323 results about "Data imbalance" patented technology

Disclosed in the present invention is an improved deep learning model-based refrigeration unit fault detection method. The method uses an LOF algorithm to remove outliers from a fault dataset, and then uses ADASYN technology to solve the problem of data imbalance. In addition, in respect of the problems that existing refrigeration unit fault diagnosis deep learning models are prone to network degradation, and refrigeration unit fault diagnosis models generally lack weighting critical features, the present invention first alleviate, on the basis of ResNet, the problem of network performance degradation which is prone to occur in deep neural network training processes, and then integrates a CBAM for capturing critical features in fault data, so as to improve the feature extraction capability of a network. Experimental results show that the LOF-ADASYN-ResNet-CBAM method provided by the present invention effectively diagnoses refrigeration unit faults.

The invention discloses an industrial part defect sample accurate generation method based on a conditional diffusion model, and belongs to the field of image processing and artificial intelligence. The method forms a closed-loop cooperative system by constructing four deep coupling modules of physical constraint noise scheduling, multi-scale feature coupling, double-domain feedback optimization and adaptive weight adjustment; a defect physical forming mechanism is converted into a dynamic noise scheduling strategy, deep interaction between condition information and a feature map is established at multiple levels of a diffusion network, quality closed-loop optimization is achieved through dual evaluation of a pixel domain and a frequency domain, and training weight is dynamically adjusted according to defect scarcity. And multi-scale accurate control is realized, a quality guarantee closed loop is established, the problem of data imbalance is effectively solved, and the performance of an industrial defect detection model is remarkably improved.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention belongs to the technical field of rainfall prediction, and discloses a short and temporary rainfall prediction method based on multi-model random scheduling integration, which develops a robust training and pushing framework based on a continuous rolling prediction strategy, and decomposes long-sequence prediction into manageable stages. According to the method, training is carried out through teacher forcing and planned sampling, error propagation is relieved, and the training process is stabilized. The invention further designs asymmetric encoder-decoders (DSE and AFD) that achieve lower FLOPs than competitive baselines under standardized assessment, where DSE selectively compresses significant features and AFD stepwise reconstructs details to mitigate excessive smoothing problems. Finally, an intensity weighted Gaussian KL divergence loss function is designed, and the key problem of data balance is solved by modeling and predicting on a distribution level and endowing a large weight to a meteorological important heavy rainfall event.

The invention relates to the field of Internet of Vehicles security, and particularly discloses a lightweight Internet of Vehicles intrusion detection method integrating multi-teacher distillation and boundary perception enhancement, which comprises the following steps: firstly, acquiring in-vehicle CAN bus flow data and out-vehicle network flow data, and dividing the data into a training set, a test set and a verification set; and the traffic data is converted into two-dimensional image data by using a Gramian Angular Field technology. Then, constructing a lightweight student model LAD Net, and adopting a multi-path feature extraction structure, a CBAM convolution attention module and a time convolution network TCN module; meanwhile, a multi-teacher fusion model is constructed based on a ResNet32 network, and optimization training is performed on the LAD Net through a boundary perception dynamic feature enhancement mechanism and an adaptive distillation temperature adjustment mechanism. And finally, the trained LAD Net is used for performing prediction and performance evaluation on the test set, so that the problems of high model complexity, data imbalance and insufficient generalization ability in the prior art are effectively solved.

The invention discloses a dynamic multivariate time sequence-oriented infrastructure load prediction method, which comprises the following steps of: predicting an infrastructure load by adopting a trained prediction model, segmenting acquired multivariate time sequence data, querying based on time steps and nodes of input segmented data to obtain a space-time pattern vector, and predicting the load of the infrastructure by adopting a dynamic multivariate time sequence-oriented infrastructure load prediction model. A sequence mode vector is obtained based on input segmented data query, the space-time mode vector and the sequence mode vector are spliced into a dual-mode vector output by a dual-mode pool module, and then in a dynamic MTS encoder, the dual-mode vector and segment embedding representation are spliced to obtain a final feature vector; and finally, inputting the final feature vector into a multi-scale decoder for decoding to obtain a prediction result. Aiming at challenges such as data imbalance, data missing and space-time dependence erosion, the accuracy of a prediction result is improved under the condition that repeated training is not needed, repeated training needed during topological change is avoided, and the robustness, prediction precision and calculation efficiency of the model are improved.

