A medical environment air quality management method, system, equipment and medium

Through real-time monitoring and intelligent processing of hospital air quality data, and using machine learning and optimization algorithms to generate customized improvement solutions, the problems of low efficiency and slow response in the existing technology are solved, and more efficient and intelligent air quality management is achieved.

CN119289489BActive Publication Date: 2025-03-18CHENG DU QING AN YI LIAO KE JI YOU XIAN GONG SI +1
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Patent Information

Application Number
CN202411474580.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-22
Publication Date
2025-03-18
Estimated Expiration
2044-10-22

AI Technical Summary

Technical Problem

The existing hospital air quality management methods are inefficient, costly, slow response, and difficult to adapt to changes in air quality in real time, which cannot meet the needs of modern hospitals for rapid response to air quality.

Method used

By monitoring the air quality data around the hospital in real time, using machine learning algorithms for pattern recognition and feature extraction, intelligently generating customized air quality improvement solutions with management strategies, using proximity strategy optimization algorithm and PID control algorithm for equipment operation scheduling and dynamic adjustment, and monitoring and processing abnormal warning signals in real time.

Benefits of technology

Real-time monitoring and optimization of air quality inside and outside the hospital is realized, the efficiency and response speed of air quality management are improved, energy consumption is reduced, and more intelligent and efficient air quality management solutions are provided.

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Abstract

The present invention provides a medical environment air quality management method, system, equipment and medium, which relates to the field of environmental air quality management technology, including obtaining a distribution map reflecting indoor and outdoor pollutant concentrations based on real-time data from the hospital's surrounding environment monitoring station and the air quality monitoring device inside the hospital after data fusion and calibration processing; using a machine learning algorithm to perform pattern recognition on the distribution map to obtain key features of medical environment air quality, and intelligently generate customized air quality improvement plans; processing through a proximal strategy optimization algorithm to obtain an equipment operation scheduling strategy; using a PID control algorithm for dynamic processing to obtain dynamically adjusted equipment operation parameters; and processing through an anomaly detection algorithm, thereby managing the medical environment air quality accordingly. The present invention can provide hospitals with a more intelligent and efficient air quality management solution.
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