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

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.