem that calibration and registration are difficult due to the fact that the imaging quality of the infrared thermal image is low is solved, and high readability and user friendliness of an output result are guaranteed.

The invention provides an infrared and visible light image end-to-end registration method based on phase consistency enhancement, and belongs to the technical field of electric digital data processing. The invention discloses an infrared and visible light image end-to-end registration method based on phase consistency enhancement, which comprises the following steps of: firstly, preprocessing infrared and visible light source images in an image database, and dividing the infrared and visible light source images into a training set and a test set; and constructing a feature extractor based on a phase consistency attention mask, realizing cross-modal consistency feature extraction, and focusing an important space region by using phase information. A self-defined ResSobeNet network is adopted for parameter estimation, and Haar wavelet transform is used for replacing pooling operation, so that information loss in a traditional down-sampling method is avoided. A double-branch generator structure is designed, affine parameters and an optical flow field are generated respectively, meanwhile, constraint on rigid registration and non-rigid registration is achieved, and finally a registration result is obtained through resampling of a space conversion module. According to the method, the problems that cross-modal consistent features are difficult to extract in the infrared and visible light image registration process and explicit registration steps are complicated and difficult to generalize are solved, and the method has good universality and practicability.

The invention relates to the technical field of substation defect detection, in particular to a substation defect detection method and system and a storage medium. The method comprises the following steps: acquiring a substation equipment image by adopting real-time image acquisition equipment, and establishing an equipment image database; performing denoising and edge detection through image processing software to generate preprocessed image data; detecting an abnormal area of the substation equipment through feature extraction and texture analysis, and training a defect recognition model; after real-time image information is input into the model, an abnormal defect area of the transformer substation is recognized, and fault risk gradient deterioration is estimated; calculating a risk index based on a pre-estimation result, and if the risk index exceeds a set threshold value, triggering an early warning function, providing defect types and positions, and suggesting repair measures; according to the invention, the transformer substation defect detection method is optimized, so that the transformer substation defect identification is more accurate.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention discloses an underwater concrete structure crack detection method based on edge calculation and deep learning, and the method comprises the steps: carrying out the pixel preprocessing of underwater concrete structure crack image data in an image database, and obtaining a preprocessed image; constructing a lightweight image segmentation network to perform image enhancement processing on the preprocessed image to obtain an image enhancement data set; constructing an ASF-ODConv-YOLOv8 network model, and performing network training and verification according to the image enhancement data set to obtain an intelligent crack recognition algorithm model; based on the crack intelligent identification algorithm model, obtaining a predicted crack category label of the underwater concrete structure crack according to the test set; and constructing a digital twinborn model of the underwater concrete structure crack so as to detect the underwater concrete structure crack. The problems that a traditional recognition method has limitations in the aspects of space-time coverage, environmental adaptability, recognition efficiency and precision, depends on subjective judgment and is prone to causing missing recognition and wrong recognition are solved.

The invention provides an image searching method, which comprises the following steps of: when a new label is added to an image in a first image database, acquiring a first semantic feature of the new label; based on the first semantic feature, performing image retrieval in a first image database to obtain a first retrieval result and a confidence coefficient of the first retrieval result; if the confidence coefficient is smaller than the preset confidence coefficient, performing secondary retrieval in the first retrieval result based on the existing tag in the first retrieval result to obtain a second retrieval result; based on the second retrieval result, adding the newly added tag into an image corresponding to the secondary retrieval result to obtain a second image database; and after an image search instruction of the user is obtained, performing image retrieval in the second image database based on the image search instruction. The problems that in the database maintenance process, a new label needs to be added, the workload of adding the label again to the data is increased while the new label is added, and the database is difficult to maintain in an existing method are solved.

