1124 results about "Microscopic image" patented technology

The invention discloses an improved convolution neural network model for leukocyte calculation, and relates to the field of artificial intelligence and medical image processing. Aiming at the problems of inaccurate feature extraction, strong background noise interference and insufficient multi-scale feature fusion in a traditional leukocyte calculation method, the model realizes fine processing and feature enhancement of a medical image through a multi-level modular design; the image preprocessing module is used for completing white blood cell positioning and image adaptive division and enhancement; multi-scale edge, texture and gray features are fused by means of an image feature extraction module, and a composite feature vector is constructed; through multi-scale convolution, a double-layer attention mechanism and convolution layer calculation of the improved convolution neural network model module, precise focusing of white blood cell contours and cell nucleus features, background noise suppression and calculation efficiency optimization are realized; and finally, a calculation result is output through a visual terminal, so that the accuracy and robustness of leukocyte counting are remarkably improved, and efficient technical support is provided for medical microscopic image analysis.

The invention discloses a kit raw material quality detection method based on multi-modal data fusion, and relates to the technical field of automatic quality detection of chemical reagent raw materials. Comprising the following steps: S1, synchronously acquiring spectral data, microscopic image data and chemical detection data of a kit raw material through a spectral sensor, microscopic imaging equipment and a chemical sensor; s2, inputting the multi-modal data into a self-adaptive spatial-temporal feature calibration network, and analyzing local detail features and global time sequence features of the modal data through a multi-scale spatial-temporal feature extraction module; according to the kit raw material quality detection method based on multi-modal data fusion, by constructing a collaborative fusion system of an adaptive spatial-temporal characteristic calibration network and a layered information entropy screening mechanism, the core technical problems of misalignment of multi-modal data dynamic alignment and redundant interference accumulation are solved.

The invention discloses a microscopic image seamless splicing and enhanced reconstruction method based on deep learning, and the method comprises the following steps: S1, collecting a plurality of original images with overlapped regions, and recording the spatial position information and imaging parameters of the original images; s2, preprocessing the original image to generate a standardized image sequence; s3, inputting the standardized image into a structure perception feature extraction network, and extracting a feature map fusing textures and structures; s4, inputting the feature image and the original image into an image registration module; s5, inputting the registration image into the boundary attention splicing network; s6, inputting the seamless image into the residual hierarchy reconstruction network, and enhancing image details through hole convolution and multi-scale branches; s7, image quality evaluation is executed, and the structural similarity, the signal-to-noise ratio and the edge retention rate are calculated; and S8, constructing a training set and carrying out end-to-end training optimization based on a joint loss function. According to the method, a multi-module deep network is fused, and seamless splicing and high-quality enhanced reconstruction of microscopic images are realized.

The invention discloses a pathological feature recognition and negative elimination method based on microscopic imaging. The method comprises the following steps: S1, collecting a pathological section image and digitally generating original microscopic image data; s2, preprocessing the original microscopic image; s3, constructing a pathological image recognition network fusing converter coding and a gating dynamic receptive field mechanism, and outputting pathological feature vectors; s4, performing context modeling through an attention guidance and category perception decoder, and outputting an image classification result; s5, constructing a discriminant boundary separation model based on positive and negative sample embedding, and performing negative exclusion judgment; s6, performing confidence coefficient weighted evaluation in combination with the uncertainty and the boundary distance, setting a dynamic threshold value, and screening out low-credibility samples; and S7, coding the classification result and the negative label into structured data, and sending the structured data to a diagnosis auxiliary system. According to the method, multi-scale modeling and a negative screening mechanism are fused, and intelligent recognition and credible diagnosis output of the pathological image are realized.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention discloses a titanium alloy microstructure recognition method and system based on deep learning and a medium, and belongs to the technical field of image recognition, and the method comprises the steps: carrying out the serialization processing of a preprocessed microscopic image based on a trained image recognition model, inputting the serialized microscopic image into a Transformer encoder, and carrying out the recognition of a titanium alloy microstructure. And the features extracted by the Transform encoder are respectively output to a pixel-level phase classification branch and an image-level tissue classification branch, and phase classification and tissue classification are correspondingly carried out. The tissue type probability output by the image-level tissue classification branch is used as a gating signal, and the corresponding segmentation channel of the pixel-level phase classification branch is correspondingly enhanced or inhibited; based on an interactive attention mechanism, the classification semantics of the image-level organization classification branches are reversely projected to the middle layer of the segmentation branches of the pixel-level classification branches. According to the method, the microscopic structure analysis efficiency and the standardization degree are remarkably improved, and good practicability is achieved.

