1516 results about "Image matching" patented technology

The invention relates to the technical field of agricultural knowledge base retrieval, and discloses an intelligent retrieval system for a knowledge base, which comprises an image preprocessing unit, an image adjusting unit, an image matching unit and a retrieval judgment unit, and is characterized in that a binary mask matrix is generated through image enhancement and pixel clustering, a modular entropy value is calculated in combination with parameters, and a basis is provided for feature extraction; multi-scale feature self-adaptive extraction is achieved through dynamic scale adjustment, feature distortion is avoided, feature points are evenly distributed through density dynamic adjustment and quality evaluation, high-quality feature points are screened, double screening of matching pairs is achieved by constructing a comprehensive evaluation mode, and the defects of an SIFT algorithm are overcome by dynamically adjusting a similarity threshold value and sorting. And the accuracy of image retrieval in a complex scene is improved.

The invention discloses a navigation matching correction method based on an inspection robot, and the method comprises the steps: building a feature database through the deployment of a physical calibration object and the recognition of a natural feature object, providing a reliable positioning reference for a robot, achieving the coarse positioning through the fusion of visual and laser radar data in a positioning process, and introducing a dynamic credibility evaluation mechanism. The positioning reliability is quantified in real time through an exponential decay model, when the credibility is lower than a threshold value, the system automatically triggers a compensation behavior to re-search features, in the aspect of multi-robot cooperation, secondary positioning correction is achieved through track matching and data fusion, high-confidence-coefficient reference data are screened through a clustering algorithm, the group positioning precision is improved, and the positioning accuracy is improved. Aiming at a key inspection area, multi-angle image matching is adopted to realize fine positioning, a positioning error is dynamically corrected through a sliding window, the continuity and accuracy of robot navigation in a complex environment are remarkably improved through closed-loop correction and self-adaptive optimization, and the method is suitable for intelligent inspection requirements of railway trains.

The invention provides a three-dimensional modeling processing method based on unmanned aerial vehicle oblique photography. The method is applied to the technical field of three-dimensional modeling, and comprises the following steps: obtaining a serialized image set containing geographical coordinate information according to original image data collected by an oblique photography camera carried by a multi-rotor unmanned aerial vehicle; according to time-space synchronization parameters of the serialized image set, determining a multi-view image matching relation matrix with an overlapping degree quantitative index; determining mixed three-dimensional point cloud data fusing sparse point cloud and dense point cloud according to geometric constraint conditions of the multi-view image matching relation matrix; determining an initial three-dimensional grid model with multi-level details according to the topological connection relationship of the mixed three-dimensional point cloud data; and determining an optimized three-dimensional model based on adaptive texture mapping according to the surface curvature distribution characteristics of the initial three-dimensional mesh model. In this way, the efficiency of three-dimensional modeling can be improved.

The invention provides an unmanned aerial vehicle oblique photogrammetry data intelligent processing method and system, and relates to the technical field of unmanned aerial vehicle photogrammetry, and the method comprises the steps: carrying out the preprocessing of an oblique photogrammetry original image, obtaining the exterior orientation elements of a camera, carrying out the scene semantic segmentation through a deep convolutional neural network, adaptively determining the optimal three-dimensional reconstruction parameters of each region, and obtaining the optimal three-dimensional reconstruction parameters of each region. On the basis of multi-view image matching, feature point parallax information is calculated to generate dense point clouds, different areas are processed by adopting an adaptive grid division strategy and a corresponding noise reduction algorithm, and finally, a three-dimensional scene model with real textures is constructed, so that high-precision three-dimensional reconstruction of different scene areas is realized.

