The implementation and application verification of an intelligent recognition system oriented toward edge computing

Abstract

With the deep integration of the Internet of Things and artificial intelligence technologies, intelligent recognition systems face severe challenges such as resource constraints and dynamic environmental changes in edge computing scenarios. This paper conducts research on the implementation of intelligent recognition systems for edge computing and proposes a distributed computing architecture featuring end-edge-cloud collaboration. This architecture achieves load balancing and efficient adaptation through task decoupling and heterogeneous resource abstraction. To address the challenges of model deployment, this study employs techniques such as structured pruning, knowledge distillation, and quantization-aware training to achieve model lightweighting, and further realizes online model deployment through dynamic computation graph optimization. Additionally, this paper designs a resource demand prediction model based on the characteristics of recognition tasks, a dynamic priority scheduling mechanism under latency constraints, and an adaptive update mechanism for data drift, thereby ensuring stable system operation under dynamically changing resources. The aforementioned research provides a comprehensive technical solution for the implementation of edge intelligent recognition systems, effectively balancing the trade-offs among recognition accuracy, resource consumption, and real-time responsiveness.