Parallel Macao field observatory: autonomous systems for global intelligent observation and emergency management

Huang Jun; Joseph Hun-Wei Lee; Ni Qinghua; Lin Fei; Tian Yonglin; Liu Yuhang; Lyu Yisheng; Wu Naiqi; Thales S. W. Theseus

doi:10.11959/j.issn.2096-6652.202609

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Parallel Macao field observatory: autonomous systems for global intelligent observation and emergency management

Special Column\: Ecological Intelligence and Society | 更新时间：2026-04-27

- Parallel Macao field observatory: autonomous systems for global intelligent observation and emergency management
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 1, Pages: 117-130(2026)
- 作者机构：
  
  1.澳门科技大学创新工程学院工程科学系，澳门 999078
  2.青岛智能产业技术研究院，山东青岛 266109
  3.澳门科技大学环境研究院，澳门 999078
  4.中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
  5.中国科学院自动化研究所多模态人工智能系统全国重点实验室，北京 100190
  6.欧布达大学平行智能DeSci中心，匈牙利布达佩斯 H-1034
- 作者简介：
- 基金信息：
  
  The Science and Technology Development Fund, Macau Special Administrative Region (SAR)(0093/2023/RIA2;0157/2024/RIA2;0145/2023/RIA3);Beijing Municipal Natural Science Foundation-Huairou Innovation Joint Fund(L245025);The "Agentic/Autonomous Intelligence and Robotics for Parallel Ecology" Project(DeSCII-0028-0123-2025)
- DOI：10.11959/j.issn.2096-6652.202609
  CLC： TP181
- Received：28 October 2025，
  
  Revised：2026-03-22，
  
  Accepted：24 March 2026，
  
  Published：15 March 2026
- 稿件说明：
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黄峻,Joseph Hun-Wei Lee,倪清桦等.平行澳门野外观测站：全域智能观测与应急管理自主系统[J].智能科学与技术学报,2026,08(01):117-130.

Huang Jun,Lee Joseph Hun-Wei,Ni Qinghua,et al.Parallel Macao field observatory: autonomous systems for global intelligent observation and emergency management[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):117-130.
黄峻,Joseph Hun-Wei Lee,倪清桦等.平行澳门野外观测站：全域智能观测与应急管理自主系统[J].智能科学与技术学报,2026,08(01):117-130. DOI： 10.11959/j.issn.2096-6652.202609.

Huang Jun,Lee Joseph Hun-Wei,Ni Qinghua,et al.Parallel Macao field observatory: autonomous systems for global intelligent observation and emergency management[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):117-130. DOI： 10.11959/j.issn.2096-6652.202609.

摘要

澳门海岸带生态环境国家野外科学观测站（澳门野外站）在支撑海岸带生态环境与气候变化研究方面发挥着关键作用，重点围绕海岸带环境过程监测与模拟、污染物在多环境介质间迁移与转化机制及其环境效应、环境复合污染防控理论与技术，以及气候变化背景下海岸带环境过程调控与生态修复技术开展系统化科研工作。然而，传统的环境监测系统仍面临诸多挑战，包括全域多模态观测数据采集与融合困难、环境过程建模复杂、检测设备能力受限以及大规模数据处理与分析能力不足等。此外，现有灾情预警与应急管理系统对于快速演变的突发事件响应能力有限。为应对上述难题，基于人工系统-计算实验-平行执行（ACP）方法与大语言模型，提出了平行智能驱动的全域智能观测与应急管理系统。该系统通过人工系统构建生态观测与突发事件模型，用计算实验评估应急策略，并通过平行执行实现生态监测与应急处置的动态联动。系统融合平行传感、全域感知、云-边协同计算、三类人（生物人、数字人和机器人）协同管控、社会雷达等关键技术，构建满足安全性、安保性、可持续性、灵敏性、服务性与智能性（6S）的全域智能观测与应急管理体系。该体系能够显著提升海岸带生态监测和灾害应急管理的科学性、前瞻性与响应效率，为环境治理和风险防控提供强有力的技术支撑和科学依据。

Abstract

The Macao national observation and research station for coastal ecological environment (Macao field station) played a critical role in advancing research on coastal ecological environments and climate change. Its primary research focused include monitoring and modeling coastal environmental processes

investigating the mechanisms and environmental impacts of pollutant migration and transformation across multiple environmental media

developing theories and technologies for the prevention and control of compound environmental pollution

and exploring regulatory mechanisms and ecological restoration strategies for coastal environments under climate change. However

traditional environmental monitoring systems faced several persistent challenges

such as difficulties in acquiring and integrating large-scale multimodal data

the complexity of environmental process modeling

limitations in sensing equipment

and insufficient capacity for large-scale data processing and analytics. Furthermore

existing disaster early-warning and emergency management systems often struggle to respond effectively to rapidly evolving emergencies. To address these issues

a parallel-intelligence-driven framework for holistic intelligent observation and emergency management was proposed

grounded in the ACP (artificial societies

computational experiments

and parallel execution) method and large language models. The system constructed artificial models of ecological observation and emergency scenarios

evaluated response strategies through computational experiments

and enabled dynamic coordination of monitoring and emergency actions via parallel execution. By integrating key technologies such as parallel sensing

holistic perception

cloud-edge collaborative computing

coordinated control among biological humans

digital humans

and robots

and social radar

the proposed system established a comprehensive observation and emergency-management architecture that satisfied the requirements of security

safety

sustainability

sensitivity

serviceability

and intelligence (6S). This framework substantially enhanced the scientific rigor

foresight

and responsiveness of coastal ecological monitoring and disaster emergency management

providing robust technical support and scientific foundations for environmental governance and risk mitigation.

关键词

Keywords

references

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Related Author

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Related Institution

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DeSci Center of Parallel Intelligence, Obuda University

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