文献类型：期刊文献

中文题名：基于关系转移和增强学习的时空大数据动态预测

英文题名：Dynamic prediction of spatiotemporal big data based on relationship transfer and reinforcement learning

作者：郑子君[1,2];冯翔[1,2];虞慧群[1,2];李修全[3]

机构：[1]华东理工大学计算机科学与工程系,上海200237;[2]上海智慧能源工程技术研究中心,上海200237;[3]中国科学技术发展战略研究院,北京100038

年份：2021

卷号：51

期号：2

起止页码：105

中文期刊名：山东大学学报（工学版）

外文期刊名：Journal of Shandong University（Engineering Science）

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金项目(61772200,61772201,61602175);上海市浦江人才计划(17PJ1401900);上海市经信委“信息化发展专项资金”(201602008)。

语种：中文

中文关键词：时空数据;复杂工作流;关系转移;特征学习;增强学习

外文关键词：spatiotemporal data;complex workflow;relationship transfer;feature learning;reinforcement learning

摘要：为了解决较大时空范围内的动态预测无法获得精确解的问题,采用支持较复杂工作流模式的群智计算方式,提出一种基于关系转移和增强学习的动态预测算法,解决时空数据中的优化问题。设计一个关系转移块,通过对时空数据进行特征提取来学习关系转移概率。建立一个预测增强学习块,随时间序列并行处理转移关系概率,根据特征偏好对时空数据进行优先排序,进而预测问题状态趋势。采用一种深度多步迭代策略优化方法,获得合理的解。从理论上详细地分析和讨论所提出算法的收敛性和收敛速率。在专利转移数据上的试验结果验证了该方法的优势,并证明通过应用关系转移块和预测增强学习块排序精度能得到明显地改善。
A dynamic prediction algorithm based on relationship transfer and reinforcement learning was proposed to alleviate the problem that the dynamic prediction in a large spatiotemporal range fails to obtain an accurate solution. The algorithm adopted a crowd intelligence computing manner with complex workflow models to solve the spatiotemporal data optimization problem. A relationship transfer block was designed to learn the probability of relationship transfer by extracting features from spatiotemporal data. A prediction reinforcement learning block was established along with the time series to process the transition relationship probability in parallel and prioritize the spatiotemporal data according to feature preferences that predict the problem status trend. A deep multi-step iterative strategy optimization was adopted to obtain a reasonable solution. Theoretical analysis and discussion of the convergence and convergence rate of the proposed algorithm were conducted. Experimental results on patent transfer data verified this approach′s strengths and demonstrated that the ranking accuracy could be significantly improved by applying the relationship transfer block and prediction reinforcement learning block.