智能检索技术在电网调度本体知识库中的应用
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摘要
构建电网调度本体知识库,提高电网信息的智能检索能力和调度能力,提出基于语义本体特征融合和关联映射的电网调度本体知识库构建方法,采用电网资源分布信息重组方法构建电网调度的数据库模型,根据电网资源信息分布的数据结构形式进行语义信息融合,提取电网资源信息的关联规则特征。采用模糊概念集匹配方法进行电网资源调度的本体知识库重构,实现电网资源调度的本体特征自适应优化分配,提高电网调度的均衡性和连通性。在本体知识库中提取电网资源信息分布的属性特征,根据特征提取结果进行模糊聚类,在聚类中心实现电网资源信息准确检索。仿真结果表明,采用该方法进行电网资源调度和检索的查准性较好,信息检索的时间效率较高。
The power grid dispatching ontology knowledge base is constructed to improve the intelligent retrieval ability and dispatching ability of power network information. A method of constructing power grid dispatching ontology knowledge base based on semantic ontology feature fusion and correlation mapping is proposed. The database model of power grid dispatching is constructed by using the method of recombination of grid resource distribution information.The semantic information fusion is carried out according to the data structure form of grid resource information distribution,and the association rule feature of power grid resource information is extracted. The fuzzy concept set matching method is used to reconstruct the ontology knowledge base of power grid resource scheduling,and the ontology feature adaptive optimization allocation of power grid resource scheduling is realized. In order to improve the balance and connectivity of power grid dispatching,the attribute feature of power grid resource information distribution is extracted from ontology knowledge base,and the fuzzy clustering is carried out according to the result of feature extraction. The simulation results show that the proposed method is more accurate in power grid resource scheduling and retrieval,and the time efficiency of information retrieval is higher.
The power grid dispatching ontology knowledge base is constructed to improve the intelligent retrieval ability and dispatching ability of power network information. A method of constructing power grid dispatching ontology knowledge base based on semantic ontology feature fusion and correlation mapping is proposed. The database model of power grid dispatching is constructed by using the method of recombination of grid resource distribution information.The semantic information fusion is carried out according to the data structure form of grid resource information distribution,and the association rule feature of power grid resource information is extracted. The fuzzy concept set matching method is used to reconstruct the ontology knowledge base of power grid resource scheduling,and the ontology feature adaptive optimization allocation of power grid resource scheduling is realized. In order to improve the balance and connectivity of power grid dispatching,the attribute feature of power grid resource information distribution is extracted from ontology knowledge base,and the fuzzy clustering is carried out according to the result of feature extraction. The simulation results show that the proposed method is more accurate in power grid resource scheduling and retrieval,and the time efficiency of information retrieval is higher.
引文
[1]陆兴华,郑永涛.基于非线性时间序列分析的电力系统负荷预测模型[J].电力与能源,2016,37(2):197-201.
[2]辛宇,杨静,汤楚蘅,等.基于局部语义聚类的语义重叠社区发现算法[J].计算机研究与发展,2015,52(7):1510-1521.
[3]陆兴华,陈平华.基于定量递归联合熵特征重构的缓冲区流量预测算法[J].计算机科学,2015,42(4):68-71.
[4]王小英,刘庆杰.关系型数据库中数值数据的密文检索模型研究[J].计算机仿真,2013,30(11):409-411.
[5]Chong F T,Heck M J R,Ranganathan P,et al.Data center energy efficiency:improving energy efficiency in data centers beyond technology scaling[J].IEEE Design&Test,2014,31(1):93-104.
[6]卫星,张建军,石雷,等.云计算数据中心服务器数量动态配置策略[J].电子与信息学报,2015,37(8):2007-2013.
[7]侯森,罗兴国,宋克.基于信息源聚类的最大熵加权信任分析算法[J].电子学报,2015,43(5):993-999.
[8]罗亮,吴文峻,张飞.面向云计算数据中心的能耗建模方法[J].软件学报,2014,25(7):1371-1387.
[9]章登义,吴文李,欧阳黜霏.基于语义度量的RDF图近似查询[J].电子学报,2015,43(7):1320-1328.
[10]陈雯雯,王亚林,周杰.三维统计信道中的多普勒效应及其信号分析[J].计算机科学,2017,44(3):84-88,131.
[11]HE Y,ZHANG C S,TANG X M,et al.Coherent integration loss due to pulses loss and phase modulation in passive bistatic radar[J].Digital Signal Processing,2013,23(4):1265-1276.
[12]HAO H.Multi component LFM signal detection and parameter estimation based on EEMD-FRFT[J].Optik-International Journal for Light and Electron Optics,2013,124(23):6093-6096.
[13]胡俊,胡贤德,程家兴.基于Spark的大数据混合计算模型[J].计算机系统应用,2015,24(4):214-218.
[14]李锋刚,魏炎炎,杨龙.基于和声算法异构Hadoop集群资源分配优化[J].计算机工程与应用,2014,50(9):98-102.
[15]张明,程科,杨习贝,等.基于加权粒度的多粒度粗糙集[J].控制与决策,2015,30(2):222-228.
[2]辛宇,杨静,汤楚蘅,等.基于局部语义聚类的语义重叠社区发现算法[J].计算机研究与发展,2015,52(7):1510-1521.
[3]陆兴华,陈平华.基于定量递归联合熵特征重构的缓冲区流量预测算法[J].计算机科学,2015,42(4):68-71.
[4]王小英,刘庆杰.关系型数据库中数值数据的密文检索模型研究[J].计算机仿真,2013,30(11):409-411.
[5]Chong F T,Heck M J R,Ranganathan P,et al.Data center energy efficiency:improving energy efficiency in data centers beyond technology scaling[J].IEEE Design&Test,2014,31(1):93-104.
[6]卫星,张建军,石雷,等.云计算数据中心服务器数量动态配置策略[J].电子与信息学报,2015,37(8):2007-2013.
[7]侯森,罗兴国,宋克.基于信息源聚类的最大熵加权信任分析算法[J].电子学报,2015,43(5):993-999.
[8]罗亮,吴文峻,张飞.面向云计算数据中心的能耗建模方法[J].软件学报,2014,25(7):1371-1387.
[9]章登义,吴文李,欧阳黜霏.基于语义度量的RDF图近似查询[J].电子学报,2015,43(7):1320-1328.
[10]陈雯雯,王亚林,周杰.三维统计信道中的多普勒效应及其信号分析[J].计算机科学,2017,44(3):84-88,131.
[11]HE Y,ZHANG C S,TANG X M,et al.Coherent integration loss due to pulses loss and phase modulation in passive bistatic radar[J].Digital Signal Processing,2013,23(4):1265-1276.
[12]HAO H.Multi component LFM signal detection and parameter estimation based on EEMD-FRFT[J].Optik-International Journal for Light and Electron Optics,2013,124(23):6093-6096.
[13]胡俊,胡贤德,程家兴.基于Spark的大数据混合计算模型[J].计算机系统应用,2015,24(4):214-218.
[14]李锋刚,魏炎炎,杨龙.基于和声算法异构Hadoop集群资源分配优化[J].计算机工程与应用,2014,50(9):98-102.
[15]张明,程科,杨习贝,等.基于加权粒度的多粒度粗糙集[J].控制与决策,2015,30(2):222-228.