The invention provides a multi-modal geographic positioning method based on a knowledge graph. The method comprises the following steps: graph construction: constructing a geographic knowledge graph; image positioning: calculating the entity similarity between the to-be-queried image and each sub-image so as to select an image positioning candidate sub-image, calculating the image matching similarity between the to-be-queried image and each image positioning candidate sub-image so as to determine an image positioning target sub-image, and taking the latitude and longitude coordinates of each image positioning target sub-image as a positioning result; text positioning: converting a to-be-queried text into map representation, calculating the map similarity between a text query sub-graph and each sub-graph to select a text positioning candidate sub-graph, and calculating the semantic similarity between the to-be-queried text and each text positioning candidate sub-graph, and calculating a text matching similarity corresponding to each text positioning candidate sub-graph based on the graph similarity and the semantic similarity corresponding to each text positioning candidate sub-graph so as to determine a text positioning target sub-graph, and taking latitude and longitude coordinates of each text positioning target sub-graph as a positioning result.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.

Provided are a multimodal image matching method and system, terminal device, and storage medium. The method includes: performing self-supervised feature extraction on an optical image and a synthetic aperture radar (SAR) image to obtain a repetitive feature point between the optical image and the SAR image; segmenting the optical image and the SAR image into a first image block sequence based on the repetitive feature point, and performing feature extraction on the first image block sequence through a dual-branch network to obtain feature description vectors of the optical image and the SAR image respectively, where the dual-branch network includes a first branch network for extracting a global feature and a second branch network for extracting a local feature; and performing feature matching on the optical image and the SAR image based on the feature description vectors to obtain a matching point pair between the optical image and the SAR image.

The invention relates to the technical field of semiconductor manufacturing, in particular to an accurate detection method and system for a flat-edge sheet wafer placement rotary chuck, and the method comprises the steps: when a wafer is placed on the rotary chuck, scanning the lap joint condition of a wafer edge and a chuck contact area through a laser sensor, and obtaining the real-time distribution data of an edge lap joint sheet; flat edge position information is extracted from the edge lap joint distribution data, an image matching algorithm is adopted to compare a preset orientation reference, and a specific angle value of orientation deviation is determined; according to the orientation deviation angle value and the edge lapping piece distribution data, the rotating speed and the angle of the rotating chuck are adjusted through a cooperative control algorithm, and a final positioning result of the wafer on the chuck is obtained; after a final positioning result is obtained, the in-place state of the wafer is classified and judged through a deep learning model, and whether the residual deviation or wafer lapping phenomenon exists or not is determined; and extracting abnormal data from a classification judgment result, and adjusting collaborative parameters of carrying and placing according to the abnormal data to obtain an optimized process control scheme.

The invention relates to an automatic visual alignment method and system based on a photoetching machine and a storage medium, and relates to the technical field of optical alignment. The automatic visual alignment method comprises the following steps: sequentially focusing image taking areas according to a preset multi-light-source visual focusing mechanism, recording corresponding global focusing parameters, and collecting corresponding original single-layer images; screening all the original single-layer images according to a preset image definition threshold value to obtain a to-be-detected image, and extracting corresponding key channel features; calculating a comprehensive feature error of the key channel features according to a preset image hierarchy template, and judging an initial focusing state in combination with a preset parameter error interval; correcting the global focusing parameter according to the initial focusing state in combination with a preset cooperative calibration mechanism, generating a corresponding control signal, and cooperatively tuning the corresponding visual equipment; through multi-light-source multi-time visual image capturing, the problem of poor alignment precision caused by light source errors and image capturing errors is reduced, the image matching accuracy is improved, and the visual alignment precision is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention discloses a multi-modal remote sensing image matching method, particularly relates to the technical field of remote sensing image processing, and is used for solving the problem of multi-modal image matching. The method mainly comprises the following steps of: 1, improving a phase consistency model, and constructing a phase-moment weighted joint direction feature in combination with a maximum moment and a minimum moment to replace the feature expression of the traditional image gradient; 2, implementing a point product fusion strategy on the phase-amplitude characteristics extracted by the phase consistency model and the maximum moment, and constructing phase-moment weighted joint amplitude characteristics; and 3, on the basis of the steps 1 and 2, identifying the direction of the feature points and screening local peak values to determine the main direction. Three values adjacent to a peak value are selected, and the peak value position is interpolated through parabola fitting so as to improve the matching precision; and step 4, constructing a logarithm polar coordinate descriptor based on regularization non-uniform partition to generate a feature description vector. Through the mode, high-precision and high-efficiency matching of the multi-mode remote sensing image can be realized.