The invention relates to the technical field of hot galvanizing, and discloses an automobile hot galvanizing process parameter optimization method and system based on artificial intelligence, and the method comprises the steps: carrying out window division processing on initial cooling parameters, constructing a cooling rate matrix, and finely depicting heat exchange states of different cooling sections; the problem of insufficient cooling control precision under different cooling speed alternating conditions is solved, a microscopic image is combined to extract the grain main axis direction, calculate the mean value and range of included angles, generate a consistency index set and quantitatively describe the dispersion problem in the grain growth direction, the cooling speed and the consistency index are jointly matched, and a cooling speed structure offset matrix is constructed. The method comprises the following steps: identifying a space mutation region, accurately positioning abnormal coarse or abnormal arrangement positions of crystal grains, and finally screening target cooling parameters according to a cooling index value corresponding to an abnormal region to realize local adjustment of a cooling process, thereby reducing the abnormal growth rate of the crystal grains and improving the direction consistency of the crystal grains.

The invention discloses a path planning method for a palletizing robot based on a multi-path convolutional neural network, and relates to the technical field of convolutional neural networks, and the method comprises the steps: employing the three-source data of a quality inspection report image, a damaged video and a microscopic image, extracting features through a parallel convolutional neural network, generating a dynamic maximum curvature constraint value after the fusion, and obtaining the path planning of the palletizing robot. And self-adaptive path planning of material characteristics is realized. And nodes conforming to curvature constraint are screened based on a probability route map (PRM) to generate a track, and the vibration spectrum energy of the mechanical arm is monitored in real time. When vibration exceeds a dynamic safety threshold value, subsequent path constraint is tightened in real time through a curvature penalty factor, and the damage risk is reduced by 90%; a closed loop of material analysis, path generation, vibration feedback and parameter correction is established, and balance of safety and efficiency is achieved in a high-speed stacking scene.

The invention provides a microscopic image representation method based on a dynamic pluggable mask self-supervision encoder. The microscopic image representation method comprises the following steps: step 1, establishing a network based on the dynamic pluggable mask self-supervision encoder and an MAE decoder; 2, constructing a loss function to train the network, and carrying out the combined optimization of image reconstruction and classification; step 3, using a pre-training dynamic pluggable mask self-supervision encoder to extract deep feature representation of the microscopic image; and further connecting a decoder of a downstream task, and carrying out mineral microscopic image super-resolution reconstruction, inclusion automatic analysis, intelligent diamond cleanliness rating and general cell segmentation. According to the method, the feature extraction quality of the field with the data volume disadvantage is improved through the pluggable module, and the problem that most categories of cross-field data sets are unbalanced is effectively solved. Meanwhile, compared with the addition of branches, the pluggable module reduces the extra calculation overhead brought by the addition of a structure by 50%, and endows the model with extremely strong domain mobility.

The utility model discloses a micro-fluidic chip imaging positioning device, and belongs to the technical field of micro-fluidic chip positioning. According to the technical scheme, the system comprises a microcosmic illumination module, a microcosmic imaging module, a chip moving and macroscopic illumination module, a macroscopic imaging module and an optical path multiplexing module; the microscopic illumination module comprises a fly's-eye lens assembly, the fly's-eye lens assembly comprises two fly's-eye lenses, the optical axes of the two fly's-eye lenses are parallel to each other, and the focus of each lens array element in one fly's-eye lens coincides with the center of the lens array element at the corresponding position of the other fly's-eye lens. The micro-fluidic chip clamp is applied to the aspect of micro-fluidic chip imaging positioning, overcomes the defects caused by an existing micro-fluidic chip clamp, solves the technical problems of universality, automation and high precision of micro-fluidic chip positioning, and has the characteristics of rapidness, automation, high precision and universality.

