428 results about "Multi-label classification" patented technology

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The invention discloses a person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

The invention discloses a material identification method based on a multi-modal large model, and particularly relates to the technical field of data processing. The method comprises the following steps: inputting image data and associated text data of advertisement materials into a multi-modal large model, and carrying out preliminary preprocessing and feature extraction; respectively extracting visual features and semantic features of the image and the text by using a convolutional neural network and a natural language processing technology, and optimizing a fusion effect of the image and text features through a multi-modal contrast learning strategy; the feature alignment condition is verified by comparing a loss function, and the recognition precision of the model is improved by combining a double-path fine adjustment mechanism and multi-label classification supervision; finally, an improved cue word mechanism is adopted to carry out accurate classification on the advertisement materials, a putting engine and a feedback mechanism are combined, the auditing effect of the advertisement materials is monitored and optimized in real time, the accuracy of advertisement compliance management is remarkably improved, auditing errors are reduced, and the customer satisfaction degree is improved.

The invention discloses a pipeline robot pipe network defect detection method and system based on deep learning, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a pipeline inner wall image in real time, and constructing a high-quality pipeline inner wall image sample; constructing a shallow classification model to perform binary classification on the high-quality pipeline inner wall image samples, and marking the inner wall image samples containing defects as defect image samples; extracting local features and global features of defect image samples, fusing to obtain fine-grained features, obtaining weights of defects belonging to different defect labels, constructing label characterization, enabling the graph convolutional network to construct a multi-label classification model, adaptively modeling correlation information between the labels, and obtaining a defect label prediction result. According to the method, correlation information between the labels is modeled in a self-adaptive mode through the graph convolutional network, classification of various pipe network defect types is achieved, the efficiency and accuracy of pipe network defect detection are improved, and the actual application requirement is better met.

The invention discloses a multi-modal multi-label emotion recognition method and system based on modal contribution assessment, and the method comprises the steps: obtaining original feature sequences of a text mode, a visual mode and an audio mode, mapping the original feature sequences through an encoder, and obtaining high-dimensional feature embedding of the three modes; calculating a marginal contribution degree of each mode to emotion prediction at a sample level by utilizing emotion prediction values embedded by high-dimensional features of the three modes; according to the marginal contribution degree of each modal, fusing the features of each modal to form a fused feature, and processing the fused feature through a multi-modal encoder to obtain a multi-modal integrated feature; and through a trainable label embedding sequence, learning a dependency relationship between emotion labels, embedding and inputting the multi-modal integrated features and the labels into an emotion decoder to obtain a multi-modal emotion representation of mixed emotion, and inputting the multi-modal emotion representation into a multi-label classifier to obtain a predicted emotion label. According to the invention, the efficiency of multi-modal feature fusion and the accuracy of emotion recognition can be improved.

The invention discloses a power distribution terminal equipment fault intelligent diagnosis method and system based on the Internet of Things. The method comprises the following steps: S1, collecting multiple items of operation data of a power distribution terminal to construct a time sequence sample; s2, extracting power disturbance characteristics based on a sliding window, generating a behavior coupling matrix and a topological connection matrix, and calculating node redundancy; s3, fusing the two types of relationships to construct a dynamic graph; s4, inputting a graph diffusion network, fusing a structure and a behavior diffusion result, and generating a node state feature vector; s5, inputting a multi-label classification model to identify the fault type and probability; s6, generating a response instruction in combination with the fault type and the redundancy; s7, response is executed, the relation matrix and the classification model are updated, and the diagnosis process is optimized in a closed-loop mode. According to the invention, accurate fault identification and quick response of the power distribution terminal are realized, and the intelligent operation and maintenance level of a power supply system is improved.

