612 results about "Microscopic image" patented technology

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 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 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 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 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.

A deep learning-based super-resolution fluorescence lifetime imaging microscopy (SR-FLIM) method includes the steps of: S1, performing fluorescence microscopic imaging on a sample to obtain confocal intensity images and stimulated emission depletion (STED) intensity images at a same location; S2, co-registering the acquired confocal and STED intensity images; S3, pairing the co-registered confocal and STED intensity images as input (Input) and ground truth (GT) to assemble a dataset; S4, partitioning the dataset into training and validation sets following a predefined ratio; and S5, constructing a network, and selecting hyperparameters and an optimizer. This method may achieve SR-FLIM within a conventional confocal FLIM system, surpassing spatial resolution limitations of FLIM, breaking through resolution barriers of conventional optical microscopy, while preserving normal fluorescence lifetime characteristics of fluorescent probes.

The invention discloses a living cell super-resolution imaging method and system based on self-supervised learning. The method comprises the following steps: S1, obtaining a fluorescence microscopic image through a standard fluorescence microscopic system; s2, for the acquired single noise image, generating positive and negative sample pairs of self-supervised training data through an autonomously designed and optimized self-supervised strategy; s3, constructing a front denoising neural network and a rear deconvolution network, and performing network training; and S4, inputting a newly obtained noise image into the trained front denoising network to obtain a denoised image, and inputting the denoised image into the rear deconvolution network to obtain a final super-resolution reconstructed image. According to the invention, the standard fluorescence microscopic system and the self-supervised denoising processing module are combined, so that high-quality denoising and super-resolution reconstruction of the fluorescence microscopic image can be realized in a low-photon signal scene.

The invention discloses a multi-mode ultrasonic microscopic image contrast enhancement method fused with expert priori knowledge, and the contrast, definition and detection reliability of an ultrasonic microscopic image are improved. The method comprises the following steps: a multi-modal feature coding module based on a CLIP framework extracts feature representations of an ultrasonic microscopic image and an expert cue word, and maps the feature representations to a unified semantic space through a cross-modal alignment mechanism; designing a semantic guidance prompt module, constructing semantic elements which highlight detection demand guidance, and combining an image-quality description sample to carry out few-sample fine tuning so as to enhance the response capability of the model to specific semantics; an image enhancement and reconstruction module is designed, a coding-decoding structure and a cross-layer feature connection mechanism are adopted, contrast enhancement, detail reconstruction and noise suppression are realized under the guidance of a CLIP semantic vector, self-learning correction is carried out based on a standard grooving plate to improve contrast performance, and optimization training is carried out in combination with structural similarity loss and semantic consistency loss.

The invention relates to the technical field of industrial detection, and discloses a lithium niobate metasurface defect intelligent identification method based on reactive ion beam etching, which systematically solves the problem of scarcity of experimental data through physical driving simulation and a physics-based rendering algorithm, and provides large-scale field specific training data for a deep learning model. The bidirectional coupling simulation model generates defect morphology prediction data conforming to an etching dynamics law based on real process parameters and material parameters, the prediction data is converted into a synthetic image with real microscopic image statistical characteristics by a physical rendering algorithm, and the quality and consistency of the synthetic data are ensured by an automatic labeling and statistical verification process. According to the method, the number of samples of the ternary association database is multiplied, and extreme process conditions and rare defect modes which are difficult to obtain through experimental collection are covered.

The invention relates to the technical field of image enhancement, in particular to a rhizosphere microflora distribution analysis method based on image enhancement, which comprises the following steps: acquiring a continuous microscopic image sequence of a rhizosphere microenvironment, converting the continuous microscopic image sequence into a gray data space, and constructing a standard input frame set for optical flow calculation. According to the method, the stability score of each feature is calculated by using the signal-to-noise ratio stability measurement function, and the long-life-cycle feature is screened and reserved, so that the random imaging noise of the short life cycle can be filtered fundamentally, and the topological structure of the target community is obtained; and mapping the topological structure back to an original image space by using an inverse persistence mapping algorithm to be fused with a continuous microscopic image sequence, and finally, remarkably enhancing weak biological signals while keeping original form details, thereby realizing high signal-to-noise ratio analysis of microflora spatial and temporal distribution in a complex rhizosphere environment.

