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With the development of cloud computing technology, infrastructure resources are showing a trend of diversified and ubiquitous deployment. The integrated management and control system for infrastructure sites involves fixed cameras, edge-side smart devices, and cloud-based intelligent analysis centers. The important issues that need to be addressed by this system are how to coordinate resources in various aspects, achieve cloud-edge collaborative computing, minimize costs, and provide real-time warnings of hazards. This article proposes an adaptive grouping-based particle swarm optimization algorithm to address the problem of traditional particle swarm optimization easily falling into local optimal solutions. A cloud-edge multi-level resource scheduling model is designed to achieve integrated resource scheduling solutions for cloud-edge computing power. The model is applied in the comprehensive management and control system of infrastructure sites, and the results confirm that the system can achieve real-time warning and instant response for dangerous behaviors on site, while minimizing overall costs.
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