9results about How to "Ensure training efficiency" patented technology

The invention is suitable for the technical field of computer application, and provides an image classification model construction method and device based on a random forest, electronic equipment and a storage medium, and the method comprises the steps: constructing an initial image classification model; generating a feature layer feature matrix through the feature forest layer; generating an enhancement layer feature matrix through the enhancement forest layer; inputting the feature layer feature matrix and the enhancement layer feature matrix into an output layer, and determining the prediction precision of the initial image classification model; when the prediction precision reaches a preset precision condition, determining the initial image classification model as a target image classification model; and otherwise, increasing the number of the first random forest groups and / or the second random forest groups so as to generate a target image classification model. Therefore, the image classification model is constructed by using the integrated structure of the multilayer random forest groups, the model precision is improved by increasing the number of the random forest groups, and the model training efficiency, the generalization ability and the image classification precision of the target image classification model are improved.

The application discloses a kind of reconfigurable intelligent surface auxiliary wireless environment modeling method based on three-dimensional Gaussian splash technology, belong to wireless communication field, this method will three-dimensional Gaussian splash technology from computer graphics field innovatively migrate to RIS auxiliary wireless communication system modeling field, the explicit parameterization thought of 3D-GS is applied to radio frequency electromagnetic field modeling, break through the limitation of traditional deep learning "black box" modeling;Adopt two-stage joint modeling framework: for the characteristics of RIS system, "TX→RIS" and "RIS→RX" cascade modeling strategy is designed, can accurately capture the electromagnetic regulation effect of RIS;With explicit physical parameterization: replace large-scale neural network weight with Gaussian parameter with clear physical meaning, realize light weight;In addition, by constructing complete differentiable rendering process, support efficient gradient descent optimization, ensure training efficiency and convergence.

This invention discloses an implicit reconstruction method for point cloud surfaces based on a piecewise learning strategy. The invention employs a piecewise surface representation and trains an implicit reconstruction network for point cloud surfaces targeting the local symbolic distance field of a 3D shape. The method includes the following steps: using discrete point cloud data as input, a farthest-point sampling strategy is used to generate initial patches; the offset of each sampling point within each patch relative to the patch center is calculated as the relative position of the sampling point. The latent features of the patch are obtained in the neural network encoder, and the symbolic distance values ​​of the relative positions of each sampling point are obtained in the neural network decoder. The relative positions of sampling points located in overlapping areas of different patches are weighted and summed to obtain their corresponding symbolic distance values. A mesh model of the 3D shape is obtained using the Marching Cube algorithm. This invention can reconstruct the overall shape of an object while preserving the fine details of the original shape, and it is robust to point cloud normals and noise.

This invention proposes a structure-condition-driven low-resource multilingual multimodal model training method and system, belonging to the field of artificial intelligence. The method includes: S1: Extracting image structural attributes from multimodal training samples, constructing structure-conditional text sequences, and obtaining text hidden states and visual features; S2: A route selector determines the training path based on the resource and structural attributes of the samples; S3: Freezing the multimodal large-scale model backbone network and dynamically activating a lightweight script adaptation module to update parameters; S4: Aligning visual and textual representations through a structure-conditional cross-attention module to generate a joint representation; S5: Embedding the joint representation, task prefix, target language identifier, and its language into the input decoding and multi-task output modules to obtain the generated result; S6: Constructing a total training objective function to obtain the trained multimodal model. This invention improves the overall performance, stability, and security of multimodal models in low-resource, multilingual, and complex structural scenarios.

