微型电脑应用

海量、冗余的电力交易数据极大地阻碍了电力交易决策，为此，研究基于加权FCM聚类算法的电力交易数据动态提取模型。汇聚预处理电力交易数据，压缩数据体量，基于全排列理论排序处理电力交易数据，提取电力交易数据特征(波动性、趋势性与变动性特征)作为FCM聚类算法的加权依据，获得对应的加权矩阵，应用加权FCM聚类算法聚类提取需求的电力交易数据，实现电力交易数据的动态提取。实验数据表明，该模型获得的电力交易数据聚类参数DBI数值较小，DVI数值较大，电力交易数据动态提取时间较短，应用性能更佳。

Massive and redundant power transaction data greatly hinder the decision-making of power transaction. Therefore, a dynamic extraction model of power transaction data based on weighted FCM clustering algorithm is studied. This paper gathers the preprocessed power transaction data, compress the data volume, sort and process the power transaction data based on the full arrangement theory, extract the power transaction data characteristics(volatility, trend and variability characteristics), take them as the weighting basis of FCM clustering algorithm, obtain the corresponding weighting matrix, and apply the weighted FCM clustering algorithm to cluster and extract the required power transaction data. The dynamic extraction of power transaction data is realized. The experimental data show that the clustering parameter DBI value of power transaction data obtained by the model is small, the DVI value is large, the dynamic extraction time of power transaction data is short, and the application performance is better.