基于语义多尺度的矿山地下空间建模方法

李雯静; 张馨心; 林志勇; 邱莉; 邱立强

doi:10.13272/j.issn.1671-251x.2021110012

基于语义多尺度的矿山地下空间建模方法

doi: 10.13272/j.issn.1671-251x.2021110012

李雯静^{1, 2,},
张馨心¹,
林志勇³,
邱莉¹,
邱立强¹

1.
武汉科技大学资源与环境工程学院, 湖北武汉　430081
2.
冶金矿产资源高效利用与造块湖北省重点实验室, 湖北武汉　430081
3.
武汉大学遥感信息工程学院, 湖北武汉　430079

基金项目: 湖北省高等学校优秀中青年科技创新团队计划项目（T2020002）。

详细信息

作者简介:
李雯静（1978-），女，湖北武汉人，教授，博士，博士研究生导师，主要研究方向为智慧矿山、矿山数据组织及三维地学建模，E-mail：liwenjing@wust.edu.cn

中图分类号: TD67
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- 文章访问数: 119
- HTML全文浏览量: 22
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-04
- 修回日期: 2022-03-08
- 网络出版日期: 2022-03-22

Mine underground space modeling method based on semantic multi-scale

1.
School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
2.
Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources, Wuhan 430081, China
3.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

摘要

摘要: 针对基于语义的地下矿山实体建模方法细节层次划分不够详细、模型配置自由度较小、缺乏多尺度精细化表达的问题，将参数化建模和语义多尺度思想应用到矿山多细节层次（LOD）模型构建中，提出基于语义多尺度的矿山地下空间建模方法。确定建模对象为矿山地下空间内的实体要素，依据实体要素位置划分为巷道模型和巷道内模型，巷道模型包括巷道主体模型及其他人工构筑物模型，巷道内模型包括矿山设施模型和设备模型；根据实体要素的语义信息定义不同的语义类，语义类向下划分成族，再按照功能将族分解为组件元素；通过语义属性、几何属性、外观属性、特征属性等可量化指标来描述每个语义类。设计了矿山LOD模型，该模型包括6个层次的离散化LOD模型：巷道网络模型、巷道粗略模型、巷道精细模型、巷道内主要设施模型、巷道内主要设备模型、巷道内其他设备模型，将离散化LOD模型与各种属性进行组合，可清晰显示矿山LOD模型的详细程度和主次关系。基于语义多尺度的矿山地下空间建模方法能够提供不同细节丰富度的实体要素模型，降低场景渲染的计算复杂度，提高模型构建的效率和灵活性。
- 矿山三维建模 /
- 参数化建模 /
- 语义多尺度 /
- LOD /
- 语义划分 /
- 精细化表达
Abstract: In order to solve the problems of semantic-based underground mine entity modeling method, such as the lack of detail level division, the small degree of freedom of model configuration, and the lack of multi-scale fine expression, parametric modeling and semantic multi-scale ideas are applied to the construction of mine multi-level of detail (LOD) model, and a mine underground space modeling method based on semantic multi-scale is proposed. It is determined that the modeling object is the entity element in mine underground space. The modeling objects are divided into roadway model and in-roadway model according to the position of the entity element. The roadway model comprises a roadway main body model and other artificial structure models, and the in-roadway model comprises a mine facility model and an equipment model. The different semantic classes are defined according to the semantic information of the entity elements. The semantic classes are divided into families, and then the families are decomposed into component elements according to their functions. Each semantic class is described by quantifiable indicators such as semantic attributes, geometric attributes, appearance attributes and characteristic attributes. The mine LOD model is designed, which includes six levels of discrete LOD models, namely roadway network model, roadway rough model, roadway fine model, main facility model in roadway, main equipment model in roadway, and other equipment models in roadway. Combining the discrete LOD model with various attributes can clearly show the level of detail and the primary and secondary relationships of the mine LOD model. The mine underground space modeling method based on semantic multi-scale can provide entity element models with different detail richness, reduce the computational complexity of scene rendering and improve the efficiency and flexibility of model construction.
- mine 3D modeling /
- parametric modeling /
- semantic multi-scale /
- LOD /
- semantic division /
- fine expression

HTML全文

图 1 矿山地下空间实体要素

Figure 1. Mine underground space entity elements

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图 2 矿山语义类定义

Figure 2. Mine semantic class definition

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图 3 SLOD与ALOD的组合

Figure 3. Combination of SLOD and ALOD

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图 4 SLOD0—SLOD2的可视化场景

Figure 4. Visualization scene of SLOD0-SLOD2

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图 5 SLOD3—SLOD5的可视化场景

Figure 5. Visualization scene of SLOD3-SLOD5

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图 6 SLOD3与ALOD组合建模场景

Figure 6. SLOD3 and ALOD combined modeling scene

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表 1 巷道和巷道内SLOD和ALOD组合

Table 1. Combination of SLOD and ALOD of roadway and in-roadway

SLODn	SLODnx
SLODn	ALODa	ALODb	ALODc
SLOD0	SLOD0a	×	×
SLOD1	SLOD1a	SLOD1b	SLOD1c
SLOD2	SLOD2a	SLOD2b	SLOD2c
SLOD3	SLOD3a	SLOD3b	SLOD3c
SLOD4	SLOD4a	SLOD4b	SLOD4c
SLOD5	SLOD5a	SLOD5b	SLOD5c

下载: 导出CSV

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