井下位置服务系统设计

杜志刚; 储楠; 罗克

doi:10.13272/j.issn.1671-251x.2021040070

井下位置服务系统设计

doi: 10.13272/j.issn.1671-251x.2021040070

杜志刚^{1, 2,},
储楠^{1, 2},
罗克^{1, 2}

1.
中煤科工集团常州研究院有限公司, 江苏常州　213015
2.
天地(常州)自动化股份有限公司, 江苏常州　213015

基金项目: 中国煤炭科工集团科技创新基金重点项目（2018-TD-ZD005）；天地（常州）自动化股份有限公司科研项目（2020GY001-5）。

详细信息

作者简介:
杜志刚（1986-），男，山东泰安人，工程师，硕士，主要从事矿井通信、计算机软件设计与开发方面的工作，E-mail：568850188@qq.com

中图分类号: TD67
计量
- 文章访问数: 179
- HTML全文浏览量: 31
- PDF下载量: 27
- 被引次数: 0
出版历程
- 收稿日期: 2021-04-21
- 修回日期: 2022-03-13
- 网络出版日期: 2022-03-05

Underground location service system design

DU Zhigang^{1, 2
,},
CHU Nan^{1, 2},
LUO Ke^{1, 2}

1.
CCTEG Changzhou Research Institute, Changzhou 213015, China
2.
Tiandi(Changzhou) Automation Co., Ltd., Changzhou 213015, China

摘要

摘要: 位置服务旨在提供目标对象精准的实时位置信息，是建立在定位基础上的服务，然而目前井下定位系统存在定位精度低、实时性差、容量不足、数据库承载能力有限、仅支持一维定位等问题。为避免井下定位系统对位置服务的影响，设计了一种井下位置服务系统。该系统采用基于Docker的微服务架构，克服了传统单体式架构开发迭代和性能瓶颈问题，松散各业务之间的耦合性；采用多标签多锚节点同时测距方法，在保证测距精度的同时提高了测距效率和定位系统容量；采用多源数据融合定位算法提高标志卡相对锚基站方向的判别准确性；采用基于卡尔曼滤波和加权LM法的定位算法和低复杂度的特征提取方法对定位结果进行优化，降低噪声干扰，去除冗余数据，提高定位精度；引入时序数据库进行数据混合存储，将历史轨迹等时序数据存入InfluxDB，提高系统数据访问性能；采用发布订阅模式进行消息异步传输，增加公共信息的重用性和共享性；对位置服务接口采用Bearer验证，保护系统数据安全和井下敏感数据。实际应用结果表明，该系统可提供井下各类目标高精度实时位置信息，为工作面限员监测系统、人机接近保护装置、辅助运输系统、自动驾驶系统提供了重要的数据支撑。
- 位置服务 /
- 井下精确定位 /
- 位置信息 /
- UWB /
- 微服务 /
- 时序数据库 /
- 接口安全
Abstract: The location service aims to provide accurate real-time position information of target objects, which is based on positioning. However, the current underground positioning system has some problems, such as low positioning precision, poor real-time performance, insufficient capacity, limited database carrying capacity, only supporting one-dimensional positioning and so on. In order to avoid the influence of underground positioning system on location service, an underground location service system is designed. The system adopts a Docker-based micro-service architecture, which overcomes the problems of development iteration and performance bottleneck of the traditional monolithic architecture and looses the coupling between businesses. The system adopts the simultaneous ranging method of multi-label and multi-anchor nodes, which improves the ranging efficiency and the capacity of the positioning system while ensuring the ranging accuracy. The system uses multi-source data fusion positioning algorithm to improve the discrimination accuracy of the direction of the sign card relative to the anchor base station. The system adopts the positioning algorithm based on Kalman filter and weighted LM method and the low-complexity characteristic extraction method to optimize the positioning results, reduce noise interference, remove redundant data and improve positioning precision. The system introduces the time series database for mixed data storage, and stores time series data such as historical track in InfluxDB to improve system data access performance. The system adopts the publish-subscribe mode for asynchronous message transmission, which increases the reusability and sharing of public information. The system adopts Bearer verification for the location service interface to protect system data security and underground sensitive data. The practical application results show that the system can provide high-precision real-time position information of various underground targets, and provide important data support for the working face limit monitoring system, human-machine approach protection device, auxiliary transportation system and automatic driving system.
- location service /
- underground precise positioning /
- position information /
- UWB /
- micro-service /
- time series database /
- interface security

HTML全文

图 1 井下位置服务系统架构

Figure 1. Architecture of underground location service system

下载: 全尺寸图片幻灯片

图 2 微服务架构

Figure 2. Architecture of microservice

下载: 全尺寸图片幻灯片

图 3 扰动数据处理流程

Figure 3. Disturbance data processing flow

下载: 全尺寸图片幻灯片

图 4 发布订阅模式

Figure 4. Publish and subscribe mode

下载: 全尺寸图片幻灯片

图 5 OAuth授权验证基本流程

Figure 5. Basic process of OAuth authorization verification

下载: 全尺寸图片幻灯片

参考文献(17)

