摘要
结合现代智能电能表的制造特点,以其出厂检定数据、首次检定数据、随机抽样复检数据、连续运行八年后的复检数据为基础,建立了适当的大数据分析模型,以图示的形式展示了相应数据的分布情况。以上述大数据为基础建立了高风险表计、中风险表计的识别程序,根据识别结果并依据相应的技术规范对表计实施了现场校验和甄别,连续跟踪的结果及风控措施支持了智能电能表的轮换周期由八年延长至十二年乃至十六年的方针政策的实施,支撑了节能减碳工作的深入,助推了节约型社会的建设。
Combined with the manufacturing characteristics of smart meters,an appropriate big data analysis model was established based on the factory verification data,primary verification data,random sampling re-verification data,and reverification data after eight years of continuous operation,and the distribution of the corresponding data was shown in the form of a diagram.Based on the above big data,the identification procedures of high-risk meters and medium-risk meters were established,and on-site calibration and screening of meters were carried out according to the identification results and corresponding technical specifications.The results of continuous tracking and risk control measures supported the implementation of the policy of extending the replacement cycle from 8 years to 12 or even 16 years,which supported the deepening of energy-saving and carbon-reduction work and boosted the construction of a conservation-oriented society.
作者
李金嗣
李海涛
周文斌
刘士峰
LI Jinsi;LI Haitao;ZHOU Wenbin;LIU Shifeng(Beijing Institute of Metrology,Beijing 100029,China;State Grid Beijing Electric Power Research Institute,Beijing 100162,China)
出处
《计量科学与技术》
2022年第3期70-75,共6页
Metrology Science and Technology
关键词
首检合格率
零位误差区
加权平均正差率
高风险表计
中风险表计
qualified rate of primary verification
electrical error interval of null position
weighted average rate of positive error
high risk meters
moderate risk meters