The invention discloses a global enhancement method for a wide-area astronomical image. The method comprises the following steps: carrying out high-frame-frequency image acquisition on a starry sky area; converting the reference star from a star catalogue position to an observation position, and resolving a linear negative film model; geometric distortion correction is carried out based on the resolving result; based on the image after geometric distortion correction, matching and identifying a reference star in a full view field, and obtaining celestial coordinate information of all fixed star images in the view field; on the basis of celestial coordinate information, correcting a poorer astronomical effect of the astronomical image, establishing an ideal coordinate system taking the center of a view field as a tangency point, and projecting a time sequence observation image to the ideal coordinate system; translating the processed graph, accumulating all translated images, and averaging all accumulated pixel positions to realize image superposition enhancement, thereby effectively improving the dynamic range of a detector and the detection capability of a wide-area astronomical observation system; and meanwhile, the signal-to-noise ratio of the astronomical image can be effectively improved, and the astronomical image centering and light measuring precision can be further improved.

The invention relates to the technical field of foundation pit detection, and discloses a foundation pit displacement monitoring method and system based on machine vision and monitoring equipment. The method comprises the steps that a high-definition camera collects image data of a foundation pit area in real time, the collected image data are preprocessed, reference points in the image data are analyzed through an optical flow method, displacement of the reference points in a foundation pit is estimated, and three-dimensional position changes of the reference points are calculated through the stereoscopic vision technology and image matching. The system comprises an image acquisition module, an image preprocessing module, a displacement calculation module, a filtering optimization module, an anomaly detection and early warning module and a data storage module. The equipment comprises a high-definition monitoring camera, a data processing terminal, a storage unit, an alarm device and a communication module. According to the invention, through image acquisition of a multi-camera system and a stereoscopic vision technology, comprehensive monitoring of the displacement of the foundation pit is ensured, high-quality image data can be provided in various environments, and precise acquisition of three-dimensional depth information is realized in combination with stereoscopic vision.

The invention relates to a weak supervision text-pedestrian image matching method based on local and global dual-granularity identity association. The method comprises the following steps: generating an information asymmetric sample pair which comprises an information imbalance image and an information imbalance text description; extracting original image features, information imbalance image features, original text description features and information imbalance text description features; constructing a global image feature library and a global text description feature library; training a cross-modal image-text feature alignment capability based on a contrast learning loss function; constructing an intra-batch cross-modal identity association relationship in the local granularity, and reinforcing the fine-granularity feature difference capturing capability through inter-modal identity constraint; a dynamic cross-modal identity association network is constructed by taking a visual modal as an anchor point in global granularity, and weak association sample recognition sensitivity is improved in combination with a confidence coefficient dynamic adjustment mechanism; fusing information asymmetric sample pairs to construct a consistency learning mechanism; according to the method, the cross-modal matching precision is remarkably improved, and an efficient solution is provided for text-pedestrian image matching.

Disclosed in embodiments of the present disclosure are a video generation method, an apparatus, a device, a medium, and a product. The method comprises: first acquiring a reference image, audio driving information and a historical image corresponding to the audio driving information; performing image feature prediction processing according to image features of the reference image and image features of the historical image to obtain predicted image features corresponding to the audio driving information, so that the predicted image features can represent image features matching the audio driving information; performing image generation processing according to the reference image and the predicted image features to obtain a generated image corresponding to the audio driving information, so that the generated image can better represent an image matching the audio driving information; and finally, generating a video according to the generated image and the historical image, so that the corresponding time of the generated image in the video is later than the corresponding time of the historical image in the video.

