Method and system for constructing special-shaped arrangement matrix of urban indoor three-dimensional semantic model

Abstract

The invention provides a method for constructing a special-shaped arrangement matrix of an urban indoor three-dimensional semantic model. The method is characterized by comprising the following steps: S1, constructing a special-shaped arrangement matrix of an urban building contour semantic model; s2, constructing an indoor three-dimensional semantic model of the urban building; s3, embedding the urban building indoor three-dimensional semantic model into the corresponding urban building contour semantic model, and completing construction of the urban indoor three-dimensional semantic model. A simplified artificial intelligence network model is utilized to obtain a contour prediction model network of an urban building, an urban building contour semantic model is established, indoor three-dimensional semantics are embedded in the urban building contour semantic model, and an accurate urban three-dimensional semantic model is obtained. The method is simple in algorithm, the actual building position is accurately reflected through the construction method, and spatial distribution visualization and searchability of the urban indoor three-dimensional semantic model are achieved.

Description

technical field [0001] The invention relates to a method for constructing an indoor three-dimensional semantic model, in particular to a method for constructing an indoor three-dimensional semantic model of a city and a system thereof, and belongs to the field of graphical semantic construction. Background technique [0002] The semantic model is to establish a mapping relationship between the construction model and the feature or abstract identifier, and can accurately identify and retrieve the element objects in these models. It has become a popular technology in urban big data and even the realization of smart cities. On the one hand, the existing technology only considers the semantic construction of a single building or a single community building when building an indoor semantic model, which cannot constitute a complete urban semantic big data. On the other hand, although with the aid of mathematical models, such as matrix, it can accurately locate, roughly describe th...

AI Technical Summary

Problems solved by technology

On the one hand, the existing technology only considers the semantic construction of a single building or a single community building when building an indoor semantic model, which cannot constitute a complete urban semantic big data

On the other hand, although with the aid of mathematical models, such as matrix, it can accurately locate, roughly describe the orientation relationship between buildings, and describe the functional categories of urban buildings (apartment, villa, factory, complex, etc.), and retrieve each The architectural components (such as the roof) of a building can be used to determine the type of architectural style. However, the mathematical matrix cannot accurately describe the semantic model of urban architecture from the perspective of architectural topology.

so that it is impossible to visually present the appearance of the city

Traditional drawings can only describe the natural landscape and the distribution of a few buildings from the position, but cannot accurately and topologically describe

Constructing the outline of a single building is the basic work of urban indoor building topology. The algorithm for calculating the most probable outline point of a building outline using a neural network is complex. For a large number of urban buildings across the country, it will generate a large amount of calculation load, and its construction cost is huge.

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