483 results about "Image structure" patented technology

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention relates to the technical field of video coding, in particular to a video coding method and system based on multi-channel concurrent software and hardware mixing, and the method comprises the following steps: analyzing brightness distribution and edge structures, screening effective frames, calculating frame priorities, dividing image blocks and combining processing units, counting the number of frame streams, and analyzing frequency concentration. And evaluating image coherence and a load state, and outputting a processing channel adjustment record. According to the invention, through screening of image content features, occupation of redundant frames on coding resources is reduced, through dynamic configuration of frame priorities, timeliness and accuracy of target frame scheduling are improved, an aggregation strategy of a block structure is combined, picture consistency after image processing is enhanced, and task frequency trend identification is utilized, so that image processing efficiency is improved. According to the method, accurate resource matching of a high-load flow section is achieved, joint judgment of image boundary continuity and structure hopping frequency is adopted, an allocation strategy of a processing channel is optimized, an image structure and channel scheduling form linkage, and stability and adaptive capacity are improved.

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The invention discloses a robot system for steel structure welding, relates to the field of steel structure machining equipment, and aims at solving the problem that a steel structure can be welded by collecting robot track data and a welding seam image in the welding process, constructing a track continuity evaluation model, identifying a path change trend and fusing an image structure and track change information. The position and confidence of a track interruption section are recognized, so that the high-precision recognition and automatic recovery capability after path interruption is improved; a smooth compensation track is generated by adopting third-order Bessel interpolation based on starting and ending points of an interruption section, curvature and posture consistency registration with an original track is completed, and continuity and stability of a welding path are ensured; multi-modal data such as current, voltage, temperature, acoustics and welding seam images are collected through multiple channels, welding abnormity is recognized, state feature vectors are formed, and the problems such as splashing abnormity and welding seam deviation can be found in real time; through multi-dimensional analysis and dynamic adjustment of the whole welding process, the stability, intelligence and adaptability of the welding system are remarkably improved.

The invention relates to the technical field of image recognition, in particular to a lightning arrester fault thermal imaging picture recognition method and system combined with gaze prediction, and the method comprises the steps: carrying out the down-sampling of an original thermal imaging image to a fixed size, sequentially passing through a multi-layer convolution and a Spatial Softmax layer, and outputting a predicted gaze point track sequence, extracting a key area and a non-key area according to the intensity of the fixation point track sequence; reconstructing the key area to obtain a compressed and recombined target area; inputting the non-key region into a variational auto-encoder to obtain a low-dimensional potential feature vector; and inputting the image-structure joint feature representation into an EffiCroprViT model, and finally obtaining a lightning arrester fault classification result. Local and global features are efficiently fused, the calculation complexity is reduced, background noise interference is effectively suppressed, real-time and accurate identification and early warning of the fault state of power equipment are realized, and the fault classification method has the advantages of high efficiency, high reliability and high reliability. Therefore, the safety and stability of power grid operation are ensured.

The invention relates to the technical field of defect detection, in particular to a heat-conducting adhesive tape defect detection method and system based on machine vision, and the method comprises the following steps: obtaining a surface image of a heat-conducting adhesive tape, extracting a texture trend and marking an aggregation block, adjusting the trend and offset of a connecting line segment, and analyzing the angle change of a sideband node. And recognizing a skeleton trend reverse section and closing a boundary, and extracting a continuous direction changing region to obtain a defect structure image annotation layer. According to the method, the turning position in the sideband region is extracted by constructing the direction aggregation graph of the texture aggregation block, enhancing the direction identification of the direction of the regional texture, combining the grouping organization and coordinate offset processing of the line segment path, enhancing the continuous association of the image structure, and by means of the repeated distribution characteristic of the angle mutation node; and through a skeleton trend reverse paragraph and boundary closing mode, an identification range of a structure disturbance block is expanded, and coherent tracking and multi-level labeling of a structure abnormal region are completed by combining a spatial relationship between a contour trend difference and a turning point.

The invention relates to the technical field of image recognition, in particular to a mechanical casting detection method and system based on image data analysis processing, and the method comprises the following steps: obtaining a casting image gray value, classifying according to a gray scale standard, generating a multi-layer image structure and a layering graph, setting a grid, counting pixels to form a density sequence, extracting a trend region, and building an index table; and extracting multi-angle image boundary point matching intersection points to form a coordinate matrix, screening a stable intersection point set, searching an original image comparison trend coordinate to extract an overlapping region, and generating a defect annotation image result. According to the method, an image is subjected to gray scale division to form a multi-layer structure, texture differential expression is enhanced, a pattern layer grid statistics density sequence is used for extracting a trend region, abnormal distribution positioning is achieved, boundary mutation points in a multi-view image are uniformly mapped, boundary recognition consistency and robustness are improved, and a stable boundary and the trend region are compared and marked; defect expression intuition and credibility are enhanced, and detection precision and stability are improved.

