392 results about "Class imbalance" patented technology

The invention relates to the technical field of industrial intelligent inspection driven by artificial intelligence, and discloses a multi-modal property management scene risk point detection system based on artificial intelligence, and the system comprises a multi-modal data collection module which is used for synchronously obtaining visible light images, infrared thermal imaging and Internet of Things sensor data in building facilities; the domain self-adaptive defect generation module is based on a decoupling type generator and a StyleGAN2-ADA framework; a multi-modal feature fusion module; an attention enhancement detection module; a lightweight model compression frame; a real-time detection module; and a continuous learning module. High-fidelity defect samples are generated through the decoupling generator and the StyleGAN2-ADA, the problems of sample scarcity and class imbalance are solved, samples are generated through the cyclic generative adversarial network to cover multiple defect types, the acquisition period of the defect samples is shortened, and the requirement for training real samples is reduced.

The invention discloses a cascade multi-scale convolution and modal enhancement brain tumor segmentation method based on a Mama framework, and relates to the technical field of brain tumors. According to the method, an MCME-UNet model integrated with an MCMS module is provided, through a hierarchical feature extraction mechanism and a multi-modal feature fusion strategy optimized by an MEM module, the segmentation precision is remarkably improved while the calculation efficiency is kept, and particularly, an EEM module innovatively applies Sobel operator to be connected with residual errors, so that the tumor boundary continuity and the segmentation accuracy are synchronously improved; moreover, a novel training normal form of a focus Tversky loss function is introduced, so that the problem of class imbalance is effectively solved, the sensitivity of the model to a small tumor region is enhanced, a smoother segmentation boundary can be generated, and the stability and accuracy of a segmentation result are greatly improved.

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 irregular small target identification method under a non-high-definition complex background image. A multi-scale feature pyramid is constructed through bidirectional feature fusion, so that the feature expression ability of a small target is enhanced; applying a space-channel attention module to adaptively highlight target features and suppress complex background interference; by introducing a composite loss function including class balance focus loss and enhanced bounding box regression loss, model training is optimized to deal with class imbalance and improve the positioning precision of an irregular target. A self-adaptive multi-scale detection head is adopted, and dynamic feature fusion and scale perception branches are utilized to realize accurate detection of targets with different sizes; according to the method, the problems of low recognition precision and poor adaptability caused by weak features, background interference and irregular shapes of irregular small targets in low-resolution and complex background images are effectively solved, and the monitoring performance in actual applications such as unmanned aerial vehicle aerial photography and remote monitoring is remarkably improved.

The invention discloses an equipment anomaly tracing method and system based on a digital twinborn and graph neural network, and belongs to the technical field of industrial intelligent operation and maintenance and fault diagnosis. The invention provides an innovative solution integrating digital twin high-fidelity simulation and a graph structure deep learning algorithm, aiming at the technical bottlenecks that the generalization performance of an existing data driving method is insufficient under the conditions of fault sample scarcity and category imbalance and the traceability accuracy of unknown and composite faults is poor. The method comprises the following steps: constructing a high-fidelity digital twin integrating multi-dimensional physical attributes and a system topology structure; based on a fault mode, influence and harmfulness analysis method system, constructing a fault mode library comprising a plurality of single fault modes and composite fault modes, and generating an enhanced training data set with accurate labels through an automatic fault injection mechanism; training a graph neural network model with a multi-level attention mechanism by using the data set so as to learn a propagation rule of a fault in a complex system topology; and finally deploying the model to carry out abnormity traceability analysis on real-time industrial Internet of Things monitoring data. According to the method, the fault diagnosis generalization ability and the positioning precision under the sample imbalance condition are remarkably improved.

The invention discloses an electrocardiogram arrhythmia classification method and system based on a residual shrinkage network, and relates to the technical field of arrhythmia classification. According to the method, efficient electrocardiogram arrhythmia classification is achieved through multi-link cooperation, and a fine preprocessing, data balance strategy and multi-attention mechanism fusion model is designed; preprocessing provides high-quality input through wavelet denoising, precise R-wave detection and the like; the under-sampling-over-sampling mixed strategy is used for solving class imbalance and improving minority class recognition; the ResTCL-Net is fused with CNN, GRU, RCA, TSA and CLA modules, and signal features are mined in multiple dimensions; the optimization training strategy gives consideration to efficiency and stability, and is matched with comprehensive evaluation to guarantee performance. According to the scheme, the classification accuracy and generalization ability are remarkably improved, and abnormal heart beat recognition is enhanced.

