452 results about "Unbalanced data" patented technology

A full-link reconciliation method based on a snowflake algorithm, an apparatus, a device, and a medium are provided. The method includes: responding to a reconciliation request of a cross-border service and acquiring bill source data from a service end; parsing the bill source data, and converting the bill source data into target bill data; calling a reconciliation rule engine to acquire a corresponding reconciliation rule, selecting a corresponding reconciliation algorithm, and performing a full-link reconciliation task on the target bill data to acquire a reconciliation result; transmitting unbalanced data to a corresponding exception processing system for a check processing of abnormal data; generating, based on an optimized snowflake algorithm, a unique target ID to perform a corresponding bill association with the reconciliation result, automatically penetrating, according to the unique target ID corresponding to the reconciliation result, full link data of an information flow and a fund flow, and tracing, according to the unique target ID, a whole process of the corresponding bill flowing between the fund flow and the information flow, to achieve a complete closed-loop of reconciliation.

The invention discloses a main and distribution cooperative fault detection method and device based on a Mama network, and a medium. The method comprises the following steps: S1, measuring fault data of a main network and a distribution network, and constructing a training set; s2, representing a topological structure of the main and distribution network through an adjacent matrix of the graph, constructing the adjacent matrix and an edge feature matrix of the graph, and defining the weight of edges to obtain an edge feature matrix E and a feature matrix X as priori knowledge; s3, constructing a Mama network model based on the prior knowledge and the measurement data, wherein the Mama network model comprises a time sequence feature extraction network LSTM, a graph convolutional neural network, a prior knowledge and feature fusion module, a Softmax layer and a Mama network; and S4, training and testing the Mama network model by using the training set and adopting a joint loss function. According to the method, information propagation can be optimized, the calculation complexity is low, the unbalanced data processing capacity in the main and distribution cooperative power grid is effectively improved, and the model training precision and effect are improved.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention discloses an unbalanced data-oriented short-term wind power federated learning distributed prediction method, and relates to the field of artificial intelligence application of a power system. Abnormal data in the regional multi-wind-power-plant data set is cleaned through a quartile method and a cubic spline interpolation method; a local model under a federated learning framework is constructed based on a VMD-TPA-LSTM algorithm, space-time coupling features in a data set are fully extracted, and prediction precision is improved; and finally, optimizing a local model parameter uploading process of the federated learning framework based on a differential privacy protection mechanism, optimizing an aggregation direction of all local model parameters based on a Scaffold mechanism, finally completing distributed training, and perfecting a global model of federated learning. According to the method, important space-time coupling characteristics of Non-IID and unbalanced and insufficient wind power data sets are fully extracted, the limitation of multi-source numerical weather forecast NWP data acquisition and fusion is solved, and the power prediction precision of multiple wind power plants in a region is improved.

The invention relates to a bearing unbalance fault diagnosis method and system cooperatively guided by double classifiers, and the method comprises the steps: collecting bearing vibration signals in each health state under different working conditions, carrying out the fast Fourier transform, converting the signals into frequency domain signals, obtaining a data set, and carrying out the classification of the data set; dividing the data set into source domain data and target domain data under different working conditions, and marking the source domain data; constructing a fault diagnosis model, wherein the fault diagnosis model comprises a shared feature extractor, a balance classifier and a standard classifier; constructing a loss function based on the source domain data and the target domain data; training the fault diagnosis model by using a loss function to obtain a trained fault diagnosis model; and inputting a vibration signal of the bearing to be detected into the trained fault diagnosis model to obtain a fault diagnosis result. The problems that in the prior art, the performance of a diagnosis model is scarce by labels, data distribution is unbalanced, and the generalization ability of the model is improved can be solved.

The invention discloses a heart sound-electrocardio multi-modal fusion detection method based on federal learning. According to the method, collaborative fusion of multi-modal information of heart sound and electrocardio data is achieved through a cross-modal collaborative attention mechanism, the comprehensive performance of federal learning is effectively improved, and the detection accuracy of a federal model is remarkably improved; meanwhile, by introducing a weight distribution mechanism, the adaptability of the model to the imbalance of each modal data is enhanced; a differential privacy protection technology is introduced to enhance the safe aggregation of model parameters; and the method has better robustness and privacy protection in practical application.

