8572 results about "Machine vision" patented technology

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

The invention discloses a machine vision-based resistor disc defect online detection system and a grading method, relates to the technical field of industrial machine vision detection, and solves the defect problems in the aspects of multi-scale defect dynamic perception, cross-level feature interaction and process adaptive optimization in the prior art. According to the scheme, metal reflection interference is inhibited through Retinex illumination correction and a combined denoising model; adopting a deformable convolution kernel and cavity space pyramid pooling to realize gradient entropy driving dynamic sensing of the multi-scale defect; constructing a bidirectional cross-layer attention network to realize early fusion of high-resolution details and high-level semantics; modeling local-global feature physical association based on a graph attention network and a self-supervised message passing mechanism; integrating reinforcement learning and a memristor random calculation unit to form a closed-loop parameter optimization system; according to the method, the multi-scale defect detection precision, the cross-modal feature fusion efficiency and the system adaptive capacity under complex working conditions are remarkably improved.

The invention provides a printing piece defect detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: determining an optimal detection scale through image enhancement processing, multi-scale gradient pyramid construction and mutual information calculation, extracting key points through a curvature value, and carrying out the shape discrimination. And finally, generating a laser etching compensation path based on defect characteristics and performing layered compensation processing. According to the method, the automobile part surface printing flaws can be accurately identified, the compensation strategy is adaptively generated according to the defect types, and the defect repairing precision and efficiency are improved.

The invention relates to the technical field of image processing, and discloses a dried persimmon quality detection method and system based on machine vision. The method comprises the following steps: performing multi-angle multi-spectral imaging through an industrial camera array to generate a standard image set; extracting quantitative dried persimmon appearance characteristics; the internal quality is subjected to nondestructive testing by using a spectrum; identifying defects and grading; analyzing a quality trend and establishing an early warning mechanism; and correlating process parameters to optimize the production process, and constructing a quality tracing system. According to the invention, comprehensive nondestructive testing of the appearance and internal quality of the dried persimmons is realized, a closed-loop system of quality early warning and process optimization is formed through data analysis and mining, and the quality stability and traceability of dried persimmon production are improved.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention discloses an integrated Beidou monitoring machine system integrating panoramic photographing and machine vision deformation monitoring, and relates to the technical field of safety monitoring. The system comprises a hardware structure unit and a software function unit, the hardware structure unit comprises a Beidou positioning module, an inertial navigation compensation module, a panoramic vision acquisition module, a machine vision processing module, a multi-mode communication transmission module and a power management module; the software function unit comprises a multi-source data fusion engine, a panoramic three-dimensional modeling sub-module, an intelligent early warning decision module and a cloud collaborative resolving interface. According to the invention, through real-time fusion of Beidou positioning data and panoramic vision imaging, an improved dense optical flow method is adopted to track local crack propagation, and global displacement monitoring of Beidou is combined, so that cross-scale synchronous sensing from macroscopic displacement to microscopic deformation is realized, and the comprehensive monitoring precision is improved.

The invention discloses a mechanical arm positioning and grabbing method based on machine vision, and relates to the technical field of machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-view camera array, and generating three-dimensional point cloud data through data fusion; an improved LSD algorithm and a PnP algorithm are adopted to calculate the initial pose of the target object, illumination distortion is eliminated in combination with the generative adversarial network, and three-dimensional coordinates are output; a mechanical arm motion error transfer model is constructed based on Monte Carlo simulation, and a candidate grabbing scheme set is generated through reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control instruction set are generated. Through multi-modal data fusion and a nonlinear optimization algorithm, the technical problems of large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.

