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期刊号: CN32-1800/TM| ISSN1007-3175

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电网营销资产配送需求计划动态平衡研究

来源:电工电气发布时间:2021-07-19 13:19浏览次数:467

电网营销资产配送需求计划动态平衡研究

许杰雄,王江辉,颜思宇
(江苏方天电力技术有限公司,江苏 南京 210096)
 
    摘 要:各省的电力公司计量中心需要设计完善的营销资产配送需求计划的体系结构来增强需求计划的精确性,实现库存、需求与配送之间的动态平衡,使营销资源得到有效管理。通过分析传统营销资产的需求审批、资产配送存在的问题,提出了基于人工智能知识库和推理机模块的营销资产配送需求计划的业务流程编制方法,给出了基于人工智能的营销资产配送需求计划系统的结构方案,该方案可实现库存、需求与配送之间的动态平衡,使营销资源得到有效管理。
    关键词:营销资产; 人工智能;配送需求计划
    中图分类号:TM727 ;TP311.1     文献标识码:A     文章编号:1007-3175(2021)07-0063-05
 
Research on Dynamic Balance of Power Grid Marketing Assets
Distribution Demand Plan
 
XU Jie-xiong, WANG Jiang-hui, YAN Si-yu
(Jiangsu Fangtian Power Technology Co., Ltd, Nanjing 210096, China)
 
    Abstract: Aiming at the dynamic balance among the inventory, demand and distribution of marketing assets, the measurement centers of electric power companies in every provinces need to design a complete systematic structure of marketing assets distribution demand plan to increase its accuracy, so that the marketing assets could be managed efficiently. By analyzing the existing problems of the used demand approval and asset distribution of marketing assets, a method to design business process is put forward which is based on artificial intelligence knowledge base and marketing assets distribution demand plan of reasoning module. Furthermore, a structure scheme based on artificial intelligence to establish the marketing assets distribution demand plan system is proposed, which could achieve the dynamic balance among inventory, demand and distribution and could manage marketing assets efficiently.
    Key words: marketing assets; artificial intelligence; distribution demand plan
 
参考文献
[1] 岳衡,骆国荣,薛娟萍. 电力物资智能配送实现路径[J]. 物流技术,2020,39(5) :130-136.
[2] 何超. 探析信息化技术在电力资产管理中的应用[J]. 数字通信世界,2019(8) :185.
[3] 和军梁,米晨旭,许爽,于仝,高小淇. 基于电力现货市场风险管理的新能源电力现货辅助决策系统设计[J]. 中外能源,2020(11) :28-33.
[4] 马诚. 贵阳地铁运营施工调度信息化系统计划功能需求分析[J]. 中国新通信,2020(13) :105.
[5] 邓晨曦,蒋一锄. 试论自动化控制中人工智能算法的应用[J] . 科技创新与应用,2020(32) :164-165.
[6] 韩海轩. 人工智能技术对物流业效率的影响及差异性分析[J]. 商业经济研究,2020(22) :105-108.
[7] 王青松,姜富山,李菲. 大数据环境下基于关联规则的多标签学习算法[J] . 计算机科学,2020(5) :90-95.