The invention provides an electric power insulator defect detection method and system based on deep learning, and belongs to the technical field of electric power equipment insulation control detection. The method comprises the following steps: synchronously acquiring an original visible light image, an original infrared thermal imaging image and original ultrasonic data of a target electric power insulator; after preprocessing, data enhancement is carried out, then standard data is transmitted to a feature extraction module, and visible light features, infrared features and ultrasonic features are extracted; performing feature splicing, mapping to a shared space, inputting into a multi-head attention mechanism, generating a combined parallel result, and based on the combined parallel result, generating a fusion feature vector from the visible light feature, the infrared feature and the ultrasonic feature through a feedforward neural network; and outputting the defect type of the target power insulator through an output layer of the cross-modal image matching network. The method provided by the invention has higher precision and stronger robustness.

The invention provides an inspection task dynamic scheduling system based on endurance prediction and a load balancing method, and belongs to the technical field of train inspection. The system comprises an endurance prediction module, a task decision-making module, a load balancing scheduling module, a credibility calculation module, a position correction module and the like. Residual endurance time and stop time are generated through an endurance prediction module, and a task decision module generates a task transfer data packet at the stop time; the load balancing scheduling module screens candidate robots based on the total demand energy consumption and the time adaptability, and determines a replacing robot by combining the comprehensive load value of task interval correction; the credibility calculation module generates a position credibility score through image matching and track calibration, and the position correction module dynamically adjusts the position to guarantee the precision. According to the method, the adaptation precision and the position evaluation accuracy of the candidate robots are improved, the global resource configuration is optimized, the task interruption rate is reduced, and the inspection task execution efficiency and stability are enhanced.

The invention discloses an infrared and visible light image fusion method based on text supervised contrast learning. Comprising the steps that an infrared image, a visible light image and text description of the infrared image and the visible light image of the same scene are acquired, and multi-modal image features are extracted through linear embedding and cross attention Mama modules by adopting a preprocessing and image block division strategy; a pre-training text encoder is used for obtaining text semantic features, dimension alignment and weighted fusion of images and text features are achieved through an image-text feature alignment fusion module, and a fused image is generated. End-to-end training is carried out through image-text contrast learning loss in combination with pixel reconstruction and infrared intensity retention loss, and the semantic consistency and the thermal target retention capability of the fused image are effectively improved. The fusion process further comprises image-text matching weight calculation, feature weighting adjustment and fusion convolution operation. The method has the advantages of being high in semantic guiding capacity, excellent in fusion effect, accurate in hot target expression and the like, and is suitable for a multi-modal image processing task.

The invention relates to the technical field of multi-modal image matching, and discloses a multi-modal image matching method based on a parallel multi-scale cascade Transform. The method comprises the following steps of: extracting coarse-level and fine-level features from an input multi-modal image pair by using a feature extractor; performing association modeling on the coarse-level features through a parallel multi-scale cascade Transform, and dynamically adjusting channel weights by using a channel weight adaptive module in the coarse-level features so as to optimize feature representation; a local feature dynamic enhancement module is used for enhancing local information expression, and the detail capture capability and the multi-scene adaptability of features are improved; constructing a score matrix based on the enhanced coarse-level features, and realizing coarse matching through a mutual nearest neighbor criterion; by introducing a refining layer, a rough matching result is gradually optimized and adjusted on fine-level features, and finally high-precision matching is achieved; the method shows excellent performance in a multi-modal image matching task, can effectively cope with cross-modal characteristic changes in a complex scene, and is suitable for various application scenes in computer vision.

The invention discloses a cylindrical roller trajectory tracking method and device, and the cylindrical roller trajectory tracking method comprises the following steps: obtaining an actual measurement displacement sequence of a key point on the end surface of a roller under a global coordinate system based on a multi-reference matching digital image correlation method; drawing a key point displacement curve about key points and time according to the actually measured displacement sequence; judging the motion state of a roller according to the shape characteristics of the key point displacement curve, dividing a smooth part in the key point displacement curve into a rolling area, and dividing a part with jumping or sharp points into a sliding area; calculating the actual revolution angular velocity and the actual rotation angular velocity of the roller based on the actually measured displacement sequence corresponding to the sliding area; according to the method and the device, the image matching precision can be improved, meanwhile, the rolling and sliding components of the roller are accurately distinguished, the precision of the track of the cylindrical roller is improved, and high-precision and visual experimental data are provided for existing dynamics and tribology models.

