117 results about "Data balancing" patented technology

The invention relates to an automatic driving large model training optimization method based on multi-scene data balance. Comprising the following steps: (1) constructing a real vehicle high-speed driving scene library; (2) constructing a visual language automatic driving large model, training by adopting an iterative training framework based on a real vehicle high-speed driving scene library, designing a multi-task joint loss function and a weight adaptive adjustment strategy, realizing multi-task target balance, and obtaining a trained visual language automatic driving large model; (3) dynamic simulation is carried out for the automatic driving working condition, and a high-fidelity simulation data test set is constructed based on simulation data; and (4) performing hyper-parameter optimization and lightweight processing on the trained visual language automatic driving large model according to the high-fidelity simulation data test set to obtain a scene data balanced automatic driving large model. According to the method, the large model reasoning speed is increased, and resource occupation is reduced, so that the judgment capability of the large model on dynamic working conditions and high-risk scenes is remarkably improved.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

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.

The invention relates to the technical field of data signal processing, and discloses a data equalization system and method, and the method comprises the steps: inputting an input signal into an improved equalization network in response to a signal compensation request, carrying out the affine transformation of the input signal through a neuron, and obtaining a transformed signal, according to a trainable coefficient of a B-spline primary function learned during affine transformation, a nonlinear activation function is determined, then weighted summation is carried out on transformed signals, nonlinearity is introduced through local interpolation and recursive definition of the B-spline primary function in the weighted summation process, nonlinear mapping signals are obtained, and then through judgment and error code detection, the non-linear mapping signals are obtained. And outputting the balanced data signal meeting the error rate threshold. According to the method, the input signal is primarily processed through affine transformation, nonlinear mapping is carried out by using the nonlinear activation function based on the B-spline primary function, and the complex high-dimensional signal processing problem is decomposed into the combination of a plurality of low-dimensional nonlinear functions in the process, so that the calculation complexity is reduced.

The invention discloses a PHM-oriented equipment state monitoring and predicting method. The method comprises the following steps: acquiring a sound signal in equipment through a sensor; transmitting the sound signal data to a storage and analysis module, and storing the sound signal data in a database after the storage and analysis module receives the sound signal data; the storage analysis module inputs the sound data into the preprocessing module for preprocessing operation; inputting the preprocessed sound signal into a diffusion probability model for data balance processing; performing feature extraction and classification on the balanced data by adopting a parallel dynamic convolution-sequential network model; and judging the health state of the equipment according to the classification result. A wavelet denoising module, a diffusion probability model and a deep learning model are integrated in an equipment health state monitoring system, and acquired equipment sound data are processed and analyzed step by step. According to the method, the accuracy and reliability of fault prediction can be effectively improved, and more accurate decision support is provided for health management of equipment.

The invention relates to the technical field of visual identification, and discloses an operation stage identification method based on a dynamic data balance strategy, and the method comprises the following steps: S1, collecting operation original data, and carrying out the annotation of the collected original data; s2, preprocessing the annotated original data by utilizing a dynamic data balance strategy so as to form a data set; s3, constructing an Xception-double flow LSTM (Long Short Term Memory) recognition model; and S4, training and optimizing the Xception-double-flow LSTM recognition model by using the data set. According to the method, the problem of bias learning is effectively relieved, the influence of data volume difference on a model learning process is effectively reduced, and deviation is reduced, so that higher classification performance is shown in an operation stage identification task; the method effectively improves the overall recognition precision of the operation stage, can provide more structured support for operation training, quality control and clinical decision making, and further lays a foundation for research and development of an intelligent operation auxiliary system.

The embodiment of the invention relates to the field of distributed technologies, and discloses a distributed vector data balancing method, device and equipment based on Redis and a storage medium, the method comprises the steps that at least one to-be-migrated data subset is obtained, and the data subset is obtained by dividing a vector data set according to data dimensions and the value domain range of each data dimension; obtaining an available resource total score corresponding to the at least one candidate Redis node in real time; sequentially storing the at least one to-be-migrated data subset to the candidate Redis node with the highest available resource total score according to a preset sequence; and generating an index directory according to the value domain range corresponding to the at least one to-be-migrated data subset and the correspondingly stored candidate Redis nodes. By acquiring the total score of available resources corresponding to each candidate Redis node in real time, the load of the node is fully considered, the data subset is stored to the node with lower load, the load balance of the candidate Redis nodes is realized, the utilization rate of each candidate Redis node is improved, and the stability of a system where the candidate Redis nodes are located is improved.

