346results about How to "Improve discrimination ability" patented technology

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention relates to the technical field of image classification, and discloses a breast image classification evaluation method and system based on deep learning, and the method comprises the steps: carrying out the preprocessing of sample breast image data, and obtaining comprehensive feature data; performing feature enhancement based on the comprehensive feature data to obtain enhanced feature data; performing multi-modal bidirectional feature fusion on the enhanced feature data to obtain multi-modal fusion feature data; based on the multi-modal fusion feature data, constructing and training a semi-supervised local feature reservation dense connection context-aware graph convolutional network to obtain a graph volume integral class model; according to the multi-modal fusion feature data, constructing and training a multi-branch residual attention reconstruction network to obtain a multi-scale feature fusion model; through the image volume integral class model and the multi-scale feature fusion model, classification evaluation and prediction result integration are carried out on the real-time mammary gland image data to obtain a target mammary gland image classification result, and the method improves the accuracy and reliability of mammary gland image classification.

The invention relates to a multi-modal implicit sentiment analysis method based on a multi-granularity attention mechanism, which belongs to the field of artificial intelligence, and comprises the following steps: S1, extracting visual features in image information by using a visual feature extraction strategy of multi-scale attention fusion; s2, generating a text description of the image information by adopting an image translation engine, splicing the text description with an original text, and extracting text features through a hierarchical attention mechanism; s3, carrying out normalization processing; s4, performing multi-modal fine-grained correlation fusion on the visual features and the text features by adopting a bidirectional cross attention mechanism, and extracting fine-grained features in the visual features and the text features through a multi-head attention mechanism and residual connection; and S5, fusing the fine-grained features by using an adaptive multi-modal classification module to complete sentiment classification. The multi-modal sentiment analysis method solves the problems that multi-modal sentiment analysis is insufficient in inter-modal correlation mining, insufficient in fine-grained information capture and large in noise interference.

The invention relates to the technical field of medical image processing, and particularly discloses a pathological tissue image-based tumor analysis method, which comprises the steps of data acquisition and preprocessing, network construction, image enhancement, adjacent layer feature fusion, image classification and knowledge distillation. According to the scheme, features are extracted from different levels by adopting an image enhancement and comparative learning method, the capability of extracting global features and capturing local details of the model is improved, and by calculating strong and weak enhancement difference loss, it is ensured that enhancement operation does not excessively change feature representation of the image, and overfitting of the model is prevented; features are extracted and fused from each layer of feature map of the teacher network through an attention mechanism, and the detail capturing ability of the model to a pathological image is enhanced; a knowledge distillation method is used, knowledge of a teacher network is introduced, and a student network not only pays attention to classification of hard tags, but also can capture a relative relationship between classes, so that the classification accuracy is improved, and the calculation resource demand of a model is reduced.

The invention provides a remote sensing image target detection method based on an improved FCOS, and relates to the technical field of target detection, and the method comprises the following steps: carrying out the convolution and pooling operation of a remote sensing target image based on a pre-trained convolutional neural network, generating a preliminary feature, and carrying out the enhancement processing of the preliminary feature through a twin multilayer perceptron; carrying out fusion processing on different levels of feature information in the multi-level feature map through a feature map splicing mode and a feature map enhancement mode; and determining the position of the remote sensing image target according to the analysis result to obtain a target detection result. According to the method, the feature pyramid network is combined with an innovative coordinate attention mechanism, fusion of high-level semantic information and low-level detail information is realized, an enhanced multi-scale feature map is generated to cope with diversity of target scales in a remote sensing image, multi-scale fusion and an anchor-frame-free design are combined, false detection is effectively reduced, and the detection accuracy is improved. Therefore, the method shows efficient, accurate and wide application potential in the field of remote sensing image target detection.