The invention provides a multi-source heterogeneous data fusion analysis method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring multi-source heterogeneous event knowledge, and extracting entity information and attribute information of multi-source heterogeneous data; fusing the attribute information, constructing a multi-label classification model, and extracting a sequential relationship of the multi-source heterogeneous data through the multi-label classification model; a time sequence label is added to the time sequence relation, and then an initial knowledge graph of the multi-source heterogeneous data is constructed; obtaining an entity time sequence state sequence of the multi-source heterogeneous data through the initial knowledge graph and the entity information; extracting features of the entity time sequence state sequence; and processing the characteristics of the entity time sequence state sequence to update the initial knowledge graph to obtain the time sequence knowledge graph of the multi-source heterogeneous data. Massive and diversified knowledge is organized and expressed orderly, uniformly and associatively through an entity and attribute extraction technology of multi-source heterogeneous information, a time sequence multi-label relation extraction technology and a time-space big data standardization expression technology.

The invention discloses a cable state monitoring and fault diagnosis device and method, equipment and a medium, and the device comprises a data collection module and an intelligent diagnosis unit, and the data collection module collects multi-source data used for diagnosing the state of a cable; the method comprises the following steps: converting multi-source data into a space-time matrix through an intelligent diagnosis unit, carrying out feature extraction, outputting a fault type and a corresponding prediction probability of a cable by adopting a preset multi-label classification and dynamic threshold decision-making mechanism according to the extracted features, and sending out early warning signals of different grades according to the prediction probability. The diagnosis device solves the problems that in the prior art, multi-dimensional data are not fully fused, so that the diagnosis result is not comprehensive and accurate enough; and new fault features caused by cable aging or operation environment change cannot be self-adapted, the model generalization ability is limited, and the model adaptability is poor.

The invention provides a large-model multi-label classification method, system and device based on ReAct and a vector library, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a multi-level label mapping tool according to each label data table corresponding to a preset multi-level label system; and constructing a label retrieval tool based on a pre-trained text similarity model and a preset label vector database. Constructing a ReAct tool chain based on a preset label dynamic adjustment tool, a multi-level label mapping tool and a label retrieval tool; the preset label dynamic adjustment tool is constructed on the basis of cue words and a large language model and is used for updating candidate labels according to the to-be-labeled text corpus, the candidate labels output by the label retrieval tool and label rules; and based on a preset ReAct cue word project, the large language model and the ReAct tool chain, constructing a large model multi-label classification module so as to input the to-be-labeled text corpus from the user terminal into the large model multi-label classification module for multi-label classification.

The invention belongs to the technical field of bearing fault diagnosis, and particularly discloses a press machine bearing fault diagnosis method and system, a terminal and a medium, and the method comprises the steps: collecting multi-source signal data of a press machine bearing in an operation process; performing denoising and standardization processing on the signal, and extracting time domain and frequency domain features; different sensor features are fused, and multi-dimensional feature representation is constructed; inputting the features into a deep learning model combining a convolutional neural network and a long-short-term memory network for training and reasoning, and introducing a multi-kernel maximum mean value difference strategy to realize feature distribution alignment; and performing fault identification on the real-time signal based on a multi-label classification mode, and triggering an alarm when the prediction probability meets a preset condition. According to the method, fusion modeling of multi-source data and accurate recognition of composite faults are achieved, and the method has high cross-working-condition adaptive capacity and real-time diagnosis capacity and is suitable for intelligent operation and maintenance scenes of press equipment.

The invention discloses an incomplete multi-view multi-label data classification method based on semantic enhancement and pseudo-label uncertainty perception, and the method comprises the steps: employing a dual-channel feature extraction and decoupling module to obtain the shared semantic representation and specific representation of each view in each sample for a constructed incomplete multi-view multi-label data classification network model; performing cross-view fusion on the shared semantic characterization and the specific characterization, obtaining a unified shared characterization and a unified specific characterization corresponding to each sample, performing feature fusion, obtaining a fusion characterization of each sample, inputting the fusion characterization of the sample output by the dual-channel feature extraction and decoupling module into a classifier for multi-label prediction, and performing multi-label prediction on the fusion characterization of the sample. Therefore, a multi-label classification prediction result is obtained, and model training is carried out based on a total contrast learning loss function and a joint supervision classification loss function. According to the method, the classification performance and the model robustness on incomplete multi-view multi-label data are remarkably improved through training learning under the guidance of semantic enhancement and uncertainty.

