528 results about "Multi-label classification" patented technology

According to the multi-energy complementary power distribution system data analysis method based on deep learning, firstly, preprocessing and feature extraction are carried out on multi-source heterogeneous sensor data, and key indexes reflecting the health state of equipment are obtained; and obtaining globally consistent equipment state representation through a spatio-temporal data fusion technology. And then a multi-fault mode knowledge base is constructed, and a multi-label classification algorithm of deep learning is adopted to identify fault types. And finally, establishing a mapping relation between an equipment topological structure and a fault propagation path by using an attention mechanism and a graph convolutional network, and reasoning a specific position of equipment damage through fault traceability and influence analysis. The robustness and generalization ability of the model are improved by adopting methods of data enhancement, transfer learning and the like, and real-time and reliable fault location is realized through edge calculation. According to the method, the equipment fault in the multi-energy complementary power distribution system can be accurately positioned, and the reliability and safety of the system are improved.

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.

A computing system is configured to obtain a video that includes text elements and visual elements. The computing system is further configured to generate a plurality of text tokens representative of audio spoken in the video and a plurality of frame tokens representative of one or more frames of the video. The computing system is further configured to generate a set of features that includes a text feature, a frame feature, and a multi-modal feature, wherein the multi-modal feature is representative of multi-modal elements of the video, and wherein generating the set of features is based on the plurality of text tokens and the plurality of frame tokens. The computing system is further configured to associate the set of features with one or more labels to generate a multi-label classification of the video. The computing system is further configured to output an indication of the multi-label classification of the video.

The invention discloses a bioactive peptide function prediction method based on multi-view multi-modal representation learning. The method comprises the following steps: extracting polypeptide amino acid sequence information by using a multi-scale expansion convolution CNN and a bidirectional LSTM; extracting structure and functional characteristics of polypeptide molecules by using an ESM-2 model; the molecular fingerprint information is processed through a convolutional CNN and a Mamba structure; topological information of the polypeptide molecular graph is extracted through a traditional convolutional CNN, and node features are processed through a graph convolutional neural network. All the multi-view features are finally fused into aggregated feature representation through feature fusion, and then multi-label classification is carried out through a full connection layer and by applying a sigmoid function. The extracted features are finally subjected to feature splicing and fusion to form a comprehensive polypeptide molecule feature representation so as to predict various biological activity features of the polypeptide. The multi-view and multi-modal feature integration method not only enhances the prediction ability of the model, but also improves the flexibility and accuracy of processing complex biological data.

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.

A multi-label classification system performs multi-label classification using an adapted multi-class classification model in conjunction with pre-classification and post-classification processing and data transformation. An original, multi-label training data set in which objects are classified into at least one but possibly multiple original classification sets is transformed into an adapted, multi-class training data set in which the same objects are each classified into a combined classification set representing a combination of all of the individual, original classification sets into which the object is classified in the original training data set. The adapted training data set is used to train a multi-class classification model, which trained model is used to generate a multi-class classification of objects with respect to an input data set. The multi-class classification is transformed into a multi-label classification.

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 belongs to the technical field of cerebral apoplexy diagnosis, and particularly relates to a cerebral apoplexy patient tongue picture automatic identification system and method based on multi-task learning, and the method comprises the steps: carrying out the multi-label classification of a tongue picture, dividing the tongue picture into a training set and a test set, and carrying out the data enhancement; constructing an LGAN model by using the tongue picture features, extracting picture features, capturing correlation between the features, and generating a classification result of each label; and optimizing model parameters by calculating a loss value between a prediction score vector and a real label, and evaluating a model performance index. According to the method, the problems that in the prior art, standardization and objectification of tongue picture recognition of a stroke patient are insufficient, diagnosis is inconsistent and accuracy is poor are solved, and by combining multi-task learning and an attention mechanism, accuracy and robustness of feature extraction are improved, dependence on doctor experience is reduced, and standardization and automation of tongue picture diagnosis are achieved.

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 the technical field of data classification and prediction, in particular to a cardiovascular disease prediction system based on multi-modal fusion, which comprises an ECG signal processing module; a text processing module; a multi-modal fusion module; the classification module is used for extracting time sequence features of ECG signals through a convolutional neural network, processing semantic information in a medical text report by adopting a large language model and better capturing key features through prompt fine tuning, and the system realizes deep fusion of ECG and text features through a Transform encoder, exerts complementarity of two modals and improves prediction accuracy; meanwhile, the system predicts various cardiovascular diseases by using a multi-label classification model, is suitable for complicated prediction scenes of complications or multiple diseases, and remarkably improves the comprehensiveness and accuracy of cardiovascular disease prediction by introducing a large language model and prompt fine tuning compared with a traditional prediction method of a single ECG signal.

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.

A system and method for generating a multi-label classifier of textual data is presented. The method includes training a plurality of single-label classifiers, wherein each of the plurality of single-label classifiers is trained to classify textual data to a single predefined revenue-based label; and training the multi-label classifier using labeled data output by the plurality of single-label classifiers, wherein the multi-label classifier is trained to classify textual data to a vector including revenue-based labels and their respective classification scores.

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.

According to one aspect, intersection scenario description may be implemented by receiving a video stream of a surrounding environment of an ego-vehicle, extracting tracklets and appearance features associated with dynamic objects from the surrounding environment, extracting motion features associated with dynamic objects from the surrounding environment based on the corresponding tracklets, passing the appearance features through an appearance neural network to generate an appearance model, passing the motion features through a motion neural network to generate a motion model, passing the appearance model and the motion model through a fusion network to generate a fusion output, passing the fusion output through a classifier to generate a classifier output, and passing the classifier output through a loss function to generate a multi-label classification output associated with the ego-vehicle, dynamic objects, and corresponding motion paths.

The invention discloses a training method and device of a multi-level label generation model and a text classification method and device. The training method of the multi-level label generation model comprises the following steps: acquiring a plurality of training samples; the training samples are input into an original model, the original model comprises a sequence-to-sequence model and a multi-label classifier, and the input of the multi-label classifier is the output of an encoder in the sequence-to-sequence model; and training to obtain a multi-level label generation model according to the difference between the non-level prediction label of each standard text output by the multi-label classifier and the matched non-level labeling label and the difference between the multi-level prediction label of each standard text output by the sequence-to-sequence model and the matched multi-level labeling label. According to the technical scheme provided by the embodiment of the invention, the potential relationship between the text and the tag to which the text belongs is mined to the greatest extent, and the prediction accuracy of the multi-level tag can be further improved.

The invention discloses a training method of an image multi-label classification model and an image classification identification method, and the method comprises the steps: carrying out the label labeling of each original sample image in an original sample image set, obtaining a first sample image set, and enabling each first sample image in the first sample image set to be associated with at least one positive label; configuring position information for each positive label associated with each first sample image; positioning a target object corresponding to each positive label in each first sample image according to the position information of the positive labels, removing the positioned and determined target object from the first sample image, and updating the corresponding positive label to a negative label to obtain a second sample image set; and training the network model according to the first sample image set and the second sample image set to obtain an image multi-label classification model, and carrying out image classification identification according to the image multi-label classification model. According to the invention, the recognition accuracy of the model on the small target object in the image can be improved.

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.