The invention relates to the technical field of intelligent lock recognition, in particular to an intelligent lock image feature recognition method and system based on deep learning. The recognition method comprises the following steps: constructing a multi-branch deep neural network model, wherein the multi-branch deep neural network model comprises a backbone network, a feature extraction branch and a feature fusion branch; a framework training model is used to learn identity recognition, attitude estimation and environment perception tasks; and identifying the comprehensive feature representation by using a trained framework training model, and generating a control instruction of the intelligent lock according to an identification result. According to the intelligent lock image feature recognition method based on deep learning provided by the invention, comprehensive analysis of multi-dimensional information such as identity, posture and environment is realized by constructing the multi-branch deep neural network model.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention provides a medical image classification unsupervised domain self-adaption and multi-granularity alignment system and a medical image classification unsupervised domain self-adaption and multi-granularity alignment method. According to the method, a coarse-to-fine progressive feature optimization strategy is provided, that is, a fine-grained feature encoder is initialized by using the training weight of a coarse-grained feature encoder, and alignment optimization of fine-grained features is guided while original semantic information is reserved. Specifically, a staged and layer-by-layer unfreezing fine adjustment method is adopted, and the adaptive capacity of fine-grained features is enhanced on the premise that the invariance of a coarse-grained domain is not damaged. According to the strategy, the generalization and convergence efficiency of the model on target domain data is remarkably improved. And experimental verification is carried out on a plurality of cross-domain medical image classification data sets. Results show that no matter on a small-scale data set or a large-scale data set, the method provided by the invention can obtain performance obviously superior to that of an existing method, and generalization ability and robustness are stronger.

The invention discloses a coronary heart disease preliminary screening method and device based on facial feature analysis and a medium, and relates to the technical field of image understanding, and the method comprises the steps: extracting each part sub-region of a human face, and obtaining a tensor image of each sample data through the stacking of the sub-regions; obtaining the flattened feature maps of the tensor images, and integrating the feature maps to obtain a feature matrix of the corresponding sample data; calculating a feature mean value in the image dimension direction according to the feature matrix, and taking the feature mean value as a global feature tensor of a corresponding sample; carrying out dimension reduction processing and nonlinear transformation on the global feature tensor, and carrying out binary classification probability distribution output of relevance between the subregions and the coronary heart disease; performing updating iteration of the classification weight of the sub-region through cross entropy loss function constraint and a stochastic gradient descent optimizer; and performing primary screening of the coronary heart disease through the iterated primary screening model of the coronary heart disease. According to the method, the key feature capturing capability of the model is improved, and then the judgment capability on the coronary heart disease risk is enhanced.

The invention relates to the technical field of big data processing, in particular to an online state grid user classification method, system and device based on a graph convolutional neural network and a storage medium. The invention discloses an online state grid user classification method based on a graph convolutional neural network. The method comprises the following steps: extracting user explicit and implicit features; the method specifically comprises the following steps: firstly, constructing a user graph model by taking online state grid user data as input, extracting feature information in graph data by using a stack network formed by a convolutional neural network, and propagating vector representations of aggregated users and neighbor users thereof by using a cyclic convolution operation; classifying users; according to the method, the graph convolutional neural network model is used for constructing the portrait of the state grid user on the Internet, dominant and implicit features of the user are deeply mined, and an accurate user feature vector is formed. Meanwhile, user vector subdivision is achieved according to probability representation output by the full connection layer, and potential user groups are effectively recognized.

The invention discloses a speech emotion recognition method based on multi-modal feature fusion, and the method comprises the steps: designing a multi-modal speech emotion recognition model, enabling an audio to serve as a main mode, employing the function of wav2vec2.0, and combining a text mode with a visual mode to enhance the performance of emotion recognition based on the audio; the self-attention mechanism, the GRU and the LVC are combined to extract global and local information from a visual mode, the global information is extracted only from text features, visual clues and text clues related to emotion can be comprehensively understood, the extracted text and visual features are integrated into a wav2vec2.0 model, multi-mode representation of multi-level audio information is formed, and the multi-mode representation of the multi-level audio information is realized. And meanwhile, the discrimination capability of the model is enhanced by utilizing multi-modal homogeneous feature difference learning, and the proposed model is evaluated on two data sets, so that the model is proved to have excellent robustness and generalization compared with the existing most advanced model.