The invention belongs to the field of artificial intelligence and finance, and relates to a text multi-label classification method, which comprises the following steps: preprocessing an original text data set, obtaining classification labels of the preprocessed text data set, marking the classification labels, obtaining a marked text data set, and dividing the marked text data set into a training set and a verification set; constructing a low-rank adaptive classification model according to the classification label, inputting the training set into the low-rank adaptive classification model, and obtaining a classification prediction result fusing a semantic feature extraction result and a low-rank decomposition result; fine-tuning the low-rank adaptive classification model based on the classification prediction result, and continuing to train to obtain a fine-tuning classification model; verifying the fine-tuning classification model to obtain a final text label classification model; and inputting the to-be-classified text into the text label classification model for classification. The invention further provides a text multi-label classification device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the tagged text data set can be stored in a block chain. The text multi-label classification accuracy can be improved.

The invention discloses a training method and device of a cross-domain target detection model for an image and a cross-domain target detection method. The training method comprises the following steps: acquiring source domain data and target domain data; inputting the source domain data and the target domain data into a deep learning network to obtain a first feature map; based on a cross-channel cross attention strategy, adjusting the attention weight in the first feature map to obtain a second feature map with the adjusted attention weight; inputting the second feature map into a domain classifier network for image-level feature alignment and an image-level multi-label classifier network for specific category feature alignment to obtain a third feature map; inputting the third feature map into a first prediction network used for source domain instance-level feature alignment and a second prediction network used for target domain instance-level multi-label prediction to obtain a fourth feature map; and according to the source domain data, the target domain data and the first to fourth feature maps, adjusting model parameters to obtain a trained cross-domain target detection model.

The invention discloses a self-adaptive classification method for dynamic changes of micro-seismic signal features, and relates to the technical field of intelligent monitoring and information. According to the technical scheme, an Ada-VIT model integrating a feature extractor, a multi-label classifier and a domain discriminator is constructed; the features are optimized by using a self-adaptive mechanism so as to improve the recognition precision of the micro-seismic signals in a complex change environment; performing model training by using the clean micro-seismic signals and the pure noise signals as source domain samples, and performing identification and classification by using the micro-seismic signals which are more complex and contain noise after feature change as target domain samples; and verifying the adaptive ability of the model in a complex change environment. The method has the beneficial effects that the recognition precision of the microseismic signals and the model adaptive capacity are improved, the multi-label classification performance is optimized, the calculation efficiency and the resource utilization rate are improved, and comprehensive evaluation indexes are adopted for performance verification; therefore, the method has important application value and popularization prospect in the field of geological disaster monitoring and early warning.

The invention relates to the technical field of artificial intelligence auxiliary medical treatment, and discloses a child tooth trauma intelligent grading and dynamic processing decision auxiliary system. A trauma identification module; an injury condition grading module; a processing suggestion generation module; a prognosis risk dynamic evaluation module; and a man-machine interaction module. The method comprises the following steps: acquiring an image containing child tooth trauma information and clinical data; utilizing a multi-label classification model to identify a trauma type; calculating a comprehensive injury condition score through a hierarchical multi-modal scoring network and determining an injury condition grade; calling a knowledge graph and a rule engine to generate and dynamically correct a processing suggestion; and continuously updating the prognosis risk assessment result based on the time sequence review data. The invention aims to solve the problems of strong evaluation subjectivity and complex decision in the diagnosis and treatment of the tooth trauma of children, provides standardized and intelligent decision assistance for clinic, and realizes closed-loop prognosis management.

The invention relates to a transformer substation flood prevention case text classification method and device, equipment and a medium, belongs to the field of natural language processing text classification, and is used for carrying out multi-label classification on transformer substation flood prevention case texts to be predicted based on a completely trained improved BERT model to obtain disaster cause labels of the flood prevention case texts. The improved BERT model comprises an embedded layer, an encoder layer, a TextCNN (Convolutional Neural Network) layer and a Seq2Seq layer; the transformer substation flood prevention case text classification method comprises the steps of extracting a text vector of a to-be-predicted transformer substation flood prevention case text based on an embedded layer, and compressing the text vector by adopting a lightweight strategy; extracting global semantic features of the compressed text vector based on an encoder layer; performing feature extraction on the global semantic features based on a TextCNN layer to obtain feature vectors; the feature vector is coded and decoded based on the Seq2Seq layer, the disaster-causing cause label of the flood prevention case text is obtained, the overall classification performance is enhanced, and the accuracy of the single cause label is improved.

