Data model for geological spatiotemporal big data expression and storage management

Liu Gang; Wu Chonglong; He Zhenwen; Weng Zhengping; Que Xiang; Tian Shanjun; Li Yang

doi:10.19509/j.cnki.dzkq.2020.0118

Volume 39 Issue 1

Jan. 2020

Turn off MathJax

Article Contents

Article Navigation > Bulletin of Geological Science and Technology > 2020 > 39(1): 164-174

Liu Gang, Wu Chonglong, He Zhenwen, Weng Zhengping, Que Xiang, Tian Shanjun, Li Yang. Data model for geological spatiotemporal big data expression and storage management[J]. Bulletin of Geological Science and Technology, 2020, 39(1): 164-174. doi: 10.19509/j.cnki.dzkq.2020.0118

Citation:

Liu Gang, Wu Chonglong, He Zhenwen, Weng Zhengping, Que Xiang, Tian Shanjun, Li Yang. Data model for geological spatiotemporal big data expression and storage management[J]. Bulletin of Geological Science and Technology, 2020, 39(1): 164-174. doi: 10.19509/j.cnki.dzkq.2020.0118

Citation:

PDF( 2522 KB)

Data model for geological spatiotemporal big data expression and storage management

doi: 10.19509/j.cnki.dzkq.2020.0118

Liu Gang^{1a, 1b
,},
Wu Chonglong^{1a, 1b},
He Zhenwen^{1a, 1b},
Weng Zhengping^{1a, 1b},
Que Xiang²,
Tian Shanjun³,
Li Yang^{1a, 1b}

Received Date: 14 Nov 2019

Abstract

Abstract

Geological big data is a typical spatiotemporal big data.Most of present geological information systems only support the storage and expression of the static status of geological exploration or production in a specific period.However, with the development and application of multiple sensors, real-time monitoring and other high-tech in the geological exploration, which results in more and more dynamic and multi-source heterogeneous geological data being produced.In practical application, it is in urgent need to deal with the real-time increasing big data in the geological exploration and production, which can strongly support the analysis and judgement of the geological process.In this paper, we study the traditional data model for geological spatiotemporal expression and storage management with the target of a new hybrid model, which can not only deal with the multi-sources and multi-dimensional data, but also support the time associated and time granularity.According to the characteristics of geological big data and the combinations of static and dynamic geological process data, we took both the object-oriented and event based method and put forward a geological event multi-factors driven model.We also designed the corresponding geological data logic model for distributed storage.Based on the geological data logical model and NoSQL database technology, we designed a non-relational distributed database schema.With the use of 3D geological modeling technology, real-time dynamic monitoring information visualization technology, we build a necessary virtual simulation environment for the hybrid model.By binding the observation data source, which is operation as an events emitter based on the OPC interface, the dynamic mining process is simulating as the geological objects responding to their corresponding events.Under the support of geological modeling data and reports and other data in the first mining area of Wang Jialing coal mine, we tried to implement the model application with multi geological spatiotemporal events.The results show that the data model is feasible, applicable for the expression and data management of mine dynamic mining process.
- data model,
- expression of geological spatiotemporal data,
- storage management of geological big data,
- NoSQL

FullText(HTML)

References(36)