The invention provides a machine vision-based heavy-load large holding tool driving wheel multi-mode damage detection method, and relates to the technical field of automatic detection, and the method comprises the steps: collecting the image, vibration and temperature data of a driving wheel, extracting the visual features of the image through a multi-scale pyramid network, and obtaining the multi-mode damage of the driving wheel; inputting a cross-modal feature fusion network in combination with eccentricity and runout features of the vibration signals and stress concentration features of the temperature data to obtain fusion features; inputting the fusion features into a multi-task detection module to identify the damage, and measuring the damage area, depth, crack growth rate and stress concentration degree based on image segmentation, a photometric stereo method, vibration signal analysis and temperature gradient analysis; and finally, in combination with the equipment operation condition and the damage evolution law, an improved evidence theory is adopted to fuse the evaluation indexes, and an evaluation report including the damage state, the development trend and the maintenance suggestion is generated.

The invention relates to the technical field of intelligent detection, in particular to a container surface damage detection method and device based on machine vision, and the device consists of a multi-modal data acquisition module, a dynamic compensation processing module, a multi-modal data fusion module and a damage classification and positioning module. The multi-modal data acquisition module generates a high-density three-dimensional point cloud through double-laser line scanning, and acquires a multispectral image, an infrared thermogram and a real-time motion state. The dynamic compensation processing module integrates an optical flow method and acceleration data to realize sub-pixel-level motion compensation, and combines adaptive exposure control to optimize the imaging quality under complex illumination. The multi-modal data fusion module strengthens defect feature expression through space-time alignment and a double-branch collaborative attention mechanism, and model parameters are reduced through lightweight network design. And the damage classification and positioning module adopts an improved deep network to realize defect classification and complete millimeter-level three-dimensional positioning. The automatic detection requirement of a port is met, and the problems of poor container detection precision, poor adaptability and the like are solved.

The invention relates to the technical field of industrial intelligent monitoring, in particular to a remote online monitoring method and system based on machine vision and artificial intelligence, and the method comprises the following steps: synchronously collecting a visible light video stream, infrared thermal imaging data and three-dimensional vibration spectrum data through an edge calculation node, and forming a multi-modal sensing data set; performing cross-domain feature alignment processing on the data set to generate a space-time synchronous composite feature matrix; inputting the composite feature matrix into a cascaded deep learning model, and outputting a three-dimensional diagnosis vector which comprises an equipment health state, an abnormal region and a fault probability; and matching the three-dimensional diagnosis vector with a historical reference vector through a DTW and cosine similarity combination algorithm to generate a deviation index, and outputting a self-adaptive alarm threshold and a maintenance suggestion scheme according to the deviation index. The method has the advantages of high multi-modal fusion degree, light diagnosis model and intelligent and adjustable alarm mechanism, and is suitable for real-time remote operation and maintenance in a high-risk industrial scene.

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention relates to the technical field of image processing, and discloses a stranded wire production quality monitoring method and system based on machine vision. The method comprises the following steps: acquiring the surface of a stranded wire at multiple angles through a high-speed camera array to obtain an original image; filtering high reflection and noise to obtain a feature map; performing edge detection, segmenting and identifying defects, and generating a position mapping table; quantitatively calculating defect parameters to form a quality score; performing correlation analysis to adjust process parameters; and constructing a defect-process database to realize quality tracing early warning. According to the invention, full-process automatic control from defect identification, quantitative evaluation to process parameter optimization is realized, the defect-process relational database is constructed, and the tracing and early warning functions of production quality are supported, so that the quality stability and the intelligent level of stranded wire production are improved.

The invention provides a human body posture detection method and equipment based on machine vision, which are applied to posture recognition in physical fitness detection, and are used for generating a dynamic human body region set of a target user by acquiring a multi-frame continuous image sequence of a physical fitness training scene and performing human body region positioning processing on the multi-frame continuous image sequence. Performing posture feature extraction processing on the dynamic human body region set to obtain a space-time posture feature set of the target user, performing posture state recognition processing on the space-time posture feature set based on a preset physical fitness evaluation model to generate a posture state recognition result of the target user, and generating physical fitness evaluation parameters according to the posture state recognition result, and the physical fitness evaluation parameters are fed back to the physical fitness training guidance system. According to the method, the identification capability of fine posture change in the fitness action of the complex body can be improved, the synchronous evaluation of the action specification degree and the physiological load state is realized, and the limitation of the traditional single-dimensional action evaluation is broken through.