This application provides an unsupervised detection method and system for electrical appliance operation anomalies in a virtual power plant, relating to the field of equipment operation status monitoring. The method includes: collecting and preprocessing operating power sequences and timestamp sequences, then concatenating them to form a feature vector set. A single-classification model is constructed, mapping samples to the feature space using a Gaussian radial basis function kernel, and training to obtain a parameterized hypersphere model describing the distribution of normal data. The Lagrange duality problem of the single-classification model is solved to obtain the hypersphere's center vector, radius parameter, and Lagrange multiplier subset. Real-time acquisition of the target electrical appliance's operating data generates a real-time feature vector, and the determination distance between the real-time feature vector and the hypersphere's center is calculated. If the target electrical appliance is determined to be in an abnormal state, the abnormal device is removed from the virtual power plant's resource scheduling pool, and a health warning message is sent. This addresses the technical problems of existing technologies that rely on scarce labeled abnormal data, have high training costs, and poor generalization ability.

The application provides an I / O feature prediction method, a model training method and related devices. The job submission script of a job is converted into a grayscale image, and a deep learning method of image recognition is used to realize the prediction task of the I / O feature. When the I / O feature is predicted, the processing method accumulated in the field of deep learning can be reused, including various classical image recognition network structures, to process the above-mentioned grayscale image to obtain the I / O feature of the job. Compared with the existing method of using a "job name-user name-computing node number" triplet as the I / O feature and using a clustering algorithm to process a large-scale data set for prediction, the I / O feature can be predicted based on the job submission script, the time consumption of the prediction of the I / O feature is reduced, and the I / O feature of the job in the high-performance computing cluster is accurately and efficiently predicted.

The invention discloses a double-layer adaptive training recommendation system and method based on standardized expression increment and a dynamic baseline, and relates to the technical field of artificial intelligence technology, education technology and acknowledgement science, the system has a double-state switching mechanism, provides a clear initialization path for a new user, and ensures the user experience from 0 to N; a standard performance score SPS is introduced into the baseline storage module, and the defect that cross-game types are incomparable is overcome through mean value and variance calibration; the game fatigue GFS model can prevent children from being tired due to repetition; the method comprises the steps of determining an initialization state or a dynamic self-adaptive state by detecting the total number of games of a user; a training load is set, a training course package is generated by using an SPS sequence and NPD data of recent k games, calculating SPSlevel and SPStrend and determining the training load N, finally, difficulty regulation and control in the games are achieved based on DGB, instant excitation of skipping is provided, fatigue switching can be conducted, and training enthusiasm is guaranteed.

The invention discloses a self-organizing network-oriented decentralized device-level federated forgetting task scheduling method, which comprises the following steps of: performing decentralized device-level federated forgetting processing on the basis of task scheduling information through a device-level federated forgetting task scheduling model to obtain a task scheduling scheme, and realizing task scheduling of a self-organizing network through the task scheduling scheme; the equipment-level federal forgetting task scheduling model comprises the following steps: partitioning clients in a self-organizing network by adopting a heuristic client dynamic partitioning strategy based on a DDFU-SON algorithm; and dynamically evaluating the data heterogeneity of the self-organizing network to perform dynamic aggregation so as to realize task scheduling. The invention discloses a self-organizing network-oriented decentralized device-level federation forgetting task scheduling method and system, a storage medium, an edge device forgetting method focused on the dynamic access condition of a client, a partition selection and topology construction strategy of a newly accessed client, and a training stability guarantee mechanism.

The present application relates to a kind of main hoist remaining life prediction method and device, wherein, method includes: S1, the vibration time series data and working condition parameter time series data of the main hoist to be measured are acquired, and the measured vibration signal and measured working condition parameter signal of single operation are segmented, detection data group is constructed and time information is generated;S2, using detection data group to identify working condition, input the first self-encoding model corresponding to working condition, obtain the vibration signal after reconstruction and physical constraint residual, and the model is based on physical constraint dynamics equation to build;S3, total deviation is calculated according to reconstructed signal and physical constraint residual, and deviation sequence is obtained according to time sequence;S4, failure curve is generated based on deviation sequence, and then the remaining life is obtained.The present application can effectively separate working condition influence, fusion physical priori knowledge, realize accurate and reliable life prediction under the condition of variable working condition and variable load.