15results about How to "Solve the imbalance" patented technology

The invention provides a management method and system for automatically generating standardized OT data based on a large model. The method comprises the steps that S1, an OT metadata ontology library is constructed into an OT term vector space according to standard regularized metadata meeting preset requirements; s2, mapping a natural language instruction to an OT term vector space through a large model to obtain standard structured OT data; and S3, carrying out quality verification and enhancement processing on the standard structured OT data. According to the method, the data consistency can be improved, the naming conflict in the integration process is solved through the unified cloud standard specification, and the data interoperability is remarkably enhanced; the efficiency revolution can be implemented, the data cleaning workload is effectively reduced, the qualitative change from a project system to productization is realized from traditional manpower integration, and the basic support of high-quality training data is provided for large-scale industrial deployment.

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 invention discloses a multi-coal-source coal blending data processing method based on a WSMOTE algorithm, and belongs to the technical field of coal processing data processing, and the method comprises the steps: data collection and classification: collecting multi-dimensional data of a raw coal floating and sinking test, a coal blending test and the like, carrying out the preprocessing, dividing the data into a missing data set and a complete data set, carrying out the interpolation optimization of a missing value, and carrying out the calculation of the complete data set. And carrying out adaptive processing according to data distribution types, or carrying out weighted combination normalization according to feature importance, analyzing the number and distribution characteristics of minority class samples, dynamically adjusting the neighbor sample size and the new sample synthesis amount, and completing data enhancement and standardization. According to the method, standardized parameters are dynamically adjusted to adapt to data distribution changes, abnormal values are accurately processed, the combined interpolation model is subjected to weight optimization, errors are reduced, and data integrity is improved. WSMOTE parameters are adaptively combined with data distribution to synthesize samples, data are balanced, and the over-fitting risk is reduced. In practical application, the quality of coal blending model training data can be improved, and optimization of a multi-coal-source coal blending scheme is assisted.

The invention discloses a lightweight network traffic intrusion detection method fusing TrafficFormer-knowledge distillation, and aims to solve the problem of model lightweight demand and network traffic data imbalance in a resource limited scene. The method comprises the following steps: firstly, segmenting and filtering pcap-format original traffic data, dividing a data set according to a preset proportion, and then carrying out data balance on a training set by adopting an RIFA method; performing effective data extraction and byte-level processing on all the data sets to generate a standardized token sequence; then, a teacher model is built through fine tuning on the basis of a TrafficFormer pre-training model, and the model is pre-trained through MBM and SODF double-self-supervision tasks and has the excellent flow feature representation capacity; then, a four-layer Transform encoder is adopted as a lightweight student model, and teacher model knowledge is migrated to the student model through a double-loss function of KL divergence distillation loss and Focal supervision loss; and finally evaluating the performance of the obtained student model.

This invention discloses a method, system, device, and medium for automatic extraction of black soil layer interfaces using ground-penetrating radar (GPR) based on an improved U-Net, belonging to the field of GPR data processing and intelligent interpretation technology. The method includes acquiring and preprocessing GPR B-scan image data, constructing a training dataset, building a layer extraction model based on an improved U-Net neural network model, constructing a hybrid loss function including at least two combinations of weighted binary cross-entropy loss, Dice loss, structural similarity loss, and smoothness loss, training the layer extraction model based on the training dataset and the hybrid loss function, and inputting the GPR B-scan image data to be detected into the trained layer extraction model to achieve automatic extraction of black soil layer interfaces using GPR. This invention effectively overcomes signal attenuation and noise interference in GPR images, achieving high-precision and continuous soil layer interface extraction.

The invention relates to a document tampering detection method based on dynamic kernel fusion and adaptive gradient modulation, and belongs to the technical field of digital forensics and computer vision. The method comprises the following steps: obtaining an RGB image of a to-be-detected document, constructing a feature encoder containing a double-branch feature extraction and feature updating module, taking ConvNeXt V2-Base as a backbone, combining visual and frequency domain features with a cross-domain feature calibration module, and obtaining optimized multi-scale features through learnable wavelet decomposition; a frequency domain adaptive feature decoder is constructed, dynamic kernel fusion is completed through Fourier frequency band grouping weight, kernel element precise modulation and space-frequency band dynamic matching, and a tampering prediction map is output; and during training, joint optimization of adaptive gradient cosine loss, cross entropy loss and LoWitz loss is adopted. According to the method, the frequency abnormity and tiny traces of document tampering can be accurately captured, the problem of class imbalance is effectively relieved, and the method has high detection precision and robustness in different compression scenes.