The invention discloses a CSAR self-focusing imaging method based on ABP sub-aperture processing and incoherent superposition, and the method comprises the steps: generating a plurality of sub-aperture data according to the to-be-imaged data, and carrying out the coarse imaging of all sub-aperture data, and obtaining a corresponding coarse image; performing feature extraction on all the coarse images to obtain corresponding projection vectors; performing self-focusing processing on all the projection vectors to obtain corresponding self-focusing images; and obtaining an imaging image corresponding to the to-be-imaged data through image matching and incoherent superposition processing according to the self-focusing imaging picture. According to the invention, the precision and efficiency of CSAR imaging can be improved.

The invention discloses a microscopic image scanning and parallel efficient splicing method based on a spanning tree, which considers the characteristic that the scanning visual angle of a microscope is distortionless and the characteristic that the corresponding relation between the physical scale and the pixel scale of a microscope platform is unchanged, and has the advantages of an image matching algorithm based on a template. Automatically dividing an overlapping region range for a template matching algorithm, fully considering the matching result credibility of the pictures on the surrounding adjacent sides, and calculating a global splicing point location by using a spanning tree algorithm; picture splicing types are divided through rows and columns, the problem of memory access conflicts is avoided, the computing power improvement brought by multiple threads is fully utilized, and the picture splicing efficiency is improved. According to the method, adjacent image matching, high-precision matching of global splicing point locations based on the spanning tree and a multi-thread high-speed image splicing technology are realized.

The invention relates to a multi-modal image matching method and system based on learning features and epipolar geometric constraints. The method comprises the following steps: carrying out edge enhancement processing on an input image through wavelet transform; extracting a multi-scale dense feature map based on the transformed convolutional neural network, and generating a feature descriptor with rotation and scale invariance in combination with principal direction normalization; adopting an FLANN algorithm and dynamic distance constraint to realize preliminary feature matching; and introducing a basic matrix construction and epipolar geometric consistency verification mechanism, and eliminating mismatching point pairs in combination with an RANSAC affine constraint model. According to the method, image enhancement, deep learning and geometric verification strategies are fused, the problems of radiation nonlinearity and geometric distortion caused by imaging mechanism differences among multi-modal images are effectively solved, the matching precision and robustness are improved, and the method is suitable for remote sensing application scenes such as optical-SAR registration, multi-source image fusion and earth surface change detection.

The embodiments of the present disclosure relate to a method and an apparatus, a device, a vehicle, and a medium for generating a classification model. The method for generating a classification model according to the embodiments of the present disclosure comprises detecting a plurality of images in association with a target text using a semantic text-image matching model, wherein the target text indicates a target scenario. The method further comprises generating an image sample set comprising image samples with category labels by determining the category to which each image of the plurality of images belongs. The method further comprises generating a classification model for the target scenario based on the image sample set, wherein generating the classification model is based on a pre-trained model using linear recognition.In this way, it is possible to quickly generate customized classification models for specific scenarios that meet actual requirements, improving flexibility and accuracy while ensuring the efficiency and stability of the model generation process.

The invention discloses a multi-lens picture automatic splicing fusion processing method in video communication, which belongs to the technical field of multimedia communication, and comprises an acquisition module for synchronously acquiring video images of different visual angles through a plurality of cameras, and an analysis module for identifying overlapping areas and key feature points in each path of video, carrying out image matching, and sending the images to the video communication module. The fusion module is used for splicing and fusing the multiple paths of videos according to the image matching result to generate a panoramic picture, and the output module is used for outputting the spliced and fused panoramic picture to terminal equipment in real time. According to the invention, through a frame-level synchronization system with a unified time reference and standardized video parameter configuration, time sequence deviation and chromatic aberration distortion of multiple paths of videos are eliminated, the feature matching reliability in a complex scene is remarkably improved, the positioning precision of an overlapping region is ensured, splicing traces can be efficiently eliminated, and meanwhile, processing delay is reduced; and intelligent adaptation of the panoramic content on different terminal devices can be realized, and the degree of freedom of user control is improved.