The invention discloses a grouting parameter intelligent feedback method based on rock mass groutability evaluation, which comprises the following steps: collecting geological condition data, rock mass quality data, field test data and grouting process data, and constructing a rock mass groutability grading data set; an SMOTE algorithm is adopted to process data imbalance, input indexes are screened through Pearson correlation analysis, and data are normalized through deviation standardization; an XGBoost classification model is constructed, hyper-parameters are optimized through grid search, the model is trained through K-fold cross validation, and the rock mass groutability grade is evaluated; constructing an XGBoost regression model by taking a rock mass groutability grade, grouting construction data and monitoring data as input, and predicting the maximum value and the minimum value of grouting pressure and slurry density by using an optimal model; and generating a grouting parameter interval according to the predicted maximum value and minimum value, and dynamically feeding back to the grouting equipment to regulate and control parameters. According to the method, scientificity and accuracy of rock mass groutability evaluation and grouting parameter regulation and control can be effectively improved.

The invention provides a diabetic retinopathy grading method based on a vision-language pre-training model and a ranking perception prompt strategy. According to the method, an eye ground color image and a corresponding DR grading label are converted into image features and text embedding through an image and text dual-coding mode, matching is carried out through the similarity between the image and the text, and accurate DR grading is achieved. In order to solve the problems of class imbalance and natural sequence modeling, a ranking perception prompt module and a similarity matrix smoothing module are introduced, ranking perception prompt ensures that the model can learn and maintain the sequence relation between different DR levels, and an SMS module effectively relieves the influence caused by data imbalance. The method disclosed by the invention can show relatively high accuracy and robustness in various DR grading tasks, especially under the condition of few samples or unbalanced categories. The method not only improves the grading accuracy, but also provides an effective auxiliary tool for clinical diagnosis, and has a wide application prospect.

The invention provides a self-adaptive sampling method for fault diagnosis under data multi-class imbalance and related equipment, and effectively solves the problems of low sample generation quality, high parameter dependence, high calculation complexity, poor adaptability to complex working conditions and the like. The method comprises the steps of coping with different data feature scenes through a parameter adaptive calculation mechanism, then exploring a global optimal solution in multi-classification modeling accuracy model solution optimization by an evolution mechanism through employing a Newton-Raphson optimizer thought, and finally converting an optimal solution set into various fault samples by using a feature recombination mechanism. Therefore, high-quality sample equalization is realized, small sample multi-class imbalance fault diagnosis is carried out by combining MAESTE and multi-class LS-SVM, and the interpretability of the model is improved.

The invention discloses an electric energy metering data anomaly detection method based on a generative adversarial network and multi-task optimization, which solves the problem of data imbalance in an electric energy metering data anomaly detection scene by utilizing a dynamic resampling strategy, and trains the generative adversarial network in combination with electric energy metering characteristics. Pseudo samples consistent with real distribution are generated through adversarial training of a generator and a discriminator, and an abnormal sample set is expanded, so that the detection capability of the model on abnormal data is enhanced. By constructing a multi-task learning framework, sharing a feature extraction module and jointly optimizing an anomaly detection task and a load prediction task, the accuracy of anomaly detection and the precision of load prediction are remarkably improved. The method specifically comprises the following steps: a data preprocessing step, a dynamic resampling step, a generative adversarial network training step, a multi-task joint optimization step, and an anomaly detection and load prediction step.

The invention discloses a few-sample target detection method and system based on an aggregation variational prototype. The method comprises the steps of constructing a data set containing a base class and a new class, dividing the data set into a support set and a query set, generating a class prototype by utilizing a P-VAE module in combination with CLIP semantic features and a feature discriminator, realizing bidirectional fusion of the support set and the query set features by means of an MFM module, fusing the query features and the class prototype, and inputting the fused query features and the class prototype into a detection head to complete target detection. The system comprises a data set construction module, a priori variational automatic encoder P-VAE module, a mutual fusion module MFM, a feature fusion module and a target detection module. According to the scheme, by introducing semantic priori, optimizing prototype generation and feature interaction, the problems of data imbalance and insufficient new class feature representation in a few-sample scene are solved, improvement of new class detection precision is verified on PASCAL VOC, MS COCO and other data sets, and an effective solution is provided for target detection of sample scarce scenes such as medical images and rare species monitoring.