The invention relates to the technical field of civil engineering structure safety monitoring and aircraft image detection, and discloses a safety quantification nondestructive monitoring method and system based on an aircraft concrete structure, and the method comprises the steps: constructing a multi-target path optimization model, and generating a track point sequence; the aircraft collects an image and calculates an image reliability score; identifying crack and spalling defects by using a deep learning model; calculating a structure risk index SFI based on a partial least squares regression model; a monitoring report is generated and uploaded to the cloud. The technical problem that in the prior art, the safety quantitative evaluation based on the in-flight image structure cannot be realized by depending on a manual inspection or static image recognition mode, especially under the conditions of a special-shaped curved surface structure, shielding of a complex area or violent illumination change is solved. Due to the fact that three-dimensional path optimization, an image reliability scoring mechanism and AI defect recognition are fused, intelligent recognition of structure surface covering and crack peeling is achieved, and the automation degree of concrete structure monitoring is improved.

The invention discloses a urinary calculus image recognition and analysis system based on deep learning, which relates to the technical field of urinary calculus image recognition and comprises a structure communication mapping module, an edge disturbance analysis module, a structure fidelity coding module, a trusted path assignment module and a self-adaptive screening regulation and control module. The edge disturbance analysis module is used for constructing an edge direction difference matrix based on the region connectivity vector, carrying out gradient direction analysis on boundary pixels in the enhanced image and extracting edge frequency disturbance characteristics; and the structure fidelity coding module is used for correspondingly fusing the region connectivity vector and the edge frequency disturbance characteristics according to position indexes, constructing a structure integrity description vector and constructing a structure fidelity kernel function based on the structure integrity description vector so as to generate a structure fidelity score. According to the method, the problem of training misleading caused by incomplete enhanced image structure is solved, structural integrity screening and path optimization of training samples are realized, and the model recognition precision and stability are improved.

The invention discloses an automatic software function testing and evaluating method based on a multi-modal large model, and particularly relates to the field of non-embedded software function testing and evaluating, which comprises the following steps: collecting software texts, images and structured data, generating corresponding feature vectors, aligning through a cross-modal comparison algorithm, and constructing a multi-modal testing and evaluating data set; deep features of all modes are extracted through a mode exclusive encoder, and a unified multi-mode function vector is output based on information entropy dynamic weighted fusion; constructing triple training data, and optimizing the multi-modal large model by combining a joint loss function with an enhanced training strategy; and finally, deploying the model at a cloud end and an edge node, carrying out real-time triggering test by docking a CI / CD process, and realizing functional integrity verification, interactive compliance detection and accurate defect positioning through semantic vector comparison, graph isomorphism rate calculation and cross-modal attention backtracking. According to the scheme, the test comprehensiveness and precision are improved, and the rapid iteration requirement of software is met.

The invention relates to the field of computer technology and medical image processing, in particular to a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism. In order to solve the problems of scarcity of annotation data, weak model generalization ability, inaccurate segmentation boundary and the like in a current medical image segmentation task, the invention provides a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism, which is called an RCDE segmentation model. The model adopts a shared encoder and double decoder structure, combines a structure-level disturbance generation strategy, enhances the perception ability of the model for image structure change by mixing and recombining labeled images and unlabeled images, generates pseudo labels by utilizing a teacher network, and introduces a dynamic confidence coefficient screening mechanism, so that low-quality pseudo labels are effectively eliminated, and the robustness of the model is improved. The training stability and the pseudo-supervision effect are improved, preprocessing such as size normalization and image enhancement is carried out on the image, and the consistency and robustness of model input are improved.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention discloses an energy field intelligent knowledge base question answering system based on multi-model cooperation, and aims to solve the problems of data type identification, image structured extraction and cross-modal indexing in energy field multi-modal document question answering. The system comprises 13 core modules, dynamic cooperation of a multi-mode large model and a large language model is achieved through a collaborative scheduling module, data types involved in user problems can be recognized, and corresponding modules can be called; structured information of images such as charts and flow charts can be extracted from documents and coded; and cross-modal retrieval is realized through a unified semantic vector. When answers are integrated, an attached source is quoted, and traceability is ensured. The system improves the accuracy and efficiency of complex document question answering in the energy field, and is suitable for professional document question answering scenes containing multiple types of images.

