营商环境评估的企业级复合区块链构建方法
Enterprise composite blockchain construction method for business environment evaluation
李素 1陈泽 1宋宝燕 1张浩林1
作者信息
- 1. 辽宁大学信息学院,辽宁沈阳 110036
- 折叠
摘要
针对现有营商环境评估系统中企业数据可信度低、易篡改的问题,提出营商环境评估的企业级复合区块链构建方法,采用链上和链下数据协同的方式,对企业原始数据进行存储.改进区块链哈希函数,提出基于SHA256 算法的企业原始数据加密方法.引入Key-Value存储模式进行链下基于非易失性内存的Level DB存储,降低系统的通信和存储压力.提出数据链上存储方法,分别将Level DB中的Key值对应存储到基于DAG的Con-flux公有链,企业状态数据对应存入到联盟链,为营商环境评估提供可信的存证数据.通过与改进前的Level DB数据库和现有的区块链存储模型进行实验对比,实验结果表明,提出的企业级复合区块链构建方法在读写性能、存储效率两方面均优于现有方法.
Abstract
An enterprise composite blockchain construction method for business environment evaluation was proposed in order to address the problems of low credibility and easy tampering of enterprise data in the existing business environment evaluation system.The synergistic approach of on-chain and off-chain data was adopted to store the original enterprise data.The blockchain hash function was improved,and an encryption method based on SHA256 algorithm was proposed for enterprise raw data.Key-Value storage mode was introduced for off-chain non-volatile memory-based Level DB storage in order to reduce system communication and storage pressure.The data on-chain storage method was proposed to store the Key values in Level DB corresponding to the DAG-based Conflux public chain and the enterprise state data corresponding to the consortium chain so as to provide trustworthy depository data for the evaluation of the business environment.Experimental comparison with the Level DB database before improvement and the existing blockchain storage model was conducted.The experimental results show that the proposed enterprise composite blockchain construction method has significantly improved read and write performance and storage efficiency.
关键词
营商环境评估/Level/DB/机器学习/公有链/联盟链Key words
business environment evaluation/Level DB/machine learning/public blockchain/consortium blockchain引用本文复制引用
基金项目
国家重点研发计划(2023YFC3304900)
辽宁省应用基础研究计划(2022JH2/101300250)
辽宁省教育厅高等学校基本科研项目(理工类)面上项目(揭榜挂帅服务地方项目)(JYTMS20230761)
教育部产学合作协同育人项目(230701160261310)
出版年
2024
NETL
NSTL
维普
万方数据