Research on key technologies of multi-element monitoring data integration in intelligent mine
-
摘要: 目前大部分煤矿监控系统采用私有数据采集协议，相互之间无法兼容。针对该问题，从数据采集、数据融合、数据存储3个方面入手，探讨了智能矿井多元监控数据集成关键技术。数据采集：为了加强系统的开放性、兼容性，可将私有协议封装为驱动动态链接库(DLL)，通过加载适配OPC，MQTT等协议及挂接私有协议驱动的方式实现各业务系统的数据采集，并采用多线程技术满足多通道、多协议数据传输的高效性、实时性要求。数据融合：可将各系统之间共享频率高的数据进行统一规范，形成煤矿主数据，以保证各系统之间数据的一致性。数据存储：对实时性要求高的数据可选用时序数据库，对实时性要求不高的数据可选用关系型数据库，经对比分析，InfluxDB更适用于煤矿监控数据的实时存储，MySQL Community更适用于对实时性要求不高的数据存储；可运用Redis缓存技术实现数据高效缓存，以保证煤矿监控数据的完整性。Abstract: Currently, most coal mine monitoring systems adopt private data acquisition protocols, which are incompatible with each other. In order to solve this problem, the key technologies of multi-element monitoring data integration in intelligent mine are discussed from three aspects of data acquisition, data fusion and data storage. Data acquisition: In order to strengthen the openness and compatibility of the system, the private protocol can be encapsulated into a driver dynamic link library (DLL). The data acquisition of each business system can be realized by loading and adapting OPC, MQTT and other protocols and hooking the private protocol driver. The multithreading technology can be adopted to meet the requirements of high efficiency and real-time of multi-channel and multi-protocol data transmission. Data fusion: The data with the high frequency of sharing among various systems can be unified and standardized to form the master data of the coal mine. This will ensure the consistency of data among various systems. Data storage: For data with high real-time requirements, the time series database can be selected. For data with low real-time requirements, the relational database can be selected. Through comparative analysis, InfluxDB is more suitable for real-time storage of coal mine monitoring data, and MySQL Community is more suitable for data storage with low real-time requirements. Redis cache technology can be used to achieve efficient data cache so as to ensure the integrity of coal mine monitoring data.
-
-
![]()
图 1 智能矿井多元监控数据集成方案
Figure 1. Intelligent mine multi-dimensional monitoring data integration scheme
表 1 时序数据库对比
Table 1 Comparison of the time series databases
数据库 105条记录存储时间 支持的系统 TDengine 单线程，1.3～1.4 s
5个线程，0.45～0.55 sLinux ClickHouse 0.3 s Windows和Linux InfluxDB 0.109 s Windows和Linux 
下载: 导出CSV
-
[1] 崔亚仲,白明亮,李波. 智能矿山大数据关键技术与发展研究[J]. 煤炭科学技术,2019,47(3):66-74. DOI: 10.13199/j.cnki.cst.2019.03.009 CUI Yazhong,BAI Mingliang,LI Bo. Key technology and development research on big data of intelligent mine[J]. Coal Science and Technology,2019,47(3):66-74. DOI: 10.13199/j.cnki.cst.2019.03.009
[2] 毛善君,刘孝孔,雷小锋,等. 智能矿井安全生产大数据集成分析平台及其应用[J]. 煤炭科学技术,2018,46(12):169-176. DOI: 10.13199/j.cnki.cst.2018.12.027 MAO Shanjun,LIU Xiaokong,LEI Xiaofeng,et al. Research and application on big data integration analysis platform for intelligent mine safety production[J]. Coal Science and Technology,2018,46(12):169-176. DOI: 10.13199/j.cnki.cst.2018.12.027
[3] 贺耀宜,刘丽静,赵立厂,等. 基于工业物联网的智能矿山基础信息采集关键技术与平台[J]. 工矿自动化,2021,47(6):17-24. DOI: 10.13272/j.issn.1671-251x.17798 HE Yaoyi,LIU Lijing,ZHAO Lichang,et al. Key technology and platform of intelligent mine basic information acquisition based on industrial Internet of things[J]. Industry and Mine Automation,2021,47(6):17-24. DOI: 10.13272/j.issn.1671-251x.17798
[4] 荣雪,黄友锐,储怡然,等. 基于OPC UA的煤矿安全生产监控系统信息模型[J]. 工矿自动化,2022,48(3):112-117. RONG Xue,HUANG Yourui,CHU Yiran,et al. Information model of coal mine safety production monitoring system based on OPC UA[J]. Journal of Mine Automation,2022,48(3):112-117.