The application relates to an energy consumption anomaly monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring first data, which is the energy consumption data of a building within a first time. The first time is segmented to obtain a plurality of time periods, and each time period in the plurality of time periods has the same time interval. A first matrix is obtained based on the plurality of time periods, and the first matrix is an energy consumption data matrix arranged based on the plurality of time periods. The first matrix is supplemented to obtain a second matrix, and the second matrix is an energy consumption data matrix meeting the training requirements of a convolutional neural network. The second matrix is subjected to self-encoding training through the convolutional neural network, and the trained second matrix is clustered through a K-means algorithm to obtain a corresponding convolutional neural network model. The convolutional neural network model is called to classify newly input building energy consumption data to determine whether the newly input building energy consumption data is abnormal.

This invention discloses an upward-rotating and downward-rotating spray gun, belonging to the field of agricultural irrigation technology. Based on the original double-rotating spray gun, this invention is optimized for high-sediment-content water environments. It includes: an outer cylinder (1), a central water supply pipe (2), a drive rotating shaft (3), a planetary reduction mechanism (4), a high-speed recoil drive arm (5), and a low-speed spraying arm (6). The improvements are as follows: the base (7) is provided with first and second sealing rings (701, 702) to isolate sand and dust; the upper end face of the base and the central groove are provided with sand discharge holes (703); the lower end of the first water distribution chamber (501) is provided with a double sealing ring layer (504), and the high-speed recoil drive arm at the corresponding position is provided with a debris discharge hole (505); the top of the high-speed recoil drive arm is provided with a sealing thread sleeve (506) to cooperate with the upper sealing ring (507); the inner wall of the central water supply pipe (2) is provided with ribs (202) that have both reinforcement and flow guiding functions; the low-speed spray arm (6) consists of two bifurcated arms of different heights, with nozzles of different sizes installed at angles of 30-38° and 5-20° respectively to balance the reaction force. While maintaining the advantages of the original double swirl field, the present invention significantly improves the anti-clogging ability and operational balance, and is especially suitable for surface water irrigation with high sand content.

The invention discloses a multi-source attack detection method for computing power cluster security protection, and belongs to the technical field of host security attack detection. The method comprises the following steps: inputting an operating system auditing log of a computing node in a computing power cluster to be identified into a pre-trained time sequence anomaly detection model to calculate an anomaly probability score of the operating system auditing log, when the anomaly probability score exceeds a threshold value, judging that the operating system auditing log is attacked, and otherwise, judging that the operating system auditing log is safe; the pre-trained time sequence anomaly detection model is obtained through the following steps: collecting an operating system audit log of each computing node, analyzing an event in the log into a standardized quintuple event, and obtaining a global cause and effect graph based on the event; active entity nodes in the global causal graph are screened, and multi-dimensional feature coding is carried out to generate a feature sequence; and inputting the feature sequence into a pre-constructed time sequence anomaly detection model to obtain a trained time sequence anomaly detection model. According to the invention, the problem that the existing detection technology is difficult to restore a cross-host complete attack link is solved.

The invention discloses an intelligent voltage regulating transformer fault diagnosis method and system based on an improved CNN-BiLSTM model. The method comprises the following steps: firstly, collecting historical data of a voltage regulating transformer for preprocessing; then training is carried out based on historical data of the voltage regulating transformer to obtain a transformer fault diagnosis model, the transformer fault diagnosis model is an improved CNN-BiLSTM model, and an ECA-TAM attention mechanism is introduced into the CNN-BiLSTM model; and finally, inputting the current operation data of the voltage regulating transformer into the transformer fault diagnosis model to obtain a fault diagnosis result of the voltage regulating transformer. According to the method, an ECA-TAM attention mechanism is introduced into the CNN-BiLSTM network model for voltage regulating transformer fault diagnosis, purer and more representative feature input can be provided, interference of redundant information on time sequence modeling is reduced, the key dynamic learning ability is improved, efficient and accurate diagnosis of voltage regulating transformer fault types is achieved, and the fault diagnosis efficiency is improved. The accuracy and timeliness of transformer fault prediction are improved, and stable and safe operation of an electric power system is guaranteed.

