基于数据挖掘的电力服务质量评估方法
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Power Service Quality Evaluation Method Based on Data Mining
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    摘要:

    针对目前电力服务质量评估时存在数据类型多、评估精度低等问题,提出一种基于数据挖掘的电力服务质量评估模型。基于电力服务社交媒体数据,提出一种电力服务情感词典生成方法;基于数据挖掘中信息检索技术和深度学习技术,提出一种客户态度分析模型,衡量客户对电力服务质量的满意程度。实验结果表明:所提模型可综合考虑文本特征以及语音特征,有效提升评估性能,为电力服务质量量化分析提供了一定借鉴。

    Abstract:

    In order to understand the distribution characteristics of meteorological disasters, realize the effective and accurate early warning of meteorological disasters, and reduce the losses caused by meteorological disasters to animal husbandry, a model for extracting the distribution characteristics of meteorological disasters in animal husbandry based on spectral clustering was proposed. By calculating the degree of impact, 11 meteorological disaster indicators are determined. Standardization, vacancy supplement and noise removal are carried out for meteorological disaster indicators. Spectral clustering algorithm is used to calculate the characteristic value of meteorological disasters, and the corresponding risk degree of meteorological disasters is obtained. The regions 1, 2, 5 and 7 show an upward trend, and the risk degree increases year by year, while the disaster characteristic values of regions 6 and 10 show a downward trend, and the risk degree decreases year by year; The disaster characteristic values of regions 3, 4, 8 and 9 have been maintained at a relatively stable level, of which the risk level of regions 3, 4 and 8 has been maintained at a low risk level, and the risk level of region 9 has been maintained at a moderate risk level. The low risk area occupies a larger area and is mostly concentrated in the northwest corner, the moderate risk area is mostly concentrated in the middle, and the high risk area is mostly concentrated in the southeast. The results show that the model has a good application effect and can effectively extract the distribution characteristics of meteorological disasters in animal husbandry.

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汤成佳.基于数据挖掘的电力服务质量评估方法[J].,2025,44(10).

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  • 收稿日期:2024-10-11
  • 最后修改日期:2024-11-10
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  • 在线发布日期: 2025-12-02
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