3results about How to "Alleviate needs" patented technology

The application discloses a face soft tissue defect point cloud completion method based on a PF-Net, and belongs to the technical field of face soft tissue defect completion. The method comprises the following steps: S1, a data preparation stage: a data enhancement algorithm is designed to obtain a paired data set of incomplete input and defect labels; S2, a model construction stage: a dynamic graph convolution and a boundary attention mechanism are introduced into an encoder to optimize a point cloud completion network model PF-Net; S3, a model training stage: a training set is input into the PF-Net model for training; S4, a model testing stage: a test set is input into a generator for testing; and S5, a model inference stage: for clinical face soft defect point cloud data, multi-scale input data are acquired and completion is performed. The application realizes autonomous simulation design of face soft tissue defects, solves technical barriers in existing digital prosthetic technology, such as dependence on healthy side mirror image data or standard organ models, and solves the problem of unstable repair effect caused by dependence on doctor experience and aesthetic literacy.

This invention discloses a process for producing converter slagging agent from lithium extraction residue of electrolytic aluminum ash. Addressing the characteristic that lithium extraction residue of aluminum ash contains multiple fluxing components for converter steelmaking, this invention employs an innovative process method. Utilizing the principles of calcination dehydration and reduction desulfurization, the harmful sulfur component in the lithium extraction residue is removed to a level that meets the standards for steelmaking flux, making it a steelmaking flux raw material that can be directly utilized in converter steelmaking. This invention provides a highly competitive process for the large-scale treatment of lithium extraction residue from hazardous waste of electrolytic aluminum in the steel industry, demonstrating the technological advantages of cross-disciplinary innovation.

The invention relates to the technical field of lithium ion batteries, in particular to an energy storage lithium ion battery health state prediction method, which comprises the following steps that sparse test point data are acquired through a battery aging experiment, and each test point comprises a cycle number, internal resistance, an increment health state characteristic and a health state label; systematic slicing and resampling are carried out on a time axis by utilizing an aging mining method, training samples containing different circulation history paths are generated, and a mapping relation between circulation history and health state attenuation is learned by adopting a deep learning model, so that high-precision health state prediction is realized; the method has the beneficial effects that the problem of scarcity of battery aging data is solved, and the health state prediction precision is remarkably improved; the method has good generalization ability and has flexibility and expandability.