The invention discloses a test risk identification method and system based on artificial intelligence, and particularly relates to the technical field of data processing. By introducing an adaptive learning rate and a feedback mechanism, the improved test risk identification method is provided for the problem of data imbalance existing in the test process of an existing machine learning model, and the test risk identification method is provided by comprehensively collecting test data including test case execution results and test environment states. Extracting a dependency relationship abnormal value and a network delay high-frequency value between code modules, constructing a data prediction model, and comprehensively calculating a deviation of a prediction result based on the data; by dynamically adjusting the deviation of the model to the successful use case, the identification capability of the failed use case and the high-risk area is enhanced, and the high-risk area is effectively prevented from being underestimated or the normal use case is misjudged to be high in risk, so that the prediction precision is improved, the configuration of test resources is optimized, the invalid test reexamination is reduced, the test efficiency is remarkably improved, and the cost and the period are reduced.

The invention relates to a bearing unbalanced sample fault diagnosis method based on dynamic modeling and a causal interpretable GAN, and belongs to the technical field of mechanical intelligent detection. Aiming at the problems of low model diagnosis precision caused by data imbalance in the prior art and deficient physical significance and poor interpretability of existing GAN network generation samples, the invention provides a technical scheme of fusing dynamic modeling and an interpretable generative adversarial network. The method comprises the following steps: generating a fault response signal with physical significance through a two-degree-of-freedom dynamic model; constructing a GabcauACGAN network, combining a causal loss function to restrain the relevance between the generated features and fault tags, and extracting time-frequency features by adopting a learnable Gabor filtering layer. The method has the technical effects that the time-frequency similarity SSIM of a generated fault sample and a real signal reaches 99%, and the diagnosis accuracy is improved to 98% or above.

The invention relates to the technical field of data processing, and discloses a defect identification method and device for defect data, equipment and a medium, and the method comprises the steps: dynamically adjusting the attention weight of a defect identification model for difficult-to-classify samples based on the feature distribution and inter-class relationship of each sample in a training sample set; extracting text features and image features of the input data through a multi-mode encoder, and generating a fusion control signal; based on the fusion control signal and the attention weight, generating synthetic defect data consistent with target defect semantics through a controllable diffusion model; mixing the synthesized defect data with an original training sample set, and training a defect recognition model after a balanced data set is constructed; and carrying out defect classification and positioning on an input image by utilizing the trained defect identification model, and outputting defect category and position information. According to the method, the problems of data imbalance and sample scarcity can be effectively solved, and the accuracy and robustness of defect identification on the defect data are remarkably improved.

The invention discloses a large model-based production breaking data analysis method, equipment and a medium. The method comprises the following steps: acquiring multi-channel production breaking data through a distributed data acquisition system, and realizing automatic classified storage of the data by adopting OCR and text extraction technologies; a deep learning model is utilized to extract a production breaking field entity, and an enterprise production breaking knowledge graph is constructed through a mutual attention mechanism; an oversampling algorithm is applied to process a data imbalance problem, and a multi-dimensional feature system is constructed in combination with a knowledge graph; a Qwen-plus large language model and an improved V-detector algorithm are adopted to realize dynamic risk early warning; similar historical cases are retrieved based on the knowledge graph, and a personalized disposal scheme is generated in combination with rule reasoning; according to the method, the knowledge graph and the deep learning technology are creatively fused, the dynamic monitoring, accurate early warning and intelligent decision support of the production breaking risk are realized, and the accuracy and timeliness of the production breaking data analysis are remarkably improved. The method can be widely applied to the field of financial institution, court and enterprise risk management.

The invention discloses an epilepsy early warning system based on electroencephalogram signals, and relates to the technical field of nervous systems, the epilepsy early warning system comprises an information acquisition module, a data division module, a data preprocessing module, a model training module and a real-time reasoning module, the information acquisition module is used for determining a few-channel electroencephalogram signal acquisition position and acquiring electroencephalogram signal data; the data division module is used for recording the total number of times of epileptic seizure events and defining the interval of seizure, the early stage of seizure and the period of seizure for each epileptic seizure event, and the data preprocessing module is used for data denoising and dynamically adjusting the overlapping proportion of a data window by adopting a sliding window-based adaptive overlapping data slicing method so as to obtain the data of the epileptic seizure event. The model training module is used for completing a preprocessing process to relieve the problem of data imbalance, and is used for constructing and training an early warning model based on a deep learning model of time-frequency feature fusion; the complexity and wearing burden of the equipment are effectively reduced, and efficient epilepsy early warning is realized.