The invention relates to the technical field of medical image processing and computer vision, in particular to a parasitic ovum microscopic image detection method and system based on polymorphic prior, and the method comprises the steps: obtaining a to-be-detected microscopic image, and carrying out the feature extraction of the to-be-detected microscopic image through a convolutional neural network, and obtaining an initial feature map; constructing a polymorphic convolution kernel library based on preset biological morphological characteristics of the parasitic ova; performing deep convolution and feature fusion operation on the initial feature map by using a polymorphic convolution kernel library to generate a space attention map; performing feature enhancement processing on the initial feature map by using the spatial attention map to obtain an enhanced feature map; and performing bounding box regression and category prediction on the enhanced feature map to obtain a parasitic ovum detection result. According to the method, morphological priori and attention mechanisms are introduced, so that the problems of egg form similarity, background interference and the like are solved, accurate and robust automatic detection is realized, and the clinical diagnosis efficiency is remarkably improved.

The invention provides a carbon fiber composite material microcrack image segmentation method based on a two-stage super-resolution dynamic attention network, and the method comprises the steps: collecting a microscopic image of a to-be-detected carbon fiber composite material, and carrying out the preprocessing of the image, so as to improve the discrimination between a crack and a background; scanning the preprocessed image by adopting a multi-scale sliding window, carrying out microcrack feature extraction in combination with morphological Top-hat transformation, and positioning a candidate region with microcracks; cutting an image corresponding to the candidate region into sub-images, inputting the sub-images into a pre-trained super-resolution reconstruction network, and amplifying the sub-images to 2-8 times of the original size to obtain a high-resolution candidate region image; inputting the super-resolution enhanced candidate region image into a pre-trained convolutional neural network model for pixel-level crack segmentation to obtain a segmentation result of cracks in the candidate region; and mapping the segmented micro-crack region back to an original image coordinate system, marking the position and shape of the crack on the original image, and outputting a final micro-crack segmentation result.

The invention relates to the technical field of image processing, and discloses a stem cell microscopic image feature extraction method and system. The method comprises the following steps: carrying out segmentation processing on a stem cell delay 3D confocal microscopic image through a chromatin texture sensitive adaptive threshold segmentation algorithm to obtain a nucleoplasm separation mask image; performing feature extraction processing on the marker fluorescence image according to the mask image to obtain a radial distribution feature vector and a pluripotency maintenance index; performing time sequence quantification processing on the state change sequence based on the index to obtain differentiation track feature data; performing spatial feature extraction processing on the subcellular organelle region through a topological graph construction algorithm to obtain a spatial correlation network graph; and performing feature fusion processing through a multi-layer perceptron fusion algorithm to obtain a stem cell image feature classification result. The accuracy of stem cell state recognition and the reliability of differentiation trajectory prediction are improved.

The invention discloses a probiotic packaging stability control method based on deep learning, and the method comprises the following steps: collecting multi-source data in a packaging process, and carrying out the preprocessing of the multi-source data; performing time sequence feature analysis on the standardized structured data set, and extracting multi-scale time sequence features; performing target segmentation and multi-dimensional structure feature extraction on the standardized microscopic image data set; feature fusion is carried out, a probiotic packaging stability feature space is constructed, and a stable feature vector is generated; inputting a time sequence prediction network, and carrying out time sequence modeling and prediction processing; performing control parameter adjustment and constraint optimization based on a difference value between a prediction result and stability reference data; and executing and updating the packaging control adjustment scheme according to feedback to complete a packaging control closed loop. According to the method, deep learning and multi-source data analysis are fused, intelligent prediction and adaptive control of the probiotic packaging process are realized, and the method has the advantages of high stability and high precision.

The invention discloses an all-optical three-dimensional scanning confocal fluorescent microscopic imaging device and an implementation method thereof. According to the invention, through a deep learning driven adaptive regulation and control method, CNN is adopted to process spatial distribution data and dynamically regulate and control a phase hologram and the light intensity and phase compensation of a laser, so that the focal point of exciting light is subjected to aberration-free axial displacement, a bidirectional parallel optical scanning track of a two-dimensional scanning system is optimized, and all-optical three-dimensional scanning is realized; lSTM and TCN are combined to obtain a time sequence dependency relationship, a laser light source, an adjustable diaphragm, an electric focus-adjustable lens and a photoelectric detector are integrally controlled, efficient synchronization and automatic operation and high-speed axial focusing adjustment are realized, errors and time sequence mismatch are eliminated, optical characteristics of different samples and environmental interference are automatically adapted, and high-quality imaging is kept. The robustness and the applicable scene range of the system are improved; the method is used for model biological embryo real-time tracking, intracellular signal molecule dynamic visualization, cell membrane protein migration and aggregation observation and intracellular organelle interaction tracking.