Class imbalance in a training dataset may negatively impact the accuracy of a machine-learning model in classifying rare events that are underrepresented in the training dataset. Training datasets comprising time-series data present a unique challenge. Accordingly, resampling techniques for up-sampling and / or down-sampling a training dataset of time series are disclosed. The up-sampling may respect the temporal correlation of time samples in the time series, while generating synthetic time series that mimic the feature values of time series belonging to the minority class. Down-sampling may be used to fine-tune the ratio of time series belonging to the minority class to the time series belonging to the majority class.

The invention discloses an Internet of Vehicles CAN bus intrusion detection method based on noise perception active learning, and belongs to the technical field of Internet of Vehicles safety and machine learning. The invention aims to solve the technical problems of false label noise interference, high manual labeling cost, high attack missing report rate caused by class imbalance and the like. The core of the method is to execute a noise sensing mixed query strategy in an iterative loop: firstly, generating a pseudo tag through clustering and correcting by using an integrated noise detector; secondly, calculating uncertainty scores and noise probabilities of the samples, fusing the uncertainty scores and the noise probabilities to obtain a comprehensive score, and preferentially selecting the samples with high uncertainty and low noise probabilities; and then adaptively selecting a sampling strategy according to the model performance and applying category balance constraint. The query batch is used to iteratively update the model while dynamically adjusting the classification threshold to reduce the missing report rate. According to the method, the influence of pseudo label noise can be effectively suppressed, and the attack detection precision and generalization capability are remarkably improved with extremely low labeling cost.

The invention discloses a single-channel electroencephalogram signal sleep stage classification method, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining a single-channel electroencephalogram signal, carrying out the preprocessing of the single-channel electroencephalogram signal, and generating a signal segment with a fixed time length; performing multi-scale time-frequency feature extraction on the signal segment to generate a primary time-frequency feature, capturing a sleep stage conversion dependency relationship, and performing time sequence enhancement on the primary time-frequency feature to generate an enhanced time sequence feature; performing frequency spectrum statistical feature extraction on the signal segments to generate frequency spectrum statistical features; fusing the enhanced time sequence features and the frequency spectrum statistical features to generate a comprehensive feature vector; according to the comprehensive feature vector, probability distribution of different sleep stages is output through a main classifier, and a binary judgment result of the sleep stage with the minimum sample size is output through an auxiliary classifier. The method can solve the problems of class imbalance, signal complexity and calculation efficiency.

The invention provides a bearing signal expansion method and system based on a chaotic particle swarm algorithm and a generative adversarial network, and the method comprises the following steps: firstly, carrying out the preprocessing of a collected bearing acceleration signal, and carrying out the denoising through employing the discrete wavelet transform (DWT) in combination with a Bayesian soft threshold method; secondly, determining a proper signal sample length according to a Nyquist sampling theorem, slicing the signal by adopting a sliding window strategy, inputting the sliced data into a chaos particle swarm algorithm for optimization, and searching an optimized vector which is closest to the structural similarity of the sliced data; and finally, inputting slice data of a real sample into a discriminator, and superposing the optimized feature vector with random disturbance to serve as initial input of a generator. In the training process, the parameters of the generator and the discriminator are mutually confronted and updated until a preset training round is reached. And the small sample problem and the class imbalance problem in bearing fault diagnosis are effectively relieved.

The invention provides a ship noise multi-feature classifier data enhancement method and system based on a multi-fine-grained conditional diffusion model. And compressing a waveform to a potential space through VQ-VAE, extracting a ship type / ship name cross semantic vector by using ResNet, and optimizing clustering in combination with a loss function. And a one-dimensional U-Net conditional diffusion model is constructed, unconditional / conditional model output is dynamically weighted and fused, and the weight is adaptively adjusted according to training loss. In the generation stage, a semantic prototype is constructed by using a high-fine-granularity label, parameters are determined by using low / medium-granularity mean value sampling and Bayesian optimization, and fine-granularity controllable waveform generation is realized. After the generated data is converted into multiple features such as MFCC and Lofar, the generated data and original data are combined to train a classifier, and a virtual class strategy relieves class imbalance. Experiments show that the MSE of generated data and real data is reduced, the classification accuracy is improved, the data diversity and the model generalization ability are remarkably enhanced, and the method is suitable for scenes such as underwater target recognition.