The invention discloses a thromboembolism occurrence risk assessment method and assessment system. The method comprises the following steps: acquiring a blood vessel image map of a to-be-assessed patient; inputting the blood vessel image map of the patient to be assessed into a pre-trained risk assessment model; wherein the pre-training of the risk assessment model is based on a ResNet-50 model, and self-supervised contrast learning iteration is carried out by inputting positive and negative samples so as to update model parameters; extracting a feature embedding vector in the blood vessel image; according to the feature embedding vector, calculating probability distribution of embolism occurrence risk levels through a model classification head; and according to the probability distribution, selecting a risk level label and outputting the risk level label as an evaluation result. According to the method based on the time axis symmetry, the image data of the patient is collected before and after the embolism occurrence time point, the positive and negative sample pairs are generated, the problem that the embolism sample is difficult to obtain is solved, and meanwhile the sample collection cost is reduced through self-supervised learning.

The invention relates to the technical field of medical information, and discloses a pancreatic cancer risk prediction method based on machine learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a target user; performing time sequence deduction on the molecular biological detection data to generate a virtual molecular time sequence; time sequence signals are extracted from the virtual molecule time sequence and the time sequence behavior monitoring data; calculating the dynamic coupling strength between the two time sequence signals to obtain a space-time coupling coefficient; weighted fusion is carried out on the features, and unified multi-modal feature representation is constructed; carrying out multi-modal feature representation training to obtain a special risk prediction model for the target user; and obtaining a risk quantitative score, and identifying a key risk driving factor which contributes to the score most. According to the invention, through multi-modal time sequence fusion and personalized modeling, early-stage, dynamic and explainable and evaluable pancreatic cancer risks are realized.

The invention discloses a single-cell RNA sequencing data classification method and system based on deep learning. The invention provides a multi-scale neural network based on an improved Transform. The multi-scale neural network is used for classifying preprocessed single-cell RNA (Ribonucleic Acid) sequencing data. The multi-scale neural network integrates a multi-scale attention mechanism, relative position coding and a gating feedforward network, not only can the multi-level dependency relationship of data be captured, but also noise interference can be effectively reduced through feature screening. Compared with a traditional convolutional neural network or a classical Transform, the multi-scale neural network provided by the invention shows stronger robustness and generalization ability in sequence modeling and classification tasks. Through wide tests on a plurality of public data sets, the proposed multi-scale neural network shows better performance in a cell category classification task.

A method, system and device for monitoring downhole flow and moisture content specifically comprises the steps that S1, DAS signals are collected, and the obtained DAS signals are preprocessed; s2, signal features in the DAS are extracted, and time domain features, frequency domain features, time-frequency domain features and time sequence features in the DAS are extracted; s3, the DAS signal features extracted in the S2 are fused, and comprehensive feature representation is generated; and S4, identifying the DAS signal features, training an AI model to obtain a flow water content identification model, and identifying the comprehensive feature representation through the flow water content identification model to obtain flow and water content identification. The method has the advantages that four DAS signal feature extraction methods are provided, and time-frequency feature extraction, frequency-frequency feature extraction, time-frequency feature extraction and time sequence feature extraction are achieved respectively; designing a feature fusion algorithm, and realizing feature-based event discrimination capability enhancement; a deep learning algorithm implementation method is provided, and a high-precision feature recognition technology is improved.