The application provides a midpoint potential balance control method based on a notch filter and a compensator, and belongs to the technical field of converters, and comprises the following steps: determining a difference signal of a positive midpoint bus capacitor voltage signal and a negative midpoint bus capacitor voltage signal of a three-level converter DC side, and inputting the difference signal into a notch filter for processing; inputting the processed difference signal as a negative feedback signal into a compensator together with a zero reference signal; dividing a compensator output signal by a specific proportional coefficient as a zero sequence modulation signal, and superimposing the zero sequence modulation signal into three-phase modulation signals of a current converter, and generating a PWM signal according to the three-phase modulation signals superimposed with the zero sequence modulation signal, so as to control the midpoint potential balance of the three-level converter DC side; the application generates a zero sequence modulation signal by using a notch filter and a compensator, and superimposes the zero sequence modulation signal on three-phase alternating current modulation signals, so as to control the current on the neutral line, and the problem of midpoint potential imbalance of the three-level converter DC side capacitor can be solved.

The invention discloses an ejection structure for a mobile phone support product, and relates to the technical field of mobile phone support product ejection, the ejection structure comprises a mold base and a first assembly mounted on the mold base, the cross section of the mold base is U-shaped, and the first assembly comprises an ejection transmission assembly, a mold forming assembly and a guide positioning assembly; the ejection transmission assembly comprises an ejector pin bottom plate and an ejector pin panel, the ejector pin bottom plate is in transmission connection with the ejector pin panel through a guide column, and the ejector pin panel is provided with a positioning hole used for installing the ejection execution assembly; through cooperative operation of a sliding rod, a spiral groove, a limiting body and the like in the fourth assembly, linear ejection force restored by a spring is converted into stable rotary lifting motion, the ejection speed of the inclined ejection block is further reduced through the design of a large spiral lead, the lifting stability is improved, uniform power transmission is achieved in cooperation with composite ejection motion of the inclined ejection block in the 45-degree direction, and the service life of the inclined ejection block is prolonged. And the problem of unbalanced ejection of the small bracket is thoroughly solved, and the product deformation rate is reduced to an extremely low level.

The application discloses a panoramic service intelligent sensing and accurate classification method, comprising the following steps: a sensing probe collects characteristic data of different service types of service end information, and divides the characteristic data into labeled data and unlabeled data; a feature subset of the labeled data is constructed based on a mixed feature selection method, and a global optimal feature subset is obtained; based on a semi-supervised learning model of an improved K-means clustering algorithm, service end infection data is output; the sensing probe collects, stores and transmits service request information of a user end, and the service request information is used as prior information; a trained convolutional neural network model is used to output high-level features of the prior information and the service end infection data; and a trained random forest service sensing and identification model is used to classify the high-level features. The panoramic service intelligent sensing and accurate classification method can improve the accuracy of mixed service identification.

A kind of multi-flow chain type perception enhanced multimodal aspect-level sentiment analysis method is referred to as MCPE model, it is related to multimodal sentiment analysis technical field.The present application solves the problem of low accuracy of fine-grained sentiment analysis caused by the difference of information density within modal and the imbalance of information between modal in the prior art.The present application comprises: obtaining a multi-modal text-image pair to be analyzed, inputting the text-image pair into the trained MCPE model to obtain aspect words and their sentiment polarity;The MCPE model comprises: a feature extraction module, a chain enhancement module, a multi-flow interaction module and a classifier;Text features are extracted by BART and image features are extracted by Faster R-CNN in the feature extraction;The chain enhancement module suppresses image and text noise and enhances fine-grained semantics through IFE-TFE double-chain architecture;The multi-flow interaction module realizes the dynamic complementary fusion of text reasoning and visual evidence by using bidirectional cross-modal attention;The classifier outputs the analysis result based on the fusion features, and does not require external tools.The present application is suitable for sentiment analysis of social media comments.

The invention discloses a hydrogen production mode identification method and system based on a tree classifier, and belongs to the technical field of hydrogen analysis, the hydrogen production mode identification method based on the tree classifier comprises the following steps: S1, extracting the original component content of a hydrogen sample; s2, constructing engineering features based on the original component content, wherein the engineering features comprise a component proportion, a composition entropy, logarithm transformation, a component interaction item and a principal component; and S3, hydrogen production mode identification: taking the engineering characteristics as input, synthesizing minority class oversampling for relieving class imbalance, and performing hydrogen production mode judgment through a tree classifier. By adopting the technical scheme, the hydrogen production mode can be quickly analyzed by utilizing the tree classifier based on the original component content and engineering characteristics of the hydrogen sample.