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 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 an object classification method, a classification model training method, related equipment and a program product, and relates to the technical field of machine learning. The method comprises the steps of obtaining parameter data of a to-be-classified object; performing feature extraction on the parameter data to obtain parameter features; the parameter features are sent to a classification model to obtain a classification result, the classification model is obtained by training a random forest model through a reinforcement learning strategy, and the weight parameter of each decision tree in the random forest model is updated and fixed in the reinforcement learning training process. Through the reinforcement learning strategy, the weight of the decision tree can be automatically adjusted, so that the classification accuracy and robustness of the classification model on complex and unbalanced data are improved. In addition, compared with a deep learning model, the parameter quantity is smaller, and the required computing resources are lower.

The invention discloses a partial discharge fault type classification method based on a GAN adversarial network and a CNN-BiLSTM-Attention mechanism, and belongs to the technical field of fault diagnosis, and the method comprises the following steps: S1, carrying out the batch normalization processing of a collected high-frequency current signal, and carrying out the single pulse extraction of a processed waveform signal; s2, performing time-frequency analysis on the processed signal by using a wavelet transform method, constructing a data time-frequency spectrogram, and introducing an optimized GAN generative adversarial network to expand an unbalanced data set; and S3, converting the time-frequency spectrogram into a time-frequency matrix, and inputting the time-frequency matrix into a CNN-BiLSTM-Attention attention mechanism model. According to the method, the diagnosis model is adopted, the type of the partial discharge fault is accurately judged, meanwhile, the method can be applied to an unbalanced data model through data expansion processing, and it is indicated that the proposed model has good generalization ability.

The invention discloses a power transmission and transformation equipment fault risk assessment method and system in extremely severe weather, and the method and system improve the fault detection effect through the cloud-side intelligent cooperation technology, a self-learning federated learning mechanism and the construction of a risk map. Power transmission line fault detection based on cloud edge cooperation is composed of an edge end and a cloud end. The edge end comprises a hidden danger identification module, a data acquisition module and an algorithm iteration module. And the cloud is responsible for integrating and globally updating the model and the map. A self-learning federated learning method is used, the edge ends use own data to train a model, and model parameter data uploaded to the cloud end by each edge end are updated globally at the cloud end and deployed to each edge end. The phenomenon that the recognition capability is low due to unbalanced data collection at the edge end, missing of sample categories and overfitting is avoided, and the problem that the transmission pressure of a network channel is too large is solved.

The invention discloses a transformer fault diagnosis method based on a time-frequency domain feature interactive fusion network. The method comprises the following steps: data collection: collecting a data set of dissolved characteristic gas in transformer oil with a marked type; data preprocessing: removing obvious error data through data cleaning, and then processing unbalanced data by using oversampling and downsampling; fault type judgment is carried out based on three pairs of ratio coding combinations of gas; network construction: constructing an interactive fusion network based on time-frequency domain features; network training: inputting the training data set into the constructed network for network training; and fault classification: inputting the test data set into the trained time-frequency domain feature-based interactive fusion network to obtain a classification result. The method improves the feature expression capability, reduces the overfitting risk, improves the accuracy and efficiency of fault diagnosis, and can be used in the field of transformer operation and maintenance.

The invention discloses a wind turbine generator variable pitch system fault diagnosis method for data balance. The method specifically comprises the following steps: acquiring operation data and fault data of a wind turbine generator through a monitoring and data acquisition system equipped on a wind power plant; based on the obtained data, screening sensitive features related to the fault of the variable pitch system, reducing the dimension of a data sample and reducing the input of redundant information; according to the quantitative relation, dynamic balance factors are distributed for various types of samples, the dynamic balance factors are introduced into a multi-stage diagnosis model Dualformer based on double Transformers, and a DBFDformer data balance fault diagnosis model is constructed; and training a DBFDform model by using the screened feature data, and completing dynamic updating of balance factors and optimization of model hyper-parameters through a snow ablation optimization algorithm. The wind turbine generator variable pitch system fault diagnosis method can effectively improve the diagnosis precision and performance of the wind turbine generator variable pitch system fault under the unbalanced data, has excellent generalization ability and robustness, provides a new thought for the fault diagnosis of the wind turbine generator variable pitch system under the unbalanced data sample, and is helpful for improving the utilization rate of wind energy.

