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山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (2): 105-114.doi: 10.6040/j.issn.1672-3961.0.2020.233

• • 上一篇    

基于关系转移和增强学习的时空大数据动态预测

郑子君1,2,冯翔1,2*,虞慧群1,2,李修全3   

  1. 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
  • 发布日期:2021-04-16
  • 作者简介:郑子君(1994— ),女,江西抚州人,博士研究生,CCF学生会员,主要研究方向为分布并行计算,时空大数据. E-mail:mazjzheng@163.com. *通信作者简介:冯翔(1977— ),女,湖北武汉人,教授,博士,CCF会员,主要研究方向为分布并行计算,计算机网络. E-mail:xfeng@ecust.edu.cn
  • 基金资助:
    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)

Dynamic prediction of spatiotemporal big data based on relationship transfer and reinforcement learning

ZHENG Zijun1,2, FENG Xiang1,2*, YU Huiqun1,2, LI Xiuquan3   

  1. 1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China;
    2. Shanghai Engineering Research Center of Smart Energy, Shanghai 200237, China;
    3. China Academy of Science and Technology for Development, Beijing 100038, China
  • Published:2021-04-16

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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。

关键词: 时空数据, 复杂工作流, 关系转移, 特征学习, 增强学习

Abstract: 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.

Key words: spatiotemporal data, complex workflow, relationship transfer, feature learning, reinforcement learning

中图分类号: 

  • TP391
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