The invention discloses an intelligent quality detection system based on machine vision, relates to the technical field of data processing, and solves the problem that light source parameters are difficult to automatically adjust by combining and utilizing a control algorithm according to environmental parameter influence coefficients. After multi-scale fusion alignment is difficult to carry out on image data, optimized image information is obtained by adopting a Q-learning algorithm and combining image quality scores; defect identification detection and three-dimensional deformation detection are difficult to carry out; and the quality abnormal coefficient is difficult to analyze and control feedback and visualization are difficult to carry out. Through adaptive light source control and image optimization, in combination with defect detection and three-dimensional deformation evaluation, efficient and accurate quality detection is realized, and through real-time feedback and visualization, the defect and deformation state of an object can be conveniently and rapidly known.

The invention relates to the technical field of photography focusing control, in particular to a photography focusing control method based on machine vision. Comprising the following steps: collecting and preprocessing multi-frame image data; detecting a target area in the image through a machine vision technology and a deep learning algorithm, marking a main body object and dynamically adjusting a focusing priority; extracting multi-dimensional visual features by using a sparse coding algorithm, and constructing a dynamic focusing optimization model based on the features to calculate an optimal focusing parameter; the imaging definition and the focusing effect are evaluated in real time through a fuzzy logic control algorithm, and focusing parameters are adjusted according to feedback; and the photographic equipment is controlled to generate a high-resolution focused image or video data, so that the optimal imaging effect is ensured. According to the method, artificial intelligence and an optimization algorithm are utilized, the intelligent level of the photographic equipment is improved, the focusing capability and the imaging quality of the photographic equipment in a complex scene are improved, convenient and efficient photographing experience is provided for a user, and meanwhile dependence on manual operation is reduced.

The invention provides a circuit board detection method and system based on machine vision, and the method comprises the steps: obtaining an original visual data set of a to-be-detected circuit board, carrying out the visual information optimization processing of the original visual data set, and obtaining a standardized image set with unified illumination intensity and contrast, calling a pre-trained defect discrimination model to perform key feature recognition processing on the standardized image set, generating a potential defect feature set of the circuit board in the image, and determining defect types existing in the to-be-detected circuit board and position distribution feature information of defects in the image according to the potential defect feature set, and generating a detection result report containing defect positioning coordinates based on the defect type and the position distribution feature information, and outputting the detection result report to a target display terminal to complete the detection process. According to the invention, the accuracy of defect identification is improved, and the reliability of defect positioning is ensured in combination with the position marking information, so that the overall quality of a circuit board detection result is effectively improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The present invention relates to the technical field of agricultural information, and specifically to a plant factory information automatic acquisition method and system based on unmanned aerial vehicle machine vision. The method comprises the following steps: using a camera mounted on an unmanned aerial vehicle to capture an image, and using a deep learning and image segmentation technology to generate a plant region segmentation image. According to the present invention, the unmanned aerial vehicle is used to carry the camera to capture a plant image, and efficient and accurate plant region segmentation is realized by using the deep learning technology. A plant health assessment report is generated by means of a computer vision technology, and a health problem is early warned. A plant health analysis report comprehensively considering environmental factors is generated on the basis of ground sensor data and time series analysis, and comprehensive and accurate health assessment is provided. A multi-dimensional plant health monitoring model provides support for all-round health monitoring. A plant disease nutrition analysis report is generated on the basis of a machine learning classification algorithm, a problem is accurately recognized, and a fertilization and pesticide-spraying strategy is formulated on the basis of a decision support system, thereby optimizing resource utilization, and improving the yield and quality.

The invention provides a data center inspection robot monitoring analysis method and system based on machine vision, and relates to the technical field of inspection robots, and the method comprises the steps: obtaining a video stream and environment parameter data collected by an inspection robot, and carrying out the detection and recognition of an abnormal state through a deep learning target; and constructing a multi-modal data fusion analysis model to form a knowledge graph to generate a root cause analysis result, and planning an inspection path based on priority scores. According to the invention, intelligentization and precision of data center monitoring are realized, and inspection efficiency and fault diagnosis accuracy are improved.

