A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control

Jun ZHANG; Peidong XU; Siyuan CHEN; Tianlu GAO; Yuxin DAI; Ke ZHANG; Hang ZHAO; Jiemai GAO; Yuyang BAI; Jinxing LI; Haoran ZHANG; Xiang LI; Jiuxiang CHEN

doi:10.11959/j.issn.2096-6652.202237

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A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control

Special Column: Machine Learning Methods in AI 3.0 | 更新时间：2024-06-05

- A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 4, Pages: 571-583(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202237
  CLC： TP319
- Published Online：2022-12，
  
  Published：15 December 2022
- 稿件说明：
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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：

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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