Camera positions can be recovered for video frames. Keypoint tracks can be generated for a first video frame and subsequent video frames. Metric depth can be determined for the video frames. An image databased can be generated using the keypoint tracks and the metric depth. The image database can include the video frames. A first video frame having a highest number of keypoint tracks can be identified. A first projection into a three-dimensional model can be generated for the first video frame and a first matching video frame. A camera position can be generated using the first projection. A second video frame having a next-highest number of keypoint tracks can be identified. A second projection into a three-dimensional model can be generated for the second video frame and a second matching video frame. A camera position can be generated using the second projection.

The invention discloses a wind turbine blade fault diagnosis method based on aerodynamic noise and image recognition, and relates to the technical field of wind turbine blade fault diagnosis, and the method comprises the steps: building a plurality of typical fault models of a wind turbine blade based on a numerical simulation platform, and extracting simulation acoustic image data; the position of the aerodynamic noise source is positioned through an acoustic camera, and corresponding blade image information is collected by an unmanned aerial vehicle; and carrying out feature training on the image sample and the acoustic image data by using a deep neural network and executing fault matching judgment. According to the method, a fault acoustic image database under multiple working conditions is constructed through numerical simulation, outfield data acquisition is performed in combination with an acoustic camera and an unmanned aerial vehicle, and joint feature training and fault matching of acoustic frequency spectrums and images are realized by using a deep neural network. The method can break through the limitations of the prior art in the aspects of real-time performance, accuracy and intelligence, and achieves the efficient and reliable diagnosis of the fault of the wind turbine blade.

The invention discloses an image secret type cloud storage method and device based on feature block encryption, and the method comprises the steps: a cloud server receives and stores an encrypted image, an encrypted image feature set and a feature inner product, and forms an image database; the cloud server receives a feature ciphertext query feature uploaded by the authorized user when the authorized user needs to query the image; the cloud server performs retrieval according to the received ciphertext query features, separately calculates the similarity between color and texture sub-features in the ciphertext query features and features in each encrypted image feature set in the image database by using a hierarchical earth moving distance algorithm, and calculates the comprehensive similarity corresponding to each encrypted image; and screening out a plurality of encrypted images according to the comprehensive similarity and returning the encrypted images to the authorized user. According to the method, safe and efficient search of the image can be realized, and the image retrieval performance is improved.

The invention provides a weld defect intelligent detection method based on high-definition image deep learning, which comprises the following steps: S1, collecting a high-definition weld image, manually marking the selected image, and constructing a weld defect image database; s2, performing first fine tuning on the pre-training weight GLIP-L by adopting a GLIP model; s3, a self-made welding test piece is made, a high-definition camera and ultrasonic detection equipment are used for collecting a weld joint surface high-definition image of the self-made welding test piece and ultrasonic echo characteristic data of internal defects, and a high-definition weld joint image and ultrasonic multi-mode database is established; s4, introducing a multi-layer perceptron module into the GLIP model, constructing an image and ultrasonic multi-modal feature alignment mechanism, and performing second fine tuning to obtain a trained GLIP model; and S5, performing defect detection on the welding seam by using the trained GLIP model, and outputting a detection result. According to the invention, through multi-modal data fusion, the accuracy and robustness of weld defect detection are improved, and the method is suitable for automatic and accurate industrial detection scenes.

The invention provides an imaging department doctor training method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: calling a predetermined case parameterization strategy to carry out the parameterization processing of a real-time case, and obtaining a real-time parameter feature; extracting a first case in the image database, and obtaining a first parameter feature of the first case; generating a virtual case in combination with the real-time parameter features and the first parameter features, and introducing medical knowledge graph analysis to obtain a case reasonable coefficient of the virtual case; if the case reasonable coefficient is in a preset coefficient threshold value, constructing a virtual clinical environment of the virtual case; in a virtual clinical environment, image training is carried out on a target doctor in combination with a virtual case, and the problems that an existing image department doctor training system depends on fixed textbooks and standardized training cases and lacks a dynamic adjustment mechanism based on a real-time case, so that dynamic deduction and immersive interactive learning of doctors are difficult to realize, and the training efficiency is poor are solved. And the technical problem that the image diagnosis skill is difficult to improve efficiently is solved.

