3669results about "Acquiring/recognising microscopic objects" patented technology

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The invention discloses a traditional Chinese medicine identification and analysis system based on clustering analysis, and relates to the technical field of medicine analysis and identification, and the system comprises the following modules: a data acquisition module, which is used for obtaining traditional Chinese medicine multi-modal data and processing asynchronous stream data, the multi-modal data comprises microscopic images, spectral data, metabonomics data and electronic nose / tongue sensing data, and the step of processing the asynchronous stream data comprises timestamp marking, multi-device data calibration and feature caching. According to the traditional Chinese medicine identification and analysis system based on clustering analysis, through collaborative design of a multi-modal hypergraph space-time encoder and a drug property causal intervention mechanism, multi-dimensional and multi-level leap-type improvement of a traditional Chinese medicine identification technology is realized, and the core contradiction of disjunction of a traditional clustering algorithm and domain knowledge is solved; and a universal framework of cross-modal dynamic learning is provided for the field of medical informatics.

The invention relates to the technical field of medical image processing, and provides a medical image segmentation method based on wavelet enhancement and multi-scale feature fusion. According to the method, a CNN-Transform double-branch coding structure is combined, a multi-scale wavelet fusion module is provided, from the perspective of a frequency domain, Haar wavelet transform is adopted to extract an image high-frequency sub-band so as to enhance edge and texture detail expression, dynamic weighting is performed on different frequency band features through grouping convolution and a sub-band attention mechanism, and the discrimination capability is improved; meanwhile, a multi-scale cavity pyramid structure is fused in a spatial domain, and after cross attention dynamic fusion is introduced, a feature alignment mechanism of a wavelet domain and the spatial domain is established; and collaborative fusion of frequency domain and space domain features is realized. The method effectively improves the segmentation precision of the fuzzy boundary and the fine-grained structure under the complex background, has good universality and adaptability, and is suitable for various medical image segmentation tasks.

The invention relates to the technical field of cell culture data processing, in particular to a cell culture information data management method and system. The method comprises the following steps: performing culture environment multi-parameter high-frequency real-time monitoring on a cell culture environment box to obtain multi-dimensional cell culture environment time sequence parameters; performing culture environment key disturbance parameter screening on the multi-dimensional cell culture environment time sequence parameters to generate environment key disturbance parameters; acquiring historical cell culture batch data; based on historical cell culture batch data, carrying out multi-parameter coordinated regulation and control on the environment key disturbance parameters to obtain a cell culture environment regulation and control strategy; regulating and controlling the cell culture process according to the cell culture environment regulation and control strategy, and performing cell culture knowledge graph construction on historical cell culture batch data to obtain a cell culture management knowledge graph. According to the invention, intelligent management of cell culture data is realized through construction of the cell culture knowledge graph.

The invention provides a circulating tumor cell recognition model training method and system based on deep learning, and the method comprises the steps: obtaining a multi-channel pathological sample image set of circulating tumor cells, carrying out the cross-modal feature extraction of a multi-channel pathological sample image, generating a fusion feature vector containing the morphological features and microenvironment features of the cells, and carrying out the recognition of the circulating tumor cells through the fusion feature vector. A deep convolutional network with a multi-stage training strategy is constructed, network parameters of the deep convolutional network in the training process are verified and adjusted by adopting a hierarchical cross validation mechanism, and finally a final recognition model is generated based on the verified and adjusted network parameters. The final recognition model is configured to receive the clinical pathology image stream and output localization coordinates and classification confidence of circulating tumor cells. According to the method, a circulating tumor cell recognition result can conform to a cell space distribution rule and has clinical pathological diagnosis interpretability, and the problems of high false positive rate and insufficient cell subtype discrimination caused by neglecting of microenvironment interaction in a traditional method are solved.

