Multi-layer cognitive constrained high-dimensional geospatial data focusing visualization method
A geospatial data, high-dimensional technology, applied in the direction of visual data mining, neural learning methods, structured data retrieval, etc., can solve the problems of complex hierarchical structure of data information, high fatigue, large amount of information, etc., to achieve semantic recognition Knowledge and information comparison, efficient semantic cognition and information comparison, and the effect of strengthening human-computer interaction
Active Publication Date: 2020-08-14
浙江中海达空间信息技术有限公司
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[0006] A large amount of valuable information is hidden in high-complexity high-dimensional data. Mining high-dimensional data and visualizing it can help people obtain more information and its deeper meaning. However, due to the complexity of big data itself and multi-dimensional attributes, its representation presents an excessive amount of information, and the hierarchical structure of data information is complex, resulting in high fatigue and low efficiency in the cognitive process
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[0034] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:
[0035]The invention provides a focused visualization method for high-dimensional geographic spatial data with multi-layer cognitive constraints. Wherein, the high-dimensional geospatial data refers to a continuously changing geographic object or phenomenon, which is used to express the distribution of environmental data, the statistical distribution of characteristic indicators, and the integrated digital expression form of "attribute-space-time"; The visualization method of focusing on emotional cognition of high-dimensional data with multi-layer progressive constraints realizes integrated display: combining the self-encoder to express the semantic characteristics of data through layer-by-layer unsupervised learning, relying on the internal structure and autonomous characteristics of high-dimensional data; Based on emotional cognition, the hierarc...
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The invention relates to a multi-layer cognitive constrained high-dimensional geospatial data focusing visualization method. The method comprises the following steps: a) for high-dimensional spatial data with different expression features, constructing a constraint item set of a semi-supervised mechanism dimension reduction model by taking semantic information, an autonomous structure, a focusingattribute, a space feature and a time feature of the high-dimensional spatial data as cognitive features; b) constructing a deep auto-encoder of an adaptive semi-supervised mechanism by depending on cognitive features, and performing dimensionality reduction on high-dimensional data by using the model to obtain an adaptive feature matrix set; c) establishing a feature matrix normalization mappingrule by focusing the perception and attention of the human being according to the cognitive process of the human being on the visual interface from the shallow visual information to the deep emotion information, and summarizing a feature mapping rule set; and d) outputting the high-dimensional visual representation of knowledge focus. According to the method, the problems that an existing dimension reduction processing method is low in accuracy, poor in stability, high in redundancy and the like can be solved.
Description
technical field [0001] The invention belongs to the technical field of geospatial data processing, in particular to a focused visualization method for high-dimensional geospatial data with multi-layer cognitive constraints. Background technique [0002] The rapid development of the earth observation system and global change simulation has accumulated a large amount of spatio-temporal data, and presents the characteristics of multi-dimensional, multi-attribute and irregular structure. Deal with hot spots in the tech world. Geospatial data is the digital expression of geographic objects, and is generally suitable for describing continuously changing objects or phenomena. It is mostly used to express the distribution of environmental data, the statistical distribution of characteristic indicators, and the probability distribution of geophysical phenomena. Geographic spatiotemporal data is based on the above characteristics. Temporal attributes, thus increasing the complexity o...
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IPC IPC(8): G06F16/26G06K9/62G06N3/04G06N3/08
CPCG06F16/26G06N3/088G06N3/045G06F18/25
Inventor 谢潇伍庭晨张叶廷许飞徐怡婷
Owner 浙江中海达空间信息技术有限公司