平行澳门野外观测站：全域智能观测与应急管理自主系统

黄峻; Joseph Hun-Wei Lee; 倪清桦; 林飞; 田永林; 刘宇航; 吕宜生; 伍乃骐; Thales S. W. Theseus

doi:10.11959/j.issn.2096-6652.202609

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平行澳门野外观测站：全域智能观测与应急管理自主系统

专栏：生态智能与社会 | 更新时间：2026-04-27

- 平行澳门野外观测站：全域智能观测与应急管理自主系统
- Parallel Macao field observatory: autonomous systems for global intelligent observation and emergency management
- 智能科学与技术学报 2026年8卷第1期页码：117-130
- 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：
  
  [ "黄峻（1998- ），男，澳门科技大学创新工程学院工程科学系博士生，主要研究方向为平行智能、自动驾驶轨迹预测规划、提示工程、大语言模型。" ]
  [ "Joseph Hun-Wei Lee（1952- ），男，澳门科技大学校长，主要研究方向为流体力学、环境管理、水资源系统分析和管理、水力学。" ]
  [ "倪清桦（1999- ），女，澳门科技大学创新工程学院工程科学系博士生，主要研究方向为平行智能、区块链、数字孪生、智能系统等。" ]
  [ "林飞（1994- ），男，澳门科技大学创新工程学院工程科学系博士生，主要研究方向为平行系统、人工智能药物研发、大语言模型、多模态感知、生成式人工智能。" ]
  [ "田永林（1994- ），男，中国科学院自动化研究所在站博士后，主要研究方向为平行系统、自动驾驶、场景工程。" ]
  [ "刘宇航（1999- ），男，中国科学院自动化研究所博士生，主要研究方向为平行感知、3D场景理解、MLLM。" ]
  [ "吕宜生（1983- ），男，中国科学院自动化研究所研究员，主要研究方向为人工智能、机器学习、深度学习、智能驾驶、智能交通和交通大数据。" ]
  [ "伍乃骐（1949- ），男，博士，澳门科技大学创新工程学院工程科学系主任、澳门系统工程研究所所长，主要研究方向为生产计划与调度、制造系统建模与控制、离散事件系统、Petri网理论及其应用、智能交通系统、能源系统。" ]
  Thales S. W. Theseus，男，欧布达大学平行智能DeSci中心高级研究员，主要研究方向为用于教育和智能生活及工作环境的平行艺术与音乐。
- 基金信息：
  
  澳门特别行政区科学技术发展基金资助项目(0093/2023/RIA2;0157/2024/RIA2;0145/2023/RIA3);北京市自然科学基金-怀柔创新联合基金资助项目(L245025);平行生态立项(DeSCII-0028-0123-2025)
- DOI：10.11959/j.issn.2096-6652.202609
  中图分类号： TP181
- 收稿：2025-10-28，
  
  修回：2026-03-22，
  
  录用：2026-03-24，
  
  纸质出版：2026-03-15
- 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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