[1]刘文立,陈士翀,刘文思,等.基于能源大数据特征的数据评价方法研究及应用[J].计算机技术与发展,2024,34(03):22-27.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 004]
 LIU Wen-li,CHEN Shi-chong,LIU Wen-si,et al.Research and Application of Data Evaluation Methods Based on Energy Big Data Characterization[J].,2024,34(03):22-27.[doi:10. 3969 / j. issn. 1673-629X. 2024. 03. 004]
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基于能源大数据特征的数据评价方法研究及应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年03期
页码:
22-27
栏目:
大数据与云计算
出版日期:
2024-03-10

文章信息/Info

Title:
Research and Application of Data Evaluation Methods Based on Energy Big Data Characterization
文章编号:
1673-629X(2024)03-0022-06
作者:
刘文立陈士翀刘文思宣东海江丽娜沈子奇
国家电网有限公司大数据中心,北京 100052
Author(s):
LIU Wen-liCHEN Shi-chongLIU Wen-siXUAN Dong-haiJIANG Li-naSHEN Zi-qi
Big Data Center of State Grid Corporation of China,Beijing 100052,China
关键词:
能源大数据数据评价数据质量数据成本数据应用数据资产价值评估
Keywords:
energy big datadata evaluationdata qualitydata costdata applicationdata asset value evaluation
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 03. 004
摘要:
为实现对能源数据资产价值的高效准确评估,促进数据要素流通,提出一种基于能源数据资产特征的能源数据评 价方法。 通过对能源数据在技术、行业等方面特征的分析,提取数据质量、成本、应用三项资产价值化关键影响因素。 通 过引入数据质量因素,构建能源数据质量的价值联系函数,并形成业务结合的数据质量评价体系;通过引入成本和应用因 素,构建基于能源大数据应用场景和数据资产生存周期视角的成本评价方法,以及多维应用评价方法。 分析可知,数据质 量因素显著影响能源数据的应用价值,提升数据质量则会增加数据资产成本。 典型能源大数据资产化场景的应用结果表 明,提出的能源数据评价方法有效实现了质量评价、成本评价和应用评价,具备应用性和可推广性,能够支持进一步的能 源大数据价值评估。
Abstract:
To achieve efficient and accurate evaluation of the value of energy data assets and promote its circulation,a method for energydata evaluation based on the characteristics of energy data assets is proposed. We focus on the characterization of energy big data andexamine three key factors that influence asset valorization:data quality,data cost,and?
data application. Through the introduction of dataquality factors,we develop a Sigmoid function based value link function which reflects the nonlinear characteristics between?
data value utilization and data quality,and form a data quality evaluation system in conjunction with business operations. Additionally,we propose acost evaluation method based on energy data application scenarios and the data asset life cycle perspective,as well as a multi-dimensionalapplication evaluation method. Data quality factors significantly affect the application value of energy data,while improving data qualitywill increase the cost of data assets. The application results of typical energy big data assetization scenarios indicate that the energy data evaluation method proposed effectively achieves quality evaluation,cost evaluation,and application evaluation,and has applicability andscalability,which can support energy data value evaluation in the future.
更新日期/Last Update: 2024-03-10