基于图数据库的汽车质量追溯数据存储研究

Research on Automotive Quality Traceability Data Storage Based on Graph Database

汽车产业已经成为当前国民经济中的支柱产业,为了应对汽车零部件生产中的质量问题,目前产业界在生产过程中广泛引入质量追溯系统。现有的质量追溯系统大多基于关系型数据库构建,而传统的数据库在处理复杂的关系型数据时存在局限性,无法灵活应对质量追溯的多层次和复杂需求。因此,提出了一种基于图数据库的汽车质量追溯数据存储方法,选择图数据库作为数据存储方案,并设计了高度灵活的数据模型,包括汽车、部件、工序等之间的关联关系,以更好地满足汽车零部件生产质量追溯需求。实验结果表明,相较于传统数据库系统,基于图数据库的方法在查询性能以及数据插入效率方面均表现出显著优势。

The automotive industry has become a pillar industry in the current national economy,and in order to address quality issues in the production of automotive parts,the industry has widely introduced quality traceability systems in the production process.Most existing quality traceability systems are built on relational databases,while traditional databases have limitations in handling complex relational data and cannot flexibly meet the multi-level and complex requirements of quality traceability.Therefore,a storage method for automotive quality traceability data based on graph database is proposed,and graph database is selected as the data acquisition storage scheme,and the highly flexible data model is designed,including the correlation relationships between automobiles,components,processes,etc.,to better meet the production quality traceability needs of automotive parts.The experimental results show that compared to traditional database systems,methods based on graph database exhibit significant advantages in query performance and data insertion efficiency.