大数据环境下地铁自动售检票系统设计与实现

近年来,随着经济领域蓬勃发展,我国加快了现代化建设进程,交通设施建设不断推进;受互联网大数据技术变革的影响,传统地铁售检票系统无法满足高客流量、大数据流处理的高强度工作要求;在日常实践应用中,传统地铁售检票系统经常出现检票识别率低、售票信息运算处理响应速度慢、多人员、多任务操作执行准确率差的问题;针对上述问题,结合大数据资源运算能力,提出大数据环境下地铁自动售检票系统设计;采用大数据实名高检处理引擎(VBDKG)、多路分处运算模组(ICGRU)与动态身份比对算法(DBTDE),针对传统地铁自动售检票系统存在的问题进行解决;通过仿真实验测试证明,提出的大数据环境下地铁自动售检票系统设计具有较强的实施性与可操作性;同时,运行处理准确性高,运行稳定。

Big data environment metro automatic fare collection system design and implementation

In recent years, along with the economy booming development, accelerate the process of the modernization in our country, traffic infrastructure to push. Affected by Internet big data technology change, the traditional subway fare collection system cannot meet the high traffic and large data stream processing requirement for high strength work. In daily practice applications, the traditional subway fare collection system often appear check-in recognition rate is low, the ticket information processing slow response speed, more personnel multitasking operating problem of poor accuracy of execution. Aiming at the above problems, combining the resources of the large data and computing power, metro automatic fare collection system under big data environment is designed. Using big data real-name widely processing engine (VBDKG), multiplex section arithmetic module (ICGRU) and dynamic identity comparison algorithm (DBTDE) for the problem of the traditional metro automatic fare collection system is solved. Through the simulation test proves that the proposed large data environment design of metro automatic fare collection system with strong practicability and maneuverability. At the same time, higher processing accuracy, stable operation.