A vector conversion method of building indoor space data based on attribute classification

Abstract

Aiming at the problems of low data conversion efficiency, low accuracy and low data utilization rate after conversion in traditional methods, this paper proposes a vector conversion method for building indoor space data based on attribute classification. Firstly, the transformation process of data vectors is analyzed. Secondly, block matching detection and fusion recognition were carried out on the building interior space images, and fuzzy feature extraction method was used to optimize the collection and feature recognition of the building interior space data. Then, the attribute classification method is used to obtain the condition attribute and decision attribute of the data, and realize the building interior space data mining. Then, the K-means algorithm is used to cluster the indoor spatial data samples, and the wavelet transform method is used to de-noise the noisy data in advance. Finally, the obtained data is processed by vector transformation. The experimental results show that the data conversion efficiency of this method is high, and the data conversion accuracy and data utilization rate have been improved.