计算机与现代化 ›› 2024, Vol. 0 ›› Issue (05): 69-74.doi: 10.3969/j.issn.1006-2475.2024.05.012

• 网络与通信 • 上一篇    下一篇

气象服务中台关键技术研究与应用

  

  1. (1.湖南省气象信息中心,湖南 长沙 410118; 2.气象防灾减灾湖南省重点实验室,湖南 长沙 410118)
  • 出版日期:2024-05-29 发布日期:2024-06-12
  • 作者简介: 作者简介:冯冼(1978—),男,湖南湘潭人,高级工程师,硕士,研究方向:气象信息技术,气象数据处理,E-mail: dikfeng@163.com; 通信作者:方昆(1989—),男,工程师,硕士,研究方向:气象数据与图像处理,E-mail: K19890823@163.com; 屈右铭(1980—),男,高级工程师,硕士,研究方向:气象数据处理,E-mail: 5688056@qq.com; 刘晓波(1981—),男,高级工程师,学士,研究方向:网络与数据安全,E-mail: 44459141@qq.com; 施佳驰(1990—),男,工程师,硕士,研究方向:信息系统建设,E-mail: 799043496@qq.com; 文立恒(1985—),女,高级工程师,硕士,研究方向:气象数据处理,E-mail: 32026947@qq.com。
  • 基金资助:
    湖南省自然科学基金资助项目(2020JJ4397); 湖南省气象局重点科研项目(XQKJ22A006); 湖南省应急管理科技项目(202301)
      

Research and Application of Key Technologies in Meteorological Service Middle Platform

  1. (1. Hunan Meteorological Information Center, Changsha 410118, China;
    2. Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Changsha 410118, China)
  • Online:2024-05-29 Published:2024-06-12

摘要:
摘要:随着气象数据量的不断增长以及应用场景的不断拓展,传统数据处理模式难以满足多行业、融合型服务需求。为解决气象服务数据量大、处理过程复杂、应用需求多样、响应时效要求高等难点,本文开发分布式架构的湖南气象服务中台,并介绍支撑高并发服务的关键技术,包括:采用标准化流程实现多源异构数据统一处理,研发微服务并行处理模块提升数据处理效率,设计动态负载均衡算法增强并发能力,通过流量控制机制保障运行稳定性。测试结果表明:应用上述技术,在有限的基础资源支撑下,湖南气象服务中台可满足5000客户端并发访问需求,平均响应时间为1202 ms,在支撑应急管理、水利水文、自然资源等跨行业、多场景的气象服务中发挥了良好作用。



关键词: 关键词:气象服务, 高并发, 流程化, 微服务, 负载均衡, 流量控制

Abstract: Abstract: With the continuous growth of meteorological data and the expansion of application scenarios, traditional data processing models are difficult to meet the needs of various industry and integrated services. In order to solve the difficulties of mass data, complex processing, demand diversification, and high response time requirements in meteorological services, we developed the Hunan meteorological service middle platform based on distributed architecture, and introduced key technologies to support high concurrency services, including adopting standardized processes to achieve unified processing of multi-source heterogeneous data, developing microservice parallel processing modules to improve data processing efficiency, designing dynamic load balancing algorithms to enhance concurrency capabilities, and ensuring operational stability through flow control mechanisms. The test results show that with the application of the above technology and limited basic resource support, the platform can support 5000 concurrent access, displaying average response time 1202 ms. It has achieved positive application effects in supporting cross industry and multi scenario meteorological services such as emergency management, water conservancy, natural resources.

Key words: Key words: meteorological service, high concurrency, processization, microservices, load balancing, flow control

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