The invention relates to the technical field of computer vision, and discloses a visual saliency target detection method based on an RGB-D image, and the method comprises the following steps: S1, carrying out the channel alignment and spatial calibration of a mutual relation between corresponding features of different modals through an MI module, and obtaining a cross-modal representation; s2, then, an IGF module is added to the tail end of the Swin Transform encoder, and the IGF module is added to the tail end of the Swin Transform encoder; s3, the cross-modal fusion feature # imgabs0 # is subjected to channel cross fusion through a CCT module, and relevant information about significance is further obtained; and S4, finally, carrying out modeling on the remote dependency of the fusion features from a global perspective by using a decoding block based on Swin-Transform. According to the visual saliency target detection method based on the RGB-D image, by designing a modal interaction module and a channel cross fusion mechanism, the fusion effect of multi-modal features and the fine modeling capability of multi-scale features are fundamentally improved, and therefore the problems of saliency target detection precision and robustness which are difficult to overcome in the prior art are effectively solved.

The invention discloses a multi-modal false information detection method, system and device and a storage medium, and belongs to the technical field of false information detection.The method comprises the steps that a text of multi-modal information is input into a text representation network to obtain text representation, and an image of the multi-modal information is input into an image representation network to obtain image representation; and inputting the text representation and the image representation into a shared classifier to obtain a text prediction information label, an image prediction information label, a text weight and an image weight, and weighting the text prediction information label and the image prediction information label based on the text weight and the image weight to obtain a prediction information label. According to the method, the text and image modal information is combined, and an adaptive weight distribution mechanism is adopted, so that efficient and accurate multi-modal information processing is realized, the prediction performance is remarkably improved, and the application range is widened.

The invention belongs to the field of multi-label image classification, and particularly discloses a neural collapse-based long-tail multi-label image classification method, which guides learning of different types of features by using label embedding of an equiangular tight frame, so that the different types of features meet the property of neural collapse, the difference between different types is maximized, and the neural collapse-based long-tail multi-label image classification efficiency is improved. The influence of the head class on the tail class in the same sample is reduced, and learning of tail class features is fully guaranteed. Besides, a comparative learning method is also adopted, so that each class feature is clustered and collapsed to a class prototype, and the model learns more robust features. And finally, features corresponding to different classes of samples extracted by the model are combined and input into a binary classifier meeting an equiangular tight frame, so that the sample features are fully utilized, the influence of an unbalanced data set on the training of the model is avoided, the generalization ability of the model is enhanced, and the training efficiency of the model is improved. And the prediction precision of multi-mark image classification is improved.

The invention discloses a hyperspectral image classification method based on frequency domain denoising and element gradient correction, and the method comprises the following steps: carrying out the preprocessing of all hyperspectral image data, and dividing an overall training sample set formed by the processed hyperspectral images into a training set and a verification set; constructing a sample weighting model based on frequency domain denoising and element gradient correction; in the training process, a parameterization frequency spectrum gating sensing transformation module is utilized to map features to a frequency domain through discrete Fourier transform, a learnable frequency spectrum response function is utilized to adaptively suppress spectrum jitter noise, and finally pure features are reconstructed. And automatically constructing a high-confidence pseudo-clean verification set based on a Gaussian mixture model and time domain consistency. According to the method, a time domain momentum updating mechanism is introduced, the variance of statistical estimation is effectively smoothed, random interference caused by training fluctuation is resisted, and the accuracy of pseudo clean set construction and the convergence stability of overall model training are further improved.

The invention discloses a multi-task electromagnetic model based on a hybrid expert network, and belongs to a wireless communication technology. The model comprises a preprocessing module, a feature extraction module, a task output module and a pre-training-fine tuning learning strategy. The preprocessing module carries out standardization processing on the multi-source electromagnetic signals; the feature extraction module is based on a Transform structure, introduces a hybrid expert network to replace part of a traditional feedforward neural network, and dynamically selects an expert sub-network through a task specific routing mechanism; the task output module configures a special structure according to different task targets; in the pre-training stage, a mask auto-encoder is used for pre-training large-scale label-free data, and a downstream task is subjected to full-amount fine adjustment through small-scale label data. According to the method, multi-task collaborative learning and differential expression are realized, and the recognition performance, robustness and processing efficiency of the model in a complex electromagnetic environment are improved.

