中国电力 ›› 2024, Vol. 57 ›› Issue (7): 182-187.DOI: 10.11930/j.issn.1004-9649.202309045

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基于区块链的园区碳排放可信监测模型

王栋1(), 冯景丽1(), 李达1(), 牛静伟2, 李军2   

  1. 1. 国网区块链科技(北京)有限公司,北京 100053
    2. 北京信息科技大学 计算机学院,北京 102206
  • 收稿日期:2023-09-12 出版日期:2024-07-28 发布日期:2024-07-23
  • 作者简介:王栋(1988—),男,硕士,高级工程师,从事能源区块链技术研究,E-mail:wangdong@sgdt.sgcc.com.cn
    冯景丽(1993—),女,通信作者,硕士,工程师,从事能源区块链技术研究,E-mail:fengjl1024@163.com
    李达(1991—),男,硕士,工程师,从事区块链、信息技术等研究,E-mail:lida@sgec.sgcc.om.cn.com
  • 基金资助:
    国网山西省电力有限公司科技项目(52051C220004)。

A Credible Monitoring Model for Carbon Emissions in Industrial Parks Based on Blockchain Technology

Dong WANG1(), Jingli FENG1(), Da LI1(), Jingwei NIU2, Jun LI2   

  1. 1. State Grid Blockchain Technology (Beijing) Co., Ltd., Beijing 100053, China
    2. School of Computer Science, Beijing Information Science and Technology University, Beijing 102206, China
  • Received:2023-09-12 Online:2024-07-28 Published:2024-07-23
  • Supported by:
    This work is supported by Science and Technology Project of State Grid Shanxi Electric Power Co., Ltd. (No.52051C220004).

摘要:

构建了基于能源电力区块链的园区碳排放可信监测数字模型。首先,利用区块链防篡改技术保障监测数据的可信存证要求,相关接入实体指标全部由联盟链进行身份认证和权限控制,避免了数据遗失以及主体以外其他人恶意篡改的风险;其次,在具体的监测指标融合过程中,结合层次分析法,构建碳排放评价指标融合策略,对能源电力相关指标数据结合相似性聚类算法进行多源在线融合;最后,基于局部异常因子算法(local outlier factor,LOF)实现指标数据长周期异常离群检测,一定程度上解决数据畸变和错报自筛难题。

关键词: 区块链, 碳排放监测, 层次决策

Abstract:

A digital model for trusted monitoring of carbon emissions in the park based on energy and power blockchain is proposed to be built. The blockchain anti-tamper technology is first used to ensure the trusted storage requirements of monitoring data. All relevant access entity indicators are authenticated and controlled by the alliance chain, which avoids the risk of data loss and malicious tampering by others other than the subject; Secondly, in the fusion process of specific monitoring indicators, the model combined with AHP method to build a carbon emission evaluation index fusion strategy, and carried out multi-source online fusion of energy and power related index data combined with similarity clustering algorithm. Finally, LOF algorithm is used to detect long-period abnormal outliers of index data, which can solve the problem of data distortion or self-screening of misstatement to some extent.

Key words: blockchain, carbon emission monitoring, hierarchical decision