78 results about "Data balancing" patented technology

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 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 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 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.

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 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 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.

The application belongs to the field of biomedical signal processing and human-computer interaction, and discloses a muscle-derived signal gesture recognition method and system based on crown porcupine optimization VMD parameters. The method collects electromyography or muscle magnetic signals during gesture action, and constructs a sample set through sliding window segmentation, data balancing processing and standardization. The crown porcupine optimization algorithm (CPO) is used to adaptively optimize the mode number and penalty factor of variational mode decomposition (VMD), and the gesture classification accuracy is used as the fitness function. Global search and local mining are realized through the four-layer defense of vision, sound, odor and physics of CPO. The intrinsic mode function (IMF) under the optimal parameters is extracted and spliced into a multi-dimensional feature matrix, which is input into a classification model to realize gesture recognition. The application directly optimizes the recognition performance, enhances the feature discriminability, and improves the recognition accuracy and robustness of muscle-derived signals in complex scenes.

The invention discloses a data redistribution method, device and equipment for a distributed memory database and a medium, and belongs to the field of data migration. The method comprises the steps that expansion nodes are added to the distributed memory database, and data balance processing is conducted on the distributed memory database; screening target migration data from the to-be-migrated data according to the transmission time consumption calculated in real time; and performing dynamic migration processing according to the target migration data. According to the method, the data volume of each migration is determined through the predetermined acceptable service pause duration, the data can be divided into smaller data which can be flexibly split for transmission, and the granularity of the data is refined; dynamic adjustment is carried out according to the real-time migration state, resource competition with foreground transactions is reduced, resource waste is avoided, and the resource utilization rate is increased.

The invention discloses a log association judgment method and system based on TF-IDF and data balance. According to the method, a unified corpus is constructed through preprocessing, then the preprocessed BUG overview and description are spliced to serve as a query text, log fragments serve as documents, and a feature matrix is generated through TF-IDF vectorization. And further calculating the similarity between the query text and the document, and generating a final feature vector in combination with length weighting and position weighting. The problem of data imbalance is solved through undersampling or oversampling processing, and model training is carried out by using logistic regression or a linear SVM (Support Vector Machine). And finally, performing storage-friendly persistence processing on the trained model, and performing log association judgment on the processed model. According to the method, the accuracy of correlation judgment between the Chinese BUG report and the DMSG log fragment is remarkably improved, the misjudgment rate is reduced, meanwhile, the efficiency of model training and deployment is improved, and the generalization ability and engineering adaptability of the model are enhanced.

This invention belongs to the technical field of breast cancer molecular subtyping, and more specifically, relates to a method for identifying HER2 expression status in breast ultrasound videos based on spatiotemporal feature interaction. The method includes: using raw breast ultrasound videos as input data and performing multi-level data preprocessing operations; then performing data balancing on the training data to obtain a standardized training set; sequentially constructing a feature module, a multi-stage feature fusion module, and a classification module based on the UniFormerV2 network, and training the resulting model; and inputting the test set into the model to obtain the final prediction result of HER2 expression status. This invention solves the problems of existing HER2 detection methods being invasive and unable to dynamically monitor, and existing deep learning methods based on static images ignoring the spatiotemporal dynamic information in ultrasound videos, leading to insufficient accuracy in predicting HER2 expression status, especially the HER2-Low subtype.

The invention discloses a personalized health management scheme generation method based on artificial intelligence, and relates to the technical field of artificial intelligence and health management, and the method comprises the following steps: constructing a multi-modal, cross-time and cross-data-source data integration platform, the method comprises the following steps: gathering multi-source health data from drugstores in different geographic areas, cross-time sequence data and a multi-source heterogeneous data set of a third-party data source, carrying out preprocessing of cleaning, format unification and feature coding, and pre-evaluating data skewness by adopting a statistical analysis method; according to the method, multi-mode, cross-time and third-party data skew characteristics are comprehensively captured, multi-source heterogeneous data integration and skew pre-evaluation are carried out, and third-party data source skew transmission correction and data balance are carried out. The cooperative influence of multi-modal data skew, the cumulative effect of data cross-time skew and the transmission problem of third-party data source skew are effectively solved, and the accuracy and balance of data are improved.

The invention discloses an online game dialogue malicious language interpretable detection method based on knowledge distillation, and belongs to the field of natural language processing and artificial intelligence. According to the method, interpretable labels are generated through multi-stage labeling, T5 model semantic retelling is used for balancing data distribution, a combined training framework of a classifier and a reason generator is combined, and efficient interpretable harmfulness detection and dynamic enhancement of large-scale game dialogues are achieved based on focus loss function optimization and manual consistency verification. According to the method, high-precision interpretable detection is achieved through the F1 score of the classifier and the BLEU-4 score of the reason generator, the calculation cost is reduced in combination with knowledge distillation, the low-frequency malicious recognition robustness is enhanced through a data balance strategy, and triple improvement of high efficiency, transparency and adaptability of large-scale game dialogue harmfulness analysis is achieved.

