3results about How to "Reduce memory space requirements" patented technology

The invention provides a composite material cracking path calculation method based on a matrix-free pixel finite element, pixels of a composite material image are used as fine grids of the finite element, and different pixel gray values represent different material components. And coarse graining the fine grids to form coarse grids, and embedding the boundaries of the material components into the coarse grids to form embedded boundary units. A stiffness matrix of a coarse grid unit is calculated through Gaussian integral, the overall stiffness does not need to be assembled, and coarse grid node displacement is solved through a conditional conjugate gradient algorithm. And on the basis of the coarse grid node displacement, fine grid node displacement is obtained through an interpolation method, and then fine grid unit stress is obtained. Taking a maximum tensile stress criterion as a damage criterion of the unit, taking a unit region as a crack after the unit is damaged, adding a corresponding substance unit at an image boundary, and keeping substance conservation. According to the calculation method, little computer memory is occupied, and the requirement of cracking calculation for hardware resources is greatly reduced.

The application provides a kind of double-link point cloud completion method, system, equipment and storage medium, method includes: the original point cloud data is respectively input into first feature extraction branch and second feature extraction branch, and first feature vector and second feature vector are respectively obtained by different network architecture;The first feature vector and the second feature vector are fused to obtain global high-dimensional semantic feature;And the global high-dimensional semantic feature is decoded to generate the target point cloud set of completion.This application uses two branches to simultaneously extract different dimensional features of point cloud, obtain more, richer high-dimensional semantic features and local features containing global point cloud information, which can improve point cloud completion accuracy and point cloud density.

The present invention discloses a data processing method and device based on a large-scale storage system, and relates to the field of computer storage technology. The data processing method based on a large-scale storage system includes dividing the persistence layer of the large-scale storage system into a pre-write log module, an index memory cache module and an index persistence module, then writing data to the persistence layer through a write process, and then reading user data from the persistence layer through a read process. In a large-scale storage system based on a write-ahead log, the present invention optimizes the data reading and writing process of the persistence layer, improves the data double-writing problem caused by WAL, has less memory requirements, and does not increase unnecessary write delays due to the independent storage of data and index metadata; there is no need to significantly modify the WAL design, and for large I / O, only the index can be recorded, while for small I / O, user data can also be recorded in the index at the same time, reducing one data redirection, and supporting the optimization of I / O of different sizes.