The invention discloses a food colony detection and analysis method and system based on artificial intelligence, and the method comprises the steps: carrying out gradient elution-layered filtration pretreatment on a to-be-detected food sample to generate an impurity-removed colony sample; synchronously performing bacterial colony microscopic image acquisition and near infrared spectrum data acquisition on the impurity-removed bacterial colony sample to generate initial multi-modal data; performing space-time calibration processing on the initial multi-modal data to generate multi-modal data after space-time calibration; performing multi-dimensional bacterial colony feature extraction and screening processing on the multi-modal data after space-time calibration to generate a strong correlation feature subset; performing AI double-branch attention fusion analysis processing on the strong correlation feature subset to generate a bacterial colony classification result and a bacterial colony concentration estimated value; and performing multi-reference-system correction processing on the bacterial colony classification result and the bacterial colony concentration estimation value to generate a corrected detection result. According to the invention, the timeliness and reliability of the detection result are improved.

The invention relates to the technical field of optical detection and automatic control, and discloses a computer vision-assisted nano-particle recognition and micro-spectrum automatic measurement system, which comprises a sample positioning module, an imaging module, an optical measurement module and a processing control module, automatic focusing is realized on the basis of a definition evaluation value calculated by performing Fourier transform on the microscopic image; graying, binaryzation and morphological operation are carried out on the focused image so as to identify effective nano-particles and calculate centroid coordinates of the effective nano-particles; according to the coordinates of the center of mass, the X axis and the Y axis of the electric three-axis displacement table are controlled through closed-loop iteration movement, and the measured particles are accurately positioned to the center of the view field. According to the invention, the problems of low manual measurement efficiency, poor repeatability and complex operation in the prior art are solved, and high-flux, full-automatic and high-precision measurement of the spectral characteristics of the nanoparticles is realized.

The invention relates to the technical field of steel structure grain size analysis, in particular to a microscopic image segmentation result-based steel structure grain size analysis method, which comprises the following steps of: performing format conversion and size unification on a segmentation result of a deep learning segmentation model to generate a complete mask, and performing mask optimization on the complete mask; and generating a visual distribution diagram based on the distribution states of the ferrite masks and the pearlite masks in the complete masks, performing superposition and proportional scale labeling on the original microscopic images, and quantifying the distribution characteristics of the ferrite and the pearlite by combining a uniformity index and a spatial correlation index. According to the method, the ferrite and the pearlite in the microscopic image are accurately segmented through the deep learning segmentation model, the visual distribution diagram is generated on the basis, and quantitative analysis is performed on the tissue distribution in combination with the uniformity index and the spatial correlation index, so that quantitative and objective tissue structure characterization can be realized.

The invention relates to the field of computer vision, in particular to a bovine embryo quality evaluation method and system based on a time sequence microscopic image, and the method comprises the steps: collecting a microscopic image and a timestamp, carrying out the embryo segmentation and inter-frame registration of the image, and obtaining the frame quality weight, morphological characteristics, image block embedding and topological data; respectively calculating a topological distance and an optimal transmission distance, determining a change point generation event sequence in combination with a frame quality weight, and aligning and outputting a penalty term based on a time Petri network; inferring a stage sequence and a quality posteriori based on the event sequence and a penalty term, and constructing a multi-instance set based on an event time window to obtain a multi-instance learning score; and fusing quality posteriori and multi-instance learning score to obtain an evaluation result, calculating a conflict coefficient, and relocating an event time window when a threshold value is exceeded to update the evaluation result. According to the method, rating consistency, noise immunity and traceability are improved.

The invention provides an excrement sample microscopic image detection method and system based on morphological prior constraint, and the method comprises the steps: obtaining a global background region and a potential target candidate region through separation, so as to generate a standardized category morphological constraint parameter set; inputting the enhanced candidate region feature map into a preset category constraint double-branch instance segmentation network to generate an initial instance mask set corresponding to each independent visible component; performing closed-loop verification of mask integrity and category consistency on the initial instance mask set, performing iterative optimization correction on a mask region which is verified to be unqualified so as to generate an instance segmentation map, and synchronously performing spatial positioning and attribute marking on each independent mask region in the instance segmentation map; and based on an attribute marking result, performing multi-dimensional feature extraction and subdivision category identification on each independent mask region, and synchronously generating a corresponding excrement sample detection report. According to the invention, the precision, stability and complex scene adaptation capability of excrement microscopic detection can be greatly improved.

