Distribution of VLPD on region servers.

The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key–value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

智能交通系统（Intelligent Transportation Systems）所生成的海量车牌数据，广泛应用于城市交通信息系统建设领域，具备极高的科研与应用价值。近年来，依托大数据平台对这类高价值数据资源进行妥善存储、处理与开发利用，已成为研究热点方向。为解决大数据平台键值数据库（Key-Value Database）存储环境下，复杂多条件综合查询功能落地与灵活数据应用存在的难题，本文提出了一种基于跳跃哈希一致性算法（Jump Hash Consistency Algorithm）的数据访问模型。该模型采用数据分片存储、多线程滑动窗口并行读取等算法，实现了集群数据节点上海量时序数据（Time-Series Data）的均匀分布存储与快速读取。通过对数据分布均匀性与检索效率的对比分析可知，该模型可有效避免集群热点问题，能够支持各类复杂条件下的综合分析查询，并通过精准定位数据存储范围、运用并行扫描读取技术维持较高的查询效率。