The invention discloses an AI large model-based arrival person screening method and device, a medium and equipment, and belongs to the field of screening, and the method comprises the steps of firstly obtaining screening conditions input by a user, including text content, image data and qualification information requirements, and to-be-screened arrival person data; next, performing word segmentation, keyword extraction and semantic matching on the text content of the person arrival data by utilizing a preset semantic analysis model in combination with BiLSTM, CRF and LDA technologies, and outputting a semantic score; meanwhile, note styles are recognized through the text classification model, logic judgment is conducted, and styles and logic verification scores are obtained. In addition, element detection and style verification are carried out on the image data through the multi-modal recognition model, and image matching scores are output; the qualification scoring module calculates qualification scores according to a preset weight formula. And finally, fusing the multi-dimensional scores to generate a comprehensive score, and carrying out accurate screening on the arriving persons according to the comprehensive score.

The invention discloses a pose estimation method and device based on air-ground cross-view image matching, and belongs to the technical field of image matching and positioning. According to the method, a global digital elevation model (DEM) and an orthographic image are generated by obtaining an aerial image sequence of a target area and are divided into a plurality of grids, and a multi-view-angle simulation forward view image is generated for each grid. After a visual sensor carried by a ground mobile platform obtains a ground front-view image, a simulation front-view image similar to the ground front-view image is screened by using a magnetic field azimuth angle, and an optimal grid is determined through feature point extraction and matching. And resolving a pose matrix of the ground mobile platform based on the feature point coordinates of the optimal grid, and further improving pose estimation precision through track reconstruction and optimization. According to the method, the problems of matching difficulty and low pose estimation precision caused by relatively large difference between imaging angles and imaging content scales of the aerial image and the ground front view image are effectively solved.

The invention provides a camera pose estimation method based on 2D Gaussian splash. The camera pose estimation method comprises the following steps: acquiring a depth map and a normal map from a training image pose based on a pre-trained 2D Gaussian splash model; acquiring a grid ray starting point based on the depth map; generating a main ray and a hemispherical ray for each sampling point based on the normal diagram and the ray starting point; constructing a ray and image matching network and a lightweight convolutional neural network, and performing network training through a loss function; estimating the position of the camera through the trained ray and image matching network and the lightweight convolutional neural network based on the ray features of the main ray and the hemispherical ray and the image features of the query image, and constructing a rotation matrix to realize initial camera pose estimation; and optimizing the initial camera pose by adopting a depth-guided pose optimization method to obtain a final optimized camera 6D pose estimation result. The method gets rid of the dependence of an initial value, and has the advantages of high precision, strong robustness, efficient calculation and wide application.

The invention discloses an automatic generation method for health science popularization articles, and belongs to the crossing field of computer technology and health science popularization. The method comprises the following steps that S1, health authority information is collected through a multi-source crawler, and an original material pool is obtained through Sension-BERT vectorization duplicate removal and BioBERT medical entity labeling; s2, classifying nine types of health themes by using a RoBERTa fine tuning model; s3, multiple AI model supplementary materials are made into a structured package; s4, predetermining optimal selection questions in combination with AI three-dimensional scoring and editing; s5, calling the multi-dimensional knowledge base to generate a professional knowledge packet; s6, generating an outline and performing three-dimensional five-score system auditing of knowledge point fullness and the like; s7, the LLM expands and writes the text and marks knowledge sources; s8, performing double-stage auditing; s9, anthropomorphic draft moistening; s10, intelligently illustrating the picture; s11, when the matching degree is smaller than a preset threshold value, automatically updating the knowledge base; and S12, integrating and outputting. According to the method, the health science popularization article is generated, the manual participation time is shortened to be within 30 minutes, the medical error rate is reduced to be below 5%, the daily output of a 10-person team is improved by 3-5 times, the annual human cost is reduced by 60% or above, and the large-scale and high-quality science popularization requirements are met.