Lightweight reinforcement learning model construction method for plateau scene intelligent oxygen supply

By using a lightweight reinforcement learning model combined with neural networks to optimize oxygen supply decisions, the problems of large size and low computational efficiency of portable oxygen generators in high-altitude environments have been solved, achieving efficient and accurate oxygen supply control.

CN113947194BActive Publication Date: 2026-06-19NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHWESTERN POLYTECHNICAL UNIV
Filing Date
2021-10-18
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing portable oxygen concentrators are bulky in high-altitude environments, making them unsuitable for single-person carrying, and their computational efficiency is low, making them unable to effectively supply oxygen in mobile scenarios.

⚗Method used

A lightweight reinforcement learning model is adopted, which combines environmental state information and task data, and uses a neural network to estimate action benefits, thereby reducing network parameters and the number of layers and optimizing oxygen supply decisions.

🎯Benefits of technology

It achieves efficient and accurate oxygen supply decisions in high-altitude environments, adapts to complex conditions, reduces computational load, and is suitable for embedded devices.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a lightweight reinforcement learning model construction method for intelligent oxygen supply in high-altitude scenarios. The model includes several steps: environmental state input, data preprocessing, using a neural network to decide on the output action, receiving environmental feedback rewards, and updating the neural network parameters. It can comprehensively consider various factors in extreme environments such as high altitudes, improving model accuracy while minimizing computational load to ensure correct decision-making and efficiently completing the oxygen supply task. It can serve as the model basis for intelligent control of oxygen supply in oxygen supply systems.
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