面向智能电网的时序数据库优化
摘 要
随着智能电网规模的不断扩大和电力系统信息化程度的持续提升，时序数据量呈现爆炸式增长，这对时序数据库的性能提出了严峻挑战。为此，本文面向智能电网开展时序数据库优化研究，旨在提高时序数据库在海量数据存储与高效查询方面的性能。通过分析智能电网时序数据的特点，提出一种基于数据压缩与索引结构改进相结合的优化方法。该方法利用新型压缩算法减少数据存储空间，在保证数据精度的前提下实现高效压缩；同时设计多级混合索引结构以加速查询过程，将时间戳索引与数据特征索引相融合，使查询操作能够快速定位目标数据。实验结果表明，所提方法可显著降低存储成本约30%，查询响应时间平均缩短45%以上。
关键词：智能电网 时序数据库优化 数据压缩
Abstract
With the continuous expansion of the scale of smart grid and the continuous improvement of the informatization degree of power system, the amount of time sequence data shows an explosive growth, which poses a severe challenge to the performance of time sequence database. To this end, this paper carries out timing database optimization research for smart grid, aiming to improve the performance of timing database in massive data storage and efficient query. By analyzing the characteristics of timing data of smart grid, an optimization method based on data compression and index structure improvement is proposed. This method uses the new compression algorithm to reduce the data storage space and realize efficient compression under the premise of ensuring the data accuracy. Meanwhile, the multilevel hybrid index structure is designed to accelerate the query process, and integrate the timestamp index with the data feature index, so that the query operation can quickly locate the target data. The experimental results show that the proposed method can significantly reduce the storage cost by about 30%, and shorten the query response time by more than 45% on average.
Keyword:Smart Grid Time-Series Database Optimization Data Compression Multi-Level
目 录
1绪论 1
1.1面向智能电网的时序数据库优化背景 1
1.2研究现状与挑战 1
1.3本文研究方法概述 1
2智能电网时序数据特性分析 2
2.1数据采集频率与规模 2
2.2数据存储需求评估 3
2.3数据访问模式研究 3
3时序数据库架构优化设计 4
3.1存储引擎选择与优化 4
3.2数据压缩技术应用 4
3.3查询性能优化策略 5
4智能电网业务场景适配 5
4.1实时监控系统优化 6
4.2历史数据分析支持 6
4.3异常检测与预警机制 7
结论 8
参考文献 9
致谢 10
摘 要
随着智能电网规模的不断扩大和电力系统信息化程度的持续提升，时序数据量呈现爆炸式增长，这对时序数据库的性能提出了严峻挑战。为此，本文面向智能电网开展时序数据库优化研究，旨在提高时序数据库在海量数据存储与高效查询方面的性能。通过分析智能电网时序数据的特点，提出一种基于数据压缩与索引结构改进相结合的优化方法。该方法利用新型压缩算法减少数据存储空间，在保证数据精度的前提下实现高效压缩；同时设计多级混合索引结构以加速查询过程，将时间戳索引与数据特征索引相融合，使查询操作能够快速定位目标数据。实验结果表明，所提方法可显著降低存储成本约30%，查询响应时间平均缩短45%以上。
关键词：智能电网 时序数据库优化 数据压缩
Abstract
With the continuous expansion of the scale of smart grid and the continuous improvement of the informatization degree of power system, the amount of time sequence data shows an explosive growth, which poses a severe challenge to the performance of time sequence database. To this end, this paper carries out timing database optimization research for smart grid, aiming to improve the performance of timing database in massive data storage and efficient query. By analyzing the characteristics of timing data of smart grid, an optimization method based on data compression and index structure improvement is proposed. This method uses the new compression algorithm to reduce the data storage space and realize efficient compression under the premise of ensuring the data accuracy. Meanwhile, the multilevel hybrid index structure is designed to accelerate the query process, and integrate the timestamp index with the data feature index, so that the query operation can quickly locate the target data. The experimental results show that the proposed method can significantly reduce the storage cost by about 30%, and shorten the query response time by more than 45% on average.
Keyword:Smart Grid Time-Series Database Optimization Data Compression Multi-Level
目 录
1绪论 1
1.1面向智能电网的时序数据库优化背景 1
1.2研究现状与挑战 1
1.3本文研究方法概述 1
2智能电网时序数据特性分析 2
2.1数据采集频率与规模 2
2.2数据存储需求评估 3
2.3数据访问模式研究 3
3时序数据库架构优化设计 4
3.1存储引擎选择与优化 4
3.2数据压缩技术应用 4
3.3查询性能优化策略 5
4智能电网业务场景适配 5
4.1实时监控系统优化 6
4.2历史数据分析支持 6
4.3异常检测与预警机制 7
结论 8
参考文献 9
致谢 10
