994 results about "Semi-supervised learning" patented technology

A small sample steel defect detection method based on an attention feature pyramid mechanism, comprising: first acquiring initial multi-scale feature maps by means of a convolutional neural network, then by means of a top-down and transverse connection mode, using an improved multi-scale attention network to process the initial multi-scale feature maps one by one to obtain intermediate multi-scale feature maps, then by means of a bottom-up and transverse connection mode, using the improved multi-scale attention network to process the intermediate multi-scale feature maps one by one to obtain final multi-scale feature maps, and finally outputting a final classification result; using a pseudo-label semi-supervised learning algorithm to train a detection network model, using a locally linear embedding algorithm to perform dimension reduction and visualization processing on high-level features of training output results, screening for mistakenly labeled and incorrectly labeled images and removing labels, then putting the images into unlabeled image data, and performing re-training to generate pseudo labels so as to expand a training set until the training requirement is met; and inputting an image to be detected into a trained detection network model for identification.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention relates to dialect speech synthesis, in particular to a semi-supervised dialect emotion speech synthesis system based on hybrid experts, which comprises a text analysis module for preprocessing an input dialect text and generating a text representation vector through feature extraction and feature fusion; the mixed expert module is used for acquiring dialect acoustic features, rhythm features, emotional features and general acoustic features; the dynamic routing module is used for realizing intelligent cooperation among experts through a task-aware soft routing algorithm; the semi-supervised learning module is used for training supervised learning by using the marked dialect emotion voice data and training self-supervised learning by using the unmarked dialect emotion voice data; the acoustic parameter generation module is used for integrating the output of each expert to generate a complete acoustic parameter set; the neural vocoder is used for converting the acoustic parameter set into final dialect emotion voice; the method can effectively overcome the defect that the dialect emotional voice is difficult to accurately synthesize under the condition of lack of sample resources.

The invention discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

Disclosed is a method for establishing a 3D medical image segmentation model based on masked modeling and application thereof includes: establishing a semi-supervised learning network, wherein a student network includes an encoding module for extracting latent features and a segmentation decoder that predicts segmentation results, a teacher network includes an encoding module and a segmentation decoder that are structurally consistent with the student network; training the semi-supervised learning network, wherein during training, two random masking operations are performed on each image, and the image is input to the two networks respectively; optimizing and updating the weight of the student network, and transferring the updated weight to the teacher network, wherein the training loss function includes prototype representation loss, which is used to characterize the difference between the prototypes extracted and generated by the two networks; the student network may further include a reconstruction decoder and an auxiliary segmentation decoder.

The invention provides a cross-domain target detection method and system based on semi-supervised learning in extreme weather, and the method comprises the steps: enabling a student model to learn bounding box regression knowledge of a trained teacher model through distillation, so as to achieve the regression capability in extreme weather; performing instance-level feature adversarial training on a pre-training detection model which is obtained in a distillation stage and has a regression capability in extreme weather by using an intermediate-state image which is generated by a diffusion model and is closer to a target domain and has smaller noise, and determining a new teacher model and a new student model based on the detection model after the adversarial training; in the fine tuning stage, a dynamic category pseudo label screening strategy is utilized to screen pseudo labels generated by a new teacher model, and a screened target domain pseudo label is utilized to train a new student model to obtain a target detection model used for detecting a to-be-detected image in extreme weather. The phenomena of leak detection and error detection in extreme weather scenes in the prior art can be reduced, and the original reasoning speed is kept.

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses a PCBA anomaly detection method and system based on three-dimensional modeling and AI fusion, and a medium. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention discloses an industrial equipment intelligent control method and system, and the method comprises the following steps: S1, collecting and preprocessing equipment operation data, and constructing a state data set; s2, establishing a trend prediction model by adopting Gaussian process regression, and outputting a state prediction value; s3, constructing a graph structure representation model, and combining a graph convolutional network and a semi-supervised learning method to carry out joint training and identify a working condition category and an operation level; s4, based on the equipment operation data, calculating a fuzzy membership degree and a fuzzy entropy index, and generating an entropy distribution curve; s5, fusing the state prediction value, the working condition category, the operation grade and the fuzzy entropy index to generate a state evaluation result; s6, adjusting control strategy parameters according to a state evaluation result, and issuing a control instruction to an equipment control system; and S7, collecting feedback data, updating the state data set, and realizing closed-loop iterative optimization. The method has the advantages of accurate prediction, stable identification, flexible evaluation, fast control, self-learning and the like, and is suitable for complex and changeable industrial application scenes.