The invention relates to the technical field of data classification, in particular to a distributed unshared unbalanced data-oriented classification method, which comprises the following steps of: firstly, learning a converged basic global model based on a federal learning framework; then, an elastic weight solidification method is utilized to enable each local model to further learn local data distribution under the condition that important information in a previous round of global model is not forgotten so as to solve the problems of extreme class imbalance and general class imbalance data on most clients; the problem that an existing distributed unshared data classification method is basically proposed under a federated learning framework, but model disastrous forgetting is caused due to the fact that local data set end class imbalance exists under the framework and class imbalance data generally exists on most clients is not considered is solved. And a global classification model with strong comprehensive performance is inconvenient to cooperatively train by using the data.

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 provides an electroencephalogram signal anxiety emotion evaluation method based on unbalanced data driving, and relates to the technical field of anxiety emotion evaluation.The method comprises the steps that an anxiety electroencephalogram data set is obtained, preprocessed and divided into a training set and a test set; constructing a data enhancement model, and inputting the training set data into the data enhancement model for training; providing noise vectors randomly sampled from standard normal distribution and distributed labels for the trained data enhancement model, generating a least class of samples, and combining the training set and the least class of samples to obtain a balanced anxiety electroencephalogram training set; constructing an anxiety emotion evaluation model, and training the anxiety emotion evaluation model by using the balanced anxiety electroencephalogram data; and processing the electroencephalogram test set by using the trained anxiety emotion evaluation model to obtain an anxiety emotion evaluation result. According to the method, a certain type of signals can be generated in a targeted mode to obtain the balanced electroencephalogram data set, and the accuracy of electroencephalogram signal anxiety emotion evaluation is effectively improved.

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 tire defect detection method based on anomaly detection and a conditional generative adversarial network, and the method comprises the steps: inputting a to-be-detected tire image into a tire defect detection model, and obtaining a defect detection result; the tire defect detection model is obtained by taking a total training data set as a training set for training; the training data total set is obtained by expanding a real data set by applying an auxiliary classification generative adversarial network based on feature reconstruction, and attention modules with abnormal weights are embedded in a generator and a discriminator of the auxiliary classification generative adversarial network based on feature reconstruction. The attention module with the abnormal weight is a CBAM attention module combined with an abnormal weight matrix. According to the method, the quality of the training set is improved through the generative data enhancement model, and the method is suitable for a small sample unbalanced data environment; the data enhancement effect is improved through the idea of feature reconstruction and the attention module with the abnormal weight; detection precision is high and an application prospect is good.

The invention relates to the technical field of food safety, in particular to a food safety risk assessment method for class imbalance data based on small sample learning. The method comprises the following steps: firstly, acquiring a safety limit value interval of the content of a main hazardous substance, analyzing a mapping relationship between grain and oil food safety and the main hazardous substance, proposing a SWARA-CRITIC method to calculate subjective and objective weights, and calculating a comprehensive weight of the main hazardous substance through combining the weights by a game theory; secondly, researching a grain and oil food risk assessment grade division method, and carrying out risk grade objective division through a Gaussian hybrid clustering algorithm; finally, effective feature extraction is conducted on the unbalanced data set through a prototype network algorithm based on the cosine attention mechanism, a new attention mechanism based on the cosine similarity is designed, and compared with a prototype network, the new attention mechanism replaces a traditional zoom dot product attention mechanism. The new mechanism pays more attention to samples with the same characteristics in the support set and the query set, so that a more stable and effective relation is established, and high-precision risk assessment is realized on the premise of not losing key information. The invention provides a novel food safety risk assessment technology based on a small sample learning method.