The invention relates to a data balancing method and device and computer equipment. The method comprises the following steps: determining a hard disk to be migrated out and a hard disk to be migrated in based on the access heat of data blocks contained in each hard disk; obtaining a to-be-immigrated hard disk sequence based on each to-be-immigrated hard disk; respectively determining to-be-migrated data blocks corresponding to the to-be-migrated hard disks to obtain a to-be-migrated data block sequence; traversing the to-be-immigrated hard disk sequence, determining a target to-be-immigrated data block matched with the to-be-immigrated hard disk in the to-be-immigrated data block sequence for each to-be-immigrated hard disk, determining a target data block in the to-be-immigrated hard disk, and performing data migration based on the target to-be-immigrated data block and the target data block, according to the technical scheme, data balancing and capacity balancing among the hard disks can be carried out on the basis of considering the access heat among the hard disks, and the reading flow can be uniformly guided to the hard disks, so that the access heat of the data is used as a basis for balancing, and the read-write performance of the hard disks can be improved to a greater extent.

The invention discloses a lung cancer patient dynamic survival prediction model based on multi-dimensional clinical data, and relates to the technical field of cancer survival prediction, and the key point of the technical scheme is that the dynamic survival prediction model comprises a core model module and a data support module, the core model module comprises a graph convolutional neural network model, a long-short-term memory network model and a generative large language model, and the data support module comprises three data processing systems, namely a time-dependent variable, a time independent variable and a data balancing strategy. According to the dynamic survival prediction model based on combination of traditional Chinese medicine and western medicine, abundant multivariate data in EHR can be fully utilized, individualized and precise diagnosis and treatment of the patient can be realized, artificial intelligence is assisted, and dynamic prediction of the survival rate of the lung cancer patient in one year, two years and three years can be effectively realized.

The present invention relates to a method for detecting network abnormal behaviors based on dynamic data balancing and generation, including: acquiring original network traffic data, inputting the original network traffic data into a dynamic abnormal data balancing model to obtain an enhanced data set; marking the original network traffic data based on the abnormal data balancing model, performing selective undersampling on the marked data, adjusting the ratio of normal data to abnormal data to obtain a balanced data set, generating new abnormal data according to the balanced data set, and merging it with the balanced data set to obtain an enhanced data set; inputting the enhanced data set into a TimeDiT denoising model for denoising to obtain a denoised target data set, and inputting it into an LSTM-Transformer weighted training model for detecting network abnormal behaviors, weighting the abnormal data in the target data set in combination with a weighted loss function to obtain a detection result. The present invention can improve the detection efficiency and accuracy of network traffic abnormal behaviors.

The invention discloses a time sequence coal reservoir permeability prediction method based on improved deep learning, and belongs to the technical field of coal bed gas exploration and development and deep learning. According to the method, standardized preprocessing is performed on logging data, a time sequence sample is constructed by using a sliding window, stratified sampling is introduced to optimize K-fold cross validation, and an AdamW optimizer is adopted to train a double-layer LSTM network, so that the problems of low precision and poor generalization ability of a traditional prediction method are effectively solved. Experimental results show that the method can accurately capture the time sequence characteristics of the logging data, data balance is ensured through stratified sampling, overfitting is inhibited in combination with weight attenuation, and the prediction result has high stability and reliability.

The invention relates to the field of industrial network security, and particularly discloses a picture network intrusion detection method based on feature selection and data balance, which comprises the following steps of: firstly, acquiring an intrusion detection data set containing non-numeric data and numeric data, performing one-hot coding on the non-numeric data, performing maximum and minimum normalization on the numeric data, and performing one-hot coding on the numeric data; dividing a training set and a test set; performing feature selection on the training set by using a mixed feature selection strategy of a ReliefF filtering method and a Boruta packaging method, and performing resampling processing on the training set by using an SMOTE-ENN mixed sampling method; then, converting the resampled training set and the original test set into grayscale images, and performing training by using an OfficientNet image classifier to obtain a trained model; and finally, the model is used for carrying out classification prediction on gray level images of a test set. The problems of data redundancy, dimension disasters and class imbalance in an IIOT environment are effectively solved, and the accuracy and stability of an intrusion detection system are remarkably improved.