The invention provides a few-sample multi-label Android malicious software detection method based on LLM, and solves the major challenge of keeping stable malicious software detection performance under the condition of data noise and label inconsistency. Two main innovations are introduced into the provided LeoDdroid framework to deal with the challenges. Firstly, a complex core set strategy is realized, representative samples are carefully selected, and the influence of noise is reduced to the maximum extent. Secondly, the advanced reasoning ability of a large language model is utilized through a customized prompt project. In addition, a novel Multi-Sample-ACC measure is introduced, and the measure provides more meaningful evaluation for the multi-label classification performance in the malicious software detection context. The method is characterized in that a consistent MS-ACC (Maximum Sequence-Adaptive Cracking Code) score, which is realized by the LoDandroid on an anonymmouscept data set, a Drebin data set and a VirusShare data set, is higher than 0.93. Due to the powerful framework, the framework is superior to a traditional machine learning method by more than 300% in an anonymmousert data set. These results verify the framework's ability to maintain high detection accuracy with varying degrees of noise and data quality.

The invention relates to the technical field of eye fundus image processing, and particularly discloses an eye fundus image multi-label classification model and method, and the method comprises the steps: extracting the features of eye fundus images from RGB and gray eye fundus images through two branch structures based on TransNeXt; the multi-scale space-channel attention mechanisms are respectively embedded in the two branch structures, modeling is carried out after each feature extraction stage of each branch structure, and multi-scale modeling is carried out on a feature map of each stage; and the feature interaction module is used for realizing communication between the two branches, integrates a large-selectivity module to detect local details and global context information of modeling, performs feature extraction through standard convolution and expansion convolution by using a double-path architecture, and performs splicing, dimension reduction, aggregation and compression processes in sequence after feature extraction, so as to obtain the communication between the two branches. And a final output result is generated, so that the problems confronted by fundus image multi-label classification at present are solved.

The invention provides a multi-label classification method and device for an image. The method comprises the following steps: acquiring a to-be-processed image; extracting features of the to-be-processed image by using the trained first network to obtain first features; according to the first feature and a preset second feature, obtaining a prediction score of each label; and obtaining a classification label of the to-be-processed image according to the prediction score of each label and a preset score threshold. According to the method, features of an image are extracted through a trained first network, the features extracted by the first network are processed according to preset second features used for representing semantic features of all labels and correlation information of the semantic features, and therefore prediction scores of all the labels are obtained. The method not only considers the features of the image, but also considers the relevance between the labels, thereby achieving the deep fusion of the image features and the label topology, and enabling the multi-label classification result to be more accurate.

The invention discloses a resume label generation method and device and a medium, and relates to the field of natural language processing, and the method comprises the steps: carrying out the preprocessing of an original resume text, and carrying out the word segmentation of original resume data into a plurality of text words; counting word frequencies corresponding to the text words, and endowing the text words with corresponding importance weights according to the word frequencies; vectorizing the text words to obtain corresponding word vectors, and aggregating the word vectors according to the importance weight to obtain resume text vectors corresponding to the original resume text; based on a preset hierarchical label library, performing multi-label classification on the text semantic vector through a nonlinear classification algorithm, and outputting a prediction label of the original resume text; and screening the label prediction values based on a preset threshold to obtain a final resume label set. By fusing word frequency weighted semantic representation and a nonlinear classification algorithm, the accuracy and adaptability of resume label generation are remarkably improved.