An unsupervised industrial abnormal sound detection method combining self-supervised learning and a Gaussian mixture model (GMM) is provided to deal with intelligent industrial equipment monitoring challenges. Traditional diagnosis depends on manpower or rules, various faults are difficult to process, and data are uneven. According to the method, TinyCRNN and GMM are used, audio is processed into a Mel spectrogram through Librosa, normal sound features are extracted through self-supervision, then abnormity is identified through GMM clustering analysis, and performance is evaluated through AUC and the like. According to the method, dependence on labeled data is reduced, the problem of data scarcity is solved, detection accuracy and robustness are improved, the application potential in industrial fault detection is large, and especially the advantage on unknown faults is remarkable.

The invention discloses a voice and music collaborative generation method and system based on a dynamic mixed attention and expert architecture, terminal equipment and a medium, and relates to the technical field of audio generation. The method comprises the following steps: acquiring multi-modal input containing at least one of audio, text and vision, performing embedding processing on the multi-modal input, and mapping the multi-modal input to a unified space to obtain a fusion sequence; setting a plurality of attention heads, dynamically selecting the attention heads to activate experts through attention head routing gating, and weighting and aggregating calculation results to obtain mixed attention features; generating expert probability distribution through expert set routing gating, dynamically selecting an expert set to activate experts, and calculating task adaptation features; and performing audio generation processing based on the audio language modeling head to obtain a voice or music result. According to the method, collaborative generation of voice and music, dynamic allocation of computing resources, balance of domain-specific and cross-domain general knowledge learning are realized, the problems of task conflict and data imbalance are solved, and the audio generation quality and efficiency are improved.

The invention relates to the technical field of intelligent information processing, and discloses a knowledge graph-fused technical demand-achievement bidirectional matching decision-making system, which comprises the following modules: a data acquisition and preprocessing module, a knowledge graph construction module, a knowledge graph analysis module and a knowledge graph analysis module, the system comprises a demand mapping knowledge domain, an achievement mapping knowledge domain, a semantic processing module, a bidirectional matching calculation module, an evaluation index setting module and a decision suggestion generation module. According to the invention, through confrontation training of the generator and the discriminator, negative samples which cover complex conditions and are more challenging and diversified can be generated. The problem of data imbalance in bidirectional matching is effectively relieved, the manual labeling cost is reduced, the model can be assisted to mine potential features and learn complex matching boundaries, and therefore the generalization ability and robustness of the model are remarkably improved, and the model can be better matched with different data sets and actual scenes. And bidirectional matching of technical requirements and achievements can be realized more accurately and stably.

The invention discloses a multi-label medical image classification method, belongs to the technical field of medical images, and solves the problem that an existing CNN-based multi-label medical image classification method is low in classification accuracy. According to the method, the image data in the training image set and the clinical risk data in the feature set are combined, the features of which the importance ranks in the front are selected from the single labels through the univariate decision tree model to form the top-N feature set, and then the top-N feature set is input into the MedFusionNet model, so that the multi-mode learning of the MedFusionNet model is expanded, and the accuracy of the multi-mode learning of the MedFusionNet model is improved. According to the method, input features can be represented more abundant, challenges of label dependency, data imbalance and modal integration are effectively solved, meanwhile, interpretability and predictability are enhanced, and classification accuracy is improved.

The invention relates to the technical field of natural language processing, in particular to a big language model-assisted interactive text fine-grained sentiment analysis method, which is characterized in that a big language model, a convolutional neural network (CNN) and a Transform technology are fused, and adaptive processing of texts with different complexities is realized through dynamic weight adjustment. Comprising the following steps of 1, performing high cross-domain adaptability and optimized emotion granularity division; 2, improving data quality and enhancing model flexibility; 3, solving data imbalance; according to the method, the fine-grained emotion recognition capability of the network interaction text is remarkably improved, more accurate and comprehensive civil information support can be provided for government decision making, and improvement of government government capability and effective solution of public appeals can be promoted. Implementation of the method has an important influence on improvement of government public service quality, enhancement of a government and people interaction effect and promotion of social harmony and stability.