The invention discloses an amniotic epithelial stem cell screening method and device based on microscopic image feature extraction, relates to the technical field of microscopic image analysis and cell screening, and is used for solving the problem that group topology and internal activity in amniotic epithelial stem cell lossless screening are difficult to collaboratively quantify. The method comprises the following steps: firstly, performing enhancement processing on a phase difference microscopic image, and extracting a kernel centroid and a global gradient energy diagram; then, constructing a Voronoi jurisdiction, and coupling a cytoplasm texture entropy and a gray scale attenuation gradient to generate a cytoplasm compactness factor; meanwhile, calculating a cell gap topology consistency index based on mapping of the Voronoi boundary and the gradient map; and finally, weighting and correcting the compactness factor by using a topological index to obtain a dryness maintenance confidence coefficient, and outputting a target coordinate. Through dual verification of geometric topology and physical gradient, combined constraint screening of internal and external characteristics of cells is realized.

Disclosed in the present invention are a conical optical illumination-based reflective scanning super-resolution optical microscopy system and method. The system comprises a sample displacement module, an optical microscopic imaging module, an annular focused beam generation module, a beam scanning module, a super-resolution focused illumination and collection module, a super-resolution optical imaging module and a computer. By means of using the advantages of annular focused beam illumination, high focusing efficiency of traditional lenses, and high beam scanning speed, high-efficiency super-resolution focused illumination with an extended depth of focus is achieved, and further traditional optical lenses can be used to achieve fast super-resolution optical microscopic imaging; fast label-free far-field super-resolution two-dimensional microscopic imaging is implemented by means of two-dimensional beam scanning; by means of performing label-free far-field super-resolution two-dimensional microscopic imaging on samples at different axial positions, super-resolution two-dimensional microscopic images of different cross sections of samples are obtained, thereby achieving three-dimensional tomography. The present invention can be applied to fast label-free super-resolution microscopic imaging of biological samples, and can also be applied to other fields such as industrial super-resolution microscopic inspection.

A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results. Imaging visual field of the pathological section analyzer is hundreds of times that of the traditional optical microscope, a deep learning network is adopted to analyze pathological conditions of unstained pathological sections, so that the analysis process of pathological sections is simplified.

The invention relates to the technical field of image data processing, in particular to an image segmentation method and system applied to marine plankton. The method comprises the following steps: acquiring an in-situ microscopic image of marine plankton, carrying out binarization processing and connected domain analysis on the in-situ microscopic image, and screening out a biological adhesion block mass to be processed; synchronously extracting the edge contour of the biological adhesion block mass to be processed, and calculating a biological structure integrity score; selecting the candidate segmentation path with the highest biological structure integrity score as an optimal segmentation line; curvature smooth completion is carried out along the optimal segmentation line, and a single biological image is obtained; and inputting the single biological image into a pre-trained classifier to obtain a biological abundance data set. According to the method, high-precision automatic identification and abundance statistics of plankton in a complex marine environment are realized by screening and positioning the adhesion block mass, carrying out adaptive adhesion segmentation and combining multi-dimensional morphological feature classification.

The invention is suitable for the technical field of diffractive optics, and provides a self-adaptive chromatography grating dispersion correction method and system based on deep learning, and the method comprises the following steps: enabling an incident light beam to be diffracted through a chromatography grating, and obtaining a multi-stage chromatography diffraction light beam; dispersion angle compensation is carried out on the multi-stage diffraction beams through the plane straight stripe blazed grating; collimating the corrected light beam by using a double-barrel lens group and forming an image space telecentric light path to obtain an image space telecentric light beam; performing spectral beam splitting and aberration pre-correction on an image space telecentric beam through a micro-lens array, and then performing image acquisition to obtain a pre-corrected multi-spectral image; and based on a deep learning algorithm, carrying out real-time fusion processing on the pre-corrected multispectral image, dynamically adjusting the deflection angle of the microlens array, and outputting a chromatic dispersion corrected tomographic microscopic image. According to the method, the axial color difference and the transverse color difference of the chromatography system can be remarkably reduced, and the method is particularly suitable for application scenes needing wide spectrum coverage and high signal-to-noise ratio.