The invention discloses a text data balancing method based on adversarial training, and belongs to the field of data balancing, and the method comprises the steps: determining a target text sub-data set from each text sub-data set of a text data set to be subjected to data balancing; establishing a text generation cooperation strategy corresponding to the target text sub-data set based on the target text sub-data set, and performing cooperation adjustment on a text generator and a generated text discriminator respectively corresponding to the target text sub-data set based on the text generation cooperation strategy; and performing data expansion on the target text sub-data set based on the adjusted text generator to obtain a text data set after data balance. According to the method, a collaborative adjustment mechanism based on an adversarial thought is introduced, so that the balance between data diversity and simulation degree is realized while the category distribution deviation is effectively eliminated, and the problem that the data balance and the data capacity cannot be considered at the same time is solved.

The invention relates to the technical field of machine learning, and provides a prompt generation method and device, computer equipment and a readable storage medium, and the method comprises the steps: obtaining business data, rule data and historical processing records, and carrying out the preprocessing, and forming a standardized data set; obtaining a rule matrix corresponding to the standardized data set in real time by adopting increment extraction; performing dimension reduction and distribution equalization on the high-dimensional features corresponding to the rule matrix by using correlation analysis, a feature selection algorithm and a data balance technology; through loss distribution fitting, cross validation and hyper-parameter optimization, constructing a field association map of association business field semantics, rule logic and historical operation habits corresponding to the rule matrix; and when new service information is accessed, generating processing prompt information according to the field association map and the service information. According to the method, the intelligent level of a complex business process is fundamentally improved, and information processing pain points in the fields of finance, medical health, old-age care and the like are effectively handled.

This invention discloses a Bayesian inversion extraction method for the equilibrium signal of wide-span altimeter data. The method involves acquiring and preprocessing sea surface height anomaly sequences from radar interferometers and nadir altimeters. A normalized sine square window function is applied for windowing, and multidimensional spatial averaging is used to estimate the one-dimensional wavenumber power spectrum. A piecewise power law-based equilibrium signal spectrum model and a noise spectrum model constrained by dynamic sea state are constructed, and the set of spectral parameters is extracted through logarithmic domain weighted least squares fitting. A set of spatial covariance matrices is constructed using cosine integral transform and Abelian forward and inverse transforms. A graphics processor is scheduled to perform batch matrix decomposition and singular fault-tolerant regularized inversion to solve for the posterior mean vector and posterior covariance matrix of the target equilibrium signal. Window fusion and index mapping are applied to fill the gaps in nadir observations. Geostrophic dynamics parameters are calculated, uncertainty quantification is performed based on the linear error propagation law, and the knowledge base is updated based on the exponential moving average algorithm. This invention achieves suppression of observation noise and physical filling of observation gaps, improving the adaptability of the inversion system to environmental changes while preserving non-Gaussian dynamic characteristics.

The invention discloses a power transmission line fault identification method and system based on an improved PSO-SVM incremental learning strategy. The method comprises the following steps: training a PSO-SVM model by using historical fault data of a power transmission line to determine a support vector; calculating the importance degree of the support vector and the sparsity of the K-neighbor center to screen out playback data; merging the newly added fault data and the playback data into an incremental data set; new samples are generated in a sparse sample distribution area in the incremental data set through a zoning oversampling technology to achieve intra-class data balance of the incremental data set, then the incremental data set is used for training the PSO-SVM model again through multi-task cross integration learning to achieve inter-class data balance of the incremental data set, and a final fault recognition model is obtained. According to the method, the self-adaption and high-precision identification of the fault is realized under limited computing resources.

The embodiment of the invention discloses a data balance optimization method and device based on multi-satellite collaboration. A specific embodiment of the method comprises the following steps: selecting a satellite with a maximum local training data set as an optimized satellite; selecting adjacent satellites of the optimized satellites for data unloading according to the data size between the satellites and the class label difference; establishing a constraint objective function between the optimized satellite and the adjacent satellite, solving the constraint objective function, and determining a target data unloading proportion of the optimized satellite; and performing data unloading between the optimized satellite and the adjacent satellite according to the target data unloading proportion. According to the embodiment, through multi-satellite collaborative data unloading, the training burden of satellites with relatively large data volume is reduced.