Granularity index construction method for large-scale BIM building data

Abstract

The invention discloses a granularity index construction method for large-scale BIM building data, and the method comprises the steps: carrying out the analysis and arrangement of the spatial data ofa BIM building based on a building space semantic information analysis algorithm, and forming the guided semantic information of building space analysis; adopting a building space segmentation and information supplement method based on geometric calculation to carry out verifiability supplement on the leading semantic information; based on the geometric space structure of the whole BIM building, including the adjacency, communication and inclusion relationship of the geometric space, constructing the fine-grained index of the BIM building data, forming the hierarchical network diagram of the BIM building data, and realizing the quick access and management of the granularity data. According to the granularity index construction method for the large-scale BIM building data, the geometric space structure of the whole BIM building is combined, the BIM building data is recombined and optimized into the multi-level granularity data, and the granularity index construction method has the advantages of being automatic, granular and fluent.

Description

technical field [0001] The invention relates to the technical field of BIM building data processing, in particular to a granular index construction method for large-scale BIM building data. Background technique [0002] Buildings are closely related to human life. With the development of smart buildings and their core technology - BIM (Building Information Modeling), BIM data has become the core source code for smart building analysis. With the popularization and promotion of smart mobile devices, BIM Efficient analysis of buildings, especially online analysis under collaboration and visual sharing, has gradually become an important tool and decision-making basis for many building participating departments to collaborate, communicate and manage affairs. However, due to the large number of departments and extensive information involved in BIM building data, professional BIM building data has the characteristics of large data volume, diverse information, and low organizational...

AI Technical Summary

Problems solved by technology

[0004] First, the volume of data is large. BIM building data involves various information in the entire life cycle of the building. The entire BIM data format IFC contains nearly 1,000 predefined entity classes and types to describe building components and their corresponding attributes, etc., resulting in a very large volume of data;

[0005] Second, the data granularity is not uniform. Based on the concept of "digital twin", BIM building data is consistent with the actual building environment, including a large number of building components such as "walls" and "doors" and their corresponding geometric and semantic information. Different granularity in terms of information volume, expression, and actual space, which brings difficulties to data manipulation;

[0006] Third, real-time scheduling and data access are less effective. BIM construction data is more focused on the various needs of construction engineering, and less attention is paid to the efficient organization or management of data for online display. It is difficult to support real-time access and picking of data with appropriate granularity. As a result, current applications often perform data scheduling directly in the form of files or manual control granularity, and the real-time scheduling effect is not high

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