A method based on a multi-task learning model comprises the following steps: acquiring and processing histopathological image data of gastritis through a data preparation and preprocessing step; feature extraction is performed by using a self-supervised learning pre-trained model, and image blocks are coded into high-dimensional feature vectors; and constructing a multi-task learning model, learning feature representation through a full connection layer module and an attention layer module, and outputting a classification result of each task. And carrying out model training and optimization by using an optimizer, adding multitask loss through a loss function, and dynamically adjusting the model performance. The trained model can automatically detect and grade gastritis, atrophy, acute activity, intestinal metaplasia and other pathological indexes, outputs a standardized evaluation result, and assists in pathological diagnosis. According to the method, a multi-task deep learning framework based on self-supervised learning pre-training is constructed, a traditional single-task modeling mode is broken through, deep learning framework design is driven through pathological index association, and accuracy and clinical practicability of non-neoplastic disease assessment of the stomach are remarkably improved.

The invention discloses an IG-MambaUNet image segmentation model, a model training method and an application method thereof, the IG-MambaUNet image segmentation model comprises an encoder and a decoder which are symmetrically arranged in parallel, the encoder comprises three first encoding modules and a second encoding module, the three first encoding modules are sequentially connected from top to bottom, the second encoding module is connected with the first encoding module on the bottommost layer, and the third encoding module is connected with the second encoding module on the bottommost layer. The input end of the first coding module on the topmost layer is connected with a first convolution and maximum pooling operation module, the first coding module is formed by coupling an IG-Mama module and a Patch Merging module, the second coding module comprises an IG-Mama module, the IG-Mama module comprises a local feature extraction module, a global feature extraction module and a gating attention module, and the global feature extraction module comprises a local feature extraction module, a global feature extraction module and a gating attention module. The decoder comprises a second coding module and a first coding module which are sequentially connected from bottom to top, and the output end of the first decoding module on the topmost layer is connected with a second convolution and maximum pooling operation module. According to the scheme, fine segmentation is realized, and the method can be suitable for automatic cell body segmentation of histological dyed ganglion images with complex textures and fine structures.

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 an alimentary canal pathological diagnosis visual language large model construction method based on reinforcement learning and application thereof, and belongs to the technical field of medical image processing. The method comprises the following steps: firstly, extracting pathological information through layout analysis and adaptive threshold processing, and recombining the pathological information into a structured data set containing an inference chain; secondly, a visual encoder and a multi-branch classifier are used for extracting features and confidence coefficients, and dynamic structured cue words are generated; and finally, inputting the image and the cue word into a multi-modal large model, carrying out supervised fine-tuning hot start, and carrying out reinforcement learning training by adopting a group relative strategy optimization algorithm in cooperation with a composite reward function containing format, semantics and diagnosis dimensions. The problems that a general model is prone to generating illusion in a pathological scene and lacks reasoning logic are solved, and the accuracy and logicality of pathological report generation are remarkably improved.

The invention provides a cell type and cell abundance identification method and system based on cross-modal training, relates to the field of image processing, and aims to solve the problems that the existing identification technology mostly depends on non-standard factors such as manual labeling position annotation information and the like, abundant morphological modes in a tissue pathological image are not fully utilized, and the identification accuracy is poor. And the identification reliability and accuracy are influenced. The method comprises the following steps: acquiring spatial transcriptomics data matched with pathological image-gene expression, and preprocessing the spatial transcriptomics data to obtain high-expression gene expression data, local image blocks, cell types and abundance tags; constructing a cross-modal joint representation learning model, and inputting high-expression gene expression data and local image blocks into the model for training; and predicting a to-be-predicted histological image based on the trained model. According to the method, the problems in the prior art are solved, the capability of predicting the cell abundance from the histological image is improved, and the spatial distribution of fine-grained cell types is fully revealed.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention relates to the technical field of data processing. The fermentation process intelligent regulation and control method and system based on multi-modal perception are provided, and the method comprises the following steps: carrying out image feature extraction processing on microorganism image data to generate a morphological feature vector, and carrying out metabolic feature dimension reduction processing on metabonomics data to generate a metabolic feature matrix; performing time sequence alignment processing to generate a fusion feature matrix, and performing abnormal marking processing on the metabonomics data to generate abnormal marking data; carrying out correlation intensity calculation processing on the morphological change of the microorganisms and the concentration fluctuation of the metabolites to generate a dynamic correlation intensity curve; constructing a cross-dimensional anomaly recognition model and a multi-modal collaborative prediction model, and generating a regulation and control parameter suggested value; the parameters of the multi-modal collaborative prediction model are updated through a feedback learning mechanism, the feature fusion weight of the fusion feature matrix is optimized, the accuracy of anomaly detection and regulation decision is improved, and the risk of stability fluctuation in the fermentation process is reduced.

