Intelligent deployment method of air monitoring nodes based on crowd sensing

Abstract

The present invention belongs to the field of cross-technology of crowd perception, air monitoring and artificial intelligence, and discloses a method for intelligent deployment of air monitoring nodes based on crowd perception. In response to the problems of insufficient node deployment density, delayed response and monitoring blind spots in existing air quality monitoring systems, crowd perception information related to air pollution is first extracted from multi-source data such as social media and public reporting platforms, and pollution events and their spatial locations are identified through natural language processing and image recognition technology to construct a dynamic pollution perception heat map. On this basis, a deep reinforcement learning algorithm framework is designed to comprehensively consider factors such as perception coverage, deployment cost and communication connectivity, and learn and output the optimal deployment plan for air monitoring nodes. The intelligent deployment method designed by the present invention has adaptive capabilities, and dynamically adjusts the deployment strategy of spatial quality monitoring nodes according to crowd perception data, thereby improving the system's response capability to sudden pollution events.

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