The invention relates to the technical field of image processing, in particular to a degradation perception-based self-supervised underwater image super-resolution reconstruction method, which comprises the following steps of: acquiring a real underwater image, extracting multi-modal degradation characteristics of the real underwater image through an underwater degradation perception pseudo-characteristic extraction network, and then performing super-resolution reconstruction on the multi-modal degradation characteristics of the real underwater image; generating a low-quality reconstructed image of the real underwater image based on the multi-modal degradation features; freezing the underwater degradation perception pseudo feature extraction network and the super-resolution reconstruction network, unfreezing a bottleneck layer in the super-resolution reconstruction network, performing fine tuning training on a decoding layer, and performing image structure restoration on a real underwater image by using the fine-tuned super-resolution reconstruction network; in the fine tuning training process, a super-resolution reconstruction image obtained by reconstructing a real underwater image through a super-resolution reconstruction network is obtained, fine tuning is carried out on a bottleneck layer and a decoding layer based on the construction and a fine tuning loss function calculation error, and the adaptability and the image reconstruction capability in a real underwater environment are improved.

The invention discloses a CT image analysis method and system based on a neural network, and relates to the technical field of CT image analys.The method comprises the steps that an original CT image is obtained after user authorization, a Laplace operator is adopted to strengthen a focus boundary, and a circular region of interest is intercepted to remove edge sensitive information; extracting edge and texture information in the standardized image; focus area features are focused step by step; executing characteristic distillation balance based on category sample distribution, and outputting a focus characteristic graph with local perception enhancement and sample balance characteristics; segmenting the lesion feature map into serialized units, embedding position codes, inputting the serialized units into a plurality of layers of encoders, and fusing an image structure and text indication information through a dynamic adjustment mechanism; performing linear classification on the global semantic vector to output a diagnosis result, generating a focus thermodynamic diagram, and superposing the focus thermodynamic diagram to an original image for visualization; and performing dynamic optimization based on doctor feedback. The accuracy of feature analysis is improved; the overall operation efficiency of the system is improved.

The invention discloses a lightweight single-image super-resolution reconstruction method based on local and global feature collaborative enhancement perception, which is used for solving the problems of insufficient utilization of high and low frequency clues in single-image super-resolution, low feature fusion efficiency and difficulty in consideration of structural consistency and visual fidelity of reconstructed images. The network adopts a double-flow heterogeneous architecture; a local branch uses multi-type differential convolution explicit coding image edge and texture prior to enhance detail characterization capability; the global branch effectively models long-range dependency and low-frequency semantic information by integrating local, cross-regional and global multi-level spatial self-attention mechanisms. The frequency sensing fusion module provided by the invention generates a channel specific space weight based on frequency characteristics, self-adaptively fuses double-branch characteristics, accurately balances structure maintenance and detail enhancement requirements in a characteristic fusion process, and effectively reduces characteristic redundancy. According to the super-resolution network, the model complexity is remarkably reduced, high-resolution images which are consistent in structure, vivid in vision and rich in details can be generated while lightweight design is kept, and an effective scheme is provided for efficient and high-performance lightweight single-image super-resolution reconstruction model design.

The invention discloses an infrared-guided image restoration method under interference of a non-uniform scattering medium. The infrared-guided image restoration method comprises the following steps: (1) constructing a progressive guide aggregation module; (2) designing a differential amplification attention module; (3) constructing a structure guide enhancement module; and (4) designing physical coupling loss. Aiming at the problems of non-uniform distribution of interference areas and image feature redundancy, infrared image structure information and visible light texture expression capability are effectively combined, and multi-modal complementary information and physical priori knowledge are combined, so that self-adaptive enhancement and fine structure restoration of the visible light image under the interference of the non-uniform scattering medium are realized. The method provided by the invention is excellent in performance on a non-uniform interference data set, effectively relieves the problems of structure loss, color cast, modal redundancy and the like, provides a high-robustness solution for image enhancement in complex environments such as dust, sand dust, water mist and the like, and provides a new direction for research of an infrared guide image restoration method.

The invention discloses a multi-view dynamic fusion electrocardiogram diagnosis method and system based on graph embedding, and belongs to the technical field of medical signal processing. The method comprises the following steps: S1, acquiring standard 12-lead electrocardiogram data acquired under different instruments and equipment, and preprocessing the acquired data; s2, constructing a multi-view feature coding and graph structure modeling module; s3, constructing a multi-view fusion module for graph embedding; and S4, constructing a classification prediction output module, and establishing an optimization mechanism. According to the method, the image structure and the multi-view features are fused, the problems of lead modeling stiffness, fusion strategy static state and structure optimization decoupling in an existing method are solved, the disease recognition capability is remarkably improved, the multi-source heterogeneous data verification performance is excellent, and good clinical and engineering adaptability is achieved.