The invention discloses a shield tunneling intelligent auxiliary method based on stratum types. The method comprises the steps that parameters of a current shield tunneling machine in the tunneling process are collected; through Pearson's correlation coefficient calculation, correlation coefficients of all the parameters and the total thrust and the cutterhead torque are obtained respectively, and a feature combination is obtained; utilizing a K-means algorithm to calculate and obtain a stratum classification result which is clustered by taking a basic combination consisting of the total thrust and the cutterhead torque and a feature combination as a feature set; a cooperative training algorithm is adopted, and dug stratum classification based on the basic combination and the feature combination is obtained under semi-supervised learning; a machine learning algorithm is utilized to carry out learning training on types of excavated stratums, unknown stratum conditions in the tunneling direction are identified, and a reference range of total thrust and cutterhead torque is given. The method can be circularly executed in the shield tunneling process, the excavated stratum classification result is continuously updated, the method has the characteristics of practical feasibility, real-time performance and the like, and important auxiliary decision-making information can be provided for shield tunneling.

The invention discloses a semi-supervised medical image segmentation method based on uncertainty-driven dynamic correction and multi-scale consistency learning, and the method comprises the steps: carrying out the preprocessing of a medical image data set, and dividing the medical image data set into a training set and a test set; constructing a semi-supervised segmentation model of dynamic correction and multi-scale consistency learning based on uncertainty driving; inputting the training set into a semi-supervised segmentation model, and performing iterative training and parameter optimization to obtain a trained semi-supervised segmentation model; inputting the test set into the trained semi-supervised segmentation model to obtain a medical image segmentation result; wherein the semi-supervised segmentation model adopts a mean teacher model of a V-Net network, a prediction block is added behind each up-sampling block of a V-Net decoder, and a dropout layer is added; according to the method, the problems that the existing semi-supervised learning method is difficult to adapt to the complexity of annotated data and unannotated data distribution, so that effective information is lost; meanwhile, a traditional uncertainty estimation method needs multiple times of forward transmission, and the calculation cost is high.

The invention discloses a medical image segmentation method and system based on basic model assisted semi-supervised learning. In a medical image segmentation task, by introducing an innovative method of combining a visual basic model (SAM) and a U-Net model, the labeling precision and the segmentation effect are remarkably improved. Compared with an existing semi-supervised learning method, the method has the advantages that the problem of low-quality false labels is solved by introducing a reliability scoring mechanism and a foreground conflict processing strategy, and unreliable images are effectively utilized through a region exchange strategy. Finally, the invention provides a new solution for solving the problems of low false label quality and high data annotation cost in medical image segmentation, greatly improves the performance of medical image segmentation, and shows an excellent segmentation effect on a plurality of reference data sets.

A slope safety and stability analysis method based on an integrated sensor and an intelligent algorithm comprises the following steps: acquiring initial values of geometric parameters (including height, gradient and width) of a slope and basic physical parameters (weight, internal friction angle and cohesive force) of a soil body through a method of combining in-situ static sounding and an indoor test; an improved integrated sensor slope state monitoring device is adopted to continuously obtain state parameters such as water content, matrix suction, internal three-way displacement, an inclination angle and soil pressure of a slope soil body, and multi-dimensional dynamic monitoring is achieved; basic physical parameters of a soil body are updated in real time based on data collected by a sensor, and the slope stability safety coefficient is predicted through a semi-supervised learning model; safety early warning is triggered in a grading mode according to prediction results, model prediction values and measured values are recorded and analyzed, and the system evaluates the long-term stability and applicability of the model; through multi-source data fusion and intelligent analysis, theoretical support and technical guarantee are provided for safety monitoring and early warning of the loess slope.

The invention relates to a lithium ion battery health state estimation method based on semi-supervised learning, and the method comprises the steps: obtaining health factors in an original IC curve, and carrying out the preprocessing; constructing a data set based on the preprocessed health factors; wherein the data set comprises a marked sample data set and an unmarked sample data set; using the data set to train a semi-supervised Transform model, and obtaining a battery SOH prediction model; and performing lithium ion battery SOH estimation by using the battery SOH prediction model. According to the method, the effective information of the marked sample is fully mined by utilizing the advantages of the self-attention mechanism in the feature extraction of the time sequence data and the long-range dependence modeling method, the marking and utilization of the unmarked sample are realized through self iteration, and the precise prediction of the SOH of the lithium ion battery is realized.

