农网物资储备点需求趋势优化预测方法
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摘要
为了提高农网物资储备点的优化管理和调度能力,需要对农网物资储备点的需求趋势进行优化预测,提出基于关联规则调度和模糊自适应聚类的农网物资储备点需求趋势优化预测方法,构建农网物资储备点需求趋势的统计序列分布模型,采用大数据挖掘方法进行农网物资储备点需求趋势的大数据统计信息建模,提取农网物资储备点需求趋势的关联规则特征量,采用模糊聚类方法对需求趋势大数据进行自动聚类处理,建立农网物资储备点需求趋势预测的优化迭代模型,结合自适应寻优算法实现农网物资储备点需求趋势优化预测。仿真结果表明,采用该方法进行农网物资储备点需求趋势预测的自适应性较好,预测精度较高,提高了农网物资储备点的自适应调度和管理能力。
In order to improve the optimal management and dispatching ability of the agricultural network material storage point,it is necessary to optimize the forecast of the demand trend of the agricultural network material reserve point. An optimal forecasting method based on association rule scheduling and fuzzy adaptive clustering is proposed,and the statistical sequence distribution model of demand trend of material storage point in rural power network is constructed. Big data mining method is used to model big data statistical information of demand trend of material storage point in agricultural power network,and the characteristic quantity of association rules of demand trend of material reserve point of agricultural network is extracted. The fuzzy clustering method is used to automatically cluster the demand trend big data,and an optimal iterative model for forecasting the demand trend of the material reserve point in rural power network is established. The optimal forecasting of demand trend at the reserve point of agricultural power network is realized by combining the adaptive optimization algorithm with the method of fuzzy clustering. The simulation results show that this method has better self-adaptability and higher prediction precision,and improves the ability of adaptive scheduling and management.
In order to improve the optimal management and dispatching ability of the agricultural network material storage point,it is necessary to optimize the forecast of the demand trend of the agricultural network material reserve point. An optimal forecasting method based on association rule scheduling and fuzzy adaptive clustering is proposed,and the statistical sequence distribution model of demand trend of material storage point in rural power network is constructed. Big data mining method is used to model big data statistical information of demand trend of material storage point in agricultural power network,and the characteristic quantity of association rules of demand trend of material reserve point of agricultural network is extracted. The fuzzy clustering method is used to automatically cluster the demand trend big data,and an optimal iterative model for forecasting the demand trend of the material reserve point in rural power network is established. The optimal forecasting of demand trend at the reserve point of agricultural power network is realized by combining the adaptive optimization algorithm with the method of fuzzy clustering. The simulation results show that this method has better self-adaptability and higher prediction precision,and improves the ability of adaptive scheduling and management.
引文
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[2] Eldemerdash Y A, Dobre O A,Liao B J. Blind identification of SM and Alamouti STBC-OFDM signals[J]. IEEE Transactions on Wireless Communications,2015 14(2):972-982.
[3] Karami E and Dobre O A. Identification of SM-OFDM and AL-OFDM signals based on their second-order cyclostationarity[J]. IEEE Transactions on Vehicular Technology,2015,64(3):942-953.
[4] Mohammadkarimi M,Dobre O A. Blind identification of Spatial Multiplexing and Alamouti space-time block code via Kolmogorov-Smirnov(K-S)test[J]. IEEE Communications Letters,2014,18(10):1711-1714.
[5]张国英,刘阳,陆英.基于主动服务的农村电网工程物资供应模式优化研究[J].物流工程与管理,2016,38(06):175-176.
[6]井伟.基于供应链协同的配农网物资供应时效性提升工作方案[J].企业改革与管理,2017(24):218+221.
[7]金圣达,朱兆伟,赵尚书,等.蜂窝异构网络中的最优频谱分配算法[J].计算机工程,2019,45(2):70-75.
[8]易利容,王绍宇,殷丽丽,等.基于多变量LSTM的工业传感器时序数据预测[J].智能计算机与应用,2018,8(05):13-16.
[9]戴宝林,龚俊.基于初态学习的遗忘因子迭代学习控制研究[J].信息与控制,2018,47(5):547-552.
[10]田启华,黄超,于海东,等.基于AHP的耦合任务集资源分配权重确定方法[J].计算机工程与应用,2018,54(21):25-30.