摘要： 海洋计算模型具有数据源广、模型复杂度高的特点，并且涉及众多学科领域。针对多种类海洋计算模型协同计算时调用复杂的问题，开发海洋计算模型协同一体化流程管理系统。对模型进行服务化封装集成，设计通用的数据转换接口，提出基于随机森林的数据分类与转换算法实现海洋数据的协同转换，并通过加入数据预处理过程降低时间复杂度。在此基础上，设计改进的鸡群优化算法提高调度效率，利用面向服务的多粒度协作流程建模方法构建轻量级的流程自定义交互机制。实验结果表明，该系统可有效提高海洋数据分析与模拟计算的效率，其中结合数据预处理的RF算法较SVM和原始RF算法数据分类速度更快，并可保持高于91%的分类准确率，而改进的鸡群优化算法迭代次数较原始CSO算法和SJF算法减少29%~37%，可有效提高调度效率。

关键词: 海洋计算模型, 协同计算, 模型封装, 数据分类与转换, 资源调度, 流程建模

Abstract: The ocean computing model has the characteristics of wide data sources and high model complexity,and involves many disciplines.In order to solve the complex problem in the collaborative computing of multiple ocean computing models,a collaborative integrated process management system for ocean computing models is developed.The model is integrated as services,an universal data conversion interface is designed,a data classification and conversion algorithm based on Random Forest(RF) is proposed to realize the collaborative conversion of ocean data,and the time complexity is reduced by adding a data preprocessing process.On this basis,an improved Chicken Swarm Optimization(CSO) algorithm is designed to improve scheduling efficiency,and a lightweight process customization interaction mechanism is constructed by using the service-oriented multi-granularity collaborative process modeling method.Experimental results show that the system can effectively improve the efficiency of the ocean data analysis and numerical simulation.The RF algorithm combining with data preprocessing has faster data classification speed compared with SVM and the original RF algorithm,and can keep the classification accuracy higher than 91%.Compared with the original CSO algorithm and SJC algorithm,the iterations of the improved CSO algorithm is reduced by 29%~37%,which can effectively improve the scheduling efficiency.

Key words: ocean computing model, collaborative computing, model encapsulation, data classification and transformation, resource scheduling, process modeling

中图分类号: 

• TP391.9