The present disclosure relates to an automatic quality control (QC) tool that may perform assessments for microscopic images and detect quality of focus. The disclosed QC tool may perform focus assessments by leveraging a deep learning (DL) model configured to take a microscopic image, segmented into patches, and generate a predicted probability for mapping each patch to a predefined label e.g., in-focus (InF), non-tissue area (NTA) or out-of-focus (OOF). The DL model may be trained on a dataset including NTAs along with different focus depths or z-offsets images labeled by assigning InF to the patches with z-offset between a determined z-offset margin and OOF otherwise. Based on a statistical technique, QC tool may score each label by aggregating the predicted patches associated with each label within a single microscopic image. Based on the aggregation scores, the OOF images may be assigned back to the scanning device for the rescanning.

The invention relates to a high-fidelity super-resolution reconstruction method and equipment for microscopic images, and belongs to the technical field of industrial automation and machine vision. The image super-resolution reconstruction network constructed by the method sequentially comprises a shallow feature extraction module, a plurality of high-fidelity groups connected in series, and a residual connection and reconstruction module. Wherein a high-fidelity block in the high-fidelity group integrates a frequency sensing high-frequency branch and a global sensing low-frequency branch adopting four-direction two-dimensional selective scanning state space modeling, and image details and a global structure are cooperatively enhanced in a spatial domain and a frequency domain through adaptive fusion of a cross-gating fusion module. A pixel domain and frequency domain joint loss function is adopted for training. According to the method, the reconstruction fidelity and the detail recovery capability of the microscopic image are remarkably improved while the linear calculation complexity is kept, and the method is suitable for industrial detection, biomedical imaging and other scenes.

The invention discloses a lightweight lithium mineral microscopic image real-time detection and instance segmentation method, and belongs to the technical field of computer vision and image segmentation, and the method comprises the steps: constructing a sample data set; wherein the sample data set comprises lithium mineral microscopic images and corresponding segmentation labels; the YOLO11-seg segmentation model is improved, and the improved YOLO11-seg segmentation model is obtained; the sample data set is used to train the improved YOLO11-seg segmentation model; and the trained and improved YOLO11-seg segmentation model is utilized to realize lithium mineral microscopic image real-time detection and instance segmentation. The model provided by the invention can be deployed in resource-constrained equipment and embedded equipment, and can realize accurate detection and segmentation of main components (lepidolite, quartz and feldspar components) in lithium ore.

The invention provides a depth information guided multi-focal-plane microscopic imaging method and device and a medium, and the method comprises the steps: carrying out the region division of a plurality of camera fields of view of a target sample, and obtaining a plurality of field of view regions of each camera field of view; collecting area depth values of the plurality of view field areas; according to the area depth values of the plurality of field-of-view areas, obtaining a field-of-view depth range of the corresponding camera field-of-view; acquiring a plurality of view field focal plane depths of each camera view field according to the view field depth range of each camera view field and a depth-of-field parameter of the microscopic equipment; performing image acquisition according to the plurality of view field focal plane depths of each camera view field to obtain a plurality of focal plane microscopic images of each camera view field; and according to the plurality of focal plane microscopic images of the plurality of camera fields of view, obtaining a panoramic depth fusion image corresponding to the target sample. The number of times of image acquisition can be effectively controlled, redundant image data is prevented from being generated, and the efficiency of image acquisition is improved.

The invention relates to the field of image recognition, and discloses a mineral structure type recognition method, system and device and a storage medium. The method comprises the following steps: collecting an optical microscopic image of a sample; detecting the optical microscopic image by adopting a preset image processing algorithm to obtain a network-shaped characteristic graph, a mineral total particle graph, a plurality of single mineral particle graphs and respective pixel point numbers; if the number of the pixel points of the network-shaped feature map is greater than 0, performing intersection comparison on the network-shaped feature map, the mineral total particle map and each single mineral particle map to obtain a total intersection, a plurality of sub-intersections and respective pixel point numbers; and calculating a first proportion based on the number of pixel points of the network-shaped feature map and the number of pixel points of the total intersection, calculating a second proportion based on the number of pixel points of each single mineral particle map and the number of pixel points of each sub-intersection, and further identifying the structure type of each mineral in the sample, including a network structure and a crushing structure. According to the invention, the recognition efficiency of the type of the reticular structure or the crushed structure of the mineral is improved.

