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Efficiency Evaluation of Operation Analysis Systems Based on Dynamic Data Envelope Analysis Models from a Big Data Perspective

机译:基于大数据的动态数据包络分析模型的操作分析系统效率评估

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The operating environment of power grid enterprises is complex with a range of monitoring indicators. To grasp the overall operational status in time and find the key affecting factors, Balanced Scorecard Card (BSC), Interpretive Structural Model (ISM), Principal component analysis (PCA) should be applied. This paper proposed several grid enterprise operators and monitoring indicator systems (which include achievement indicators and driver indicators), and applied PCA for verification and evaluation. The achievement indicators mainly reflected the effectiveness of business operations, which included corporate value, social value, customer value, and so on. Driver indicators mainly reflected the core resources and operations process management of business operations, which have a direct impact on the achievement indicators. The driver and achievement indicators were used as input and output indicators for the provinces to assess the efficiency of operations, and appropriate measures were proposed for improvement. The results showed that the dynamic data envelopment analysis (DEA) model could reflect the time lag of the grid enterprises operating investment and income much better than the other two methods, and the static changes compared to assess efficiency had an average around 4%.
机译:电网企业的运营环境复杂于一系列监测指标。要掌握整体运行状态及发现关键影响因素,应应用平衡记分卡(BSC),解释性结构模型(ISM),主成分分析(PCA)。本文提出了几种网格企业运营商和监测指标系统(包括成就指标和驾驶员指标),并应用PCA进行验证和评估。成就指标主要反映了业务运营的有效性,包括企业价值,社会价值,客户价值等。司机指标主要反映了业务运营的核心资源和运营过程管理,这对成就指标有直接影响。司机和成就指标被用作省份的投入和产出指标,以评估运营效率,并提出适当措施进行改进。结果表明,动态数据包络分析(DEA)模型可以反映电网企业的时间滞后,比其他两种方法更好地经营投资和收入,而静态变化与评估效率相比平均约为4%。

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