The invention discloses a panchromatic sharpening image fusion method and device based on a double-domain flexible converter. The method comprises the following steps: performing multilayer space-frequency joint attention operation on a low-resolution multispectral image and a high-resolution panchromatic image to generate a feature tensor; performing double-domain feature alignment operation, aligning structural semantic features based on an attention mechanism, fusing amplitude and phase frequency spectrum information through Fourier transform, and matching modal distribution differences by using instance normalization; multi-level fusion is carried out, and splicing, convolution and space-frequency joint attention operation are carried out on each level in sequence; carrying out residual error reconstruction on the fusion features; and adding the reconstructed features and the up-sampled low-resolution multispectral image element by element, and outputting a high-resolution multispectral image. According to the method, image structure distortion and detail blurring can be remarkably reduced, the image definition and the edge reduction capability are improved, the fusion consistency and the physical authenticity are improved, and the detail expressive force of the fused image is enhanced.

The invention discloses an image processing method and system for beam crack depth based on MLP optimization, and particularly relates to the field of image data processing based on a neural network, and the method comprises the steps: carrying out the noise suppression, contrast remapping and size normalization processing of original image data, and converting the original image data into an image set with unified spatial distribution; and a structure labeling image set with a crack area label is generated through structure calibration and space mask construction. By constructing a spatial expression vector driven by image state evolution and introducing a variation MLP model with a channel decoupling and path regulation and control structure, crack region depth prediction processing dominated by image structure change is realized, so that the adaptability and prediction stability of the model to an image complex structure are improved, and the prediction accuracy is improved. The problems that an existing method is inconsistent in image semantics, lacks spatial modeling, is insufficient in depth prediction precision and the like are solved.

The invention discloses a remote sensing image segmentation method and system based on cross-normal-form feature fusion and alignment, and the method comprises the steps: carrying out the preprocessing of an input remote sensing image, and extracting an initial feature; inputting a cross-normal-form feature fusion and alignment network, and fusing multi-modal and cross-scale remote sensing image structure information through sparse channel enhancement and space alignment and space pixel refining and channel alignment to obtain a first-stage fusion feature; inputting a multi-stage cross-paradigm enhanced feature extraction network, fusing local details and global context information through multi-level information interaction and a dynamic gating mechanism, and gradually extracting a joint feature map of semantic and spatial structure collaborative expression; a final semantic segmentation result is generated through the segmentation head, and composite loss is calculated based on a real label; according to the method, the multi-stage feature extraction network and a cross-normal-form feature alignment mechanism are constructed, local textures, spatial contexts and multi-modal information are effectively fused, and the segmentation performance is enhanced while the calculation efficiency is guaranteed.

The invention discloses a texture perception state space modeling method for an image restoration task, and the method comprises the steps: 1, constructing a region selection mechanism based on texture complexity, and enabling the region selection mechanism to be used for distinguishing a flat region and a high-texture region in an image; 2, introducing a texture modulation mechanism, and performing explicit adjustment on a state transition matrix in the state space model; 3, enhancing the context modeling capability of the model through a multi-direction sensing module; and 4, by combining position embedding and a sequence modeling structure, the capability of the model in the aspects of image structure understanding and spatial information maintenance is improved. The method can effectively alleviate the problem of information loss when a traditional image restoration method processes texture details, improves the structure restoration capability of a complex region, gives consideration to the restoration quality and the calculation efficiency, is suitable for multiple image restoration scenes such as image super-resolution, image rain removal, low-light image enhancement and the like, and improves the image restoration efficiency. And the method has good engineering adaptability and actual deployment value.

The invention provides an image coding method and system based on a single-step diffusion model, and the method comprises the steps: coding a potential representation to a decoding end through employing an extremely low bit rate based on a stable diffusion model (SD-Turbo) and a depth potential representation compression model, and obtaining a reconstructed image through single-step denoising; a group of auxiliary encoders and decoders are introduced, rich and entropy-perceived pixel-level original image semantic information is extracted at an encoding end, and decoded image structure information is shared for a single-step denoising process at a decoding end; and the whole model is subjected to end-to-end optimization by adopting code rate and pixel level constraint so as to achieve the optimal subjective coding quality. According to the invention, the technical problems of high decoding complexity and poor reconstruction consistency are solved.

