271 results about "Unbalanced data" patented technology

The invention discloses an unbalanced sample-oriented interactive fault diagnosis method and device and a storage medium, and belongs to the field of fault diagnosis. The method comprises the following steps: firstly, acquiring operation monitoring data of a target object, and dynamically extracting time domain / frequency domain features through a sliding window algorithm to construct a mixed feature vector; and then, extracting a preset global key feature value from the mixed feature vector, and inputting the preset global key feature value into a trained fault diagnosis model to directly output a classification result. Wherein in the training process of the fault diagnosis model, original unbalance data is converted into a class equilibrium data set by using a CGAN network integrated with a sliding window, the global contribution degree of features is quantified based on an SHAP algorithm, a Top-K key feature set is dynamically locked in combination with man-machine interaction feedback, feature representation is enhanced through a feature adaptive weighted fusion network, and the fault diagnosis model is obtained. And a focus loss function is adopted to train a LightGBM classifier to focus a minority class of samples, so that the diagnosis performance and reliability of the fault diagnosis model under the unbalanced data are improved.

The invention discloses a pulmonary tuberculosis image classification and recognition system based on feature aggregation and focus area perception, and the system comprises the steps: carrying out the local and global feature extraction and fusion of a pulmonary tuberculosis image through a feature extraction subsystem; the method comprises the following steps: constructing a graph structure of fusion features through a pulmonary tuberculosis classification subsystem based on focus area perception, dynamically aggregating node information, realizing three classifications of health, tuberculosis and disease but non-tuberculosis in a first stage, and carrying out active tuberculosis and old tuberculosis subdivision in a second stage on a pulmonary tuberculosis image in combination with focus structure features; through an unbalanced data set processing subsystem based on characteristic aggregation and dynamic data amplification, under-sampling is carried out on the characteristic that active tuberculosis data samples are large by adopting a characteristic aggregation technology, and resampling is carried out on old tuberculosis with few data samples by implementing a dynamic data amplification strategy; and key information learned from active tuberculosis categories is reserved through a knowledge distillation mechanism, so that more accurate focus recognition and classification are realized.

The invention discloses a radiology report generation method and system based on visual collaborative enhancement and a cross-modal fusion network, and belongs to the technical field of natural language processing. According to the invention, a visual collaborative enhancement module is designed for modeling visual features from global and local perspectives to enhance the recognition of abnormal lesions in a radiology image, so that the attention deviation of an abnormal region caused by unbalanced data distribution is relieved. Meanwhile, a cross-modal information fusion device is provided, the module utilizes a novel double cross-modal communication component to promote multi-level fusion of visual and text information, the problem of modal isomerism is solved, and semantic-level feature alignment and refinement are achieved. According to the method, the problem that a model cannot capture key focus features due to unbalanced data distribution in a radiology image is solved, and the problem that effective alignment and fusion are difficult due to the fact that feature spaces of different modal information of image and text information are different is solved.

The invention discloses an unbalanced bearing fault diagnosis method and system based on dynamic feature optimization. The method comprises the following steps: S1, constructing a diagnosis system framework comprising a sample generation and enhancement module and a dynamic penalty feature optimization module; s2, bearing vibration signal data are collected, overlapped sampling preprocessing is carried out, and an unbalanced data set is constructed; s3, in a sample generation and enhancement module, a self-attention condition diffusion model is adopted to generate a balanced fault sample; s4, in a dynamic penalty feature optimization module, using a multi-scale feature extractor to extract fault features, and using a dynamic penalty feature optimization algorithm to adjust feature weights; s5, calculating the importance of each feature based on the SHAP value, dynamically updating the feature weight, and optimizing the sensitivity of the model to the key fault feature; and S6, training a fault diagnosis model by using the optimized features to realize high-precision bearing fault classification, and circularly adjusting the feature weight in the next round of training to continuously improve the diagnosis performance.

The invention discloses a privacy protection recommendation method based on graph federal learning, and belongs to the technical field of intelligent recommendation. The method mainly comprises the following steps: a client receives an initial weight, and constructs an initial user-article interaction graph based on user local data; modeling interaction between nodes on the initial user-article interaction graph based on the graph neural network and the initial weight; training the local user-article interaction model based on user local data; the server side aggregates the article embedding matrix gradient uploaded by each client side; the server side clusters the updated article embedding matrix to generate a sampling article set; and the client performs multi-task joint training based on self-supervised learning on the local user-article interaction model based on the user local interaction subgraph, and performs article recommendation based on the trained local user-article interaction model. The problem of unbalanced data distribution can be relieved, and meanwhile the risk of user privacy disclosure is reduced.