The invention discloses an electric arc detection method based on differential increase and structured attention, and the method comprises the steps: firstly carrying out the collection and preprocessing of a current signal, carrying out the differential enhancement according to a sample type, carrying out the strong enhancement of an electric arc sample, improving the generalization capability, and carrying out the weak enhancement of a normal sample, thereby avoiding the overfitting; the problem of class imbalance is relieved, and the model generalization ability is improved; secondly, obtaining six complementary feature representations of time domain waveform, frequency domain frequency spectrum, time frequency analysis, envelope features, statistical distribution and related features from the differentially enhanced current signal through a multi-modal feature extraction method, and fusing to generate a multi-modal image; then, designing a deep learning model integrated with structured attention, carrying out distinguished attention on different feature analysis areas of the multi-modal image, and directionally enhancing arc features; and finally, dynamically quantifying the trained model, reducing the size of the model and reasoning delay, and supporting efficient deployment of various edge computing devices.

The invention discloses an anti-fact generation method for processing class imbalance based on a real sample. The method comprises the following steps: preprocessing input data; performing causal feature selection by adopting a causal discovery algorithm; calculating causal feature tendency scores, and performing matching; carrying out anti-fact generation, forming a synthesized minority class set, and integrating the synthesized minority class set with original data to obtain an enhanced data set; and performing data cleaning on the enhanced data set to obtain a balanced data set. According to the method, a data set is effectively balanced by generating a high-quality and close-to-reality anti-fact sample, so that the performance of a downstream classifier on key indexes is remarkably improved; the feature values of the real instances are combined to ensure that the generated samples are located in a reasonable area of data distribution, so that the credibility and availability of the enhanced data are improved; the generated anti-fact sample is located in a boundary region between the majority class and the minority class, the decision region of the minority class is effectively expanded, and the unique post-cleaning avoids the influence of noise accumulation on model training.

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

The invention discloses a drug relocation model construction method for simultaneously predicting drug-target interaction and drug-disease incidence relation, and belongs to the field of drug research and development, and the method comprises the following steps: integrating heterogeneous networks and attribute characteristics of drugs, targets and diseases, learning multi-relation node embedding by using RGCN, and constructing a drug relocation model for simultaneously predicting drug-target interaction and drug-disease incidence relation; a Gelato algorithm is combined to enhance a network structure, an auto-covariance is introduced to calculate a potential association score, and drug-target interaction and drug-disease association are synchronously predicted; the weighted cross entropy and N-pair loss joint optimization is adopted, unbiased training is realized, the problems of class imbalance and network sparseness are solved, and the model generalization ability and prediction precision are improved.

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

The invention discloses a rolling bearing fault classification method fusing adaptive distribution perception discrimination loss (ADADL), and belongs to the technical field of rolling bearing fault diagnosis. The rolling bearing fault classification method comprises the following steps of: obtaining a rolling bearing fault, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model, and carrying out classification on the rolling bearing fault by using the ADADL as a fusion model. In a complex industrial environment, classification boundary fuzziness is often caused by noise interference and feature overlapping, and the accuracy of rolling bearing fault diagnosis is reduced. According to the method, an adaptive distribution perception discrimination loss function (ADADL) is provided, and intra-class compactness and inter-class separability are improved by adjusting intra-class distance through a dynamic threshold value and optimizing inter-class distribution through an adaptive boundary. And the cross entropy loss is combined with ADADL, so that the classification precision is further optimized, the model is helped to better process samples difficult to classify, and the robustness and the adaptive ability of the model are improved. The classification performance is remarkably improved on the CWRU data set, and particularly, excellent robustness and generalization ability are shown under the conditions of class imbalance and strong noise. Feature visualization results show that ADADL can optimize clustering boundaries of different fault categories, minimize overlapping regions, and relieve the problem of fuzzy classification boundaries.

The invention relates to the technical field of image processing, and discloses a three-dimensional medical image segmentation method, which comprises the following steps: acquiring two prediction segmentation probability graphs of each sample based on two parallel sub-networks of a dual-network segmentation model, and calculating a soft pseudo label of an unlabeled sample; utilizing a distance regression head and a hyperbolic tangent function to obtain two prediction symbol distance fields of each sample based on the decoding feature map; calculating segmentation loss and regression loss of each labeled sample, and adding the segmentation loss and the regression loss to obtain supervision loss; calculating the pseudo label consistency loss and the symbol distance consistency loss of each unlabeled sample, and adding the pseudo label consistency loss and the symbol distance consistency loss to obtain consistency loss; optimizing the dual-network segmentation model under the joint constraint of supervision loss and consistency loss; and obtaining an input segmentation result of the to-be-segmented three-dimensional medical image by using the trained dual-network segmentation model. According to the method, the problem of class imbalance in the three-dimensional medical image is effectively relieved, and the segmentation precision of the weak edge region is improved.

