Time variant data time super-resolution visualization method based on deep learning model

Abstract

The invention relates to the technical field of data processing and modeling. The invention provides a time variant data time super-resolution visualization method based on a deep learning model, and the method comprises the steps: firstly extracting key voxels based on a gradient histogram, and reducing the data scale while maintaining the spatial features of original data; next, training a multi-scale variational auto-encoder to obtain an encoder with a feature extraction function and a decoder with a volume data generation function, so that the generation problem of a time variant data sequence can be converted into the generation problem of a hidden variable sequence. Therefore, the volume data can be processed and generated in the low-dimensional feature space which is more concise and can express potential information of the volume data. In the hidden space, two thoughts are provided to fit the time sequence relationship between the data hidden variables of each time step: in one method, the data is directly projected to two dimensions, the hidden variables of new data are obtained through interpolation on the basis of the overall time sequence development trend of the data, and a corresponding volume data sequence is further obtained;.

Description

technical field [0001] The invention belongs to the technical field of data processing and modeling, and in particular relates to a time-varying data time super-resolution visualization method based on a deep learning model. Background technique [0002] Collective simulation data is large-scale time-varying multivariate volume data generated by running scientific models with different parameter combinations. By studying the multivariate data sequences of different collection members, scientists can explore and discover the development laws of various scientific phenomena, as well as special events in the development of data time series. In the analysis of aggregated simulation data, the more time-varying data that can be used for analysis, the more accurate and richer the information of the dynamic spatiotemporal characteristics of the data can be obtained, which will have a very high demand for data storage space. However, ensemble simulation data has the characteristics ...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a time-varying volumetric data time super-resolution visualization method based on a deep learning model, which is used to solve the technical problems in the above-mentioned prior art, such as: the realization of time-varying multivariate volumetric data in research at the present stage The limitation of the time super-resolution method is that the development of the data over time is regarded as a linear change, which causes a large distortion of the results generated during the period of severe data changes; and the linear interpolation method only focuses on the local time-varying data. information, ignoring the overall time-varying trend of the data

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