[1]	王国法,赵国瑞,任怀伟. 智慧煤矿与智能化开采核心技术分析[J]. 煤炭学报,2019,44(1):34-41. WANG Guofa,ZHAO Guorui,REN Huaiwei. Analysis on key technologies of intelligent coal mine and intelligent mining[J]. Journal of China Coal Society,2019,44(1):34-41.
[2]	李德毅,张天雷,黄立威. 位置服务:接地气的云计算[J]. 电子学报,2014,42(4):786-790. doi: 10.3969/j.issn.0372-2112.2014.04.025 LI Deyi,ZHANG Tianlei,HUANG Liwei. A down-to-earth cloud computing:location-based service[J]. Acta Electronica Sinica,2014,42(4):786-790. doi: 10.3969/j.issn.0372-2112.2014.04.025
[3]	包建军. 智能矿山高精度位置服务系统研究现状与展望[J]. 智能矿山,2021(3):46-52. BAO Jianjun. Research status and prospect of intelligent mine high-precision location service system[J]. Journal of Intelligent Mine,2021(3):46-52.
[4]	荆诚,王爱军. 微服务在煤矿监控类软件开发框架中的应用[J]. 工矿自动化,2020,46(2):94-99. JING Cheng,WANG Aijun. Application of microservice in development framework of coal mine monitoring software series[J]. Industry and Mine Automation,2020,46(2):94-99.
[5]	曹宏宇,胡恒. 基于微服务架构的智能终端软件架构探讨[J]. 科技创新与应用,2019(20):17-19. CAO Hongyu,HU Heng. Discussion on the software architecture of intelligent terminal based on micro-service architecture[J]. Technology Innovation and Application,2019(20):17-19.
[6]	钟俊林. 基于微服务架构的自助微商城的研究与实现[D]. 北京: 北京邮电大学, 2019. ZHONG Junlin. Research and implementation of self-service wemall based on microservices architecture[D]. Beijing: Beijing University of Posts and Telecommunications, 2019.
[7]	霍振龙. 矿井定位技术现状和发展趋势[J]. 工矿自动化,2018,44(2):51-55. HUO Zhenlong. Status and development trend of mine positioning technology[J]. Industry and Mine Automation,2018,44(2):51-55.
[8]	MAO Guoqiang,FIDAN B,ANDERSON B D O. Wireless sensor network localization techniques[J]. Computer Networks,2007,51(10):2529-2553. doi: 10.1016/j.comnet.2006.11.018
[9]	陈康,王军,包建军,等. 基于消息复用的TOF井下精确定位技术[J]. 工矿自动化,2019,45(2):1-5. CHEN Kang,WANG Jun,BAO Jianjun,et al. TOF underground accurate positioning technology based on message multiplexing[J]. Industry and Mine Automation,2019,45(2):1-5.
[10]	KIM H. Performance analysis of the SDS-TWR-MA algorithm[C]//International Conference on Wireless Communications & Mobile Computing: Connecting the World Wirelessly, Leipzig, 2009: 399-403.
[11]	包建军. 煤矿巷道多源数据融合定位算法研究[J]. 工矿自动化,2019,45(8):38-42. BAO Jianjun. Research on multi-source data fusion positioning algorithm for coal mine roadway[J]. Industry and Mine Automation,2019,45(8):38-42.
[12]	王伟. 基于卡尔曼滤波和加权LM法的井下精确定位算法[J]. 工矿自动化,2019,45(11):5-9. WANG Wei. Underground precise positioning algorithm based on Kalman filter and weighted LM algorithm[J]. Industry and Mine Automation,2019,45(11):5-9.
[13]	余修武. 矿井安全智能监测无线传感器网络关键技术研究[D]. 武汉: 武汉理工大学, 2013. YU Xiuwu. Study on key technologies of wireless sensor networks in mine underground safety intelligent monitoring[D]. Wuhan: Wuhan University of Technology, 2013.
[14]	杨婕. 时序数据库发展研究[J]. 广东通信技术,2020,40(3):46-48. doi: 10.3969/j.issn.1006-6403.2020.03.011 YANG Jie. Research on development of time series database[J]. Guangdong Communication Technology,2020,40(3):46-48. doi: 10.3969/j.issn.1006-6403.2020.03.011
[15]	韩艺坤,李富年,陈志丹. 基于InfluxDB的桥梁监测系统设计与实现[J]. 现代电子技术,2020,43(16):21-25. HAN Yikun,LI Funian,CHEN Zhidan. Design and implementation of bridge monitoring system based on InfluxDB[J]. Modern Electronics Technique,2020,43(16):21-25.
[16]	徐化岩,初彦龙. 基于InfluxDB的工业时序数据库引擎设计[J]. 计算机应用与软件,2019,36(9):33-36. doi: 10.3969/j.issn.1000-386x.2019.09.006 XU Huayan,CHU Yanlong. Design of industrial time series database engine based on InfluxDB[J]. Computer Applications and Software,2019,36(9):33-36. doi: 10.3969/j.issn.1000-386x.2019.09.006
[17]	吴龙波. 基于Oauth2.0构建空管云数据中心的资源认证授权机制[J]. 科技传播,2020,12(15):168-169. doi: 10.3969/j.issn.1674-6708.2020.15.075 WU Longbo. The resource authentication and authorization mechanism of air traffic control cloud data center is constructed based on Oauth2.0[J]. Public Communication of Science & Technology,2020,12(15):168-169. doi: 10.3969/j.issn.1674-6708.2020.15.075