推荐算法在电力设备故障修复场景中的应用

本文的主要研究目的是探究文本挖掘技术在电力数据中的应用场景，通过对电力设备运行过程中累计的缺陷数据进行分析应用，实现缺陷处理措施的自动推荐，以提升检修消缺的效率，降低工单化过程的时间成本。本文首先构建了电力设备专业词库，词库来源主要包含电力行业常用词汇、南方电网设备类别词汇和缺陷数据特征词汇。其次对非结构化的缺陷文本数据，如：缺陷表象、缺陷原因及缺陷类型等数据结合已构建的专业词库进行分词，提取出其中的关键字、并对关键程度进行排序。最后通过simhash算法与汉明距离的计算在缺陷数据库中查询层发生过的相似度最高的缺陷，推荐其处理措施作为本条缺陷的参考。本文应用上述方法，成功实现了输入缺陷处理措施的推荐，且根据专家判断该措施可以实现这类缺陷的消缺。

Application of Recommendation Algorithm in Defect Restoration of Power Equipment

The main purpose of this paper is to explore the application scenarios of text mining technology in power data. Through the analysis and application of the accumulated defects data during the operation of the power equipment, the automatic recommendation of the elimination repair processing measures is realized, so as to improve the efficiency of maintenance and elimination and reduce the time cost of the work order process. This paper first constructs a professional vocabulary of power equipment. The source of the thesaurus mainly includes the common vocabulary of the power industry, the vocabulary of the equipment category of the Southern Power Grid and the defect data feature vocabulary. Secondly, the unstructured defect text data, such as defect representation, defect cause and defect type, is combined with the constructed professional lexicon to extract the keywords and sort the key degrees. Finally, through the simhash algorithm and the Hamming distance calculation, the defect with the highest similarity that has occurred in the query layer in the defect database is recommended, and its processing measures are recommended as a reference for this defect. In this paper, the above method is applied to successfully implement the recommendation of input defect processing measures, and according to experts'' judgment, this measure can realize the shortage of such defects.