The invention provides an intrusion detection method based on boundary sensitive federated expert multi-modal detection, which comprises the following steps of: splicing, synthesizing and fusing seven types of discriminative characteristics based on original traffic characteristics, designing a multi-modal collaborative attention model MultiModalFusion, dividing the characteristics into four modals, namely a protocol state, a traffic behavior, statistical distribution and a connection relationship, and realizing cross-modal information interaction by utilizing dynamic weight learning. In order to solve the problem of data imbalance, a boundary sensitive condition generator BSGenemator is developed to guide generation of minority class samples through a dynamic boundary strategy in combination with a composite loss function method. And finally, constructing a federal element strategy expert committee, dynamically fusing decisions of four experts by adopting a learnable strategy network, and verifying the characteristic contribution degree through an SHAP interpretable module. And finally, the efficiency of the scheme is verified by using a data set UNSW-NB15, through comparison of multiple schemes, the scheme has significant accuracy, the weighted average F1 score is improved, and a new normal form is provided for a real-time intrusion detection scheme.

The invention belongs to the technical field of visual anomaly detection, and discloses an image small sample anomaly detection system and method based on computer vision. The system is composed of a cloud-edge environment perception federated learning module, an edge end training and pushing integrated lightweight detection module, a small sample adaptive loss optimization module and an edge-cloud collaborative iteration updating module, and a category balance comparison loss function is designed through the small sample adaptive loss optimization module. In combination with inverse frequency weighting, dynamic threshold adjustment and environmental context constraints, the model focuses on rare samples, sample differences are accurately measured, and abnormity is accurately judged according to the environmental background; self-supervised learning is utilized to generate a positive sample pseudo label, data enhancement is performed on a negative sample, the data quantity and diversity are expanded, the small sample utilization rate is improved, the influence of data imbalance on model training is effectively relieved, the labeling cost and subjective errors are reduced, the data quality is optimized, the generalization ability of the model to an abnormal mode is enhanced, and the detection effect is improved.

The invention discloses a nuclear power plant equipment fault diagnosis method, and the scheme can comprise the steps: firstly, collecting normal and fault data of 100% and 80% working conditions, and covering a plurality of crevasse fault types; state monitoring is carried out through KPCA, data are mapped to a high-dimensional space in a non-linear mode through a kernel function, and non-linear features are effectively captured through kernel matrix calculation, feature decomposition and anomaly judgment based on T2 and SPE statistics. A CNN-Bi-LSTM model is adopted in the fault diagnosis link, the CNN extracts local space features, and the Bi-LSTM captures bidirectional time sequence dependence; for a small sample working condition, model parameters trained by a source working condition (sufficient data) are migrated to a target working condition through migration learning, and fine adjustment adaptation is carried out. According to the scheme, nonlinear feature extraction, space-time modeling and transfer learning are fused, the accuracy and robustness of fault recognition under complex working conditions are improved, and the method is particularly suitable for data imbalance and sample scarcity scenes.

The invention belongs to the technical field of industrial monitoring, and particularly discloses a multi-source signal tool wear loss prediction method and system based on a one-dimensional residual network, and the method and system achieve the whole-process monitoring from signal collection to tool wear loss prediction. The signal acquisition module is arranged, and the precision of signal analysis is improved through multi-sensor signal conversion and wavelet transformation; a data processing module is arranged, the problems of data imbalance and small samples are solved through data balance operation, and the effectiveness of data processing is improved; a complex relation processing module is arranged, and a one-dimensional residual network is constructed to process a complex relation among multi-source signals, so that comprehensive signal analysis is realized; a feature enhancement module is arranged, and a multi-head attention mechanism is introduced, so that the feature extraction capability is enhanced, and the accuracy of tool wear loss prediction is improved; and a task execution module is arranged, a one-dimensional residual network and a multi-head attention mechanism model are combined, the tool abrasion loss is predicted, and the refined analysis capacity of fault detection is improved.