[5] 旷永龙. Modbus通信在煤矿监测系统中的应用[J]. 矿业装备,2019(2):110-111. DOI: 10.3969/j.issn.2095-1418.2019.02.047 KUANG Yonglong. Application of Modbus communication in coal mine monitoring system[J]. Mining Equipment,2019(2):110-111. DOI: 10.3969/j.issn.2095-1418.2019.02.047
[6] 王海军,丁剑明,白明亮,等. 神东煤炭生产数据标准化规划初探[J]. 中国煤炭,2018,44(2):83-86,90. DOI: 10.3969/j.issn.1006-530X.2018.02.017 WANG Haijun,DING Jianming,BAI Mingliang,et al. Preliminary study on coal production data standardization and planning of Shendong Group[J]. China Coal,2018,44(2):83-86,90. DOI: 10.3969/j.issn.1006-530X.2018.02.017
[7] 贺耀宜,王海波. 基于物联网的可融合性煤矿监控系统研究[J]. 工矿自动化,2019,45(8):13-18. DOI: 10.13272/j.issn.1671-251x.17458 HE Yaoyi,WANG Haibo. Research on coal mine fusion monitoring system based on Internet of things[J]. Industry and Mine Automation,2019,45(8):13-18. DOI: 10.13272/j.issn.1671-251x.17458
[8] 左文康. 基于多线程技术的水泥企业生产数据采集系统[D]. 济南: 济南大学, 2017. ZUO Wenkang. Production data acquisition system of cement enterprises based on multithreading technology[D]. Jinan: University of Jinan, 2017.
[9] 荣宝,魏德志,于海成,等. 露天煤矿安全生产大数据存储与流式计算技术[J]. 工矿自动化,2021,47(增刊1):101-102,109. RONG Bao,WEI Dezhi,YU Haicheng,et al. Open-pit coal mine safety production big data storage and streaming computing technology[J]. Industry and Mine Automation,2021,47(S1):101-102,109.
[10] 董雪,高远,敖炳. 基于TDengine的智能电网监控系统数据存储方法研究[J]. 电气应用,2021,40(8):68-74. DONG Xue,GAO Yuan,AO Bing. Research on data storage method of smart grid monitoring system based on TDengine[J]. Electrotechnical Application,2021,40(8):68-74.
[11] 高翔. 基于Clickhouse的大数据对比分析应用案例[J]. 电子技术,2022,51(5):31-35. GAO Xiang. Case stduy on comparative analysis of big data based on Clickhouse[J]. Electronic Technology,2022,51(5):31-35.
[12] 张世贤,张少春,谢晓东. 基于InfluxDB的监控设备通用运维管理平台[J]. 计算机系统应用,2021,30(12):123-127. DOI: 10.15888/j.cnki.csa.008201 ZHANG Shixian,ZHANG Shaochun,XIE Xiaodong. General operation and maintenance management platform for monitoring equipment based on InfluxDB[J]. Computer Systems & Applications,2021,30(12):123-127. DOI: 10.15888/j.cnki.csa.008201
[13] 王续法. 基于Redis的一致性分析与改进[D]. 成都: 电子科技大学, 2017. WANG Xufa. Analysis and improvement of data consistency based on Redis[D]. Chengdu: University of Electronic Science and Technology of China, 2017.
-
期刊类型引用(11)
1. 王举重，聂倩，李敏，赵海龙，任丹彤. 煤矿设备数据管理系统设计及性能测试. 煤矿机械. 2025(01): 23-25 . 
百度学术
2. 曹现刚，段雍，王国法，赵江滨，任怀伟，赵福媛，杨鑫，张鑫媛，樊红卫，薛旭升，李曼. 煤矿设备全寿命周期健康管理与智能维护研究综述. 煤炭学报. 2025(01): 694-714 . 百度学术
3. 尚伟栋，王海力，张晓霞，王浩，徐华龙. 基于对象模型的煤矿数据采集融合共享系统. 工矿自动化. 2024(01): 17-24+34 . 本站查看
4. 苏雄，李小龙，贺杰伟. 基于三维GIS的煤矿数据集成自动化监测系统. 自动化与仪表. 2024(03): 121-125 . 百度学术
5. 王启飞，赵逸涵，刘帅，刘昊霖，孙英峰，李成武. 煤矿事故大数据驱动的风险治理模式研究综述. 中国安全科学学报. 2024(07): 28-37 . 百度学术
6. 丁震，孙继平，张帆，王鹏，胡而已，邓文革，高静，郑耀涛，王波，高秋秋，李系民，钱海军，柳建华，乔少利，鲍震，杨永生，杨振宇，李玉雪，李昱翰，邵光耀. 智能化矿山通信接口与协议技术规范研究. 工矿自动化. 2023(02): 6-13 . 本站查看
7. 谭章禄，王美君，叶紫涵. 智能化煤矿数据治理体系与关键问题研究. 工矿自动化. 2023(05): 22-29 . 本站查看
8. 包乌云毕力格. 基于5G技术工作面智能安全监测技术研究. 煤炭技术. 2023(10): 153-155 . 百度学术
9. 付翔，秦一凡，李浩杰，牛鹏昊. 新一代智能煤矿人工智能赋能技术研究综述. 工矿自动化. 2023(09): 122-131+139 . 本站查看
10. 王磊，黄晴，尚伟栋，苌延辉，张晓霞. 面向微服务架构的煤矿生产监控数据采集系统设计. 电子技术应用. 2023(12): 31-37 . 百度学术
11. 黄超，林爱芳，朱丹，尤瑞杰. 煤矿井下供水自动化启停、补水、泄压装置的应用. 能源科技. 2022(06): 40-44 . 百度学术
其他类型引用(6)
计量
- 文章访问数: 292
- HTML全文浏览量: 47
- PDF下载量: 47
- 被引次数: 17