A system and method for facilitating training of large language model based recommender systems are provided. The system may utilize one or more LLMs to create probability distributions for binary classification tasks associated with specific user-item pairs. The probabilities may be utilized to rank one or more tasks directly. The training of the one or more LLMs may involve the use of Knowledge Distillation methods and may be based on incorporating a dual-label system such as, for example, hard labels and soft labels. The one or more LLMs training data may consist of user-item pairs and their corresponding features. The labels used in the training process may include binary classification labels and their respective probabilities. The system may further implement the trained one or more LLMs to determine rankings or recommendations associated with user engagement of one or more content items.

The invention provides a bridge design method and system based on pushing displacement and rigidity inversion, and belongs to the technical field of bridge design. A theoretical formula among the axial force of each span of steel box girder, the displacement of each pier and the anti-pushing rigidity of the piers under the condition of system temperature change after each pushing in the walking pushing construction process is deduced, and actual monitoring data of the beam length change of the steel box girder in the walking pushing construction process are combined; numerical analysis software is used for carrying out inversion analysis on the anti-thrust stiffness of each pier, the curve trend of the anti-thrust stiffness changing along with the temperature difference under the theoretical condition is obtained, the theoretical analysis of the influence of the system temperature effect on the longitudinal bridge direction in the walking incremental launching construction process is perfected, and data support is provided for implementation of measures such as pier stiffness adjustment and displacement release in the follow-up process; by calculating the sparse parameters of the missing data, the adaptability to unbalanced data and the data noise redundancy are higher, and the method is suitable for missing data supplementation in the field of bridge design which is susceptible to natural factors.

The invention relates to an unbalanced data-oriented electric power system lightning disaster risk detection method, which comprises the following steps of: S1, gridding a target detection land parcel to obtain a plurality of grid units; S2, based on latitude and longitude coordinates of each grid unit, obtaining line parameters and environment characteristics of each grid unit in combination with a line database and a geographic information base, wherein the line parameters comprise tower height, span, grounding resistance, loop number, lightning arrester number and transformer capacity, and the environment characteristics comprise soil conductivity, building height and land utilization type; and S3, inputting the line parameters and the environment characteristics of each grid unit into a trained first detection model to obtain the thunder and lightning probability and the maximum peak current of each grid unit. Compared with the prior art, the multi-source data such as the tower height, the span, the grounding resistance and the soil conductivity are comprehensively considered, and the risk assessment result has higher regional adaptability and accuracy.

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 provides a motor fault diagnosis method for unbalanced data in a noise environment based on a dual-scale network, and the method comprises the steps: collecting acceleration signals of motor vibration in a normal state and a fault state, and constructing a data set; building a fault diagnosis model based on a dual-scale network, wherein the fault diagnosis model comprises a wide convolution kernel 1D convolution module, an attention-based dual-scale module, a convolution-based feature fusion module, an attention 2D convolution layer and a classification module which are connected in sequence; based on the data set, using a category weighted loss function to train a fault diagnosis model based on a dual-scale network to obtain a trained fault diagnosis model; and inputting a motor vibration signal acquired in real time into the trained fault diagnosis model for fault diagnosis to obtain a fault type. According to the method, the influence of noise interference and uneven data on the neural network model is weakened by mining deep fault features and endowing different weights to the loss function.

The invention relates to a dynamic density guided generative adversarial network unbalanced data processing method (DAG-WGAN). The DAG-WGAN realizes unbalanced data processing through data preprocessing, dynamic density estimation and weight distribution, potential structure learning based on a variational auto-encoder (VAE), and density guide generation and dynamic feedback optimization based on WGAN-GP. The DAG-WGAN adaptively evaluates the sample density by using kernel density estimation (KDE) and a Gaussian kernel function, and allocates a weight for a generation process, thereby emphatically enhancing the low density and discriminating the sample generation of a difficult region. The VAE learns a potential manifold structure of a minority class of samples, realizes density-guided generation of a potential space under a WGAN-GP framework, and ensures diversity and manifold consistency of generated samples. In addition, a dynamic feedback mechanism is introduced, the weight and the gradient penalty coefficient are adaptively adjusted and generated, and the training stability and the sample generation robustness are improved.