The invention provides a pathological image visual positioning method and system, equipment and a storage medium, and belongs to the technical field of image recognition, and the method comprises the steps: extracting visual features based on a target pathological image, and determining a semantic feature vector and a knowledge feature vector based on first text description; the target pathological image is a pathological image to be subjected to target area positioning, and the knowledge feature vector is used for representing knowledge information associated with the content of the target pathological image; fusing the semantic feature vector and the knowledge feature vector to obtain a fused text feature; performing cross-modal fusion on the fused text features and the visual features to obtain fused multi-modal features, and obtaining fusion representation based on the fused multi-modal features; and based on the fusion representation, positioning a target area in the target pathological image through a multi-layer perceptron to obtain position information of a bounding box of the target area. The method can improve the capability of accurately and flexibly positioning the pathological image region level.

The invention discloses an intelligent stem cell extraction monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent medical monitoring. The stem cell extraction intelligent monitoring method and system based on the Internet of Things comprises the steps that S1, multi-source data in the stem cell extraction process is collected and preprocessed; s2, evaluating a modal response slope, sudden change delay and disturbance intensity, and dynamically adjusting a sampling strategy; s3, analyzing modal abnormal fluctuation sensitivity, and driving event anchor point judgment and trend identification; s4, judging a multi-source feature coupling state and a stability level, and triggering a visual mode; and S5, identifying trend abnormity, constructing a dynamic baseline, and executing semantic correction and path reconstruction. The core problems of feature mismatching and decision misleading caused by advanced image variation and chemical response delay in the prior art are solved, and the multi-modal fusion quality and the accuracy and stability of joint anomaly detection are remarkably improved.

The invention relates to a single cell image segmentation method based on a minimum circumcircle, which solves the technical problem of how to improve the precision, robustness and adaptability of an image-based single cell segmentation method, and comprises the following steps of: firstly, obtaining an original cell image, and secondly, converting the original cell image into a gray level image; preprocessing and binarization processing are carried out on the grayscale image to obtain a binarized image, a cell edge contour image is obtained through processing, a minimum circumcircle is drawn for each contour in the cell edge contour image to obtain a cell circumcircle image, and finally, the center of the minimum circumcircle is used as the center point of a cutting area to obtain a cell edge contour image. And determining the boundary of the cutting area by taking the background including the cells and within a certain range around the cells as a standard, cutting the cell circumcircle image, and finally obtaining a single cell image. The method is suitable for single cell identification and segmentation in a microscope image, and can be widely applied to the fields of cell biology research, medical diagnosis, drug screening and the like.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention relates to the technical field of medical image analysis, in particular to a CAR-T cell culture monitoring system based on image recognition, which comprises a cell region division module, a cell morphology analysis module, a cell behavior dynamic analysis module and a cell population collaborative monitoring module. According to the method, through cell image gradient intensity analysis, refined cell edge detection and dynamic adjustment of a segmentation threshold value, high-precision extraction of cell boundaries is ensured, noise interference is avoided, cell shapes, textures and geometric features are refined, the identification degree of morphological features is improved, and tiny changes of cell morphologies are accurately captured; dynamic tracking and trend evaluation are carried out on cell behaviors based on adjacent frame images, tiny dynamic changes such as cell division, aggregation and migration are accurately reflected, excessive smoothness in the dynamic process is avoided, cell population behavior monitoring is combined with interaction and corresponding speed calculation, the population synergistic effect change trend is comprehensively evaluated, and the cell population behavior monitoring effect is improved. And finer cell culture environment optimization and clinical research support are provided.