The invention relates to a semi-supervised semantic segmentation method for multi-scale patch classification for sea target identification, and belongs to the technical field of computer vision. Inputting the marked data and the unmarked data into a teacher-student framework to carry out weak disturbance and strong disturbance, and then carrying out prediction; the teacher MPMC module performs multi-scale pooling operation on the extracted features and connects the features after multi-scale pooling; classifying the featured receptive field patches through a convolutional network and a linear layer, and calculating an adaptive weight according to a classification result; the self-adaptive weight sum is used for adjusting the teacher model and the student model; calculating the supervision loss between the teacher model prediction result and the student model prediction result, and calculating the multi-label classification supervision loss of the MPMC to the marked data; updating parameters of the student model through an optimization algorithm; and the student segmentation network predicts the input image to obtain a final prediction result. According to the method, the data annotation requirement is reduced, the segmentation precision is improved, and the robustness and generalization ability of the model are enhanced.

The invention relates to an incomplete multi-view multi-label classification method based on task enhancement and missing view completion. The method comprises the steps of obtaining to-be-classified multi-view data; inputting the to-be-classified multi-view data into the missing view completion and multi-label classification model, and outputting a multi-label classification result; wherein the missing view completion and multi-label classification model comprises a task-oriented feature selection module, a missing view recovery module, an adaptive learning fusion module and a multi-label classification module, and the missing view completion and multi-label classification model is obtained based on training of a training set, information bottleneck loss, reconstruction loss and target classification loss. The training set is incomplete multi-view multi-label data. According to the method, a new two-stage framework is provided, acquisition of task related information and complementation of missing views are combined, task related features are extracted in the first stage, and complete multi-view data are obtained in the second stage, so that the accuracy of the classification process is improved.

The invention provides a multi-label text classification method, device and equipment, and relates to the technical field of deep learning, and the method comprises the steps: obtaining a plurality of text samples and label sequences corresponding to the text samples, and training an initial prediction model through the text samples and the label sequences, determining a main loss function value of the initial prediction model through a binary cross entropy loss function; determining a first auxiliary loss function value of the initial prediction model by calculating a correlation difference between a punctual point mutual PPMI correlation matrix and each tag sequence; determining a second auxiliary loss function value of the initial prediction model based on the text label similarity matrix and a boundary ranking loss function; and adjusting internal parameters of the initial prediction model based on the loss function value to obtain a multi-label text classification model. According to the method, the semantic correlation between the tags is captured and compared with the result obtained by text feature training to optimize the model prediction result, and the overall accuracy of multi-tag classification is effectively improved.

The invention relates to a bridge disease two-stage analysis and risk assessment method and system, belongs to the technical field of bridge disease analysis, and solves the problems that an existing bridge disease analysis and risk assessment method is low in bridge disease prediction precision and incomplete in risk assessment. The method comprises the following steps: acquiring data of a plurality of bridges, including geometric characteristics, material characteristics, environmental factors and disease data; preprocessing the data, and carrying out data balance processing on the preprocessed data; a two-stage analysis method is adopted to carry out feature optimization on bridge diseases, and a multi-label classification model is constructed to predict joint disease probability distribution; and based on the combined disease prediction probability, carrying out risk clustering analysis on the bridge by adopting a fuzzy C-means clustering method, dividing bridge risks into different categories, and outputting a risk clustering result. According to the method, the accuracy of bridge disease prediction and the reliability of risk assessment are improved, the adaptability is higher, and good application prospects and economic benefits are shown.

The invention relates to a multi-label text classification method based on positive and negative label learning and label correlation, and belongs to the field of multi-label classification. Comprising the following steps: constructing a feedforward neural network model with double hidden layers; initializing a model component; reading features and label information of samples in the training set, and generating a feature matrix and a label matrix; randomly initializing a weight matrix and an offset parameter of the model; inputting the feature matrix into an input layer of the model, and calculating neuron output layer by layer; calculating gradients of weight matrixes and bias parameters among layers in the model by adopting a composite error function, dynamically adjusting the gradients by utilizing an Adam optimization algorithm, and updating the weight matrixes and the bias parameters; when the error change amplitude is lower than a threshold value or reaches a preset number of iterations, stopping training; and after model convergence, predicting the test set to form a final multi-label classification result. According to the method, the limitation of traditional text classification is broken through through a deep learning technology, and high-precision and high-efficiency classification of complex text data is realized.

