蜂巢结构动态算法在智能用电信息管理系统中的应用方法研究

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Abstract In this paper, a new algorithm is proposed for the dynamic data transmission, which is based on the shortcomings of large error, poor stability, long delay time and packet loss rate. The algorithm calculates the corresponding priority according to the different kinds of business and the current network status, which is based on the different traffic information collection. Through experiments, the algorithm provides the stability of data transmission, reduces its error, long delay time and high packet loss rate, and verifies the feasibility and efficiency of the data transmission in the optimization of the electric information management system.

Key words： honeycomb structure bandwidth allocation priority intelligent information management system

Published: 09 December 2015

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	Articles by authors
	Chen Hongyu
	Li Chunhai
	Lu Bin
	Liu Xiaolong
	Cui Zhenwei

Cite this article:

Chen Hongyu,Li Chunhai,Lu Bin等. Research on the Application of Honeycomb Structure Dynamic Algorithm in Intelligent Information Management System[J]. Electrical Engineering, 2015, 16(12): 56-60.

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http://dqjs.cesmedia.cn/EN/Y2015/V16/I12/56

[1] 吕娜娜, 王翠丽. 用电采集系统及其研究[J]. 消费电子, 2014(10): 674-679.
[2] 赵莉, 何容, 蒲箭, 等. 用电信息采集异常精确定位技术研究[J]. 电气技术, 2014(1): 54-57, 80.
[3] 胡江溢, 祝恩国, 杜新纲, 等. 用电信息采集系统应用现状及发展趋势[J]. 电力系统自动化, 2014, 38(2): 131-135.
[4] 刘潇潇, 常国锋. 基于神经网络和蚁群算法融合的QoS组播路由求解[J]. 计算机测量与控制, 2013, 21(12): 3365-3367.
[5] 梁根, 梁活民, 秦勇. 多重链路时延优化动态可用带宽分配算法[J]. 计算机应用研究, 2012, 29(10): 3926-3928.
[6] 赵维佺, 李迪. CAN网络化运动控制系统的动态带宽分配算法[J]. 华南理工大学学报(自然科学版), 2010, 38(10): 61-67.
[7] 高政南, 樊卫华, 陈庆伟, 等. 基于模糊反馈的网络控制系统分层调度策略[J]. 南京理工大学学报(自然科学版), 2010, 34(4): 496-502.
[8] Chunlin Sha, Hongyong Zhao, Fengli Ren. A new delayed projection neural network for solving quadratic programming problems with equality and inequality constraints[J]. Neurocomputing. 2015, 168: 1164-1172.
[9] Guo Dongsheng, Zhang Y, Xiao Zhengli, et al. Common Nature of learning between BP-type and Hopfield-type neural networks[J]. Neurocomputing, 2015, 167: 578-586.
[10] 喻宗泉. 神经网络控制[M]. 3版. 西安: 西安电子科技大学出版社, 2009.
[11] 杜景林, 郑若钦, 谢立. WSANs中基于蜂巢结构的移动容错恢复算法[J]. 物理学报, 2015(1): 416-425.
[12] 解基源, 王建中, 李彬. GPON在智能配电网络中的应用及其带宽分配[J]. 高技术通信, 2013, 23(10): 1083-1088.
[13] 李超. EPON上行接入中动态带宽分配算法研究[D]. 成都: 电子科技大学, 2006.
[14] 江晓明, 朱娜, 董亮, 等. 改进的神经网络EPON动态带宽分配方法[J]. 计算机工程与应用, 2012, 48(36): 112-115.
[15] 张琦毓. 基于GPON的配用电通信组网方案及带宽分配研究[D]. 保定: 华北电力大学, 2013.