References

[1]	严光生, 薛群威, 肖克炎, 等.地质调查大数据研究的主要问题分析[J].地质通报, 2015, 34(7):1273-1279. doi: 10.3969/j.issn.1671-2552.2015.07.004
[2]	肖克炎, 孙莉, 李楠, 等.大数据思维下的矿产资源评价[J].地质通报, 2015, 34(7):1266-1272. doi: 10.3969/j.issn.1671-2552.2015.07.003
[3]	王登红, 刘新星, 刘丽君.地质大数据的特点及其在成矿规律、成矿系列研究中的应用[J].矿床地质, 2015, 34(6):1143-1154. http://d.old.wanfangdata.com.cn/Periodical/kcdz201506005
[4]	周永章, 黎培兴, 王树功, 等.矿床大数据及智能矿床模型研究背景与进展[J].矿物岩石地球化学通报, 2017, 36(2):334-339. http://d.old.wanfangdata.com.cn/Periodical/kwysdqhxtb201702016
[5]	Carr G R, Andrew A S, Denton G J.The "Glass Earth":Geochemical frontiers in exploration through cover[J].Australian Institute of Geoscientists Bulletin, 1999, 28:33-40. https://cn.bing.com/academic/profile?id=7d2f0272938a957ebf839da4f81479d8&encoded=0&v=paper_preview&mkt=zh-cn
[6]	吴冲龙, 刘刚."玻璃地球"建设的现状、问题、趋势与对策[J].地质通报, 2015, 34(7):1280-1287. doi: 10.3969/j.issn.1671-2552.2015.07.005
[7]	郭华东, 王力哲, 陈方, 等.科学大数据与数字地球[J].科学通报, 2014, 59(12):1047-1054. http://d.old.wanfangdata.com.cn/Periodical/kjcgglyyj201608018
[8]	Lake B, Salakhutdinov R, Tenenbaum J.Human-level concept learning through probabilistic program induction[J].Science, 2015, 266:1332-1338. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c50ea5bb88b1527b4c774daffe17c013
[9]	赵鹏大.大数据时代数字找矿与定量评价[J].地质通报, 2015, 34(7):1255-1259. doi: 10.3969/j.issn.1671-2552.2015.07.001
[10]	吴冲龙, 刘刚, 张夏林, 等.地质科学大数据及其利用的若干问题探讨[J].科学通报, 2016, 61(16):1797-1807. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kxtb201616010
[11]	张旗, 周永章.大数据正在引发地球科学领域一场深刻的革命:《地质科学》2017年大数据专题代序[J].地质科学, 2017, 52(3):637-648. http://d.old.wanfangdata.com.cn/Periodical/dzkx201703001
[12]	黄少芳, 刘晓鸿, 孙玲, 等.初论大数据时代地质资料信息集成与服务[J].中国矿业, 2016, 25(2):170-172. doi: 10.3969/j.issn.1004-4051.2016.02.033
[13]	Langran G.Time in geographic information systems, technical issues in geographic information systems[M].Abington:Taylor & Francis, 1992.
[14]	曹闻.时空数据模型及其应用研究[D].郑州: 解放军信息工程大学, 2011.
[15]	Koubarakis M.Spatio-temporal databases:The chorochronos approach[M].Berlin:Springer Science & Business Media, 2003.
[16]	Worboys M F.A unified model for spatial and temporal information[J].The Computer Journal, 1994, 37(1):26-34. doi: 10.1093/comjnl/37.1.26
[17]	郑扣根, 谭石禹, 潘云鹤.基于状态和变化的统一时空数据模型[J].软件学报, 2001, 12(9):1360-1365. http://d.old.wanfangdata.com.cn/Periodical/rjxb200109013
[18]	Peuquet D J, Duan N.An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data[J].International Journal of Geographical Information Systems, 1995, 9(1):7-24. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/02693799508902022
[19]	Chen J, Jiang J.An event-based approach to spatio-temporal data modeling in land subdivision systems[J].GeoInformatica, 2000, 4(4):387-402. doi: 10.1023/A:1026565929263
[20]	孟令奎, 赵春宇, 林志勇, 等.基于地理事件时变序列的时空数据模型研究与实现[J].武汉大学学报:信息科学版, 2003, 28(2):202-207. http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb200302015
[21]	龚健雅, 李小龙, 吴华意.实时GIS时空数据模型[J].测绘学报, 2014, 43(3):226-232. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=chxb201403002
[22]	舒红, 陈军, 杜道生, 等.面向对象的时空数据模型[J].武汉测绘科技大学学报, 1997, 22(3):229-233. doi: 10.3321/j.issn:1671-8860.1997.03.009
[23]	易宝林, 冯玉才, 曹忠升.2003.基于对象行为的时空拓扑模型[J].小型微型计算机系统, 2003, 24(6):1046-1049. doi: 10.3969/j.issn.1000-1220.2003.06.027
[24]	尹章才, 李霖, 艾自兴.基于图论的时空数据模型研究[J].测绘学报, 2003, 32(2):168-172. doi: 10.3321/j.issn:1001-1595.2003.02.015
[25]	龚健雅.GIS中面向对象时空数据模型[J].测绘学报, 1997, 26(4):289-298. doi: 10.3321/j.issn:1001-1595.1997.04.002
[26]	阙翔.面向动态过程模拟和实时表达的地质时空数据模型研究[D].武汉: 中国地质大学(武汉), 2015.
[27]	田善君.面向地质大数据存储管理的时空数据模型研究[D].武汉: 中国地质大学(武汉), 2016.
[28]	Bristol S, Euliss N H, Booth N L.Science strategy for core science systems in the U.S.Geological Survey, 2013-2023-Public Review Release[R].Denver: U.S.Geological Survey, 2012. https://www.researchgate.net/publication/271508464_Science_Strategy_for_Core_Science_Systems_in_the_US_Geological_Survey_20132023Public_Review_Release
[29]	Wang L Z, Chen D, Hu Y, et al.Towards enabling cyberinfrastructure as a service in clouds[J].Computers & Electrical Engineering, 2013, 39:3-14. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=81bf0a5efdc4c0f540f7e7d0a2dc5715
[30]	吴冲龙, 刘刚, 田宜平, 等.论地质信息科学[J].地质科技情报, 2005, 24(3):1-8. http://d.old.wanfangdata.com.cn/Periodical/dzkjqb200503001
[31]	吕霞, 李健强, 龚爱华, 等.基于云架构的中国地质调查信息网格平台关键技术研究与实现[J].地质通报, 2015, 34(7):1323-1332. doi: 10.3969/j.issn.1671-2552.2015.07.010
[32]	李超岭, 李健强, 张宏春, 等.智能地质调查大数据应用体系架构与关键技术[J].地质通报, 2015, 34(7):1288-1299. doi: 10.3969/j.issn.1671-2552.2015.07.006
[33]	陈建平, 李婧, 崔宁, 等.大数据背景下地质云的构建与应用[J].地质通报, 2015, 34(7):1260-1265. doi: 10.3969/j.issn.1671-2552.2015.07.002
[34]	谭永杰.地质大数据与信息服务工程技术框架[J].地理信息世界, 2016, 23(1):1-9. doi: 10.3969/j.issn.1672-1586.2016.01.001
[35]	He Z, Wu C, Liu G, et al.Decomposition tree:A spatio-temporal indexing method for movement big data[J].Cluster Computing, 2015, 18:1481-1492. doi: 10.1007/s10586-015-0475-3
[36]	李婧, 陈建平, 王翔.地质大数据存储技术[J].地质通报, 2015, 34(8):1589-1594. doi: 10.3969/j.issn.1671-2552.2015.08.018

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article Metrics

Article Views(918) PDF Downloads(3595)

Data model for geological spatiotemporal big data expression and storage management

doi: 10.19509/j.cnki.dzkq.2020.0118

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Data model for geological spatiotemporal big data expression and storage management

doi: 10.19509/j.cnki.dzkq.2020.0118

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content