| 引用本文: | 赵松燕,曲朝阳,郭晓利,等.基于MapReduce的输电监测数据智能检索模型[J].电力系统保护与控制,2023,51(22):177-187.[点击复制] |
| ZHAO Songyan,QU Zhaoyang,GUO Xiaoli,et al.Intelligent retrieval model of power transmission monitoring data based on MapReduce[J].Power System Protection and Control,2023,51(22):177-187[点击复制] |
|
| 摘要: |
| 随着新型电力系统发展，输电监测文本数据呈现出体量大、增速快等特点，且因行业数据传输协议私有化，导致数据检索性能低，影响输电线路实时决策分析。因此提出了基于MapReduce的输电监测数据智能检索模型。首先，改进了SimHash算法，实现输电线路在线监测文本数据检索向量的高效提取。并引入多属性决策以及综合评分机制，实现目标数据的精准检索，提升数据的检索精度及查全率。其次，针对数据体量大、增速快的特点，设计了基于MapReduce的电力数据检索模型。最后，通过电网实例对比分析，验证了所提方法的检索精度、查全率及检索效率。 |
| 关键词: 新型电力系统 输电线路数据 改进SimHash 智能检索 MapReduce |
| DOI：10.19783/j.cnki.pspc.230652 |
| 投稿时间：2023-06-01修订日期：2023-08-09 |
| 基金项目:国家自然科学基金项目资助(6217111)；吉林省科技发展计划项目资助(20210203195SF)；南网广西电网公司科技项目资助(GXKJXM20222017)；南网广西电网公司创新项目资助(047000KK52210036) |
|
| Intelligent retrieval model of power transmission monitoring data based on MapReduce |
| ZHAO Songyan1,QU Zhaoyang1,GUO Xiaoli1,YU Tong3,LI Xin2,XIE Ming1，2,YU Fu4 |
| (1. School of Computer Science, Northeast Electric Power University, Jilin 132012, China; 2. China Southern Power Grid Guangxi
Power Grid Co., Ltd. Research Institute, Nanning 530023, China; 3. People's Bank of China Qinghai Provincial Branch,
Xining 810000, China; 4. Bijie Power Supply Bureau, China Guizhou Power Grid Co., Ltd. Bijie 551700, China) |
| Abstract: |
| With the development of new power systems, transmission monitoring text data presents the characteristics of large volume and fast growth rate, and because of the privatization of industry data transmission protocols, the performance of data retrieval is low. This affects the real-time decision analysis of transmission lines. An intelligent retrieval model of power transmission monitoring data based on MapReduce is proposed. First, this paper innovates and improves the SimHash algorithm to achieve efficient extraction of retrieval vectors for transmission line online monitoring text data, and introduces multi-attribute decision-making and comprehensive scoring mechanisms to achieve precise retrieval of target data and improve data retrieval accuracy and recall. Second, from the characteristics of large data volume and fast growth rate, a power data retrieval model based on MapReduce is designed. Finally, the retrieval accuracy, recall rate and retrieval efficiency of the proposed method are verified through comparative analysis of power grid examples. |
| Key words: new power systems transmission line data improve SimHash intelligent retrieval MapReduce |