The invention provides a multi-center data standardization method for evaluating the performance of an AI ultrasonic model, and relates to the technical field of ultrasonic data processing, and the method comprises the steps: collecting original ultrasonic image data from different data sources, and carrying out the standardization preprocessing, and obtaining a standardization preprocessing image; a multi-task quality evaluation model is constructed and pre-trained; applying a pre-trained multi-task quality evaluation model to carry out quantitative evaluation on the image to obtain a quality evaluation index, and based on the quality evaluation index, carrying out quality screening by adopting a batch adaptive threshold strategy and a multi-stage screening mechanism to obtain a qualified labeled image; and layered sampling is carried out on the labeled images with qualified quality, and a standardized multi-center ultrasonic evaluation data set is obtained. According to the method, the problems that quality evaluation is difficult due to heterogeneity of multi-center ultrasonic data sources, a fixed threshold strategy cannot adapt to quality baseline differences of different data sources, artifact evaluation lacks spatial position consideration, and a multi-center data balance sampling mechanism is lacked can be solved.

This disclosure describes a module and method for configuring a generative adversarial network-based continual oversampling (GAN-COS) module to augment a training dataset. The augmented training dataset may be used to train a classifier module to detect defects in a dataset. During the configuration of the GAN-COS module, datapoints associated with tasks in a training dataset are balanced using a data balancing module before the balanced dataset is then processed by a shared generator and a private generator of the GAN-COS module. The latent vectors produced by these two types of generators then undergo latent space oversampling and are used in the training of a discriminator of the GAN-COS module. The latent vectors may then be combined and used to form the augmented training dataset.

The invention discloses an intelligent equipment management system integrating charging and data dump, and relates to the technical field of equipment management, and the system comprises an identity authentication module which is used for carrying out the dual authentication of a user identity through a biological feature and an authority token, extracting the three-dimensional feature of a task from a production system, and storing the extracted three-dimensional feature; calculating the adaptation degree of the task and the equipment through a feature matching algorithm to generate a candidate equipment pool; the working condition sensing module is used for generating working condition fingerprints, and comparing historical fingerprints through a dynamic time warping algorithm to judge an abnormal level; the charging transmission and storage module is used for constructing an energy data balance model according to the priority score and the task characteristics, dynamically adjusting the charging power and the dump bandwidth, and scheduling equipment through a predictive migration mechanism; and the resource management module is used for constructing a full file of the equipment, dynamically adjusting an equipment matching rule and a user permission based on an association rule algorithm, and generating a predictive maintenance work order, so that the use safety, the matching accuracy and the maintenance efficiency of the equipment are remarkably improved.

The invention relates to the technical field of enterprise data analysis, in particular to an enterprise data intelligent analysis and traceability method, which comprises the following steps: performing enterprise data acquisition on a target management enterprise to obtain initial enterprise data, obtaining historical enterprise data of the target management enterprise and classifying the historical enterprise data to obtain a plurality of enterprise data analysis areas, according to data fluctuation parameters determined by the enterprise data in the current detection period of each type of enterprise data analysis area, determining an abnormal trend type; selecting and determining an analysis mode corresponding to the enterprise data analysis area according to the abnormal trend type so as to determine abnormal enterprise data, performing enterprise data balance verification on the data source, and determining whether a current enterprise data statistical inspection result is valid or not according to a verification result so as to determine whether the abnormal data source is traced or not. According to the invention, through a layered and classified enterprise data intelligent analysis and dynamic traceability mechanism, the accuracy and efficiency of data anomaly detection are significantly improved.

The invention discloses a femoral head necrosis prediction system and method based on Transform, and belongs to the technical field of image processing, the system comprises a data acquisition module, a data processing module, a data balancing module, a feature extraction module, a feature merging module and a necrosis classification module; the method comprises the following steps: acquiring preoperative hip joint CT data, pre-processing to unify specifications, adopting directional data enhancement to solve the problem of class imbalance, extracting features by virtue of Video Swin Transform, merging and modeling a spatial relationship through volume blocks, and finally outputting a necrosis classification result. According to the femoral head necrosis prediction system and method based on Transform provided by the invention, the prediction accuracy and robustness are effectively improved, the problem of model instability caused by strong subjectivity, hysteresis and data imbalance in the prior art is solved, an objective and efficient prediction tool is provided for clinic, and the risk of postoperative complications is reduced.

This invention belongs to the field of image registration technology, specifically relating to a medical image registration method and system based on a large language model prompt. The method includes: acquiring multi-regional medical images and corresponding medical image description text; inputting the medical images and corresponding medical image description text into a multimodal adaptive feature encoder to extract medical image features and medical image description features; fusing the medical image features with given target medical image features to generate hybrid features, predicting an initial deformation field based on the hybrid features, performing multi-level deformation optimization on the initial deformation field to generate an optimized deformation field, and obtaining a medical image registration model; based on the multi-regional medical images, employing a dynamic data balancing strategy to amplify the medical image data of different regions, and inputting the amplified multi-regional medical images into the medical image registration model to obtain multi-regional medical image registration results.

