Big data classification method of non relational distributed submission information under differentiated requirements

Lu HAN; Weiyu CHEN; Fei ZHANG; Jianfeng HE; Huaizhen SU

doi:10.11959/j.issn.1000-0801.2023122

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Big data classification method of non relational distributed submission information under differentiated requirements

Research and Development | 更新时间：2024-06-05

- Big data classification method of non relational distributed submission information under differentiated requirements
- Telecommunications Science Vol. 39, Issue 6, Pages: 114-121(2023)
- 作者机构：
  
  1. 国网甘肃省电力公司，甘肃兰州 730030
  2. 国网思极飞天（兰州）云数科技有限公司，甘肃兰州 730020
  3. 国网甘肃省电力公司定西供电公司，甘肃定西 743000
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023122
  CLC： TP311
- Published Online：2023-06，
  
  Published：20 June 2023
- 稿件说明：
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Lu HAN, Weiyu CHEN, Fei ZHANG, et al. Big data classification method of non relational distributed submission information under differentiated requirements[J]. Telecommunications science, 2023, 39(6): 114-121.
DOI：

Lu HAN, Weiyu CHEN, Fei ZHANG, et al. Big data classification method of non relational distributed submission information under differentiated requirements[J]. Telecommunications science, 2023, 39(6): 114-121. DOI： 10.11959/j.issn.1000-0801.2023122.

摘要

针对多源异构、分布广泛报送信息差异化应用需求较多、无法区分可用性信息的问题，研究了差异化需求下的非关系型分布式报送信息大数据分类方法。首先，分析了非关系型分布式报送信息数据库的可用性、开放性和拓展性等特征，结合字段类型的基本要求，采用非结构化数据库存储文本检索信息处理（TRIP）存储非关系型分布式报送信息；然后，分析了汉明散列家族内散列过程，在线性级要求约束下，利用多吸引子优化元胞自动机，通过遗传算法改进多吸引子元胞自动机分类器的最优参数，进而改进大数据分类方法。实验结果表明，该方法能够有效识别并分类非关系型分布式报送信息中的结构化数据与非结构化数据，具有较高的分类精度。

Abstract

The classification method of non-relational distributed submitted information big data under the differentiated demand was studied

aiming at the problem of multi-source heterogeneous

widely distributed submitted information with more differentiated application requirements and inability to distinguish the available information.Firstly

the usability

openness and expansibility of the non-relational distributed submission information database were analyzed.The unstructured database storage TRIP was used to store non-relational distributed submission information by combining the basic requirements of field types.Then

the hashing process within the Hamming hash family was analyzed.Under the constraint of linearity level requirements

cellular automata with multiple attractors were used to optimize the system.The optimal parameters of the multiple attractor cellular automata classifier were improved through genetic algorithm

thus improving the big data classification method.Experimental results show that this method can effectively identify and classify structured data and unstructured data in non relational distributed submission information

and has high classification accuracy.

关键词

Keywords

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