The invention provides a semi-supervised image semantic segmentation method and system based on a visual basic model, and the method comprises the steps: constructing a multi-task model which comprises a visual basic model and a depth estimation basic model, and the visual basic model is connected with a task solution head, an adapter parameter efficient fine tuning module and a multi-modal cross fusion module; the task solution head comprises a semantic segmentation head and a depth estimation head; extracting semantic hierarchy features and a depth feature map of the RGB image, performing cross attention fusion on the semantic hierarchy features and the depth feature map, and inputting obtained fusion features into a semantic segmentation head and a depth estimation head respectively; semi-supervised learning is adopted to train a multi-task model, only parameters in the adapter parameter efficient fine tuning module and the multi-modal cross fusion module are trained, and a multi-task loss function is adopted. The image semantic segmentation model obtained through training can improve semantic segmentation performance, reduce training cost and is suitable for different tasks.

The invention provides an image segmentation method and system based on semi-supervised learning and detail enhancement. The method comprises the following steps: collecting a medical image and labeling part of data; constructing a medical image segmentation model; inputting the marked image into a medical image segmentation model to calculate the cross entropy loss of the marked image; weak disturbance and strong disturbance are applied to the unlabeled image; inputting the weak disturbance image and the strong disturbance image into a medical image segmentation model, outputting the weak disturbance image through an encoder, adding a feature disturbance strategy to the weak disturbance image, and calculating consistency loss by using an output result obtained by unlabeled data; the total loss of the set model comprises cross entropy loss and consistency loss; performing optimization and reverse gradient propagation on the total loss, and updating model network parameters; and inputting the target image into the medical image segmentation model, wherein the medical image segmentation model outputs the category to which each pixel point of the target image belongs. According to the invention, the efficiency of medical image segmentation can be improved, and higher precision and robustness can be realized.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.

The invention discloses a target detection domain adaptation method based on random context consistency reasoning. A semi-supervised learning teacher-student model framework is adopted, slow updating is performed through index moving average of a teacher model, so that the model can obtain information from main knowledge learned by student models, and a self-supervised effect is achieved. A random complementary mask module is introduced to mask partial local visual features which the network seriously depends on, so that the network is encouraged to utilize other visual clues in the context. In addition, the phenomenon that most self-training frameworks seriously depend on pseudo tags is avoided through an internal context consistency reasoning module, and the self context reasoning ability of the student model is improved by constructing a self-supervised training normal form of the student model. And finally, constructing a self-training framework based on a teacher-student model through an external context consistency reasoning module, and encouraging a student model to make correct prediction on a masked area by utilizing a pseudo tag, with global priori knowledge, of the teacher model.

The invention discloses a semi-supervised learning flow field prediction method based on a Gaussian mixture model. The method comprises the steps of obtaining a flow field calculation data set through a CFD calculation method; dividing the data set into a marked flow field data set, an unmarked flow field data set and a verification set; according to the marked flow field data set, constructing a flow field prediction model based on deep learning; training the flow field prediction model to obtain pseudo-labeled flow field data; respectively training the mixed data set by using the flow field prediction model and a pre-constructed decoder model; obtaining a simple sample in the pseudo-labeled flow field data through a bipartite Gaussian mixture model based on a design loss function; adding the simple samples into the flow field prediction model in an equal proportion until the training is completed; and verifying the flow field prediction effect by using the verification set to obtain a modeling flow field. By effectively utilizing the unmarked data, the prediction capability of the model under the condition of processing an unknown flow field is greatly enhanced while the prediction precision is maintained.

The invention discloses a learning achievement prediction method based on learning behavior data mining and a computer device. The method comprises the steps of collecting multi-dimensional learning behavior data of students, performing preprocessing and feature extraction, constructing a structured learning behavior feature set, then constructing a target machine learning model suitable for different prediction tasks, inputting structured learning behavior features of the students to be predicted into the model, and obtaining a continuous cumulative risk prediction curve; converting the cumulative risk prediction curve into discrete classification labels, and generating a classification prediction result with reserved time sequence characteristics; and expanding the structured learning behavior feature set by adopting a pseudo-label semi-supervised learning algorithm, iteratively training a target machine learning model, inputting the structured learning behavior features of a to-be-predicted student into the trained model to obtain a comprehensive prediction result, and generating a visual learning achievement prediction scheme according to a classification prediction result. The method can provide a comprehensive and accurate prediction result.