The invention provides a complementary determining region 3 and immune epitope binding prediction method, and belongs to the field of bioinformatics. According to the method, multi-modal heterogeneous graph modeling, a graph attention network (GAT) and multi-objective loss function collaborative optimization are fused, and the method aims at breaking through the limitation of the prior art in the aspects of heterogeneous data modeling, class imbalance, prediction precision and the like. A heterogeneous graph of 3 nodes of a complementary determining region and immune epitope nodes is constructed, GAT is introduced to realize cross-modal feature interaction, and a dynamic weight optimization strategy of a focus loss function and an AUC loss function is combined, so that the recognition capability and the overall sorting performance of the model on difficult samples are improved. And meanwhile, interpretable analysis combined with the hotspot residues is realized through the attention weight. According to the method, the prediction accuracy of the binding specificity of the complementarity determining region 3 and the immune epitope is remarkably improved, and the method can be widely applied to cancer vaccine design, individualized immunotherapy and autoimmune disease research and has important theoretical significance and practical application value.

The invention relates to the technical field of image recognition and anti-counterfeiting, and particularly discloses a two-dimensional code anti-counterfeiting label printing source recognition method and system aiming at the problem of sample imbalance. In order to solve the problem that in the prior art, due to the fact that sample categories are distributed unevenly, the recognition capacity of a model in a few categories of printers is insufficient, and the reliability and robustness of a system are affected, the invention creatively provides a solution integrating multiple advanced technologies. The perception and capture capability of the model on the fine texture features of the two-dimensional code is improved from multiple angles, so that the recognition sensitivity on the features of a few types of printers is enhanced; secondly, an optimized CLIP fine tuning model is adopted, deep fusion of image and text multi-modal features is achieved, visual and semantic information in a two-dimensional code label is fully utilized, and the discrimination ability of the model in different categories and complex environments is improved; and finally, designing and optimizing a loss function, dynamically fusing label smoothing and a contrast learning strategy, effectively relieving training deviation caused by class imbalance, remarkably reducing excessive dependence of the model on majority class samples, and improving identification accuracy of minority classes and fairness of the whole system. Through the technical means, the identification bottleneck caused by sample imbalance in two-dimensional code anti-counterfeit label printing source identification is solved, the reliability, robustness and fairness of the system in a real complex scene are remarkably improved, the misjudgment and missed judgment risks are reduced, and the method has wide application prospects and important practical value.

The invention discloses a small sample target detection method based on class knowledge constraint-self-adaption, and the method comprises the steps: introducing a class self-adaption RoI-Head module, dynamically aggregating and querying a class prototype with the strongest feature correlation based on a multi-head attention mechanism, and improving the RoI feature expression capability; a cosine normalization classifier is adopted to unify discrimination boundaries among different categories, and classification deviation caused by category imbalance is relieved; meanwhile, an old knowledge constraint loss function is designed, constraint is kept through the angle of basic category embedding, original knowledge representation is maintained, and disastrous forgetting is reduced. Experimental results show that the method shows excellent incremental detection performance on a plurality of public data sets, achieves the detection capability of newly added categories under the condition of limited training samples, and maintains the recognition performance of basic categories.

The invention relates to a network intrusion detection method based on improved WGAN sampling and ensemble learning, and solves the defects that for high-dimensional and class-unbalanced network flow data, a base learner of an integrated model is insufficient in adaptive capacity, noise interference is difficult to restrain, and key attack modes are difficult to mine in the prior art. The method comprises the following steps: acquiring network flow data; performing data enhancement based on a DDWGLO framework; constructing a network intrusion detection model based on Stacking; training a network intrusion detection model; and detecting network intrusion in real time. According to the method, the DDWGLO is adopted for data enhancement, the weight is adaptively allocated based on the Newton-Raphson optimization algorithm improved on the basis of Circle chaotic mapping, and then the accuracy of network intrusion detection is improved.

The invention discloses a deep learning-based disordered material identification method and device, electronic equipment and a program product. The method is realized based on a trained identification model, a C3k2-ASL module is introduced into a backbone network, ASL Block in the module can model long-range structure dependence in the width direction and the height direction at low calculation overhead, and the perception ability and identification robustness of the model to slender, dispersed and low-contrast disordered material features are enhanced. In order to improve the modeling capability of the model on the overall spatial distribution of the disordered materials, an MGCA module is introduced into the neck network, and the accurate recognition capability of the model on different types of disordered materials in a complex city scene can be improved by extracting multi-dimensional global context information and realizing fusion through a dynamic attention mechanism. Aiming at the problem of class imbalance, a loss function is improved based on a constraint logarithm reweighting modulation mechanism, so that the model is degraded into uniform weighting during data equalization and is still stably optimized during long-tail distribution, and the generalization performance and the training stability of the model are improved.