The invention discloses a processing method and system for rejecting and hanging work order data, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the rejecting and hanging work order data and a real label corresponding to the rejecting and hanging work order data; inputting the rejected work order data into a trained basic text model to obtain a reasoning result, the reasoning result comprising a prediction label and a confidence coefficient; screening out the rejected work order data of which the confidence coefficient is smaller than a preset value and the predicted tag is inconsistent with the real tag from the reasoning result as low-quality data; determining a quality problem type of the low-quality data based on a quality problem determination rule; performing iterative optimization on the basic text model by adopting a corresponding optimization strategy based on the quality problem type to obtain a multi-label classification model; and inputting the to-be-improved work order rejecting and hanging data into the multi-label classification model to obtain an optimal reasoning result, thereby facilitating solving the problem that the reasoning result of the work order rejecting and hanging data cannot be accurately obtained in the prior art.

The invention belongs to the technical field of multimedia analysis, and discloses a multi-modal media tampering detection method, system and device based on multi-view comparative learning and a medium, and the method comprises the steps: obtaining a training data set which comprises a training image-training text pair and a corresponding tampering category label; a cross encoder is introduced on the basis of a vision-language model, a plurality of multi-layer sensor head structures are arranged, and three kinds of comparative learning of noise enhancement, prototype-based and multi-label tampering classification are designed to obtain an initial multi-view comparative learning framework; training the initial multi-view comparative learning framework based on the training data set to obtain a trained multi-view comparative learning framework; and based on the trained multi-view contrast learning framework, executing a tampering detection task of the to-be-detected image-text to the data. According to the technical scheme, the accuracy and robustness of multi-label classification can be improved.

The invention discloses an incomplete multi-view incomplete multi-label learning and classification method based on view information quality estimation. The method comprises the following steps: acquiring a plurality of incomplete multi-view incomplete multi-label data; constructing an incomplete multi-view incomplete multi-label learning model, inputting the incomplete multi-view incomplete multi-label data into the incomplete multi-view incomplete multi-label learning model for training, and obtaining a trained incomplete multi-view incomplete multi-label learning model; and obtaining complete or incomplete multi-view data to be detected, and inputting the complete or incomplete multi-view data to be detected into the trained incomplete multi-view incomplete multi-label learning model to complete multi-label classification of the multi-view data. According to the method, the complete label of each input sample is accurately predicted according to the partial or complete multi-view features of each input sample.

The invention relates to the technical field of artificial intelligence (AI) and computer vision, and particularly discloses an intelligent bus shelter passenger behavior analysis system, which is characterized in that bus shelter video streams, radar point clouds and environmental parameters are acquired through a high-definition camera, a millimeter wave radar and a temperature and humidity sensor and are preprocessed; aligning video frames and radar point cloud spatio-temporal information, and fusing features based on an attention mechanism; detecting positions of passengers by using an improved YOLOv8n network to generate a detection frame; binding a detection frame and the radar point cloud through a Hungary algorithm, generating a detection frame with a unique identity (ID) in combination with environmental parameters, and identifying behavior tags by using a Softmax multi-tag classifier; calculating the regional density to obtain a density grade signal; and displaying the abnormal behavior, the density thermodynamic diagram and the environmental parameters through a real-time visual interface, and triggering graded early warning according to the density grade and the abnormal behavior. According to the invention, accurate analysis and real-time monitoring of behaviors of passengers in the bus shelter are realized, and safety and management efficiency are improved.

The invention discloses an OCR (Optical Character Recognition) optimization method based on multi-label classification and active learning, and relates to the technical field of image recognizing.The method comprises the steps that an original image-text image is obtained and preprocessed, problem type classification is performed on problems existing in the preprocessed image based on a multi-label classification model, corresponding image problem types are obtained, and meanwhile, priorities are generated; performing targeted enhancement on the image according to the priority of the image problem classification result, repairing various problems existing in the image, and identifying the repaired image to obtain a structured character identification result list; and verifying an identification result, screening out difficult sample data, entering an active learning link according to the number of the sample data, and optimizing a multi-label classification model at the same time, thereby realizing dynamic cooperation of image quality improvement and OCR performance optimization. The problem that the accuracy of OCR recognition is reduced due to the multi-source quality degradation problem of the image in the prior art is effectively solved.