数据与计算发展前沿 ›› 2019, Vol. 1 ›› Issue (2): 75-85.

doi: 10.11871/jfdc.issn.2096-742X.2019.02.007

所属专题： “人工智能”专刊

• 人工智能专刊 • 上一篇    下一篇

多粒度认知计算——一种大数据智能计算的新模型

王国胤,于洪*()   

  1. 重庆邮电大学,计算智能重庆市重点实验室,重庆 400065
  • 收稿日期:2019-09-23 出版日期:2019-12-20 发布日期:2020-01-15
  • 通讯作者: 于洪
  • 作者简介:王国胤,重庆邮电大学教授,计算智能重庆市重点实验室主任,长江学者特聘教授,万人计划领军人才。主要研究方向为粒计算、知识发现、认知计算、智能信息处理、大数据智能。
    本文贡献：提出了多粒度认知计算模型,参与文章整体框架设计,以及论文的写作与修改。
    Wang Guoyin is currently a Professor at Chongqing University of Posts and Telecommunications, Dean of Chongqing Key Laboratory of Computing Intelligence, Chang Jiang Scholar and Innovation Talent of the National High-level Personnel of Special Support Program of China. His main research interests include granular computing, knowledge discovery, intelligent information processing, big Data intelligence.
    Contribution: He proposed the multi-granularity cognitive computing model, participated in the framework design of the article, as well as the writing and revision of the paper.
    E-mail: wanggy@cqupt.edu.cn|于洪,重庆邮电大学教授,主要研究方向为工业大数据智能决策、知识自动化、粒计算、三支决策、数据挖掘。
    本文贡献：参与了文章的框架设计,开展了多粒度认知计算在流程工业知识自动化中的应用研究,以及论文的写作与修改。
    Yu Hong is currently a Professor at Chongqing University of Posts and Telecommunications. Her research interests include industrial big data for intelligent decision making, knowledge automation, granular computing, three-way decisions and data mining.
    Contribution: She participated in the framework design of the article, researches on multi-granularity cognitive computing in process industry knowledge automation, as well as the writing and revision of the paper.
  • 基金资助:
    国家自然科学基金重点项目“概念嵌入：基于概念森林的深度表达学习可解释性研究”(61936001);“基于大数据与云计算的铝电解知识自动化决策系统设计方法与应用验证”(61533020);国家自然科学基金应急项目“流程工业过程操作优化决策的知识自动化方法及应用”(61751312);国家自然科学基金面上项目“工业大数据的三支多粒度智能决策模型与方法”(61876027);“知识与数据双向驱动的大数据多粒度学习模型与方法”(61772096)

Multi-Granularity Cognitive Computing—A New Model for Big Data Intelligent Computing

Wang Guoyin,Yu Hong*()   

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065,China
  • Received:2019-09-23 Online:2019-12-20 Published:2020-01-15
  • Contact: Yu Hong

RichHTML

98

PDF

964

摘要：

【目的】分析大数据智能计算的研究背景和面临的主要挑战问题,从认知计算的角度介绍一种大数据智能计算的新模型——多粒度认知计算。【方法】阐述大数据智能计算是实现大数据价值的必由之路,分析传统大数据智能计算模型所采用的数据计算机制,分析其与人类大脑认知机制不一致的问题。介绍统一满足人类大脑“大范围优先”认知机制（由粗粒度到细粒度）与计算机系统信息计算处理机制（由细粒度到粗粒度）的大数据智能计算研究新模型——多粒度认知计算,并介绍数据驱动的粒认知计算DGCC计算框架。【结果】发现建立数据驱动的粒认知计算模型,实现数据与知识双向驱动和变换,需要研究多粒度空间的描述问题、多粒度联合求解问题、人机认知机制融合等三个科学问题。【结论】通过在流程工业智能制造上进行的初步探索表明,多粒度认知计算是解决大数据智能决策面临“数据-知识”融合难题的一种有效的新模型。

关键词: 大数据智能, 认知计算, 粒计算, 数据挖掘

Abstract:

[Objective] This paper analyzes the research background of big data intelligent computing and associated challenging problems, then introduces multi-granularity cognitive computing, a novel model for big data intelligent computing in the view of cognitive computing. [Methods] Big data intelligent computing is shown to be a way to utilize the value of big data. The data computing mechanism of most traditional big data intelligent computing models is found inconsistent with the cognition mechanism of human brain. This paper introduces the multi-granularity cognitive computing model, which is a model for big data intelligent computing and unifies the “global precedence” law of human brain’s cognition mechanism (from coarse granularity to fine granularity) and the information processing mechanism in computer systems (from fine granularity to coarse granularity). The framework of data-driven granular cognitive computing (DGCC) is introduced. Furthermore, some application examples in intelligent manufacturing process industry are introduced. [Results] It is found that three kinds of scientific problems need to be studied for establishing the data-driven granular cognitive computing model and the integration of knowledge driven and data driven computing mechanisms. Those are multi-granularity space description, multi-granularity joint problem solving and human-computer cognitive mechanism integration. [Conclusion] Through its preliminary exploration on intelligent manufacturing in process industry, it is shown that multi-granularity cognitive computing is an effective new model to solve the problem of data-knowledge fusion in intelligent decision-making based on big data.

Key words: big data intelligence, cognitive computing, granular computing, data mining