The invention discloses a method for identifying sewage in a lake-near strip mine. The method comprises the following steps: establishing a three-dimensional digital model based on an initial database; establishing a clear sewage migration image database; and constructing a clean water and sewage identification model. Compared with the prior art, in the aspect of model construction, multi-source data are fused, and a high-precision three-dimensional terrain and geologic model is constructed through investigation data and on-site unmanned aerial vehicle laser radar scanning data; according to the invention, clean sewage is identified from the source, so that corresponding clean sewage diversion engineering measures can be built according to the identification result; according to the method, a numerical simulation result and an on-site sewage electrochemical monitoring result are fused to construct clear sewage migration images of different pollution source types; the sewage identification model is constructed by using the deep learning algorithm, and the method has the advantages of low learning cost, high prediction precision and the like.

The invention discloses a power equipment state monitoring method based on image recognition, which belongs to the field of power equipment monitoring, and comprises the following steps: firstly, acquiring surface images of power equipment according to a preset interval through an image acquisition module, and constructing a time sequence image database; performing alignment and standardization preprocessing on the image to generate a standard time sequence image sequence; inputting the sequence into a differential convolutional neural network, and extracting the feature variation of the defect region to obtain a defect evolution feature sequence; the sequence is analyzed based on a defect evolution dynamic model, the evolution rate of the defect is quantified, and the residual safe life of the equipment is predicted; and finally, automatically generating a graded early warning and maintenance decision according to a life prediction result. Therefore, the problem that the dynamic evolution rule of the gradual change defect of the equipment is difficult to capture and quantify in the prior art is solved, the transformation from post-event monitoring to predictive maintenance is realized, and the accuracy of state monitoring and the perspectiveness of operation and maintenance decision are remarkably improved.

Provided is a system and method for digital content generation and recommendation. A first plurality of stories are collected from various content sources over a network. A second plurality of stories are selected from the first plurality of stories such that a degree of similarity between any two stories of the second plurality of stories is below a determined threshold. A summary and a headline are generated for a first story of the second plurality of stories. A set of images are selected, from an image database, for the first story based on the generated summary and headline. A multimedia item including the summary, the headline, and a first image of the set of images is created. The multimedia item is presented on a lock screen interface of a user device, displaying the generated summary, the generated headline, and the first image on the lock screen interface.

The invention provides a quick image recognition method for the water quality condition in a water supply pipeline, and the method is characterized in that a high-definition camera with a light source is used for obtaining a color image in the underground water supply pipeline, the color image is converted into a gray image through an OpenCV method, the gray image is converted into a high-resolution image through a super-resolution method, and the high-resolution image is used for recognizing the water quality condition in the underground water supply pipeline. Carrying out segmentation and feature enhancement on a distribution region of suspended particulate matters in the high-resolution image by applying an average signal-to-noise ratio, a local self-adaptive thresholding algorithm and a denoising algorithm, extracting a region related to a water quality turbidity parameter, comparing a feature region containing the water quality turbidity parameter with a standard water quality turbidity grade image database, and obtaining a water quality turbidity grade image; and judging the turbidity grade of water in the in-service water supply pipeline. According to the invention, rapid identification and real-time monitoring of the pipeline water quality turbidity grade in the water supply network are effectively realized, and a new method is provided for rapid identification and condition monitoring of the water quality of the water supply network.

