Application of time series database technology in coal mine safety monitoring system
In the informationization construction of coal mine safety production, the real-time collection, storage, and analysis of massive time-series data is a key technical bottleneck that restricts the efficiency improvement of safety monitoring systems. Traditional relational databases are unable to meet...
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Editorial Office of Safety in Coal Mines
2025-08-01
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| Series: | Meikuang Anquan |
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| Online Access: | https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20250450 |
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| author | Hongliang ZHANG |
| author_facet | Hongliang ZHANG |
| author_sort | Hongliang ZHANG |
| collection | DOAJ |
| description | In the informationization construction of coal mine safety production, the real-time collection, storage, and analysis of massive time-series data is a key technical bottleneck that restricts the efficiency improvement of safety monitoring systems. Traditional relational databases are unable to meet the high concurrency and high stability monitoring requirements of complex underground environments due to issues such as insufficient write throughput, low storage efficiency, and real-time query latency. Aiming at the time series characteristics, high-frequency writing, and long-term storage challenges of multi-source heterogeneous sensor data streams in coal mines, a coal mine safety monitoring data management solution based on time-series databases is proposed. By analyzing the temporal characteristics of underground environmental parameters (gas concentration, temperature and humidity, wind speed), equipment status (fan speed, power supply current, pressure), and safety devices (gas sensors, power-off devices), a specialized temporal data model for coal mine scenarios is constructed. A timeline partitioning storage mechanism is designed to separate equipment tag metadata from time series data, reducing redundancy by up to 40%. A dynamic time sharding storage strategy is proposed for 2 Hz high-frequency data streams, combined with an improved run length encoding compression algorithm (RLE-X), to achieve a stable write throughput of ≥ 1 000 data streams per second and a storage space compression rate of over 85%. At the level of query optimization, establish a hierarchical index structure based on timestamp range, supporting millisecond level real-time data retrieval (average response time ≤ 50 ms) and multi-dimensional historical data backtracking analysis (span query efficiency increased by 3 times). The system integrates real-time anomaly detection and trend prediction modules, dynamically identifying gas concentration mutation events through a sliding window mechanism, with a warning accuracy rate of 92.6%. Actual deployment has shown that this solution can support efficient management of daily 2 GB level data, with a 70% increase in historical data query efficiency compared to traditional solutions. It provides high reliability technical support for building a preventive coal mine safety monitoring system and effectively reduces the risk of underground safety accidents. |
| format | Article |
| id | doaj-art-4a292cbbc6584287b129d593c8a7b13b |
| institution | Kabale University |
| issn | 1003-496X |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | Editorial Office of Safety in Coal Mines |
| record_format | Article |
| series | Meikuang Anquan |
| spelling | doaj-art-4a292cbbc6584287b129d593c8a7b13b2025-08-20T03:41:01ZzhoEditorial Office of Safety in Coal MinesMeikuang Anquan1003-496X2025-08-0156821422010.13347/j.cnki.mkaq.20250450jMKAQ20250450Application of time series database technology in coal mine safety monitoring systemHongliang ZHANG0China Coal Technology and Engineering Group Shenyang Research Institute, Fushun 113122, ChinaIn the informationization construction of coal mine safety production, the real-time collection, storage, and analysis of massive time-series data is a key technical bottleneck that restricts the efficiency improvement of safety monitoring systems. Traditional relational databases are unable to meet the high concurrency and high stability monitoring requirements of complex underground environments due to issues such as insufficient write throughput, low storage efficiency, and real-time query latency. Aiming at the time series characteristics, high-frequency writing, and long-term storage challenges of multi-source heterogeneous sensor data streams in coal mines, a coal mine safety monitoring data management solution based on time-series databases is proposed. By analyzing the temporal characteristics of underground environmental parameters (gas concentration, temperature and humidity, wind speed), equipment status (fan speed, power supply current, pressure), and safety devices (gas sensors, power-off devices), a specialized temporal data model for coal mine scenarios is constructed. A timeline partitioning storage mechanism is designed to separate equipment tag metadata from time series data, reducing redundancy by up to 40%. A dynamic time sharding storage strategy is proposed for 2 Hz high-frequency data streams, combined with an improved run length encoding compression algorithm (RLE-X), to achieve a stable write throughput of ≥ 1 000 data streams per second and a storage space compression rate of over 85%. At the level of query optimization, establish a hierarchical index structure based on timestamp range, supporting millisecond level real-time data retrieval (average response time ≤ 50 ms) and multi-dimensional historical data backtracking analysis (span query efficiency increased by 3 times). The system integrates real-time anomaly detection and trend prediction modules, dynamically identifying gas concentration mutation events through a sliding window mechanism, with a warning accuracy rate of 92.6%. Actual deployment has shown that this solution can support efficient management of daily 2 GB level data, with a 70% increase in historical data query efficiency compared to traditional solutions. It provides high reliability technical support for building a preventive coal mine safety monitoring system and effectively reduces the risk of underground safety accidents.https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20250450coal mine safety monitoringtime series databasebig datainternet of thingshigh concurrencymassive data |
| spellingShingle | Hongliang ZHANG Application of time series database technology in coal mine safety monitoring system Meikuang Anquan coal mine safety monitoring time series database big data internet of things high concurrency massive data |
| title | Application of time series database technology in coal mine safety monitoring system |
| title_full | Application of time series database technology in coal mine safety monitoring system |
| title_fullStr | Application of time series database technology in coal mine safety monitoring system |
| title_full_unstemmed | Application of time series database technology in coal mine safety monitoring system |
| title_short | Application of time series database technology in coal mine safety monitoring system |
| title_sort | application of time series database technology in coal mine safety monitoring system |
| topic | coal mine safety monitoring time series database big data internet of things high concurrency massive data |
| url | https://www.mkaqzz.com/cn/article/doi/10.13347/j.cnki.mkaq.20250450 |
| work_keys_str_mv | AT hongliangzhang applicationoftimeseriesdatabasetechnologyincoalminesafetymonitoringsystem |