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

The invention relates to a semi-supervised text classification method and system based on multi-encoder generative adversarial learning, belongs to the technical field of supervised learning, and introduces a multi-encoder structure to provide multiple output representations for the same text sample. The generator part converts a vector sampled from random noise into a vector representation having the same dimension as the output of an encoder, and constructs an unsupervised and supervised loss combination module on a discriminator in order to realize semi-supervised text classification, and continuously promotes the generator to generate approximately real adversarial samples by utilizing generative adversarial learning, so as to realize semi-supervised text classification. And meanwhile, the capability of discriminating the real text sample and the adversarial sample generated by the generator by the discriminator and the capability of category classification are improved. In addition, through joint optimization of the generator and the discriminator, the text semantic representation capability of the encoder is further improved.

The invention discloses an ICBL-based intelligent fault diagnosis method for a gear box of a cutting unit of a coal mining machine. The method comprises the following steps: firstly, collecting vibration data of different fault types; randomly dividing the data into a training data set and a test data set, and setting an unbalance degree in the training data set; then, complex structure information of the vibration data of the gearbox of the cutting part of the coal mining machine can be quickly and efficiently embedded into the improved cascade width learning model by using a random hypergraph convolution mechanism, so that the discrimination of fault features learned by the improved cascade width learning model is greatly improved; meanwhile, a class specificity weight distribution strategy is designed according to proportion information between data classes, an improved cascade width learning model can pay more attention to a few class samples, the fault diagnosis performance of unbalanced data is improved, and the fault diagnosis efficiency of the unbalanced data is improved through a mode of combining a random hypergraph convolution mechanism and the class specificity weight distribution strategy. And finally, the fault diagnosis precision of the gear box of the cutting part of the coal mining machine is effectively improved.

The invention relates to the technical field of electric data processing, in particular to a data blood relationship automatic tracking method and system based on a table in AI data, and the method comprises the steps: obtaining metadata in the table in the AI data, and analyzing a task log of a calculation engine to construct an initial directed acyclic graph; the initial directed acyclic graph comprises data nodes and explicit blood edges connected with the data nodes; and calculating a flow entropy coupling index between any two data nodes. According to the method, a flow entropy coupling index based on update time correlation and logic evolution divergence based on key entity distribution similarity are constructed, a graph comparison learning model is combined, under the condition that an explicit SQL grammatical structure is lacked, the implicit dependency of upstream and downstream nodes is automatically identified, and the flow entropy coupling index and the logic evolution divergence are combined. And end-to-end full-link data consanguinity complete tracking in a complex AI feature engineering scene is successfully realized.

The invention relates to the technical field of AI visual detection, and discloses a glass fiber filament flying and breaking early warning and disposal method based on AI visual analysis, which comprises the following steps: collecting a normal operation video of a coil base, processing the video according to frames to form a plurality of pictures, manually marking the pictures containing broken filaments, and training a model by using the marked pictures to obtain a broken filament early warning and disposal result; the coil base is detected in real time through the model, when it is detected that the number of broken wires reaches a threshold value, the audible and visual alarm of the coil base where the broken wires are located is started, alarm information of the coil base where the broken wires are located is obtained, the alarm information is processed through a definition rule, a hexadecimal instruction is generated, and the instruction is sent to a corresponding worker. And meanwhile, the work number information of the worker is judged through an optical character recognition model.

The invention discloses a tailing dry beach automatic detection and early warning method based on multi-sensor image combined processing, and belongs to the field of image data processing. According to the method, an unmanned aerial vehicle carrying an RGB camera, a multispectral camera and a laser radar is used for collecting RGB images, multispectral images and three-dimensional point cloud data, then various feature data are obtained, boundary information is obtained through fusion and a random forest classifier, then the length of a dry beach is calculated, and early warning is carried out. According to the method, the prediction accuracy and efficiency are improved, the advantages of various data are fully utilized, the tailings pond monitoring has higher reliability and flexibility, and meanwhile the method has the advantages of being good in random forest classification effect, high in discrimination capability, low in false drop rate and the like.