The invention discloses a robot detection method and system based on a sample equalization strategy and a heterogeneous graph, belongs to the technical field of robot detection, effectively relieves the problem of unbalanced data distribution in robot detection through the sample equalization strategy, and combines the multi-relation modeling capability of the heterogeneous graph to improve the robot detection efficiency. And attention behaviors and semantic similarity among the users are fully mined, and the recognition precision of the social robot is remarkably improved. And feature information in different relational graphs is layered and aggregated by using a graph neural network, and multi-modal data is dynamically fused through a semantic attention mechanism, so that the analysis capability of the model on a complex social network relationship is enhanced. Meanwhile, noise interference is reduced through cooperative application of oversampling and undersampling, the generalization performance of the system on the camouflage behavior of the novel robot is further improved, and the robustness and reliability of a detection result in different application scenes are ensured.

The invention discloses a target activity prediction method of sgRNA, which comprises the following steps: step 1, preparing a data set, and obtaining an sgRNA activity sequence data set; 2, performing sequence feature extraction on the sgRNA active sequence data set to obtain multiple pieces of feature information; 3, fusing the multiple pieces of feature information to obtain a feature set; 4, constructing an unbalanced data set processing algorithm based on the graph weighted adversarial network; 5, introducing ensemble learning, and constructing a deep learning prediction model based on a voting algorithm; and 6, carrying out conservative motif analysis on the sgRNA high-activity sequence. According to the method, the prediction effect and robustness of the model can be ensured, different characteristics of data can be captured through diversified base learners, the over-fitting risk is reduced, sequence characteristics can be efficiently extracted, and the prediction precision and efficiency are remarkably improved; in addition, the biological significance of the model is explored, and an interpretable analysis attempt is carried out on the model.

The invention discloses a cooperative medical prediction system for a heterogeneous data center, and relates to the field of intelligent medical treatment, and the system comprises a distributed client set which is used for carrying out the localized training based on a local private medical image and dose data; the centralized coordination node is used for managing and coordinating a federation training process and executing aggregation of cross-client parameters; the decoupling model architecture comprises a globally shared feature encoder and feature adapters unique to a plurality of clients; the alternative training scheduling module is configured to periodically switch between a global aggregation mode and a localization adaptation mode, and the adaptive aggregation weighting module is used for dynamically calculating and distributing the weight of each client in global aggregation based on the statistical difference between data distribution and overall distribution of each client. According to the scheme, the overall generalization performance and prediction stability of the model on heterogeneous multi-center data can be improved, and the performance difference between centers caused by unbalanced data distribution is relieved.

The invention discloses a PFMCGAN-DNN-based unbalanced network flow data anomaly detection method, and the method comprises the steps: obtaining an unbalanced network flow data set with marked data categories, segmenting the unbalanced network flow data set to form a training set, a verification set and a test set, and carrying out the preprocessing of the training set, the verification set and the test set; building an improved generative adversarial network model, namely a PFMCGAN model, training the model by using training set data subjected to feature selection, calculating a loss function and updating model parameters through reverse gradient propagation, after training is completed, generating new data by using the PFMCGAN model, and fusing the new data with original training set data to obtain a balanced training set; building a DNN classification model, training the DNN model by using the balanced training set, calculating a loss function and updating parameters; obtaining a classification threshold by using the verification set; and evaluating model performance, inputting test set data into the model to obtain a prediction category of each piece of input data, and calculating a classification evaluation index. According to the method, the feature extraction method and the deep learning model are combined, the data complexity of the model is reduced, and the accuracy of unbalanced data network traffic anomaly detection is improved.