The invention discloses an equipment state prediction method based on weak supervised learning. According to the method, through innovative means such as a hierarchical labeling strategy, an active learning module and an anomaly detection and automatic labeling module, the requirement for manual data labeling is greatly reduced. Meanwhile, various semi-supervised learning models are fused, data distribution is comprehensively learned, a multi-modal fusion attention mechanism is innovatively introduced, heterogeneous data are comprehensively utilized, and the prediction precision is improved. According to the method, a data enhancement and expansion technology is further designed, the generalization ability of the model is enhanced, online incremental learning is supported, and continuous optimization of the model is achieved. According to the method, the future state of the equipment can be efficiently and accurately predicted under the condition that the annotation data is poor, the prediction cost is reduced, and the method has important theoretical value and wide application prospects.

The invention discloses an ultra-high resolution remote sensing image semi-supervised change detection method based on pseudo label boundary quality improvement. The change detection aims to explore a change region of an image at a later moment compared with an image at a previous moment in a dual-time image; according to the method, semi-supervised learning guidance is enhanced through a high-quality pseudo tag, and the method comprises the following steps: 1, preparing a semi-supervised learning training data set; 2, building a teacher model and a student model; 3, starting training, reasoning data of the training data set by the student model, and generating corresponding rough and false labels by utilizing the teacher model; 4, refining the rough and false labels by using a boundary optimization method, and calculating the consistency loss of the labels and the reasoning result loss of the student model; 5, calculating the total loss, and updating the parameters of the student model and the teacher model; 6, finishing training, and performing change detection on the remote sensing double-time image by the student model; according to the method, the semi-supervised change detection performance which is limited by rough false label boundaries and cannot be further improved is improved.

The invention relates to the field of semantic tag generation, and discloses a semantic tag generation method based on semi-supervised learning, which comprises the following steps: collecting supervised data with tags and unlabeled data without tags, and extracting features generated by semantic tags from original data; training a supervised learning model by using the labeled data, and predicting the unlabeled data by using the trained model to generate a preliminary label; taking a preliminary label generated by the model as a pseudo label, adding the pseudo label into the unlabeled data, and jointly training the model by using the labeled data and the unlabeled data with the pseudo label; in each round of iteration, the labeled data and the unlabeled data with the high-confidence-coefficient false labels are trained together, the labels with the high confidence coefficient are used for label updating, and the false labels with the low confidence coefficient are removed; and predicting all data through the trained semi-supervised learning model to generate a final semantic tag. The method has the advantage of improving the accuracy of tag generation.

The invention provides a semi-supervised target detection method for a visible light-infrared multi-mode fusion scene. The method comprises the following steps: constructing a semi-supervised visible light-infrared multi-modal image data set based on an LLVIP data set; on the basis of a YOLOv11 model architecture, constructing a target detection model oriented to multi-modal image feature fusion, and training the target detection model by using a semi-supervised visible light-infrared multi-modal image data set in a deep learning end-to-end mode to obtain a trained target detection model; and inputting a to-be-detected multi-modal image into the trained target detection model, and outputting a target detection result of the to-be-detected multi-modal image by the trained target detection model. The method is based on a semi-supervised learning normal form, so that the precision and robustness of target detection in a multi-modal fusion scene are improved, and the requirements for high efficiency and reliability of target recognition in practical application scenes such as intelligent traffic and intelligent security and protection are met.

The invention relates to the technical field of industrial magnesium smelting intelligent control, and provides a semi-supervised learning and free threshold value-based electric smelting furnace working condition identification method, which comprises the following steps of: acquiring historical working conditions and video picture sample data of an electric smelting furnace, and dividing the historical working conditions and the video picture sample data into labeled data xa and unlabeled data xb; performing feature enhancement extraction on the preprocessed picture data by adopting a ViT feature extraction network based on a global channel space attention module; performing CNN model training of a time sequence-based dual-time fusion mechanism on the labeled feature extraction picture Xaout to obtain an initial model; sending the unlabeled image data Xbout into the initial model to predict the category probability, and generating a qualified pseudo-label data set by adopting a free threshold mechanism; and updating the training data set, carrying out model training again, calculating training loss, and carrying out iterative training continuously until the model performance meets a preset requirement. According to the method, the manual marking cost can be greatly reduced, and the working condition of the electric smelting magnesia furnace can be efficiently and accurately identified.