The invention relates to the technical field of farmland fertilization optimization, and discloses a farmland fertilizer application amount analysis and diagnosis method based on machine learning. The method comprises the following steps: firstly, acquiring near infrared spectrum data and surface microscopic image data of farmland soil, and generating a soil comprehensive risk area; analyzing the change gradient of the migration rate of the nitrogen element in the region to evaluate the offset state, and analyzing the interaction strength of the phosphorus element and the potassium element to evaluate the asymmetric coupling state of the phosphorus element and the potassium element; calculating the farmland fertilizer demand degree according to the evaluation result; analyzing the matching deviation between the operation behavior of the peasant household and the fertilizer demand degree in combination with historical fertilization operation records, and generating an operation deviation value; a fertilization optimization strategy is generated in combination with the fertilizer demand degree and the operation deviation value, and execution parameters of the fertilizer applicator are dynamically adjusted through a self-adaptive control algorithm. According to the method, the multi-dimensional information of the soil can be synthesized, the fertilizer demand condition is accurately evaluated, the fertilization strategy is optimized, and the fertilization operation is dynamically adapted.

The invention discloses a multi-mode image information fusion method based on deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining image data of an optical microscopic mode and an electronic microscopic mode, and carrying out the spatial resolution alignment; a quantum entangled state light field coding method is adopted for the optical microscopic image data to generate quantum enhancement features, and topological persistent coherence analysis is performed on the electronic microscopic image data to extract multi-scale structural features; the features are mapped to a tensor space, cross-modal feature fusion is carried out through a tensor ring decomposition method of dynamic rank adjustment, a joint representation tensor is generated, physical constraint reconstruction is carried out on the joint representation tensor, and a super-resolution fusion image is generated. Through fusion of quantum enhancement features and multi-scale topological features, tensor ring decomposition of dynamic rank adjustment and Tucker decomposition dimension reduction compression, efficient fusion of high-resolution images is achieved, calculation redundancy is reduced, and processing efficiency is remarkably improved.

The invention relates to the cross technical field of artificial intelligence and mineralogy, and discloses an AI mineral automatic identification method based on an optical microphotograph, comprising the following steps: step S1: data acquisition and preprocessing; s2, data classification and labeling: constructing a rock microscopic image data set by adopting a two-stage labeling process; s3, building and training a deep learning model; s4, model optimization and quantitative feature learning; s5, developing a software platform; and S6, hardware system combination and full-process automation are carried out. In the aspect of operation convenience, a full-automatic slice scanning hardware system is constructed and combined with an intelligent software platform, so that full-process automatic operation from automatic rock slice scanning, automatic mineral analysis to automatic PDF report generation is realized, manual intervention is greatly reduced, the operation difficulty and complexity are reduced, and the working efficiency is improved. And even an operator without rich experience can easily complete complex mineral identification work.

