【摘要】 大规模和多样的电力数据存储面临效率低和内存容量不足的瓶颈问题。数据索引和数据压缩等传统数据存储优化方法各有优劣势，如何有效应用于电力数据存储是目前研究的难点。为了解决这个问题，提出了一种融合随机森林和Q-learning的多元电力数据存储优化决策方法。该方法中的关键技术包括：首先提出了基于改进随机森林算法的存储优化策略决策模型，引入信息增益方法，综合评价数据存储时对数据库的数据访问频率、查询时间、存储速度以及数据冗余率等因素影响，做出数据直接存储、数据索引存储和数据压缩存储的存储优化方法策略决策；其次提出了基于改进Q-learning算法的数据存储算法决策模型，引入多尺度学习机制、优先经验放回机制和正负向奖励机制，决策数据索引存储时适用的索引算法以及数据压缩存储时适用的数据压缩算法。本方法有效融合了数据索引与数据压缩的技术优势，大幅提升数据存储效率并节约存储空间，为大规模多元电力数据管理提供新的解决方案。更多还原

【Abstract】 Storage of large-scale and multivariate power data faces challenges such as low efficiency and insufficient memory capacity. Traditional data storage optimization methods like data index and data compress methods have their own advantages and disadvantages. How to effectively apply these methods to power data storage is a core problem in current research. To solve the problem, a storage optimization decision method for multivariate power data that integrates random forest algorithm and Q-learning algorithm was proposed. Key technologies of this method were these as follows. Firstly, a storage optimization strategy decision model based on an improved random forest algorithm was developed. By the introduction of information gain method, the model could evaluate the data access frequency, query time, storage speed and data redundancy rate during database data storage, and made choice on storage optimization strategies like direct data storage, data index storage, and data compress storage. Secondly, a data storage algorithm decision model based on the improved Q-learning algorithm was proposed. By the introduction of multi-scale learning mechanism, prioritized experience replay mechanism and positive/negative reward mechanism, the model could determine the suitable index algorithm for data index storage and the compress algorithm for data compress storage. The proposed method effectively integrates the technological advantages of data index and data compress storage, can improve data storage efficiency and save storage space significantly, and thus provide a new solution for the management of large-scale and multivariate power data.更多还原