The invention discloses a flue-cured tobacco leaf state prediction method and system based on a space-time sequence algorithm, and relates to the technical field of tobacco production. The flue-cured tobacco leaf state prediction method based on the space-time sequence algorithm comprises the following steps: carrying out color calibration on a flue-cured tobacco leaf image by adopting black field calibration and white balance calibration; performing data set segmentation on the tobacco leaf image after color calibration according to a preset proportion, and performing data cleaning on the segmented data set to generate a baking image database; constructing a baking image data set with time sequence features based on the optimized baking image database; and utilizing a space-time sequence prediction algorithm. According to the invention, in the baking process, technicians need to continuously observe the form changes such as yellowing and water loss of tobacco leaves through an observation window, and dynamically control the operations such as temperature rise, temperature stabilization and moisture removal; the mode depends on subjective experience of visual perception of baking personnel, has relatively large subjectivity and limitation, and is difficult to ensure accuracy.

The invention discloses a medical image curative effect evaluation system based on artificial intelligence, and the system is characterized in that an acquisition module obtains a clinical vascular medical image, builds an image database according to the clinical vascular medical image, and builds a high-resolution vascular three-dimensional image according to the image data in the image database; a positioning recognition module receives the blood vessel three-dimensional image and inputs the blood vessel three-dimensional image into a trained blood vessel positioning model, and the blood vessel positioning model automatically recognizes the blood vessel position and level in the blood vessel three-dimensional image; the evaluation module is used for cutting and calculating the blood vessel three-dimensional image according to the recognized blood vessel position and level and a preset size, and evaluating whether the lesion position and lesion type of the blood vessel with blood vessel stenosis, blood vessel dilatation and blood vessel thickening occur or not according to a calculation result; the technical problem that the prior art is difficult to accurately evaluate the curative effect of vascular diseases is solved.

The invention discloses a lychee image classification method based on a YOLO11 model, and the method comprises the steps: employing a lychee image database to train the YOLO11 model, and the YOLO11 model comprises a backbone network, a neck network, and a decision network; wherein the backbone network comprises a convolutional layer and a HaloAttention module, and the convolutional layer comprises a lightweight adaptive extraction convolution (LAE); the neck network comprises a CARAFE feature sampling block; and carrying out litchi variety prediction by adopting the trained YOLO11 model. According to the method, the local attention HaloAttention is introduced into the backbone network of the YOLO11 model, so that the feature expression capability in a local range is improved; the Conv convolution is replaced with lightweight adaptive extraction convolution LAE, and feature complementation between channels is enhanced; a CARAFE feature sampling block is introduced, and the sampling process is dynamically adjusted according to input features; the method can accurately predict the variety of the litchi fruit, and has the advantages of rapid identification, high identification accuracy and strong stability.

The invention discloses an intelligent spirulina quality detection system and method, and belongs to the field of spirulina detection. The invention discloses an intelligent spirulina quality detection system and method. The system comprises a data collection unit, a model construction unit, a model application unit, a model updating unit and a result output unit. The method solves the problem that a large amount of time, manpower and material resources are consumed in the existing detection technology, the spirulina image database can be constructed by collecting the spirulina images and performing processing, labeling, feature extraction, classification, screening and induction, then the intelligent detection model of the spirulina quality is established by using the deep learning technology, and the detection accuracy is improved. According to the method, the quality grade of a sample can be analyzed and judged in real time, an intelligent detection model can be updated and optimized by regularly collecting new image data so as to improve the precision of the model, finally, a detection result is recorded, a spirulina sample quality file is formed, and early warning information is issued when the quality problem of the spirulina sample is found. Therefore, the condition of the spirulina sample can be known.

The invention provides an unmanned aerial vehicle positioning method and system fused with a relative absolute vision positioning technology, and the method comprises the following steps: carrying out the feature extraction and matching of image frames continuously collected by a vision sensor, obtaining the corresponding relation between adjacent image frames according to the feature extraction and matching results, and combining with the motion information measured by an inertial measurement unit, thereby obtaining the relative absolute vision positioning information. Obtaining relative positioning information of the unmanned aerial vehicle; image information collected by the visual sensor in real time is matched and registered with remote sensing images in a pre-constructed image database, so that absolute position information of the unmanned aerial vehicle in a global coordinate system is obtained; fusing the relative positioning information and the absolute position information, and optimizing a global path of the unmanned aerial vehicle according to a fusion result; the method has the beneficial effects that by fusing the advantages of a relative vision positioning technology and an absolute vision positioning technology, the error accumulation of relative positioning and the matching error of absolute positioning are effectively reduced, and the positioning precision of the unmanned aerial vehicle in a complex environment is improved.

