371 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 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 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 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 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 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 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.

The invention relates to the technical field of artificial intelligence, and provides a protein function prediction method and device based on multi-modal protein data, and the method comprises the steps: obtaining protein multi-source data, carrying out the feature extraction of a protein sequence in the protein multi-source data, and obtaining a protein sequence feature; constructing a heterogeneous graph based on the protein multi-source data; performing feature coding on the heterogeneous graph by adopting a graph attention mechanism to obtain protein graph features; performing multi-modal fusion on the protein sequence features and the protein map features by adopting a gating fusion mechanism to obtain fusion features; and performing multi-label classification prediction based on the fusion features to obtain a protein function annotation result. The accuracy and robustness of protein function prediction can be improved, and the problems that in the prior art, multi-source protein data cannot be effectively integrated, and the method is sensitive to data noise are solved.

The invention relates to the technical field of wind turbine generator fault early warning, and discloses a wind turbine generator fault early warning method and system based on multi-modal data fusion, and the method comprises the steps: collecting the data of a multi-modal sensor, and carrying out the time-space alignment preprocessing; multi-modal features are extracted through variational mode decomposition, STL decomposition and other methods, and cross-modal fusion is achieved through dimension adaptive projection and a multi-head attention mechanism; calculating a dynamic weight based on three factors of data quality, fault type correlation and information gain, and carrying out weighted fusion; constructing a dynamic unit topological graph, and capturing cross-unit association features by using a space-time diagram convolutional network; long-time early warning with confidence is realized through double-branch gating fusion in combination with a Bayesian neural network; a multi-label classification identification multi-fault mode is adopted, and an operation and maintenance decision is optimized through an adaptive large neighborhood search algorithm. According to the method, the long early warning window of the offshore wind turbine generator can be realized, and uncertainty quantification and intelligent operation and maintenance decision support are provided.

The invention discloses a multi-label classification method and system for an order sending scene, and the method comprises the steps: carrying out the feature extraction of an input work order text, and generating a global text semantic representation; based on a pre-constructed semantic embedding vector of each preset service label, performing label knowledge enhancement on the global text semantic representation to realize alignment of the text representation and the label semantics in a semantic space; based on the aligned text semantic representation, calculating an original confidence coefficient corresponding to each preset service tag through a main classifier; for each preset service label and the work order text, dynamically generating a classification threshold value corresponding to the service label; and judging the original confidence degree based on the classification threshold, and outputting a multi-label classification result corresponding to the work order text to realize a differentiated order dispatching decision. Therefore, by means of the personalized classification threshold value generated dynamically, the problems of misdispatch and missed dispatch caused by the fixed threshold value in the dispatch scene are effectively solved, and the accuracy of multi-label classification is remarkably improved.

The invention discloses a multi-layer circuit board quality inspection method and system based on machine learning, and the method comprises the steps: carrying out the time-space registration of collected multi-source data through an adaptive weighted fusion algorithm, and generating a multi-mode quality inspection data set containing a line topological structure and material characteristics; outputting a fused circuit board defect sensitive feature vector set by using a pre-trained nested attention deep learning model based on the multi-modal quality inspection data set; inputting the defect sensitive feature vector set into a twin network architecture, positioning a potential defect area through a dynamic anchor frame generation mechanism, carrying out multi-label classification on defect types in combination with a Bayesian probability model, and synchronously introducing a defect severity evaluation module to quantify the influence degree of defects on circuit performance, and outputting a detection result containing the defect position type and severity. According to the embodiment of the invention, the collaborative judgment of the type, position and severity of the defect can be realized, and the detection precision and generalization capability of the defect of the multilayer circuit board are improved.

The invention discloses a photovoltaic panel disease detection method based on multi-source spectrum feature fusion. The method comprises the following steps: constructing a multi-source spectral data collaborative acquisition system, acquiring spectral characteristics of a 400-2500nm wave band through a hyperspectral imaging module, acquiring a dynamic response spectrum of a 0.1-10kHz frequency band by adopting a distributed piezoelectric sensor array, capturing thermodynamic spectrum characteristics in combination with a thermal infrared imager, and synchronously acquiring IV curve electrical parameters; establishing a cross-spectral domain feature fusion model, extracting multi-scale depth features by using an improved ResNet-50 network, dynamically allocating weights of a spectrum, a dynamic spectrum and a thermal spectrum through an attention mechanism, and constructing a multi-modal association topology in combination with a graph neural network; and a multi-label classifier based on XGBoost is designed. The method breaks through the limitation of a single detection mode, the recognition accuracy of the subfissure disease reaches 96.2%, unmanned aerial vehicle carrying is supported to achieve full-field rapid inspection of the photovoltaic power station, and the detection efficiency is improved by more than 5 times compared with a traditional method.

