物理-数据-知识混合驱动的人机混合增强智能系统管控方法

张俊; 许沛东; 陈思远; 高天露; 戴宇欣; 张科; 赵杭; 高杰迈; 白昱阳; 李金星; 张浩然; 李湘; 陈玖香

doi:10.11959/j.issn.2096-6652.202237

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物理-数据-知识混合驱动的人机混合增强智能系统管控方法

专栏：人工智能3.0中的机器学习方法 | 更新时间：2024-06-05

- 物理-数据-知识混合驱动的人机混合增强智能系统管控方法
- A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control
- 智能科学与技术学报 2022年4卷第4期页码：571-583
- 作者机构：
- 作者简介：
  
  [ "张俊（1981− ），男，博士，武汉大学电气与自动化学院教授、博士生导师，中国自动化学会副秘书长，IEEE 射频识别理事会副主席。主要研究方向为大数据、人工智能、物联网等新一代信息技术及其在电力能源等复杂系统中的应用" ]
  [ "许沛东（1995− ），男，武汉大学电气与自动化学院博士生，主要研究方向为人工智能、智能电网" ]
  [ "陈思远（1993− ），男，武汉大学电气与自动化学院博士生，主要研究方向为电力系统运行与控制、人工智能和机器学习" ]
  [ "高天露（1994− ），男，武汉大学电气与自动化学院博士生，主要研究方向为自然语言处理、区块链和人机混合增强智能在电力系统运行分析和控制决策中的应用" ]
  [ "戴宇欣（1996− ），男，武汉大学电气与自动化学院博士生，主要研究方向为人工智能技术及其在电力系统中的应用" ]
  [ "张科（1993− ），女，武汉大学电气与自动化学院博士生，主要研究方向为人工智能技术在电力系统中的应用、可信任的人工智能系统以及人机混合智能" ]
  [ "赵杭（1995− ），男，武汉大学电气与自动化学院博士生，主要研究方向为因果推理在电力系统中的应用" ]
  [ "高杰迈（1995− ），女，武汉大学电气与自动化学院博士生，主要研究方向为综合能源系统、智能电网和物理深度学习" ]
  [ "白昱阳（1997− ），男，武汉大学电气与自动化学院硕士生，主要研究方向为深度强化学习在电力系统调度中的应用" ]
  [ "李金星（1996− ），男，武汉大学电气与自动化学院硕士生，主要研究方向为人工智能技术在电网故障定位及故障处置中的应用" ]
  [ "张浩然（1999− ），男，武汉大学电气与自动化学院硕士生，主要研究方向为人工智能在电力系统中的应用" ]
  [ "李湘（2000− ），女，武汉大学电气与自动化学院硕士生，主要研究方向为人工智能在电力系统中的应用" ]
  [ "陈玖香（2001− ），女，武汉大学电气与自动化学院硕士生，主要研究方向为人工智能在电力系统中的应用" ]
- 基金信息：
  
  科技创新2030—“新一代人工智能”重大项目;The National Key Research and Development Program of China(2021ZD0112700)
- DOI：10.11959/j.issn.2096-6652.202237
  中图分类号： TP319
- 网络出版日期：2022-12，
  
  纸质出版日期：2022-12-15
- 稿件说明：
移动端阅览
张俊, 许沛东, 陈思远, 等. 物理-数据-知识混合驱动的人机混合增强智能系统管控方法[J]. 智能科学与技术学报, 2022,4(4):571-583.

Jun ZHANG, Peidong XU, Siyuan CHEN, et al. A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 571-583.
张俊, 许沛东, 陈思远, 等. 物理-数据-知识混合驱动的人机混合增强智能系统管控方法[J]. 智能科学与技术学报, 2022,4(4):571-583. DOI： 10.11959/j.issn.2096-6652.202237.

Jun ZHANG, Peidong XU, Siyuan CHEN, et al. A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 571-583. DOI： 10.11959/j.issn.2096-6652.202237.

摘要

当代系统认知、管理与控制的核心理论、方法与技术已经转移到大数据和人工智能技术上，这导致当前人工智能技术条件局限与复杂系统认知、管理、控制的需求之间形成了一道鸿沟。因此，现实的需求催生了人工智能的一种新型形态——人机混合增强智能形态，即人类智能与机器智能协同贯穿于系统认知、管理、控制等过程的始终，人类的认知和机器智能认知互相混合，形成增强型的智能形态，这种形态是人工智能或机器智能可行的、重要的成长模式。提出了一种物理-数据-知识混合驱动的人机混合增强智能系统管控方法。从可信分布式数据、计算和算法，物理深度学习，融合系统运行规则的混合型深度强化学习，因果分析，可解释性AI与数字人5个方面详细阐述了所提方法。最后，以电力系统调控为背景，以3个应用为例分析了所提方法的应用方式和技术路径。

Abstract

The core theories

methods and technologies of contemporary system cognition

management

and control have been transferred to big data and artificial intelligence technology

resulting in a gap between the limitations of current artificial intelligence technology and the needs of complex system cognition

management

and control.As a result

a real need has spawned a new form of artificial intelligence: human-machine hybrid-augmented intelligence form

that is

the cooperation of human intelligence and machine intelligence runs through the process of system cognition

management

control

and so on.Human cognition and machine intelligence cognition are mixed together to form enhanced intelligence form.This form is a feasible and important growth mode of artificial intelligence or machine intelligence.A hybrid physics-data-knowledge (PDK) driven approach for human-machine hybrid-augmented intelligence-based system management and control was proposed.The proposed approach was illustrated by the following: trustworthy distributed data

computing

and algorithm

physics-informed deep learning

hybrid deep reinforce learning incorporating system operation rules

causal analysis

and interpretable AI and virtual digital human.In the context of power system dispatch and control

three examples were used for explaining the applications and technical pathways of the proposed PDK approach.

关键词

Keywords

references

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