The invention relates to the technical field of image retrieval, and provides an image retrieval method, device and equipment based on the endogenous relevance of an image database, and the image retrieval method based on the endogenous relevance of the image database comprises the following steps: obtaining a to-be-retrieved image input by a user; performing image semantic coding on the to-be-retrieved image to obtain a semantic coding feature vector of the to-be-retrieved image; performing semantic query response optimization on the semantic coding feature vector of the to-be-retrieved image based on a library image semantic coding feature vector set obtained by the image database to obtain an optimized semantic coding vector of the to-be-retrieved image; and generating a retrieval result based on the semantic correlation between each library image semantic coding feature vector in the set of library image semantic coding feature vectors and the to-be-retrieved image optimization semantic coding vector. By means of the mode, the internal correlation structure information in the image database can be fully utilized, and the accuracy and efficiency of image retrieval are effectively improved.

The invention provides an overlapped grape cluster instance segmentation and grasping positioning method of a table grape picking robot, and belongs to the technical field of robot picking. The method comprises the following steps: S1, constructing an image database of overlapped grape clusters in an orchard, and obtaining real edge labels and real pixel region labels of the overlapped grape clusters; s2, constructing a deep convolutional neural network model, performing instance segmentation on the overlapped grape clusters based on the real edge labels and the real pixel region labels of the overlapped grape clusters obtained in the S1, and determining a grasping direction; the deep convolutional neural network model comprises a backbone network, a fusion network and a positioning network; wherein the backbone network is used for capturing an overlapped grape cluster feature map of multi-level edge information; the fusion network is used for obtaining an instance segmentation result of the overlapped grape clusters; and the positioning network is used for determining the grasping direction of the overlapped grape clusters. According to the method, the overlapped grape bunches can be accurately picked.

The invention discloses an unmanned aerial vehicle camera repositioning method and device based on image retrieval and a storage medium. The method comprises the steps of collecting image data and constructing an image database. A global feature extraction module is used for extracting global features to obtain the global features, similarity calculation sorting is carried out on the global features and the global features of the images in the database, and the most similar images are selected to form an image pair. After local feature extraction, relative translation and relative rotation are predicted through a relative pose regression network. And finally, converting the relative pose of the retrieved picture into an absolute pose through a relative pose conversion algorithm. According to the overall method, image feature extraction and pose prediction are realized through a deep learning network, and efficient relocation of the unmanned aerial vehicle camera is realized.

The invention relates to the technical field of medical equipment management, and particularly discloses a first-aid trolley article checking system based on intelligent identification, an intelligent identification module is integrated with an image identification unit and a bar code / two-dimensional code identification unit, and the image identification unit establishes a medical article image database through a deep learning algorithm; identification of types, specifications and quantity of articles in the storage grids is realized; the latter scans and acquires information such as production date and validity period of the article. And the system processing module integrates the multi-source data, judges the quantity difference and the validity period state according to a preset rule, generates a visual inventory report and synchronizes the visual inventory report to a hospital information management system. And the alarm prompting module adopts an acousto-optic and wireless dual alarm mechanism to ensure that abnormal conditions are handled in time. The user interaction module supports touch screen operation and voice instructions, and the power management module provides dual-power switching and electric quantity monitoring. The checking efficiency and accuracy of the first-aid appliances are remarkably improved, the manual error rate is reduced, and intelligent management of medical resources is achieved.