The invention provides a diabetes risk prediction method based on an attention-enhanced deep belief network. The diabetes risk prediction method comprises the steps of preprocessing collected original data; screening key features of diabetes by using a voting integrated feature selection method combining chi-square test, mutual information gain and variance threshold; generating synthetic data for minority class data in the screened key features by using a generative adversarial network GAN; performing attention mechanism weighting processing on the feature data to generate a weighted context vector, inputting the weighted context vector into a deep belief network, and outputting a diabetes disease probability; in the training process, the cross entropy loss and the focus loss are combined to form a mixed loss function which is used for guiding parameter adjustment of the DBN module and the attention module; and outputting a diabetes risk prediction result. According to the method, the defects in the aspects of highly unbalanced data processing, feature selection and importance, model structures and loss functions in the prior art can be overcome, and the accuracy and reliability of diabetes risk prediction are improved.

The invention discloses a jamming frame detection method and system for games, and relates to the technical field of computers, and the method comprises the following steps: fusing multi-modal features by using PyTorch, generating time sequence representation through TCN modeling based on the multi-modal features, mapping into jamming probability through a multilayer perceptron structure based on the time sequence representation, and optimizing the jamming probability by using a Focal Loss loss function. And a lagging detection result is generated. The frame difference threshold is dynamically optimized by adopting DQN reinforcement learning, so that the complexity of a game scene can be self-adapted, and the false alarm rate and the omission ratio are remarkably reduced; multi-modal features are fused through a multi-scale SE attention mechanism and PyTorch, and the feature expression ability and the model robustness are effectively improved; tCN time sequence modeling is used to accurately capture a lagging related time sequence mode, the detection precision under unbalanced data is optimized, and the accuracy and practicability of lagging detection in a high-dynamic game scene are enhanced.

The invention relates to an industrial internet intrusion detection method based on a multi-discriminator condition classification generative adversarial network, and belongs to the field of industrial internet security. The method comprises the steps that a class imbalance data set of intrusion detection is acquired, and the class imbalance data set comprises a plurality of normal samples, attack samples with the number smaller than that of the normal samples and labels corresponding to all the samples; preprocessing the class imbalance data set, and dividing the data set; establishing a multi-discriminator condition classification generative adversarial network, and performing pre-training based on the divided data set; generating various attack samples through a pre-trained multi-discriminator condition classification generative adversarial network to obtain a class balance data set; establishing an intrusion detection model, and training the intrusion detection model by adopting the class balance data set; the trained intrusion detection model is used for real-time intrusion detection. According to the method, the problem of low detection rate of minority class attacks caused by unbalanced data samples in traditional intrusion detection is solved.

The invention discloses a multi-generator adversarial network intrusion detection method based on imaging variational enhancement in the technical field of network security, which comprises the following steps of: 1, preprocessing data and encoding images, converting network flow data into a two-dimensional image format, and reserving spatial-temporal characteristics and protocol characteristics of the data for subsequent model training; step 2, constructing a multi-generator adversarial network, adopting a plurality of generators to work in parallel, each generator being responsible for generating attack samples of required categories, and optimizing model parameters of the generators and discriminators through an adversarial training process to enable the distribution of the generated attack samples to be close to the distribution of real attack samples; according to the method, the sample and the classification model are generated through collaborative optimization, the robustness and generalization ability of a network intrusion detection system on an unbalanced data set are remarkably improved, and an innovative solution is provided for network security detection.

A method and device for detecting and locating bridge defects based on 2D-3D data fusion. This invention utilizes the homogeneity between images and point clouds to identify bridge defects under complex backgrounds and unbalanced data conditions. The specific implementation steps are: ① Collect multi-view images and defect images of the bridge structure; ② Reconstruct the point cloud model of the bridge structure using the multi-view images and output a depth image; ③ Build and train an ROI extraction model, and use the depth image to output the defect image after removing the complex background; ④ Build and train an improved DeepLabv3+ defect segmentation model, detect bridge defects in the defect image after ROI extraction, and output a bridge defect segmentation mask; ⑤ Realize three-dimensional visualization of the defect based on the bridge defect segmentation mask and the point cloud model. The method disclosed in the present invention can achieve high-precision detection and positioning of bridge defects, providing a powerful tool for routine inspection of bridge structures.