The invention discloses an intelligent traditional Chinese medicine quality detection method and system based on deep learning, and relates to the technical field of medicine health, a high-resolution industrial camera and a multispectral imager are used for collecting multi-angle RGB images and spectral images of different wavebands, and an oil cell aggregation area is identified based on the images. And multi-dimensional detection is carried out from the physical structure and microcellular level, so that the quality detection accuracy of the traditional Chinese medicine decoction pieces is guaranteed in multiple aspects. And deeply analyzing the distribution condition of the oil cells according to the identified oil cell aggregation region, and generating judgment information containing a distribution result and an acid range. The judgment information can accurately reflect the abnormal conditions of the to-be-detected traditional Chinese medicine decoction pieces distributed for the corresponding production places under the current batch, a more comprehensive and detailed basis is provided for traditional Chinese medicine quality evaluation, and through the analysis, the quality difference of the traditional Chinese medicine decoction pieces in different production places can be effectively distinguished, and a deterioration risk signal is generated according to a distribution result.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

The invention discloses an out-of-focus identification and restoration method for a microscopic system. The method comprises the following steps: 1, preparing a microscopic out-of-focus image data set and carrying out degradation modeling; 2, constructing an out-of-focus parameter prediction network based on multi-label parameter reasoning and microscopic system priori knowledge and an image restoration network based on a generative adversarial network, performing independent training, and then performing merging training through a circulation system formed by mutual connection based on predicted point spread function convolution and deconvolution operation; 3, model fine tuning based on a specific system and a new data set; and 4, performing model testing, performing large-view image sliding window detection and collage fusion, and explicitly outputting defocus parameters of the system during image shooting. The method can be applied to out-of-focus fuzzy recognition and restoration of microscopic imaging of various systems, and is beneficial to the accuracy of functions such as particle counting and particle size statistics of the systems, thereby further promoting the application of the full-depth-of-view microscopic system in biomedical detection.

The invention discloses a system and method for identifying microbial contamination in food and cosmetics based on AI identification, and the method comprises the steps: collecting to-be-detected food or cosmetics as a detection sample, and obtaining the bacterial colony growth state of microorganisms in the detection sample; the method comprises the following steps: acquiring a microbial growth image through microscopic imaging equipment, and preprocessing the microbial growth image to obtain a target area of a microbial colony; constructing an AI recognition model by adopting a convolutional neural network, and obtaining the species and quantity of microorganisms in the detection sample; parameters of the AI recognition model are adjusted in real time according to the types and the number of the microorganisms, a preset microorganism limited standard threshold value is combined, a visual analysis report is generated, the AI recognition model is combined with a Faster R-CNN target detection method, precise classification and number statistics of the types of the microorganisms are achieved, a complete microorganism pollution analysis report is formed, and the method is suitable for popularization and application. And the accuracy and scientificity of the final identification result are improved.

The invention discloses a thyroid tumor diagnosis method and system based on ultrasonic and cytological image conjoint analysis. The method comprises the following steps: converting a thyroid B ultrasonic image of a patient to be diagnosed into an image feature vector FUS; performing structured feature extraction on the thyroid cytological image of the patient to be diagnosed to obtain a structured feature vector FCYTO; the image feature vector FUS and the structured feature vector FCYTO are converted into a fusion Token sequence; and inputting the Token into a multi-modal feature fusion and prediction network based on a Transform architecture, and carrying out feature fusion and classification prediction so as to obtain the probability that the thyroid tumor of the patient to be diagnosed is malignant. The thyroid tumor diagnosis based on multi-modal fusion is carried out on the basis of ultrasonic and cytological images, so that the diagnosis accuracy is improved.