The invention provides a method and a system for restoring a bamboo-strip character image based on multi-granularity feature guidance, and innovatively designs a coarse-fine two-stage restoration network and end-to-end multi-task loss joint training aiming at the problems of structure-texture confusion, non-uniform degradation, low contrast ratio and the like of the bamboo-strip image. In the coarse repair stage, a font texture and structure double-reconstruction sub-network is used for separating semantics from a source; in the fine repairing stage, multi-scale dynamic range distribution diagram self-attention (Mdma) is provided, pixels are dynamically classified according to degradation intensity, and long-short range dependence joint modeling is achieved; an adaptive mask is designed to sense pixel shuffling downsampling (Ampd), sampling is guided by mask confidence, damage position information is kept, and artifacts are inhibited. Five mainstream methods are compared on a homemade 313 bamboo strip single word data set, the PSNR, the SSIM and the FID are optimal under 0-60% irregular masks, visual evaluation of real missing samples is natural in texture, the structure is complete, and the readability of the bamboo strip characters and the subsequent recognition accuracy are effectively improved.

The invention discloses a fusion graph recognition and symbolic expression-based resin-based composite material automatic native modeling and reverse design method and system, belongs to the field of composite material modeling and design, and particularly relates to a graph neural network and symbolic regression fusion-based composite material native modeling and structure reverse optimization method. The method comprises six steps of microcosmic image structure extraction, topological graph construction and graph embedding, constitutive relation symbol modeling, graph structure homogenization, performance-oriented reverse design and multi-modal performance prediction, and can realize an automatic process from microcosmic image to macroscopic performance prediction to structure optimization design. The problems that an existing method is low in efficiency, poor in interpretability and difficult in reverse design are solved, and the modeling efficiency and the design intelligence level of the composite material under multiple scales and multiple targets are improved.

The invention discloses a steel ladle hoisting safety monitoring and early warning method, system and equipment and a medium. The method comprises the following steps: acquiring double-side images in a steel ladle hoisting process; inputting the bilateral images into a multi-target detection model, and outputting bounding boxes, category labels and confidence coefficients of the plurality of key targets; obtaining a local cutting image of each key target according to the bilateral images and the bounding box, the category label and the confidence of each key target, and generating a structured text in combination with the bounding box position, the relative pixel distance and the category label of each key target; inputting the local cutting image, the structured text and the prompt word template of each key target into a multi-modal big language model, and outputting a behavior label, a risk level, a reason, a recommendation action and an instruction state of the current steel ladle hoisting scene through multi-modal reasoning; early warning is conducted according to the recommended action, and the steel ladle hoisting equipment is controlled according to the instruction state. According to the invention, intelligent monitoring and linkage early warning in the steel ladle hoisting process can be realized.

The invention belongs to the technical field of automatic equipment, and particularly relates to a novel paper tube external inspection machine. The device comprises a machine case frame, an electric control placement space and a detection space are arranged in the machine case frame, the detection space is located below the electric control placement space, an electric control assembly is arranged in the electric control placement space, a feeding port and a discharging port are formed in the left end and the right end of the detection space, and one end, extending out of the feeding port, of the detection space is arranged in the detection space. According to the surface light type multi-angle defect detection device, through cooperative work of the camera detection assembly on the vertical guide shaft and the surface light source auxiliary assembly on the transverse guide shaft, multi-angle and all-directional detection can be carried out on defects such as pits and pits in the surface of a paper tube. The area light source lamp set provides uniform and stable illumination, the detection camera shoots images from multiple angles to form a stereoscopic image structure, and compared with a traditional single-angle detection mode, the detection precision is greatly improved, micron-sized tiny defects can be detected, and missing detection and false detection are effectively avoided.

The invention discloses a multi-modal information fused high-myopia retina image classification method and system. The method comprises the following steps: acquiring an OCT image of a to-be-classified high-myopia retina and structured numerical data corresponding to the image; the OCT image is preprocessed, the preprocessed image is input into an image encoder for high-dimensional semantic representation learning, and overall semantic vector representation of the image is obtained; converting the structured numerical data into a medical language description text, and inputting the medical language description text into a text encoder for deep semantic modeling to obtain overall semantic vector representation of the text; inputting to a multi-modal fusion module, carrying out feature interaction and fusion through a bidirectional cross attention mechanism, and generating a fused multi-modal feature; and outputting a retina splitting stage category corresponding to the OCT image through a classification module. According to the method, the OCT image, the structured numerical data and the split staging definition text are utilized to perform multi-modal feature fusion, so that the accuracy of image staging recognition is improved.