The invention relates to an intelligent metallographic defect detection system and method based on improved YOLOv11, and the system achieves the automation of a whole process from the collection of a microscopic image of a metal material to the precise recognition of a defect through the integration of a metallographic image collection subsystem, a rotating frame marking and enhancing subsystem and an improved neural network processing subsystem. A rotating target detection mechanism is introduced to adapt to a tilt defect form, a progressive data enhancement strategy is designed to strengthen small target feature learning, and a replacement feature extraction module and an embedded attention mechanism are adopted to optimize a YOLOv11 network structure, so that the industrial real-time performance is ensured finally, meanwhile, the metallographic defect detection precision is improved, and the high-precision quality inspection requirement is met. The method not only solves the key technical bottlenecks of difficult inclined defect positioning, difficult small target detection, difficult model deployment, weak anti-interference capability and the like in the existing metallographic defect detection, but also realizes industrial-grade efficient and automatic metallographic defect detection, and has wide application prospects and popularization values.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention discloses a time domain compression femtosecond holographic microscopy reconstruction method based on spatial domain and frequency domain joint learning. Constructing a time domain compression femtosecond holographic microscopy system for carrying out multi-moment information time domain compression coding on a measured sample and generating a single-frame snapshot measurement graph; constructing a joint spatial domain-frequency domain learning end-to-end reconstruction model comprising a spatial frequency domain interaction module; training the model by taking a dynamic micro-nano structure hologram sequence generated by simulation as a training set until the joint spatial frequency domain loss function is converged; inputting a single-frame snapshot measurement image generated by a microscopic system into the model for processing to obtain a reconstructed hologram frame sequence; and performing frequency spectrum extraction and phase unwrapping processing to generate a dynamic three-dimensional scene video so as to realize time domain compression femtosecond holographic microscopy reconstruction. According to the invention, both the hardware imaging rate and the algorithm reconstruction precision are considered, and high-speed, high-resolution and low-cost four-dimensional holographic microscopic imaging can be realized under the conditions of extremely low data bandwidth and a common CMOS camera.

The invention discloses a method and system for recognizing the number of bacillus in a gynecological micro-ecological microscopic image, and belongs to the technical field of image recognition and micro-ecological analysis. Performing image enhancement processing on the image to improve the definition of the target area; pixel-level segmentation is carried out on the enhanced image based on the trained image segmentation model, and a suspected bacillus target area set is extracted; extracting a morphological characteristic parameter set for each target area; screening the suspected areas by combining a bacillus morphological feature discrimination model obtained by clinical labeling sample training, and removing false targets; counting the number of the effective areas, estimating the number of bacilli in the adhesion areas by adopting a skeleton endpoint analysis method, and finally outputting the total number of bacilli in the image; the method has the characteristics of high precision, high robustness and high automation degree, and is suitable for intelligent identification of the micro-ecological structure in the gynecological microscopic image.

The invention relates to a mitochondrial microscopic image modeling method based on a super-resolution generative adversarial network, and belongs to the field of medical artificial intelligence. The method comprises the following steps: obtaining a mitochondrial microscopic image, and constructing a MitSuper data set; preprocessing the images in the training set; constructing a mitochondrial microscopic image reconstruction model, wherein the model comprises a first microscopic feature extractor, a second microscopic feature extractor, a cross-scale feature extraction module, an adaptive noise suppressor, hybrid cross attention, a super-resolution encoder and a super-resolution decoder; training the model by using the training set; optimizing the mitochondrial microscopic image reconstruction model through a loss function, and adjusting model parameters by using an Adam optimizer to obtain a trained model; and a mitochondrial microscopic image in a test set is input into the trained model to obtain a super-resolution mitochondrial modeling image. According to the invention, the super-resolution modeling level of the mitochondrial microscopic image can be improved.

The invention provides a deep learning-based junction point relation network training and cell occlusion judgment method, which comprises the following steps of: performing instance segmentation on a cell microscopic image to obtain a cell instance mask, and extracting a cell boundary based on the cell instance mask; performing junction point detection on the cell boundary to obtain cell shielding junction points, and classifying the cell shielding junction points into cell T-shaped shielding points, cell Y-shaped convergent points or cell X-shaped cross points; cutting the local image patch by taking the cell shielding junction point as a center, and constructing a multi-channel sample containing the local image patch; adopting a unified relationship label to label the multichannel sample, wherein the unified relationship label comprises a shielding relationship, a same-layer relationship and an unjudgeable relationship; training a deep learning network by using the labeled multi-channel sample, wherein the deep learning network outputs probability distribution of a junction point relationship category; and inputting a multi-channel sample of a to-be-detected cell microscopic image into the trained deep learning network, and outputting a junction point relationship category and a confidence coefficient thereof.