The embodiment of the invention discloses a circuit board defect automatic detection method and system based on machine vision, which are used for improving the precision and efficiency of circuit board defect detection and repair, and the method comprises the following steps: obtaining a surface image data set of a target circuit board; performing image preprocessing operation on the surface image data set to generate a preprocessed image data set; inputting the preprocessed image data set into a pre-trained defect feature extraction model, and performing multi-level feature fusion processing on each piece of image data through the defect feature extraction model to generate a multi-dimensional defect feature set; determining a defect positioning information set of the target circuit board according to a matching degree calculation result between the multi-dimensional defect feature set and a preset defect feature template; and transmitting the defect positioning information set to a defect repair control terminal, and triggering the defect repair control terminal to generate a repair path planning instruction according to the defect positioning information set.

The invention relates to the technical field of dam deformation monitoring, and provides a dam deformation monitoring and early warning method and system based on Beidou and machine vision, and the method comprises the steps: collecting the original displacement data of a dam and the original image data of the surface of the dam; performing RTK differential processing and multi-point positioning data weighted average on the original displacement data to obtain dam displacement data, and performing multispectral analysis and deep learning processing on the original image data to obtain dam surface structure feature data; carrying out localization processing on the dam displacement data to obtain real-time displacement change data, and carrying out parallel processing on the dam surface structure feature data to obtain real-time deformation feature data; dynamically fusing the real-time displacement change data and the real-time deformation characteristic data to obtain comprehensive deformation data; and establishing a dam deformation trend prediction model according to the comprehensive deformation data, and pushing graded early warning information to a monitoring center when abnormal deformation is detected. According to the invention, the real-time performance of dam deformation monitoring and the early warning reliability are improved.

The invention relates to the technical field of part size detection, and discloses a part size detection method based on machine vision. The method comprises the following steps: synchronously acquiring multi-angle original image data of a to-be-detected part by using a plurality of industrial cameras; preprocessing the original image data to obtain a de-noised and enhanced standard image set; performing three-dimensional point cloud reconstruction on the set to obtain spatial point cloud model data of the part; through a spatial frequency domain registration algorithm, registering the spatial point cloud model data with a preset part standard CAD model, and outputting a difference point cloud distribution diagram; extracting a key dimension feature vector in the image, inputting the key dimension feature vector into the trained optical feature fusion model, and generating a multi-dimensional dimension deviation quantitative index; and according to the index matching process compensation parameter set, outputting a size detection decision report. The method can comprehensively capture the part features, improves the detection precision, achieves the cooperation of detection and production technologies, and is suitable for the field of part size detection.

The invention provides an industrial production part detection method based on machine vision, and the method comprises the steps: collecting multi-dimensional image data through a multispectral industrial camera when a part passes through a detection region, and carrying out the preprocessing of the multi-dimensional image data; constructing a three-dimensional feature space according to the structured light projection, and mapping the preprocessed multispectral image data into the three-dimensional feature space to carry out space registration operation to obtain a to-be-detected image; extracting composite parameters in the to-be-detected image; performing coarse screening identification on the parts through the geometric parameters and the texture parameters, and removing the parts with appearance defects; and carrying out microcosmic fine judgment identification on the roughly screened parts through material characteristic parameters, and rejecting the parts with quality defects. According to the method, the three-dimensional feature space is constructed through fusion of multispectral imaging and structured light projection, and composite analysis of geometry, texture and material characteristic parameters is combined, so that the problem of single detection dimension of a traditional method is solved, and the recognition accuracy and efficiency of complex defects are improved.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention discloses a machine vision model training method and system based on end-side computing power, and belongs to the technical field of machine learning, edge computing and computer vision, and the method comprises the steps: obtaining a machine vision image and constructing a pre-annotation model, so as to carry out the automatic pre-annotation of the image; manually correcting the partial pre-annotation to optimize the pre-annotation model, and obtaining a corrected annotation image; a machine vision model is constructed and is subjected to identification training based on an annotated image, training task variables can be distributed according to hardware perception, then a training task execution position (an edge end or a cloud end) is dynamically selected, and the trained machine vision model is deployed at the edge end so as to carry out an image identification reasoning process; and performing manual spot check on the reasoning result of the model to evaluate the accuracy rate of the machine vision model, starting a new round of model training when the accuracy rate is low, and taking the evaluated machine vision model as the pre-labeling model.

