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Massive remote sensing variable multi-dimensional aggregation information calculation method based on data cube model

A technology of data cube and calculation method, applied in the field of calculation of multi-dimensional aggregated information of massive remote sensing variables, can solve problems such as high memory consumption and high calculation delay, and achieve the effect of improving user experience, enhancing practicability, and saving computing resources

Active Publication Date: 2020-12-18
SHAANXI NORMAL UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of space-time independent aggregation, space-time coupling aggregation and space-time multidimensional aggregation. It is necessary to convert the complex space-time area into a multidimensional matrix first, and there are problems of high memory consumption and high calculation delay. It provides a massive data cube model based Calculation Method of Multidimensional Aggregation Information of Remote Sensing Variables

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  • Massive remote sensing variable multi-dimensional aggregation information calculation method based on data cube model
  • Massive remote sensing variable multi-dimensional aggregation information calculation method based on data cube model
  • Massive remote sensing variable multi-dimensional aggregation information calculation method based on data cube model

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Embodiment

[0071] In order to verify whether the data organization and aggregation method of the present invention satisfies the near-real-time response requirements for remote sensing variable aggregation query applications in an interactive environment, an optical fiber LAN environment and a service host (configuration: CPU i7-7700 3.6G, memory 32G, storage 1TB SSD) and several clients for testing. The original test data contains 3 variables (maximum temperature, minimum temperature, rainfall), the spatial scope is the global region, the spatial resolution is 0.04°, the time span is 58 years, the time resolution is monthly, and the total data volume is about 350GB. The data cube is established according to the data model of the present invention, the spatial level is "0.7°<1.4°<2.8°<5.6<11.2°", and the time level is "month<year<ENSO cycle". Test method adopts 3 kinds, and the inventive method is referred to as Cube for short, and two traditional methods are respectively: 1. write ArcGI...

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Abstract

The invention discloses a mass remote sensing variable multi-dimensional aggregation information calculation method based on a data cube model, and belongs to the field of data processing. According to the massive remote sensing variable multi-dimensional aggregation information calculation method based on the data cube model, the remote sensing variable information is aggregated in advance, and most aggregation calculation is converted into an existing aggregation information query process; a plurality of pieces of pre-calculated small-granularity aggregation information are automatically combined into an aggregation result corresponding to any query condition, so that the problem of calculation performance faced by a traditional method is avoided. Therefore, the remote sensing variable aggregation information can be returned at a near-real-time response speed in a human-computer interaction scene, and meanwhile, a large amount of computing resources are saved. Time aggregation and space aggregation are supported, and complex forms such as space-time independent aggregation and space-time coupling aggregation are also supported.

Description

technical field [0001] The invention belongs to the field of data processing, in particular to a calculation method for multidimensional aggregation information of massive remote sensing variables based on a data cube model. Background technique [0002] At present, the rapid development of high time / space / spectral satellite remote sensing technology and geographic simulation system has produced massive high-dimensional earth observation data and their reanalysis data sets, which are widely used in various fields such as climate disasters and environmental ecology. Under the background of increasing coupling between global climate change and man-land relationship, the joint analysis of natural observation data and human statistical data has become an important direction of interdisciplinary research. Spatiotemporal aggregation (Spatiotemporal Aggregation) is an important means to realize the integrated analysis of natural-human multivariate geographic data. It extracts and s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/14G06F16/2458G06F16/58G06F16/587G06F16/29
CPCG06F16/148G06F16/2462G06F16/5866G06F16/587G06F16/29Y02A90/10
Inventor 李继园冯霄曹小曙方登茂张苗
Owner SHAANXI NORMAL UNIV
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