基于Python语言的可视化空间数据库智能查询系统

针对传统的可视化空间数据库智能查询系统存在查询错误率高的问题，设计基于Python语言的可视化空间数据库智能查询系统．获取异构存储分布，通过融合度聚类分析，进行可视化空间数据库的异构样本特征分解，根据分解结果，采用模糊度检测和K均值聚类方法，对可视化空间数据库的差异性数据聚类，以聚类结果为基础，结合随机自适应调度和子空间压缩方法，实现对可视化空间数据库的智能查询．仿真实验结果表明，采用该方法进行可视化空间数据库智能查询的错误率较低，查准率较高，数据特征聚类性较强．

Intelligent Query System of Visual Spatial Database Based on Python Language

Aiming at the problem of high query error rate in the traditional visual spatial database intelligent query system,a visual spatial database intelligent query system based on Python language is designed. The heterogeneous storage distribution is first obtained,the heterogeneous sample characteristics of the visual spatial database through the fusion degree clustering analysis is then decomposed and finally the differential data of the visual spatial database is clustered by using the ambiguity detection and K-means clustering methods according to the decomposition results. Based on the clustering results,the intelligent query of visual spatial database is realized with the random adaptive scheduling and subspace compression methods. The simulation results show that this method has low error rate,high precision and strong clustering of data features.