基于数据挖掘的团体标准制定企业资本弹性特征识别研究
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摘要
团体标准的发展依赖于企业之间的深入协调,而对参与团体标准制定的企业特征识别,有利于对参与协商企业的本质认识,有利于协调的快速实现。但目前对制定团体标准的企业特征研究尚缺乏,制定团体标准的企业与制定国家标准的企业特征有哪些差异,也未见深入研究。本文针对团体标准制定企业的特征问题,以国家团体标准网为数据来源,以行业为基础,在竞争性强的几个行业中选取了80个企业,以注册资本、经营年数、风险信息以及专利数量这些公开企业数据作为数据维度,采用数据挖掘方法,建立了团体标准制定企业资本弹性数学模型,并且利用SPSS软件确定了模型中的参数,经检验得到了一个可信度较高的模型,资本弹性为0.577。为更进一步发现这个特征的独特性,以比较著名的国内公司作为数据来源,研究了国家标准的资本弹性特征,发现其为0.53。这是本文的最大创新点。
The development of association standards depends on the in-depth coordination among enterprises. Identifying the characteristics of enterprises participating in the development of association standards is conducive to understand the essence of enterprises participating in negotiation and the rapid realization of coordination. However, there is still a lack of in-depth study on the characteristics of enterprises developing association standards, and the feature differences between enterprises developing association standards and the ones developing national standards. Aiming at studying the characteristics of association standard-setting enterprises, based on national association standard network and industry,the paper selects 80 enterprises from several competitive industries. Data mining is adopted with public enterprise data such as registered capital, operation period, risk information and patent quantity as data dimension. A mathematical model of enterprise capital elasticity for association standard development is established, and the parameters of the model are determined with SPSS software. After testing, a model with high reliability is obtained, and the capital elasticity is 0.577. In order to further discover the uniqueness of this feature, this paper finds the capital elasticity characteristics of national standards is 0.53 by using famous domestic companies as data sources, which is the greatest innovation of this paper.
The development of association standards depends on the in-depth coordination among enterprises. Identifying the characteristics of enterprises participating in the development of association standards is conducive to understand the essence of enterprises participating in negotiation and the rapid realization of coordination. However, there is still a lack of in-depth study on the characteristics of enterprises developing association standards, and the feature differences between enterprises developing association standards and the ones developing national standards. Aiming at studying the characteristics of association standard-setting enterprises, based on national association standard network and industry,the paper selects 80 enterprises from several competitive industries. Data mining is adopted with public enterprise data such as registered capital, operation period, risk information and patent quantity as data dimension. A mathematical model of enterprise capital elasticity for association standard development is established, and the parameters of the model are determined with SPSS software. After testing, a model with high reliability is obtained, and the capital elasticity is 0.577. In order to further discover the uniqueness of this feature, this paper finds the capital elasticity characteristics of national standards is 0.53 by using famous domestic companies as data sources, which is the greatest innovation of this paper.
引文
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[13]许开成,毕丽苹,陈梦成.基于SPSS回归分析的锂渣混凝土抗压强度预测模型[J].建筑科学与工程学报, 2017,34(01):15-24.
[14]田程涛.大数据审计下统计分析方法研究[J].会计之友, 2018(01):140-145.
[15]游士兵,严研.逐步回归分析法及其应用[J].统计与决策, 2017(14):31-35.
[2]韩子燕,李键.团体标准维度探析及其实施条件——基于《中华人民共和国标准化法(修订草案)》的思考[J]标准科学, 2017(07):6-12.
[3]李佳.我国团体标准发展现状分析——基于全国团体标准信息平台数据[J].标准科学, 2017(05):23-27.
[4]程旭.培育与发展团体标准的探讨[J].品牌与标准化,2015(01):54-58.
[5]孟洁莹,乐为,周立军.我国团体标准的治理机制与运行模式对策研究[J].中国标准化, 2017(17):67-72.
[6]万雨龙,欧慧敏,魏静琼.基于全国团体标准信息平台数据的团体标准发展现状与对策研究[J].标准科学,2018,(7):73-77.
[7]李毅“.团体标准”舆情与政策供给的大数据分析——兼论广东科技服务业团体标准发展策略[J].标准科学, 2018,(3):21-26.
[8]陆杨先.构建团体标准第三方评价机制的思考和建议[J].质量与标准化, 2016(04):48-51.
[9]李学龙,龚海刚.大数据系统综述[J].中国科学:信息科学, 2015,45(01):1-44.
[10]牛灵芝,温惠英.城市道路拥堵与道路平均速度相关性分析[J].交通信息与安全, 2014,32(06):44-47+71.
[11]李华威,万庆.小流域山洪灾害危险性分析之降雨指标选取的初步研究[J].地球信息科学学报,2017,19(03):425-435.
[12]朱荣,周彩兰,高瑞.基于数据挖掘的客户关系管理系统研究[J].现代电子技术, 2018,41(01):182-186.
[13]许开成,毕丽苹,陈梦成.基于SPSS回归分析的锂渣混凝土抗压强度预测模型[J].建筑科学与工程学报, 2017,34(01):15-24.
[14]田程涛.大数据审计下统计分析方法研究[J].会计之友, 2018(01):140-145.
[15]游士兵,严研.逐步回归分析法及其应用[J].统计与决策, 2017(14):31-35.