5results about How to "Reduce update" patented technology

The present application belongs to the technical field of Internet of Vehicles and communication security, and discloses a federated learning training method based on vehicle state perception and applicable to dynamic Internet of Vehicles. The method collects vehicle running state information of vehicle clients, preliminarily screens candidate vehicle clients, calculates comprehensive scores of the screened vehicle clients, adopts a client selection strategy combining selection of the top-ranked clients with random supplementation to determine a set of vehicle clients participating in the current round of federated learning, configures local training parameters for the selected vehicle clients according to vehicle speed and computing capacity, performs local training on local data sets to obtain model updates, performs weighted aggregation on the model updates of the effective participating vehicle clients at the server end, introduces a momentum mechanism to smooth the model update process, and obtains new global model parameters. This method effectively improves the stability, robustness and training efficiency of federated learning in a high-dynamic Internet of Vehicles environment.

The embodiment of the application discloses a dynamic phasor representation DC power system random transient analysis method, relates to the power electronic field of the DC power system, and can reflect system characteristics caused by source-load random excitation conditions and multi-rate simulation, and balance the contradiction between precision and efficiency. The application comprises the following steps: obtaining DC power system parameters and setting initial parameters; obtaining circuit models of two types of lumped elements in the DC power system; establishing a dynamic phasor model of a converter station in the DC power system and obtaining a dynamic companion circuit of the converter station; using the circuit models of the two types of lumped elements and the dynamic companion circuit of the converter station, establishing a model of the DC power system; obtaining node voltage equations of the DC power system; updating a node admittance matrix and an injected current vector according to changes in a switching function of the converter station; and converting dynamic phasors output by the model of the DC power system into instantaneous values.

The utility model relates to the field of tobacco tar injection equipment, in particular to leakage-proof oil injection equipment which comprises a needle head body and a connecting piece, the needle head body is communicated with a plurality of oil injection needles, one end of the connecting piece is connected with the needle head body, and the other end of the connecting piece is connected with a sealing plug. The plane where the bottom of the sealing plug is located is lower than the plane where the tail ends of needle heads of all the oil injection needles are located. The outer diameter of the sealing plug is larger than or equal to the diameter of the heating wire assembly hole. The sealing plug is arranged, the position of the sealing plug is lower than the positions of the tail ends of the needle heads of all the oil injection needles, and meanwhile it is ensured that the outer diameter of the sealing plug is larger than or equal to the hole diameter of the heating wire assembly hole, so that the heating wire assembly hole is effectively plugged, and oil liquid is prevented from leaking from the hole. By means of the design, the problem that oil liquid is prone to leakage when the heating core is not installed in a traditional oil injection mode is fundamentally solved.

This invention provides a method for dynamic urban renewal based on deep reinforcement learning, relating to the field of urban renewal. The method includes: step S1, data acquisition; step S2, state prediction; step S3, hierarchical decision-making; step S4, execution feedback; and step S5, iterative optimization. Hierarchical decision-making, based on the urban state data in step S1 and the predicted state in step S2, implements hierarchical update schemes for macro-level, meso-level, and micro-level intelligent agents. This method transforms urban renewal from a one-time static planning process to dynamic adaptive decision-making that evolves with the urban system, thereby achieving dynamic adaptation of infrastructure and public services and avoiding negative impacts such as environmental damage, facility idleness, resource waste, and increased costs caused by mismatch.

The invention provides a three-dimensional point cloud semantic segmentation method and device and a storage medium, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: obtaining three-dimensional point cloud data in a target scene, constructing a semi-supervised average teacher network, and training the semi-supervised average teacher network through the point cloud data; performing dual-view uncertainty evaluation on each sample in the unlabeled point cloud data in the target scene by using the trained average teacher network to obtain a fusion uncertainty score of each sample, and screening out a to-be-labeled sample from the unlabeled samples by using a dual-drive selection strategy based on the score and a sample confusion degree score; and labeling the to-be-labeled sample, adding the labeled data set, and carrying out iterative training on the average teacher network until a preset condition is met to obtain a final point cloud semantic segmentation model suitable for the target scene. Based on the method, the invention further provides corresponding equipment and a storage medium. According to the invention, efficient point cloud sample selection is realized.