The invention discloses a training data balance processing method and device, equipment, a medium and a product. The invention relates to the technical field of data processing. The method comprises the following steps: when a user in a user set is associated with an object in an object set, generating associated data according to the associated user and object, and adding the associated data into an associated data set; acquiring other users of the same category as the target user corresponding to the to-be-supplemented data of the associated data set; obtaining a target object associated with the target user in the associated data set; according to each target object, determining candidate objects of the target user in other objects associated with the other users in the associated data set; and generating associated data according to the target user and each candidate object, and adding the associated data into the associated data set. The embodiment of the invention can balance the training data of the model.

The embodiment of the invention provides a data balancing method, device and equipment of a Ceph cluster and a medium, a plurality of placement groups are deployed in the Ceph cluster, each placement group comprises a master copy and a slave copy, the master copy and the slave copy of each placement group are placed on different storage devices in the Ceph cluster, and the method comprises the steps that data distribution information of the Ceph cluster is acquired; according to the data distribution information, performing a first balance operation on a primary copy of a placement group borne on each storage device in the Ceph cluster; and according to the data distribution information, carrying out second balance operation on the slave copies of the placement groups borne on the storage devices in the Ceph cluster. According to the embodiment of the invention, the code of the Ceph does not need to be modified, the number of the slave copies is balanced after the number of the master copies is balanced according to the data distribution information of the Ceph cluster, and the overall performance and the resource utilization rate of the cluster are improved and the risk caused by upgrading of the old version Ceph cluster is also avoided by ensuring the double-sided balance of the placement group in the Ceph cluster.

The invention belongs to the technical field of breast cancer molecular typing, and more specifically relates to a mammary gland ultrasound video HER2 expression state identification method based on spatial-temporal feature interaction. The method comprises the following steps: taking an original breast ultrasonic video as input data, performing multi-stage data preprocessing operation, and then performing data balance on training data to obtain a standardized training set; sequentially constructing a UniFormerV2 network-based feature module, a multi-stage feature fusion module and a classification module, and training to obtain a trained model; and inputting the test set into the model to obtain a final prediction result of the HER2 expression state. The problems that an existing HER2 detection method is invasive in operation and cannot perform dynamic monitoring, and an existing deep learning method based on static images neglects space-time dynamic information in an ultrasonic video, so that prediction accuracy of the HER2 expression state, especially HER2-Low subtype is insufficient are solved.

The application discloses a data balancing method and device, equipment and storage medium, and relates to the technical field of computers. The method comprises the following steps: performing hash processing on first samples and second samples in a data set, and mapping the first samples and the second samples into first hash buckets and second hash buckets; determining second samples matched with the first samples in the first hash buckets from second hash buckets adjacent to first hash buckets in the first hash buckets; searching in adjacent hash buckets in a counterfactual search process, reducing a search range, reducing a search amount, and improving search efficiency. Then, based on a feature relationship between the first samples and the second samples, third samples in the second samples are processed to obtain fourth samples. The labels of the fourth samples are consistent with the labels of the first samples, and data balancing is realized. Since the time required by the search process is shortened, the efficiency of data balancing is improved.

This application discloses a method for scaling up nodes in a distributed search engine, including obtaining the current information of the distributed search engine cluster and the information of the scaling nodes; calculating the total number M of shards that the scaling nodes can accommodate based on the current information of the cluster and the information of the scaling nodes; adding the scaling nodes to the cluster; performing multiple rounds of node pre-allocation, each round of node pre-allocation including: establishing a weight index and dividing the weight index into N weight shards; performing data balancing on the cluster; and completing node allocation when N reaches a preset value. The solution provided by this application solves the problem in the prior art where distributed search engines cannot scale up the cluster in-situ or on demand when the cluster data capacity or data write performance reaches its limit, resulting in resource waste.

The invention discloses a self-adaptive oversampling method combining sample local density and position information, and belongs to the technical field of data balance. According to the method, minority class samples are divided into boundary minority class samples and safe minority class samples; then, aiming at the safe minority class samples, endowing the samples which are considered to be more important with higher weights so as to generate more new samples by utilizing the samples in a subsequent sample generation process; in the synthesis stage of the security sample, screening out neighbor samples meeting conditions, and generating a new sample near the security sample; finally, for the boundary minority class samples, adopting a sampling method combined with the majority class samples to synthesize samples for the boundary minority class samples; more emphasizes are put on important minority samples, adjacent samples are adaptively selected for the important minority samples, and the problem of determining where and how to generate new samples is solved.

