面向OpenAPI的云原生时空信息处理服务

Cloud-native spatiotemporal information processing service based on OpenAPI

针对传统网络处理服务(WPS)通信协议、数据编码日趋冗余,研发运维过程效率不足等问题,本文依托OGC API-Processes标准,提出一种面向OpenAPI的云原生时空信息处理服务方法。首先,设计概念模型、执行模式与服务接口,在此基础上构建符合OpenAPI规范的时空信息处理服务平台框架;然后,依据云原生技术体系完善其架构与研发运维流程,支持分布式云端自动化部署、持续迭代等特性;最后,提出原型系统设计与实现方法。多组遥感影像耕地范围提取实验结果表明,相比传统WPS方法,本文方法可有效提升服务执行速率,并从应用程序各生命周期提升研发及运维效率,是一种更高效、轻量化的时空信息处理模型共享与互操作方法。

In response to issues such as redundant communication protocols and inefficient development and operation processes in traditional Web Processing Service(WPS),this paper proposes a cloud-native spatiotemporal information processing service approach based on the OGC API-Processes standard.Firstly,a conceptual model,execution patterns,and service interfaces are designed to establish a platform framework for spatiotemporal information processing services that conform to the OpenAPI specification.Secondly,leveraging cloud-native technologies,the architecture and development operation processes are enhanced to support distributed cloud-based automated deployment and continuous iterations.Lastly,an overall prototype system design and implementation approach are presented.Experimental results of multiple sets of remote sensing image cultivated land extraction demonstrate that compared to traditional WPS methods,the approach in this paper effectively improves service execution speed and enhances development and operation efficiency throughout the application lifecycle.This approach offers a more efficient and lightweight solution for sharing and interoperating of spatiotemporal information processing models.