The invention relates to a spiral bevel gear intelligent fault diagnosis method under an unbalanced data set, and the method comprises the following steps: collecting operation vibration signals of a spiral bevel gear in different health states, and constructing a data set; constructing an enhanced data set based on a data enhancement algorithm, fusing maximum pooling and average pooling operations to improve a deep auto-encoder, and completing characteristic pre-training of samples based on unsupervised learning; a pre-trained encoder module is adopted to process the unbalanced training data set, and data samples are mapped to a nonlinear feature space; constructing a classifier model based on a full connection layer to retrain the feature representation of the training data set, and constructing a loss function by adopting a segmented adjustable balance factor; and introducing an independent test data set on the basis of the verification set to carry out performance evaluation. The problem that the diagnosis performance is reduced due to the fact that an existing spiral bevel gear intelligent fault diagnosis method is difficult to guarantee extraction of effective fault information is solved, higher diagnosis precision is achieved, and good robustness and generalization are shown.

The invention discloses a traffic accident detection method based on a VAE model, and the method comprises the following steps: obtaining original data, and carrying out the primary processing of the obtained data; dividing a data set; converting a data format into a tensor format; performing cross validation on the data; pre-training the VAE model, adjusting and training a classifier, calculating a sample reconstruction error, KL divergence, a potential spatial distance and an output probability of the classifier, and calculating a mixed score through a mixed scoring formula; according to the method, the accuracy of a traffic accident detection model is improved, the accuracy of accident detection in an unbalanced data scene is effectively improved by fusing the characterization learning ability of the VAE and the discrimination ability of the classifier, and a mixed scoring mechanism is further introduced, so that the accuracy of the traffic accident detection in the unbalanced data scene is improved. Information of multiple dimensions such as VAE reconstruction error, KL divergence, potential spatial distance and classifier output probability is integrated, and the robustness of the model is enhanced.

The invention discloses an unweighted health degree assessment method and device based on kernel density and a medium, and relates to the technical field of Internet of Things, and the method comprises the steps: obtaining device health degree scoring index data, and carrying out the kernel density applicability analysis of the device health degree scoring index data, so as to determine an index comprehensive scoring adaptive sample; performing boundary correction on the standard kernel density estimation function to obtain a kernel density estimation model; based on the index comprehensive score adaptive sample, determining the model bandwidth of the kernel density estimation model through differentiated applicability comprehensive score analysis; according to the kernel density estimation model and the model bandwidth, through multi-key index kernel density analysis, obtaining an unweighted health degree assessment kernel density; and performing interval probability value calculation of the health degree level on the health degree assessment kernel density to determine the equipment health state probability. Through the method, the technical problem that an equipment health degree assessment method depends on weight analysis and kernel density estimation cannot process unbalanced data volume is solved.

As described herein, a base model based on imbalanced data may be selected for a machine learning process associated with a specific application. A first false positive error rate may be generated based on the selected base model. A plurality of imbalanced data sets may be generated based on the imbalanced data associated with the base model. A plurality of models may be generated based on the generated plurality of imbalanced data sets. A subset of the outputs of the plurality of models may be ensembled and a second false positive error rate may be generated based on the ensembled output of the subset of the plurality of models. The second false positive error rate may be determined to be less than the first false positive error rate.

The invention discloses a two-stage air conditioning system fault diagnosis method and system based on an improved deep residual network, and relates to the technical field of air conditioners. The method comprises the following steps: collecting historical operation data of equipment and parts of the heating ventilation air-conditioning system, and obtaining normal samples and fault samples; after the samples are preprocessed, labels are set for the samples according to categories, and normal-category samples and multi-category fault samples are obtained; constructing a deep residual network model fusing long-tail learning and an attention mechanism, inputting normal samples and various fault samples for training, outputting a predicted value of a data category label and determining a predicted fault type, thereby obtaining a trained heating ventilation air-conditioning system fault diagnosis model; and acquiring actual operation data, inputting the actual operation data into the trained heating ventilation air-conditioning system fault diagnosis model to carry out two-stage fault diagnosis, and outputting a fault type. According to the invention, rapid detection and accurate positioning of the fault of the heating ventilation air-conditioning system in a high imbalance data scene are realized.

