Intelligent correlation method of typical business data in power communication operation management

Guilong WU; Zhimin YANG; Yu HUANG

doi:10.11959/j.issn.1000-0801.2021014

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Intelligent correlation method of typical business data in power communication operation management

Engineering and Application | 更新时间：2024-06-05

- Intelligent correlation method of typical business data in power communication operation management
- Telecommunications Science Vol. 37, Issue 2, Pages: 164-172(2021)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2021014
  CLC： TP319
- Published Online：2021-02，
  
  Published：20 February 2021
- 稿件说明：
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Guilong WU, Zhimin YANG, Yu HUANG. Intelligent correlation method of typical business data in power communication operation management[J]. Telecommunications science, 2021, 37(2): 164-172.
DOI：

Guilong WU, Zhimin YANG, Yu HUANG. Intelligent correlation method of typical business data in power communication operation management[J]. Telecommunications science, 2021, 37(2): 164-172. DOI： 10.11959/j.issn.1000-0801.2021014.

摘要

电力通信运行管理过程中，会产生和存储各类相对独立的业务数据（如故障工单、值班日志、检修工单、巡检记录等），这些业务数据为电力通信网运行管理提供了重要支撑。目前大多数业务数据的统计过程相对独立，后期较少人工加以关联。选取了电力通信运行管理中值班日志与故障工单两种典型的业务数据，采用文本挖掘技术，构建无监督召回和监督分类相结合的机器学习模型，提出值班日志与故障工单的智能关联方法，并利用电力通信运行管理系统中相关历史业务数据，对智能关联方法进行实验验证，达到较好的关联效果。

Abstract

In the process of power communication operation management

various independent business data

such as trouble tickets

duty logs

maintenance tickets

and inspection records

are generated and stored.These business data provide important support for the operation management of the power communication network.At present

the statistical process of most business data is relatively independent

and there is less manual correlation in the later stage.Two typical business data of duty log and trouble ticket in power communication operation management were selected

text mining technology was used to build a machine learning model combining unsupervised recall and supervised classification

and the intelligent association method between duty log and trouble ticket was proposed.Besides

the relevant historical business data in the electric power communication operation management system was used to do the experimental verification of the intelligent association method.The results show that it can achieve positive effect.

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references

DAN J , JAMES H M . Speech and language processing (3 rd ed.d raft) [EB ] .

李生 . 自然语言处理的研究与发展 [J ] . 燕山大学学报 , 2013 , 37 ( 5 ): 377 - 384 .

LI S . Research and development of natural language processing [J ] . Journal of Yanshan University , 2013 , 37 ( 5 ): 377 - 384 .

王慧芳 , 曹靖 , 罗麟 . 电力文本数据挖掘现状及挑战 [J ] . 浙江电力 , 2019 , 38 ( 3 ): 1 - 7 .

WANG H F , CAO J , LUO L . Current status and challenges of power text data mining [J ] . Zhejiang Electric Power , 2019 , 38 ( 3 ): 1 - 7 .

邱剑 . 电力中文文本数据挖掘技术及其在可靠性中的应用研究 [D ] . 杭州:浙江大学 , 2016 .

QIU J . Research on power Chinese text data mining techndogy and reliability application [D ] . Hangzhou:Zhejiang University , 2016 .

汪崔洋 , 江全元 , 唐雅洁 , 等 . 基于告警信号文本挖掘的电力调度故障诊断 [J ] . 电力自动化设备 , 2019 , 39 ( 4 ): 126 - 132 .

WANG C Y , JIANG Q Y , TANG Y J , et al . Fault diagnosis of power dispatching based on alarm signal text mining [J ] . Electric Power Automation Equipment , 2019 , 39 ( 4 ): 126 - 132 .

刘梓权 , 王慧芳 , 曹靖 , 等 . 基于卷径神经网络的电力设备缺陷文本分类模型研究 [J ] . 电网技术 , 2018 , 42 ( 2 ): 644 - 650 .

LIU Z Q , WANG H F , CAO J , et al . A classification model of power equipment defect texts based on convolutional neural network [J ] . Power System Technology , 2018 , 42 ( 2 ): 644 - 650 .

王春柳 , 杨永辉 , 邓霏 , 等 . 文本相似度计算方法研究综述 [J ] . 情报科学 , 2019 , 37 ( 3 ): 158 - 168 .

WANG C L , YANG Y H , DENG F , et al . A review of text similarity approaches [J ] . Information Science , 2019 , 37 ( 3 ): 158 - 168 .

沈斌 . 基于分词的中文文本相似度计算研究 [D ] . 天津:天津财经大学 , 2006 .

SHEN B . Study on chinese text similarity computing based on word segmentation [D ] . Tianjin:Tianjin University of Finance and Economics , 2006 .

JONES K S . A statistical interpretation of term specificity and its application in retrieval [J ] . Journal of Documentation , 1972 , 28 ( 1 ): 11 - 21 .

SALTON G , YU C T . On the construction of effective vocabularies for information retrieval [C ] // Proceedings of ACM SIGIR Forum . New York:ACM Press , 1973 : 48 - 60 .

MIKOLOV T , CORRADO G , CHEN K , et al . Efficient estimation of word representations in vector space [C ] // Proceedings of the International Conference on Learning Representations (ICLR 2013) .[S.l.:s.n. ] , 2013 .

ROBERTSON S E , ZARAGOZA H . The probabilistic relevance framework:BM25 and beyond [J ] . Foundations and Trends in Information Retrieval , 2009 , 3 ( 4 ): 333 - 389 .

FRIEDMAN J H . Greedy function approximation:a gradient boosting machine [J ] . The Annals of Statistics , 2001 , 29 ( 5 ): 1189 - 1232 .

KE G , MENG Q , FINLEY T , et al . LightGBM:a highly efficient gradient boosting decision tree [C ] // Advances in Neural Information Processing Systems (NIPS) .[S.l.:s.n. ] , 2017 .

曹小芹 . 基于Python的中文结巴分词技术实现 [J ] . 信息与电脑 , 2019 ( 18 ): 38 - 39 , 42 .

CAO X Q . Technology implementation of Chinese jieba segmentation based on Python [J ] . China Computer ＆ Communication , 2019 ( 18 ): 38 - 39 , 42 .

陈正铭 , 霍英 . 编辑距离算法在中文文本相似度计算中的优化与实现 [J ] . 韶关学院学报 , 2019 , 36 ( 12 ): 8 - 12 .

CHEN Z M , HUO Y . Optimization and implementation of the edit distance algorithm in chinese similarity calculation [J ] . Journal of Shaoguan University , 2019 , 36 ( 12 ): 8 - 12 .

余小军 , 刘峰 , 张春 . 基于 N-Gram 文本特征提取的改进算法 [J ] . 现代计算机 , 2012 ( 23 ): 3 - 7 .

YU X J , LIU F , ZHANG C . Improved text feature extraction algorithm based on N-Gram [J ] . Modern Computer , 2012 ( 23 ): 3 - 7 .

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