The invention discloses a hyperspectral and morphological feature fused rice planthopper stage and sex identification method, and aims to improve the classification accuracy of the variety, age and sex of rice planthoppers in the initial stage of nymphs. Spectral data of rice planthoppers are obtained through a hyperspectral imaging technology, morphological characteristics are extracted by combining a morphological image processing technology, and the spectral data and morphological characteristic data are combined by adopting a multi-modal data fusion technology. And training and classifying the fused data by using a machine learning algorithm, and accurately identifying the type, age and gender of the rice planthopper. In order to solve the problem of data imbalance, an SMOTE algorithm is adopted to carry out sample oversampling, and class distribution balance is ensured. The severity of insect pests is evaluated in real time through automatic counting and population density generation, and support is provided for prevention and control measures. The screening of characteristic wave bands is expected to provide key technical support for the development of special multispectral cameras. In addition, the model interpretability is improved by adopting SHAP analysis, and the contribution of the features to the classification result is quantified.

The invention discloses a semi-supervised prognosis prediction system based on irregular sampling medical data pre-training, and the system comprises a clinical electronic medical record data collection and preprocessing module which automatically collects original clinical electronic medical record EHR data from a medical database; and the pre-training module is used for receiving the patient feature vector sequence output by the clinical electronic medical record data acquisition and preprocessing module and carrying out feature representation learning on the preprocessed clinical time sequence data by utilizing a combined multi-task self-supervised learning mechanism. And the classifier fine tuning and pseudo-label iterative optimization module is used for training the pre-trained model through fine tuning of samples with labels and carrying out iterative optimization through guidance of pseudo-labels to obtain a prediction result. And the application display module is used for displaying and outputting a post-hospital-admission vital sign sequence and a prediction result. According to the method, irregular sampling and missing data are effectively processed, information loss is avoided, and the prediction capability of the model and the performance of the model under the conditions of data imbalance and label scarcity are improved.

The invention relates to the technical field of computers, and discloses a subject classification model construction method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining academic paper bibliography data based on an academic database, and constructing an initial training data set; identifying minority category subjects of which the sample quantity is lower than a preset threshold value, generating synthetic data containing chapters and keywords, and labeling corresponding subject categories; mixing the synthetic data with real data in the initial training data set, and constructing a balanced mixed training data set; splicing a text based on the chapter and the keyword of each sample in the mixed training data set, and generating a multi-level fusion feature; and taking the multi-level fusion features as input, accessing a full-connection classification layer to construct a model, carrying out end-to-end training based on a mixed training data set, and adjusting and optimizing model hyper-parameters to obtain a final subject classification model. According to the method, data imbalance can be effectively relieved, existing labeling resources are fully utilized, and the model generalization ability is improved.

The invention relates to an unbalanced bearing data fault diagnosis method based on an improved condition Wasserstein generative adversarial network. The problem of bearing data imbalance under a small sample condition is effectively relieved. According to the technical scheme, the method comprises the following steps: firstly, processing a bearing vibration signal by adopting a sliding window overlapping sampling strategy, and converting the bearing vibration signal into a time-frequency image by applying continuous wavelet transform so as to enhance fault feature representation; secondly, an improved condition Wasserstein generative adversarial network is used for carrying out data enhancement, the network measures sample distribution difference through a Wasserstein distance, introduces gradient penalty and L1 loss, combines spectrum normalization and a self-attention mechanism, stabilizes a training process and prevents gradient explosion, and meanwhile, generates a specific category of high-quality samples through condition information; and finally, carrying out accurate diagnosis by adopting a convolutional neural network equipped with a global attention mechanism. According to the method, the common collapse and instability problems of the generative adversarial network can be avoided, and the precise diagnosis of the rolling bearing can still be realized under the conditions of small samples and unbalanced data.

The invention belongs to the field of natural language processing in computer intelligent information processing, and discloses a document level relation extraction method and system based on information gain and prototype comparative learning. The invention provides a document-level relation extraction model based on a graph structure, which considers two aspects of extracting more accurate node features and relieving data imbalance. The problem that an existing document-level relation extraction model generally adopts a graph-based model and faces inherent data imbalance is solved. At present, the problems that noise interference is caused by irrelevant nodes and edges in the node feature updating process, the learning ability of a model to a real relation is insufficient due to too many negative samples in a document, and all different relation types cannot be accurately predicted through multi-label classification exist in research.