The invention discloses a TSN network configuration detection method and device based on data enhancement and a GCN network, and the method comprises the steps: screening features through employing an MI index, and screening out the features with higher correlation degree; a CTGAN algorithm is introduced to carry out data enhancement on an original data set, and the proportion of feasible and infeasible configurations in FIFO, Manial and CP8 scheduling methods in the data set is calculated; expanding minority class samples in the data set through a generative adversarial network (GAN) after modal normalization and condition vector optimization; a GCN neural network is selected to carry out network configuration detection, features obtained through screening are used as node features, link loads are introduced to serve as edge features, adjacent nodes are gathered through two layers of graph convolution layers and normalization layers in a rolling mode, overfitting is reduced, and finally network configuration information is fully extracted through global average pooling GAP and a full connection layer MLP. According to the method, through CTGAN data enhancement and the GCN neural network, the classification capability of the model on the unbalanced data set is enhanced, and the classification accuracy is further improved.

The invention discloses a multi-part identification method and system for a nose and throat endoscope image, and relates to the technical field of medical image processing, and the method comprises the steps: inputting the nose and throat endoscope image into a lightweight channel feature extraction module, and extracting channel global features and fine granularity dependence features; adding the channel global features and the fine granularity dependency features to obtain channel features; inputting the channel features into a local feature extraction module, and extracting small receptive field local features and large receptive field local features; adding the small receptive field local features and the large receptive field local features to obtain fusion features, and performing channel transformation and compression on the fusion features to obtain channel fusion features; and inputting the channel fusion features into a classifier to obtain an identification result of each part. According to the application, through the lightweight channel feature extraction module, the local feature extraction module and the mixed loss function, the defect of low classification precision caused by insufficient channel dimension feature modeling, high similarity between nose and throat part classes and seriously unbalanced data classes is optimized.

The invention discloses an unbalanced disaster risk prediction method based on a WGAN-CNN, relates to the technical field of disaster risk prediction, and aims to improve the prediction capability by optimizing feature learning of WGAN and CNN models in order to solve the problem of predicting rare disasters by using unbalanced and heterogeneous data. Data sources comprise satellite images, radars, social media and the like, and data consistency is ensured by unifying multi-modal data timestamps and aligning time sequences through dynamic time warping; then, using an improved WGAN to generate a rare disaster sample so as to enhance unbalanced data, and realizing data security sharing through encryption gradient and differential privacy technologies; in addition, based on geological similarity cross-neg region mapping weights, risk levels are evaluated and pushed in real time in combination with a CNN discriminator. According to the method, the data quality is improved through the WGAN, accurate prediction is realized by using the CNN, a real-time risk assessment tool applicable across regions is finally formed, and the rare disaster prediction capability is effectively improved.

The invention discloses a federated learning fault diagnosis method and a federated learning fault diagnosis system for multi-source unbalanced data of a harmonic reducer, relates to a harmonic reducer fault diagnosis technology, and aims to solve the problem of low diagnosis accuracy caused by unbalanced sample numbers of different fault categories of the harmonic reducer of an industrial robot and limited single-source signal acquisition information. The method is technically characterized by comprising the following steps of: performing wavelet transform on multi-source signals of different users to construct a time-frequency graph data set; carrying out equalization processing on the unbalanced data set by utilizing an improved data enhancement method; an effective channel attention mechanism is introduced, and the output of a residual branch is weighted through a learnable weight, so that the adaptability of the model to different residual information and the extraction capability of the model to data key features are enhanced; the method comprises the following steps: mining complementary information among multi-source signals through an improved multi-mode variational auto-encoder to perform feature fusion, and constructing a multi-user personalized local model; and the server aggregates local model parameters and updates the model, and guarantees user island privacy data through federal learning, thereby performing fault diagnosis on the harmonic reducer under the multi-source unbalanced data. A harmonic reducer signal acquisition experiment platform is established for verification, the characteristics of multi-source unbalanced data can be effectively extracted by the method, information fusion is realized, the average fault diagnosis accuracy is 98.8%, and the performance is superior to that of the compared method.

The invention relates to the technical field of data mining, in particular to a density clustering analysis method, system and device based on an adaptive grid and a medium. The method comprises the following steps: dividing a data space into a structured network; a graph structure is constructed in a combined sampling mode, and it is ensured that small clusters obtain sampling coverage; and executing a first algorithm based on the sampling subset result to obtain a clustering result. Through a grid-based adaptive sampling strategy, the problem that small clusters are ignored when unbalanced data are processed by a traditional sampling method is effectively solved. Experiments are carried out on different data sets, the result shows that the sampling accuracy of the method is improved by three times compared with that of an existing method, the clustering accuracy is improved, and the clustering structure in the data can be revealed more accurately.