本文選題：電費(fèi)回收 + 交費(fèi)渠道�。� 參考：《計(jì)算機(jī)工程與應(yīng)用》2016年19期

【摘要】：電費(fèi)回收直接關(guān)系到電力公司的有序運(yùn)行,而交費(fèi)渠道的建設(shè)和用電客戶交費(fèi)渠道的偏好,直接影響電力公司的電費(fèi)回收;因此做好用電客戶交費(fèi)渠道偏好的研究至關(guān)重要�；诤诙挫乩碚摵拓惾~斯模型,提出了適合電力數(shù)據(jù)的黑洞熵模糊聚類模型。該模型的顯著特點(diǎn)是能通過(guò)最大后驗(yàn)概率推理和迭代抽樣算法求解聚類最優(yōu)參數(shù),且適用于大規(guī)模用戶數(shù)據(jù)。基于黑洞熵模糊聚類對(duì)某省用電客戶的實(shí)際抽樣數(shù)據(jù)進(jìn)行模式發(fā)現(xiàn)表明,電力公司自營(yíng)自助交費(fèi)渠道尤為值得推廣,預(yù)付費(fèi)交費(fèi)渠道最具有潛力。
[Abstract]:Electricity recovery is directly related to the orderly operation of power companies, and the construction of payment channels and the preference of customer payment channels directly affect the electricity recovery of power companies; therefore, it is very important to do a good job in the research of the preference of customer payment channels. Based on black hole entropy theory and Bayesian model, a black hole entropy fuzzy clustering model suitable for power data is proposed. The remarkable feature of this model is that it can solve the clustering optimal parameters by maximum posterior probability reasoning and iterative sampling algorithm, and it is suitable for large-scale user data. Based on black hole entropy fuzzy clustering, the actual sampling data of power customers in a certain province are found. The results show that self-service payment channels of power companies are particularly worthy of promotion, and prepaid payment channels have the most potential.
【作者單位】： 國(guó)網(wǎng)江蘇省電力公司;江南大學(xué)數(shù)字媒體學(xué)院;國(guó)網(wǎng)江蘇省電力公司南京供電公司;
【基金】：國(guó)家電網(wǎng)公司科技項(xiàng)目
【分類號(hào)】：F426.61;TP311.13
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本文編號(hào)：1951809