The invention discloses a microscopic image morphology recovery method based on a multi-scale pyramid, and the method comprises the steps: S1, obtaining a multi-focus microscopic image sequence, and obtaining a full-focus image and an initial depth image according to pyramid transformation; and S2, inputting the full-focus image and the initial depth map into a depth map optimization model with minimum energy, guiding the initial depth map to optimize through the full-focus image, and solving the depth map optimization model to generate a de-noised optimized depth map with rich details. According to the invention, a confidence-guided space focusing evaluation function is provided, an energy-minimization depth map optimization model is combined, and a full-focusing image of a multi-focusing image sequence is introduced to smooth a background region of a generated depth map. The objective of the invention is to solve the problem that the traditional focusing morphology recovery algorithm has defects in the aspects of noise suppression and spatial continuity while keeping the edge of a depth map, and the experimental result shows that the algorithm provided by the invention is significantly improved in the aspect of depth value quality index SSM.

The invention discloses a Micro-LED array surface defect detection method based on an AFT-YOLO algorithm. The Micro-LED array surface defect detection method comprises the steps that a high-resolution microscopic image is acquired, and four types of targets (chip vacancy, surface foreign matter, circuit damage and chip incompleteness) are marked; adaptive weighted downsampling is introduced at a downsampling position of a step length 2 of a backbone and a neck of the YOLOv8 baseline so as to enhance fine granularity and small target perception; constructing a feature focusing-diffusion pyramid at the neck to realize cross-scale semantic consistent fusion; a task alignment dynamic detection head is adopted at a detection head, and shared convolution, deformable convolution and scale normalization alignment classification and regression optimization are carried out. According to the Micro-LED array surface defect detection method, mAP50 is about 96% on representative industrial data, reasoning is about 99 FPS, compared with a baseline model, the precision and the speed are both improved, and the Micro-LED array surface defect detection method is suitable for high-speed online AOI detection of a high-density Micro-LED array.

The application discloses a photoacoustic spectrum signal detection system and a detection method. The system comprises a signal acquisition device and a detection signal processing terminal. The signal acquisition device comprises an ultraviolet pulse laser, a first plano-convex lens, a second plano-convex lens, a focusing objective lens, a refractive prism, a helium-neon laser, an electric displacement platform, a polarization modulation assembly, a light splitting assembly and a differential detector. The photoacoustic spectrum signal detection system uses the specific absorption of cell nuclei to ultraviolet light, obtains a differential detection signal through the differential detector and generates an analysis spectrum. The light sensing assembly collects a light sensing signal of the sample and generates a microscopic image. The detection system can realize photoacoustic spectrum analysis of a cell nucleus level micro-sized absorber, thereby obtaining a high-resolution analysis spectrum and a microscopic image reflecting the physical properties inside the cell nuclei. The label-free and non-staining photoacoustic spectrum signal detection does not need staining and can save detection time, and the accuracy of cell detection on biological samples is improved.

This invention relates to the field of fabric printing simulation optimization technology, specifically a method and system for nylon fabric printing simulation design optimization based on finite element analysis. The method includes: acquiring microscopic images and physical property parameters of the nylon fabric; constructing a three-dimensional microscopic finite element model reflecting the yarn interweaving structure; selecting a printing area on the model surface and defining ink droplet ejection and ink fluid property parameters; using these parameters as initial conditions, employing a fluid-structure coupling algorithm to simulate the dynamic process of ink droplet impact, wetting, and penetration, acquiring three-dimensional ink distribution data and fabric stress-strain response data; extracting printing quality characteristic indicators, comparing stress-strain data with fabric deformation thresholds, and evaluating the impact of printing on fabric structural integrity; and generating an optimization and adjustment scheme for printing process parameters based on the evaluation results. This method improves simulation accuracy, achieves simultaneous evaluation of printing quality and fabric structural integrity, and optimizes printing process design.

The invention provides a sludge organic matter distribution visualization method and device. The device comprises a multi-mode imaging device, a data processing unit and a display terminal. The multi-mode imaging device is used for acquiring spectrum, fluorescence and heat distribution data of the sludge sample through a near infrared spectrum imaging module, a fluorescence microscopic imaging module and a heat imaging module; the data processing unit performs fusion analysis on the multi-modal data by using a deep learning algorithm to generate a three-dimensional visual model; the display terminal displays the result. According to the method, high-precision characterization and real-time monitoring of sludge organic matter distribution are realized through multi-modal cooperation and an intelligent algorithm, the problems of difficulty in visual display and low detection precision in the prior art are solved, and technical support is provided for optimization of a sludge treatment process.