The invention discloses a keyboard key defect detection method and system based on machine vision, and the method comprises the following steps: collecting the multi-modal data of keyboard keys, and carrying out the fusion preprocessing of reflection component separation, dynamic gamma correction and multi-modal constraint alignment; constructing a double-flow deep neural network for defect detection, wherein the first branch network adopts an improved U-Net architecture embedded with a CBAM attention module to extract texture features; the second branch network adopts a PointNet + + architecture to process three-dimensional geometrical characteristics; realizing cross-modal feature association through a feature fusion layer, wherein a fusion weight is adaptively adjusted according to a material type; and outputting a detection result based on the cascade classifier, wherein the method comprises the following steps: positioning a suspected defect area by a first-stage YOLOv5 network; the second-stage ResNet50 network is used for completing defect classification; and the dynamic threshold segmentation algorithm adjusts the judgment boundary according to the material type. According to the invention, high-precision and high-efficiency detection of keyboard key defects is realized.

The invention relates to the technical field of grinding, in particular to a self-adaptive force control grinding system and method for a complex curved surface machining robot. Three-dimensional point cloud data of a workpiece are obtained through machine vision, gridding processing is carried out, and normal vector and curvature characteristic parameters are calculated; performing operation area division and operation path planning based on curvature characteristics; calculating operation parameters according to the normal vector and curvature characteristics; a self-adaptive impedance control algorithm is adopted, the polishing force is monitored in real time through a force sensor, and the rigidity and damping parameters of the robot are dynamically adjusted; and performing defect identification on the surface image of the processed workpiece by using the deep learning model, and when defects are identified, re-planning the operation path. According to the method, self-adaptive grinding of the complex curved surface workpiece is achieved, and the grinding efficiency and quality of the complex curved surface can be improved.

The invention relates to the technical field of machine vision technology and agricultural disease and insect pest diagnosis, in particular to a rice disease and insect pest identification method and system based on the machine vision technology, and the method comprises the following steps: S1, obtaining the polarization direction distribution of reflected light on the surface of a leaf, and generating a polarized light compensation image; s2, performing feature layer fusion to generate a multispectral fusion image; s3, based on the polarization phase difference value of each pixel point in the multispectral fusion image, segmenting an independent leaf area; s4, performing multi-dimensional feature extraction on the segmented leaf area; s5, performing Euclidean distance calculation, and determining a nearest neighbor matching result; and S6, according to a feature similarity threshold value in a matching result, determining the disease and pest species. According to the invention, through fusion of multi-source spectral information and structured feature comparison, high-precision identification of rice diseases and insect pests and scab space positioning are realized, and the intelligent level and practicability of disease diagnosis are improved.

The invention relates to the technical field of machine vision, and discloses a machine vision-oriented cross-modal image-text analysis method, which comprises the following steps of: partitioning an input image to generate an image block sequence; inputting the image block sequence into a visual converter for multi-scale feature extraction, and generating target visual features; encoding the input text to generate a target text feature; inputting the target visual features and the target text features into a deep reconstruction bottleneck network for compression alignment, and generating a cross-modal compression vector; and inputting the cross-modal compression vector into a large language model to generate cross-modal decoding information, so that cross-modal redundant information can be effectively filtered, compact shared semantic representation can be learned, the information integrity of the compression process is ensured through bidirectional reconstruction verification, cross-modal semantic alignment is realized, and the method has the advantages of high efficiency and high reliability. Omnibearing cross-modal content generation from the whole to details is achieved, and the requirements of different application scenes are met.