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 relates to the technical field of computers, and discloses an object classification method and device based on distributed multi-agent reinforcement learning. Dividing the training set to obtain at least two training subsets; for each training subset, reinforcement learning is carried out on the training subset through the intelligent agent corresponding to the training subset, local model parameters obtained after the training of the reinforcement learning model of the intelligent agent are obtained, and model parameters of different reinforcement learning models before training are global model parameters; on the basis of the local model parameters corresponding to the reinforcement learning models, determining global model parameters obtained by the round of training; under the condition that the training round meets a preset condition, determining a target reinforcement learning model based on the global model parameters obtained by the last round of training, and performing object classification on the input to-be-classified signal based on the target reinforcement learning model to obtain a classification result; the object classification efficiency can be ensured, and the capability of processing large-scale unbalanced data by an algorithm is improved.

The invention discloses a coal mill unbalanced data fault diagnosis method based on a Bayesian network, and belongs to the field of generalized zero sample fault diagnosis of a thermal power generation feed pump set. Aiming at the problems of unbalanced fault data distribution, difficult minority class fault diagnosis and poor result interpretability in an industrial scene, sample distribution is collaboratively optimized through SMOTE data enhancement and Dirichlet prior smoothing, a three-layer causal topology network of'fault layer-attribute layer-observation layer 'is constructed, expert knowledge constraint and data-driven learning are combined, and the fault layer-attribute layer-observation layer-based fault diagnosis method is established. High-precision fault classification is realized; a dual-mode diagnosis mechanism is designed, fault node direct inference and attribute node indirect inference are synchronously supported, and explainable physical-level diagnosis guidance is provided while the fault diagnosis accuracy is guaranteed. The method is successfully applied to a coal mill system of a coal-fired unit, and can be expanded to transparent intelligent operation and maintenance of complex industrial equipment.

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.

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 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 a micro-grid operation optimization method based on a cost-sensitive decision tree. The micro-grid operation optimization method comprises the following steps of S1, performing data acquisition of equipment parameters, load parameters and natural condition parameters; s2, constructing an unbalanced data classification model taking maximization of information gain as a target; s3, introducing a rejection cost optimization mechanism, and updating the unbalanced data classification model; s4, completing the construction of a micro-grid operation optimization model; and S5, obtaining an optimal operation scheduling scheme. According to the method, the optimized operation level of the micro-grid can be remarkably improved, and more efficient and reliable energy management is realized.

The invention relates to the technical field of leakage diagnosis, in particular to a gas pipeline leakage diagnosis method based on unbalanced data, and the method comprises the steps: collecting acoustic emission signals in different leakage states, and constructing a training and testing data set; performing principal component analysis on the time-frequency domain signal of the acoustic emission signal to obtain a related signal; reconstructing the related signal into a grayscale image; constructing a five-layer multi-layer feature extraction module of a four-layer structure convolution branch and a side convolution branch to perform feature extraction on the grayscale image; constructing a triple weighting module based on channel weighting, position weighting and identical mapping, and inputting an output feature map of the multi-level feature extraction module into the triple weighting module; and inputting the output characteristic pattern of the triple weighting module into a working condition discrimination module, and outputting the pipeline leakage type. The method solves the problem that the prediction accuracy of unbalanced data in the gas pipeline monitoring data under the normal working condition far more than the abnormal working condition needs to be improved.

The invention relates to a multi-modal medical image intracranial aneurysm segmentation method based on geometric deep learning, and the method comprises the steps: 1, designing a medical diffusion network based on a deep neural network of frequency domain convolution; 2, preprocessing the three-dimensional grid data to realize the manifold of the model and calculate the characteristics of the grid model; and step 3, using a multiple loss function to reduce the influence of the unbalanced data set, and the like. According to the method, the tedious process of manual feature design and multi-stage processing in a traditional segmentation method is avoided; the MedDiffusion Net designed by the method solves the problem of intracranial aneurysm segmentation of medical images of various modalities, and experimental results on MRA, CTA and DSA data show that the method can obtain accurate intracranial aneurysm segmentation without training data of different modalities respectively; and adaptively fusing the features in the neighborhood by learning a diffusion parameter t.