The invention discloses an AI-based network security risk identification and management and control system, which is characterized by comprising network security equipment and a server, the network security equipment is used for monitoring flow data flowing into / out of the server, and the AI-based network security risk identification and management and control system also comprises an interface conversion module which is used for connecting different network security equipment, original operation logs of different network security devices are collected; data collection: collecting multi-source data from multiple channels of network traffic, server logs, application program logs and user behavior records; preprocessing the data; preprocessing the data, removing noise data, filling missing values, and extracting useful features from the original data; simulation training: training the model to identify normal and abnormal behaviors through machine learning by utilizing supervised learning, unsupervised learning or semi-supervised learning methods; the deep neural network is used for modeling a complex data mode, and the detection precision is improved.

The invention relates to a power system network security threat monitoring and early warning method and device, power equipment, a computer readable storage medium and a program product, and belongs to the field of power system network security. The method comprises the following steps: acquiring historical data and real-time data; obtaining first network feature data based on historical data, performing unsupervised learning training on the initial detection model, and taking abnormal data as second network feature data; marking the second network feature data as third network feature data; marking the first network feature data to obtain fourth network feature data to perform self-supervised learning training on the model, and obtaining first network attack features to construct a network attack feature library; performing semi-supervised learning training on the model based on the first network feature data and the third network feature data to obtain a detection model, and updating a network attack feature library; and detecting real-time data based on the detection model and the network attack feature library. The method can improve the accuracy of power system network security threat monitoring and early warning.

The invention discloses a semi-supervised speech imagination intention decoding method based on electroencephalogram collaborative clustering. The method comprises the following steps: step 1, collecting electroencephalogram data of speech imagination; 2, performing feature extraction on the preprocessed electroencephalogram data; 3, performing feature clustering and sample clustering on the original data matrix to obtain a clustering indication matrix of features, a clustering indication matrix of samples and a coefficient matrix; step 4, completing optimization solution of the clustering indication matrix of the features, the clustering indication matrix of the samples and the coefficient matrix under a semi-supervised framework; and 5, displaying a decoding result obtained by the semi-supervised learning model based on the electroencephalogram sample and feature collaborative clustering through a visualization means. The method can fully explore and utilize a bidirectional complex interaction mode of features and samples and label information of speech imagination in speech imagination intention decoding research and system based on electroencephalogram signals.

The invention relates to the technical field of big data and data security, in particular to a cloud-edge collaboration and AI fusion data classification and grading method and system and a storage medium, cloud computing and edge computing are combined, the AI technology is used for data classification and grading, and rapid processing and efficient analysis of data are achieved; for structured data and unstructured data, different recognition and processing methods are adopted, including regular expression, keyword matching, dictionary matching, neural network, natural language processing and other algorithm models; particularly, a neural network and a natural language processing technology are combined with an unsupervised learning method and a semi-supervised learning method to realize an automatic classification and grading method of unstructured data in stages, rapid data processing and response can be carried out on an edge layer through a cloud edge collaboration mechanism, and the efficiency is improved. And deep analysis and model training are carried out by using the powerful computing power of the cloud layer.

The invention relates to a medical image automatic segmentation method based on semi-supervised learning, and belongs to the technical field of medical image accurate segmentation. The method comprises the following steps: S1, proposing an automatic segmentation task of a medical image, wherein the method comprises the following small steps: S11, establishing a DulecrASE-Net semi-supervised network structure; s12, an MSA-RFSEU-Net network structure is established; s13, designing an adversarial two-stage consistency training strategy; s14, designing a multi-scale attention component DyMAC based on dynamic convolution; s15, designing a combined loss function; s2, establishing a medical image data set to carry out model evaluation, wherein the model evaluation comprises the following steps: S21, selection of evaluation indexes; s22, verifying the effectiveness of the evaluation model; and S23, verifying the universality of the evaluation model. According to the method, the medical image focus area can be effectively segmented, the segmentation precision can be improved to a certain extent through the designed antagonistic two-stage consistency training strategy, and the method can be used for the precise segmentation occasion of the medical image.