3 results about "Grid generator" patented technology

The application discloses a power system frequency estimation method and system, which comprises the following steps: obtaining a power grid model and remote signaling data, a generator rotating speed and a PMU frequency measurement, forming a node branch model; forming an extended node admittance matrix according to the node branch model, a generator internal reactance parameter and the remote signaling data; performing node reordering on the extended node admittance matrix; forming a factor table of the reordered extended node admittance matrix by using a symbolic decomposition and a numerical decomposition method; calculating a frequency participation factor matrix F by using a multi-thread parallel technology; obtaining a reduced frequency participation factor matrix F' according to the size of element values in the frequency participation factor matrix F and a set threshold value; and calculating a node frequency by using a multi-thread parallel method. The application overcomes the shortcoming that the inertia center frequency of a power grid can only calculate an average frequency of the power grid, and has important reference significance for guiding the selection of a power generator frequency point monitored by a dispatching center.

The application provides a monocular video human body Gaussian avatar reconstruction method, and the implementation steps are as follows: obtaining a training and test sample set; constructing a reconstruction network model comprising a cascaded UV mapping module, an encoder, a decoder module, a linear blending skinning LBS module and a three-dimensional Gaussian sputtering module; iteratively training the reconstruction network model and obtaining a reconstruction result. The UV mapping module in the application cuts the reconstruction result output by the SMPL-X grid generator based on the anatomical principle through an anatomical cutter, and maps the SMPL-X parameters of the human body model into continuous and uniformly distributed two-dimensional data through a Laplace expander and a key point uniform distributor, so that the three-dimensional geometric structure can be more effectively learned from the two-dimensional features, the information loss is reduced, and the reconstruction accuracy is effectively improved; meanwhile, the application overcomes the defect of relying on camera parameters, and improves the applicability in the uncalibrated scene.

The application discloses a method for predicting the distribution of grid nodes of boundary lines of aircraft components and anisotropy parameters, relates to the field of automation and intellectualization of pre-processing of computational fluid dynamics simulation, and determines the geometric data of an aircraft CAD model, component semantic labels, calculation working conditions and each boundary line. A trained multi-task graph neural network model is used to intelligently classify the grid region types of the boundary lines, and isotropic or anisotropic classification results are obtained. Then, according to the classification results, the corresponding differential parameter prediction sub-network in the model is used to predict the corresponding grid control parameter set for each boundary line. Finally, based on the parameter set, a grid generator is driven to automatically generate high-quality surface grids. The application realizes intelligent decision-making of the whole process from semantic information to grid generation strategy and parameters, replaces the traditional method relying on artificial experience, and significantly improves the automation degree, efficiency and grid quality consistency of CFD pre-processing.