Chongqing General Artificial Intelligence Project: Large-Scale Social Simulation Platform Supports Disaster Evacuation Simulation with 100,000 Agents

The large-scale social simulation platform developed by the Chongqing General Artificial Intelligence Project supports crowd evacuation simulations in disaster scenarios such as fire, heavy rain, stampede, and flood. It can simulate up to 100,000 agents and supports modeling of social relationships like family and leader-follower dynamics. This platform has both academic research value and practical application significance, and can serve public safety decision support systems.

With the acceleration of urbanization and increasing population density, safety management of large-scale crowd gathering places has become a challenge. Traditional disaster emergency drills are costly and difficult to cover extreme scenarios, while computer simulation technology provides new ideas. Social simulation, as an interdisciplinary field of AI and computational sociology, has made significant progress in recent years. The platform of the Chongqing General Artificial Intelligence Project targets actual needs and can serve departments such as urban planning and emergency management.

The platform's core functions include ultra-large-scale simulation capability (supporting 100,000 agents) to simulate crowd behavior in high-density places; it supports multiple disaster scenarios: fire evacuation (impact of smoke diffusion), heavy rain and flood (waterlogging obstacles), stampede risk (density critical points and panic spread), and joint simulation of compound disasters.

The platform emphasizes agent heterogeneity and social attributes: distinguishing movement speed, endurance, and other characteristics of people of different age groups; the core innovation is social relationship modeling, including family relationships (gathering actions), leader-follower relationships (guiding evacuation), and peer effects (social influence on decision-making), making the simulation closer to real human behavior.

In terms of technical implementation, spatial partitioning is used to reduce computational overhead, and event-driven approach is adopted to improve efficiency; modular design supports customization: replaceable path planning algorithms, configurable behavior rules, import of real building CAD drawings, and provision of visual rendering interfaces.

Application scenarios include emergency drill and plan evaluation (optimizing evacuation plans), building design verification (identifying evacuation bottlenecks), academic research (experimental platform), and education and training (public safety education). Future directions: introducing fine physical engines to simulate building damage, integrating real-time data to support digital twins, using reinforcement learning to train emergency decision-making strategies, and enhancing agents' cognitive decision-making capabilities.

This platform represents the in-depth application of AI technology in the field of public safety. Combining multi-agent systems and social science theories, it serves social governance needs. With technological development in the future, simulation results will become more realistic, and its application value will be further enhanced.