The invention relates to the technical field of mechanical equipment fault detection, and discloses an equipment fault diagnosis method based on domain generalization and attention enhancement, comprising the following steps: step 1, preprocessing acoustic signals from multi-source domain equipment; step 2, constructing a dual-channel feature decoupling network composed of a machine feature encoder and a health feature encoder; 3, introducing a channel-space attention mechanism at the output end of the health feature encoder, and performing weighted enhancement on the health state features; step 4, constructing a multi-objective joint optimization function including signal reconstruction loss, feature redundancy suppression loss and causal aggregation loss; and 5, adopting a dynamic domain adaptive training strategy based on classification loss weighting and a class imbalance optimization method. According to the method, the expression ability of weak fault features and the identifiability of multi-domain data are improved, key fault features are effectively strengthened, and a redundant interference area is inhibited.

Application of multi-task learning technique(s) to machine logic (for example, software) used to detect financial transactions that are fraudulent or at least considered likely to be fraudulent. Some embodiments include adjustments and / or additions to conventional multi-task learning techniques in order to make the multi-task learning techniques more suitable for use in fraud detection software. One example of this is compensation for class imbalances that are to be expected as between the likely-fraud and not-likely-fraud classes of data sets (for example, training data sets, runtime data sets).

The invention provides a network attack traffic category balancing method and system based on an improved ACGAN, and the method comprises the steps: carrying out the preprocessing of a network traffic data set, and obtaining a preprocessed network traffic data set; wherein the category of the network traffic data set comprises normal traffic data and attack traffic data; carrying out preliminary enhancement on the preprocessed attack traffic data by using an oversampling technology SMOTE to obtain a preliminary balanced data set; and enhancing the attack traffic data in the preliminary balanced data set by using a generative adversarial network ACGAN to obtain a final balanced data set, and completing balancing of network attack traffic categories. According to the technical scheme, the problem that extreme categories of data samples are unbalanced in the prior art is solved.

The invention discloses an intelligent judicial multitask prediction method based on dynamic case perception, and the method constructs a multitask prediction model which comprises a shared encoder, a crime name and law article prediction layer, a case recognition module and a self-adaptive threshold adjustment mechanism. In the training stage, the model comprehensively evaluates the sample difficulty by calculating a prediction entropy value, normalizing prediction dispersion and cross-task consistency score, and identifies a difficult case by using a dynamically adjusted threshold value, and distributes a higher training weight for the difficult case. In the reasoning stage, crime name prediction and law article recommendation results are directly output. According to the method, key difficult cases can be adaptively recognized, the problem of class imbalance in judicial data is effectively relieved, and the prediction performance and overall robustness of the model for tail classes are remarkably improved.

The invention provides a construction method of a network intrusion model based on meta learning, comprising the following steps: a global server constructs a plurality of client nodes based on a federated learning framework, and constructs a node detection model in each client node; a client node collects local multivariate heterogeneous data at a fixed time interval, generates a simulated attack sample by using a GAN network, and then forms training data by using the simulated attack sample and a normal sample; performing iterative training on the node detection model by using the training data; performing meta-optimization on the trained node detection model by using an irrelevant meta-algorithm; and the global server dynamically allocates aggregation weights for each client node, and performs model aggregation in a weighted average manner to generate a global detection model. According to the method, the problems that the detection capability of a detection model on minority class attacks is insufficient and the detection model is difficult to quickly adapt to newly occurring novel network attacks due to the fact that the classes of model training samples are unbalanced in the prior art are solved.

The invention belongs to the field of machine learning and data mining, and particularly relates to a TSVM-based unbalanced data classification method, which comprises the following steps of: acquiring an original data sample of an industrial system, and preprocessing the acquired original data sample to obtain a data sample; calculating a membership value of each data sample; constructing and training a TSVM model according to the data samples and the membership values thereof to obtain a trained TSVM model; acquiring to-be-detected data, and inputting the to-be-detected data into the trained TSVM model to obtain a fault detection result; according to the method, the posterior probability of the data sample in each Gaussian component of the Gaussian mixture model to which the data sample belongs is calculated, and weighted combination is performed on the posterior probability of each Gaussian component by using the number of samples covered by the Gaussian components, so that the importance degree of the sample in the corresponding category can be better reflected; and the influence of the class imbalance problem on the model performance is relieved.

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.