The invention discloses a multi-class mechanical water treatment metering instrument reading method based on machine vision, and the method comprises the steps: collecting and preprocessing a video, building an instrument image database, building an instrument type recognition algorithm, and carrying out the recognition of the type of an instrument which is recognized as a pointer type instrument. The position of a pointer tip relative to two nearest scale numbers is obtained through instrument scale number recognition and pointer position recognition of the pointer type instrument, and then instrument reading is obtained through calculation after the distance proportion between the point of the pointer and the two nearest numbers is calculated. For the instrument identified as the floater type instrument, the position of the upper edge of the floater and two scale readings closest to the position are obtained through instrument scale number identification and floater position identification, and then the numerical value corresponding to the coordinate where the intersection point of the center connecting line of the two scale numbers and the upper edge of the floater is located is calculated. According to the method, the aim of automatically identifying various common mechanical instruments of the water treatment device can be fulfilled by fusing machine vision with machine learning, and the method has the advantages of high real-time performance, high universality and the like.

The invention discloses a remote sensing image retrieval method and device, electronic equipment and a medium. The method comprises the steps that a remote sensing image retrieval request is acquired; analyzing the retrieval request through the target large model to obtain an initial retrieval condition; according to the initial retrieval condition and a pre-constructed interest point database, determining a target geo-fence retrieved by the current image; and based on the initial retrieval condition and the target geo-fence, performing retrieval in a remote sensing image database to obtain a target remote sensing image. Therefore, retrieval request analysis of complex geographic description and fuzzy text description is realized through the target large model, so that the remote sensing image retrieval intention of a user is accurately understood, and meanwhile, an accurate target geographic fence is obtained in combination with the pre-constructed interest point database, remote sensing retrieval is locked in a more accurate geographic position range, and the remote sensing retrieval accuracy is improved. Therefore, the retrieval precision of the target remote sensing image is improved.

The present invention discloses a method and device for restoring compressed images. The method includes evaluating the image quality factor of a compressed image set based on the image performance parameters of the compressed image set, and matching an image restoration data set from an image database based on the image quality factor; under complex communication conditions, collecting the process parameters of an image processing device when executing the image restoration data set; analyzing the performance adaptation index of the image processing device based on the process parameters; performing a difference process on the performance adaptation index and a performance adaptation threshold to obtain a performance adaptation index difference, and matching the performance adaptation index difference with the device adjustment sets corresponding to each performance adaptation index difference interval in the image database to obtain a device adjustment set; executing the device adjustment set to perform adjustment feedback on the image device to obtain a target image device, and inputting the compressed image set under complex communication conditions into the target image device for image restoration. This method can effectively improve the restoration quality of compressed images.

The invention discloses a panoramic monitoring and illegal behavior recognition system and method for a water conservancy building construction site. The method comprises the steps that a data processing box calls a preset typical illegal construction behavior database and a typical illegal equipment image database; the method comprises the following steps: acquiring construction personnel behavior images, identifying and identifying the acquired construction personnel behavior images and a typical illegal construction behavior database, comparing construction equipment behavior images with a typical illegal equipment image database, and sending alarm information to a server side when the acquired construction personnel behavior images and the construction equipment behavior images are identified and judged to have illegal behaviors; the intercepted violation picture and the basic attribute information of the construction personnel and the construction equipment are obtained and sent to a server side; according to the method, the recognition result of the illegal behavior displayed on the interface, the heat map of the risk area and the like can be monitored in real time, construction site managers are assisted in quick response, and a powerful guarantee is provided for safe operation of site building construction.

The invention provides a neurosurgery data processing method and system, and relates to a data processing technology, by constructing a medical image database, recorded patient data can be marked and classified in combination with indexes of multiple dimensions, and the classification basis can comprise indexes such as disease types, lesion positions, shapes, volumes and the like. Therefore, the data in the medical image database can be mapped to the virtual medical auxiliary tool, such as a virtual operation guide plate, the operation data matched with the lesion information of the current patient is recommended to a doctor through the mapped data, more accurate operation reference is provided, and the scientificity of operation decision making is improved.