The application relates to an accident prediction method and system fusing time mixing effect and machine learning, wherein the method comprises the following steps: acquiring traffic state data containing a time category and traffic accident data, and dividing the traffic state data into accident data and non-accident data based on the traffic accident data, taking the accident data as a training data set of a data balancing model; generating balanced accident data by using the trained data balancing model, integrating the accident data, the balanced accident data and a non-accident data set to obtain a first data set, and taking the first data set after pretreatment as a training data set of a real-time accident prediction model; inputting real-time collected traffic state data into the trained real-time accident prediction model to output an accident prediction result; and the system is used for realizing the above method. Compared with the prior art, the data quality generated by the sampling method is guaranteed through data balancing processing, and the prediction result of the accident is more accurate.

The invention discloses a data balancing method and device, a server, a computer readable storage medium and a computer program product. According to the method, the node state in the machine room is monitored in real time, the node load distribution is determined by evaluating the node state, the current load condition is determined, the balancing operation is automatically triggered when the node load is unbalanced, and the balancer is dynamically started or stopped, so that intelligent balanced scheduling of data is realized, on one hand, manual intervention is reduced; on the other hand, the unbalanced condition of the node load can be found in time by monitoring the node state in real time, so that the data distribution of the cluster can be adjusted in time, and the stability and reliability of the overall operation of the system are improved; besides, in the method, each machine room runs an independent balancer instance, and only the balancer in the machine room is triggered to execute local balancing operation on the internal node of the machine room to which the balancer belongs, so that unnecessary cross-machine-room data transmission is greatly reduced, and the cross-machine-room network consumption is reduced.

The application relates to a power grid edge device instruction set data balancing method based on a federal learning framework, which comprises the following steps: collecting instruction information D of power grid edge devices and the type L to which the instruction belongs for a power grid system in n regions; constructing a feature vector matrix of the device instruction information; initializing a conditional generative adversarial network model on a server, generating an RSA public and private key pair on the server, sending the public key to the power grid system in each region, and sending the model to the power grid system in each region; training the conditional generative adversarial network using the instruction data set and the label set collected by the region; until the power grid system in each of the n regions completes the training of the conditional generative adversarial network model by using the data set; performing federal scheduling through the server; and forming a large-scale balanced data set. The application uses the large-scale balanced data set to train the power grid edge device instruction anomaly detection model of each region, and accurately evaluates the device security vulnerabilities.

The invention discloses a service report configuration method and device, equipment, a storage medium and a program product, and relates to the technical field of service processing. The method comprises the following steps: receiving a parameter configuration table sent by a client; determining at least one modified target service type in the parameter configuration table; the at least one debt item attribute is configured as a first mapping condition in a first mapping table, target debt item data corresponding to the target service category are determined according to the debt item data meeting the first mapping condition, and the first mapping table comprises a mapping relation between the debt item attribute and the debt item data; summarizing and calculating the target debt item data to obtain index data corresponding to the target business category, the index data including data balance and risk weighted assets; and obtaining a business report template, and filling the index data and the name of the target business category into the business report template to obtain a business report. According to the embodiment of the invention, the construction efficiency of the business report can be improved.

The application is an automatic driving large model training optimization method based on multi-scene data balance. It includes the following steps: (1) constructing a real vehicle high-speed driving scene library; (2) constructing a visual language automatic driving large model, based on the real vehicle high-speed driving scene library, using an iterative training framework, designing a multi-task joint loss function and a weight self-adaptive adjustment strategy, achieving multi-task target balance, and obtaining the trained visual language automatic driving large model; (3) performing dynamics simulation for the automatic driving working condition, and based on the simulation data, constructing a high-fidelity simulation data test set; (4) according to the high-fidelity simulation data test set, performing hyperparameter optimization and lightweight processing on the trained visual language automatic driving large model, and obtaining a scene data balanced automatic driving large model. The application realizes the acceleration of the large model inference speed and the reduction of resource occupation, thereby significantly improving the discrimination ability of the large model for dynamic working conditions and high-risk scenes.

A system and method for filtered visualization of dynamic load monitoring via a power distribution unit (PDU) collects power measurement data measured from each end device plugged into, and drawing operating power from, the PDU. The PDU collects the power measurement data into power state data over a line cycle or series of time intervals. Within the PDU, Kalman filtering determines a current power state estimate based on the measured power state data balanced with predictions of subsequent power state, as well as tuning parameters defining the balance from line cycle to line cycle. The resulting power state estimate may be displayed or presented to provide a more intuitive representation of operating power drawn by the end devices as opposed to raw power measurement data, which may be subject to fluctuation caused by high performance / AI pulsing loads.