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 phytoplankton microscopic image classification model construction method, and belongs to the field of intelligent detection. For the problems of unstable microscopic image quality and small difference between phytoplankton classes, a double-branch ResNeXtViT model is provided: a ResNeXt branch enhances the texture feature extraction capability through a wavelet convolution attention module, and improves the adaptability to irregular shapes in combination with an adaptive convolution module; a Transform branch is used for capturing global context information; the feature fusion module integrates local details and a global structure. A combined data enhancement strategy including noise addition and color migration is adopted, and a joint loss function is designed to optimize intra-class compactness and local discrimination capability. Experiments show that the accuracy of the method on a Chaohu lake phytoplankton data set reaches 94.11%, which is superior to that of ResNet, ViT and other models, and the method can be deployed to an intelligent analyzer to realize rapid water quality monitoring.

This disclosure provides a novel method and system for characterizing morphodynamic profiles of objects, such as biological entities. This disclosure provides a shape, appearance, and motion (SAM) phenotype Observation Tool (SPOT). SPOT establishes a standardized SAM “phenome,” image descriptors resembling single-cell transcriptomes, to comprehensively quantify a cell's instantaneous state without prior knowledge. SPOT also establishes a standardized workflow for temporal analysis. SPOT is a generalist tool, applicable to any live-cell imaging and advances biomedical discovery through its standardized, unbiased, streamlined workflow to quantify phenotypic heterogeneity and predict phenotype-genotype-function coupling.

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 artificial intelligence and the technical field of semiconductor detection, and discloses a wafer defect identification method based on feature fusion, which comprises the following steps: acquiring a first type of image and a second type of image of a wafer detection area, and performing alignment processing; performing feature extraction on the first type image and the second type image to generate a first type feature and a second type feature; fusing the first type of features and the second type of features to generate fused features; filtering the fusion features, and screening target fusion features meeting a confidence threshold condition; and determining a defect position and corresponding process layer information based on the target fusion feature. According to the invention, features of different types of images are fused, so that the characterization capability of wafer surface defects is enhanced, and the recognition precision of a detection system is improved; by screening the target fusion features meeting the confidence threshold, false detection and missing detection are reduced, and the detection reliability is improved.

The invention discloses a tunnel crack identification method and system based on laser radar and unmanned aerial vehicle photographing, an electronic device and a storage medium, and the method comprises the steps: obtaining space geometric information, photographing the surface image information of the interior of a tunnel, and constructing a three-dimensional texture point cloud model; carrying out feature extraction on geometric morphology features and surface texture features of the three-dimensional texture point cloud model to obtain a preliminary fracture feature set; classifying and preliminarily screening tunnel internal texture features based on the preliminary fracture feature set to obtain a preliminarily screened fracture region set; expanding the fissure regions based on the primarily screened fissure regions to generate an expanded fissure region set; analyzing the point cloud density in the expanded fracture region set through a Gaussian mixture model, judging the depth and width features of the fracture, and obtaining a fine screening fracture feature set; and for the fine screening fracture feature set, adopting a probability optimization algorithm based on a Markov random field to carry out joint probability distribution modeling on geometric and textural features of the fractures so as to obtain a fracture identification result.

The invention relates to the technical field of image analysis, in particular to an automatic marking robot calibration method based on visual guidance, which comprises the following steps of: establishing an image set comprising different resolution levels based on operation site image data acquired by a marking robot, and performing corner detection and straight line segment detection on each level image in parallel. According to the method, through a parallel detection mechanism of a multi-resolution hierarchical image set, angular point and straight line segment features under different scales are synchronously extracted, and a multi-scale feature point set with high robustness is constructed in combination with cross-hierarchical coordinate stability measurement and response intensity quantification. And performing dynamic screening and grouping association on the feature points based on a preset geometric constraint condition, eliminating noise interference and false detection features, and generating a candidate calibration structure set with spatial consistency. Weighted contribution value fitting is adopted, cross-scale stability and detection confidence of feature points are integrated, and geometric accuracy and anti-interference capability of calibration reference point coordinates are improved.