The invention discloses a multi-modal contrast learning fault diagnosis method for a small sample scene, and the method comprises the following steps: carrying out the enhancement of a one-dimensional signal and two-dimensional image fused fault data set through physical simulation for the small sample scene with scarce industrial fault data, and constructing a positive and negative sample pair through a plurality of data enhancement strategies; based on heterogeneous multi-modal fault data, designing a double-flow encoder architecture of a time sequence branch and an image branch, extracting depth features and mapping the depth features to a unified feature space through a projection head; performing supervised contrast learning pre-training based on intra-modal and inter-modal dual contrast loss; supervision fine tuning is carried out based on multiple loss functions such as physical guidance, so that accurate diagnosis of equipment faults is realized in a small sample scene. According to the fault diagnosis method under the unbalanced sample and limited labeling conditions, the problem that a traditional data driving model depends on large-scale labeling samples is effectively relieved through supervised comparative learning and cross-modal information alignment.

The invention discloses a multi-modal Hash retrieval method, system and equipment based on a multi-view center structure and a medium, belongs to the technical field of artificial intelligence and multi-modal Hash retrieval, and aims at achieving the technical purpose of obtaining balance between intra-class compactness and inter-class separability in the multi-modal Hash retrieval process. According to the technical scheme, the method comprises the following steps: constructing a multi-modal data set; constructing a multi-modal Hash retrieval model based on a multi-view center structure; training the model; wherein the construction of the multi-modal Hash retrieval model based on the multi-view center structure specifically comprises the following steps: modal specific prototype learning: respectively extracting refined features from corresponding modals by using an image modal depth multilayer perceptron and a text modal depth multilayer perceptron; calculating the average value of the refined features of the image modality and the text modality to obtain a modality specific prototype, and further obtaining the unique features of the image modality and the unique features of the text modality; carrying out multi-modal integrated prototype learning; and performing multi-view semantic enhanced hash learning.

The invention relates to a construction method and device of a substation anomaly detection network, equipment and a storage medium. The method comprises the following steps: acquiring a plurality of abnormal image samples of substation equipment and an abnormal label corresponding to each abnormal image sample; performing frequency domain feature extraction on the target abnormal image sample by adopting a to-be-trained substation anomaly detection network to obtain a frequency domain feature, and performing classification based on the frequency domain feature to obtain a predicted anomaly corresponding to the target abnormal image sample; calculating model loss based on the abnormal label corresponding to the target abnormal image sample and the prediction abnormality; adjusting network parameters of the to-be-trained substation anomaly detection network based on the model loss until the calculated model loss reaches a training stop condition, and obtaining a trained substation anomaly detection network; and the trained substation anomaly detection network is used for substation anomaly detection. By adopting the method, the detection precision of the multi-classification anomaly in the substation environment can be improved.

The invention provides a data resource collecting and labeling method based on big data mining, and relates to the technical field of data collecting and labeling. The method comprises the specific implementation steps of collecting multi-view traffic images, traffic flow data and vehicle speed data of a target road section in real time, preprocessing the multi-view traffic images, and dividing the multi-view traffic images into traffic image groups according to a time sequence; secondly, calculating a traffic concentration index in the first view angle traffic image, identifying a blocking region, mapping the blocking region to another view angle traffic image, and performing congestion labeling; then, through projection of an overlapped detection frame and traffic element point cloud calculation, a traffic accident is marked in combination with a vehicle sag detection model; and finally, environment consistency verification and annotation conflict verification are carried out on a congestion annotation result to ensure the accuracy and integrity of the data. According to the method, the processing accuracy of the traffic data is improved, effective support is provided for traffic management and decision making, and the operation efficiency of an intelligent traffic system is optimized.