Methods and systems for classification include performing multi-label classification on an input using a trained model to generate classification outputs corresponding to respective labels. The classification outputs are fused to generate a joint opinion. It is determined that the input is out of distribution as compared to a training dataset of the trained model based on a joint belief of the joint opinion. An action is performed responsive to the determination that the input is out of distribution.

The invention provides a pulse condition modeling enhancement system based on image recognition. The pulse condition modeling enhancement system comprises a pulse position marking module, a pulse image acquisition module, a pulse wave tracking module, a pulse condition structure modeling module and a pulse condition modeling enhancement module. The invention belongs to the technical field of image recognition pulse condition modeling, and particularly relates to a pulse condition modeling enhancement system based on image recognition, which comprises the following steps of: acquiring a pulse image sequence through three-pulse position pre-labeling and high-frame-rate image acquisition; pulse wave propagation features are extracted by adopting an image processing method combining motion amplification and phase encoding, a structured pulse condition model is further constructed, and automatic identification and visual expression of common traditional Chinese medicine pulse conditions are realized by fusing multi-dimensional feature vectors, self-encoding compression and multi-label classification.

The invention discloses a composite material reflectivity spectral information classification method and system based on a PCA-SVM algorithm. The method comprises the following steps: collecting reflectivity spectral data of a composite material sample; preprocessing data to eliminate measurement deviation and unify numerical scale; carrying out dimensionality reduction on the preprocessed high-dimensional spectral data through principal component analysis, and extracting feature components retaining main variance information; based on dimension reduction features, a support vector machine is adopted to construct a multi-label classification model according to a'one-to-other 'strategy; predicting the test sample, and generating a multi-label classification result through probability output and threshold processing; and analyzing the classification performance by using the multi-label evaluation index. The data processing module of the system executes preprocessing, dimension reduction, modeling, prediction and evaluation operations. The method is suitable for lossless identification of multi-component composite samples, both interpretability and identification precision are considered, the performance bottleneck of a traditional method under the conditions of feature overlapping, insufficient samples and the like is effectively overcome, and efficient and accurate classification of the composite materials is achieved.

The invention discloses a traditional Chinese medicine multi-label evidence element classification method and system based on a graph attention network. The method comprises the following steps: performing bidirectional context semantic modeling on a clinical text by utilizing a pre-trained BERT model, generating a context-related clinical text embedded vector, and generating an initial embedded representation of a syndrome element tag through a pre-trained word vector model; and then fusing clinical text embedding and syndrome element label embedding by adopting a graph attention network to generate a clinical information-syndrome element embedding matrix, inputting the embedding matrix into a multi-layer perceptron classifier, performing multi-label classification training in combination with a Focal Loss loss function, and outputting a prediction result of a clinical information-syndrome element relationship. According to the method, the complex association between clinical information and syndrome elements can be accurately captured, high-precision and explainable decision support is provided for traditional Chinese medicine clinical diagnosis, and the method is suitable for scenes such as personalized treatment scheme recommendation, curative effect evaluation and knowledge base construction.

The invention relates to the technical field of video quality diagnosis, in particular to a video quality diagnosis method and system based on large model extensible classification, and the method comprises the steps: carrying out the sliding window sampling of an input video stream, recognizing and positioning an abnormal frame, and obtaining a plurality of image frames; respectively extracting visual features corresponding to the image frames and adding time codes and label codes; fusing time sequence information of historical frames through time coding and label coding to generate global time sequence characteristics; based on predefined abnormal category label text features, a label semantic vector set is generated and spliced, a query vector is generated through a full connection layer, an attention mechanism is utilized to match video features and label features, and a multi-label classification result is output; dynamically triggering an optimization process according to the abnormal confidence coefficient, and generating new tag text features through Few-shot sample input and low-rank adaptation fine tuning; and adding the new label text features into the label semantic vector set. The accuracy of a classification result with a small sample size is improved.