The invention relates to the technical field of digital core modeling and artificial intelligence modeling, in particular to a weathered sandstone microscopic image generation method based on a conditional generative adversarial network, which comprises the following steps: acquiring and analyzing sandstone samples with different weathering degrees to construct a weathered sandstone multi-scale database; performing weathering geological knowledge extraction on the weathered sandstone multi-scale database to construct a sandstone feature tag set; and training the conditional generative adversarial network by using the sandstone feature tag set to obtain a weathered sandstone microscopic image generation model, and generating a weathered sandstone microstructure image with physical consistency by using the weathered sandstone microscopic image generation model. Therefore, the problems that in existing weathered sandstone physical and mechanical property modeling, the depth incidence relation between the microstructure and the macro-mechanical property is difficult to reveal, and a traditional modeling method is difficult to effectively fuse the advantages of a weathering geological mechanism and modeling generation, so that a reconstruction result lacks physical consistency and weathering response rationality are solved.

The invention provides a head and face tumor screening and classifying method and system based on artificial intelligence. The method comprises the following steps: acquiring texture distribution data of a target area through an ultrasonic imaging device, dividing wavebands of multispectral scanning to correspond to different spectral characteristics, and generating elastic modulus data of a tumor surface through strain field inversion based on multi-modal microscopic image data; and inputting the multi-modal microscopic image data, the texture distribution data and the elastic modulus data into a hybrid model formed by a lightweight convolutional neural network and a feature fusion module, performing cross-modal association mapping on spectral features, spatial features and physical features through a multi-channel attention mechanism, outputting classification features after cross-modal association mapping, and performing classification on the classification features after cross-modal association mapping. And generating a screening classification identifier for indicating benign and malignant classes of the tumor. According to the technical scheme provided by the invention, the accuracy of benign and malignant tumor screening is intelligently improved through cross-modal association mapping.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention relates to a sperm cell analysis and diagnosis system based on a multi-modal large language model, which comprises a multi-modal data co-processing unit, a cross-modal semantic alignment module, a dynamic diagnosis decision engine and a self-adaptive evolution system, the four-dimensional data processing module is used for synchronously processing microscopic images, motion trail videos, biochemical detection data and four-dimensional input data of medical record texts and comprises a feature selector based on a gating attention mechanism. According to the sperm cell analysis and diagnosis system based on the multi-modal large language model, quantitative analysis of sperm movement chaos features is realized for the first time, a nonlinear dynamic evaluation standard is established, a cross-modal knowledge distillation and meta-learning migration framework is developed, a data annotation dependence bottleneck is broken through, and an interpretable clinical decision support system is constructed; dynamic updating and probabilistic suggestion of diagnosis rules are achieved, semantic analysis of single-cell multi-omics data is achieved, and molecular mechanism research results are converted into clinically available knowledge.

The invention provides a comprehensive evaluation method for dissemination characteristics of minerals in coal of different particle sizes, and relates to the field of coal quality analysis. The method comprises the following steps: collecting coal analysis samples of different size fractions, and preparing coal polished sections of all the size fractions; the method comprises the following steps: collecting microscopic images, and constructing a representative microscopic image sample database of each size fraction; carrying out size fraction judgment, generating a component segmentation image on a single particle and a component segmentation image on a microscopic image, and extracting dissemination characteristic parameters of mineral substances in coal of each size fraction; key parameters are screened out, and comprehensive evaluation is carried out on the dissemination characteristics of the minerals in the coal of different size fractions. According to the method, an image processing technology and multivariate statistical analysis are combined, and qualitative and quantitative analysis of the dissemination characteristics of the minerals in the coal is achieved. Compared with an existing microscopic image analysis method, the method has the advantages that the human subjective influence and the labor intensity are remarkably reduced, and the analysis result has higher objectivity and reproducibility.

The invention relates to the field of image processing, and provides a seed multi-modal microscopic image acquisition method, device and equipment and a storage medium, and the method comprises the steps: carrying out the region segmentation of an obtained hyperspectral image of a plurality of seeds, and obtaining a single-seed region image; performing data extraction on the single seed area image to obtain spectral data of each seed; the spectral data comprises a seed identifier and a reflectivity value of each wave band; segmenting and extracting the acquired scanning images of the multiple seeds to obtain single seed scanning slices; performing three-dimensional reconstruction on the binary mask image to obtain seed three-dimensional physical structure information; the binary mask image is obtained by performing pixel classification processing on the single seed scanning slice. The method provides a basis for comprehensively and deeply analyzing chemical components and internal structures of the seeds, and improves the efficiency of seed detection and analysis.