Oxygen concentration regulation and control method based on hybrid intelligent model

The oxygen concentration regulation method using multimodal sensor networks, Kalman filters, and hybrid intelligent models solves the adaptability and accuracy problems of traditional oxygen regulation methods, achieving high-precision oxygen concentration regulation.

CN120949841AActive Publication Date: 2025-11-14TIBET UNIV
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Patent Information

Application Number
CN202511132546.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-13
Publication Date
2025-11-14
Estimated Expiration
2045-08-13

AI Technical Summary

Technical Problem

Traditional oxygen regulation methods suffer from poor model adaptability, limited feature extraction, and control lag, failing to meet the requirements for high-precision regulation.

Method used

A multimodal sensor network is used to collect environmental parameters, and a Kalman filter is used for optimization estimation and feature extraction. An oxygen concentration prediction model is constructed by combining a bidirectional LSTM network and a Transformer model, and an adaptive proportional-integral-derivative controller is used to generate control commands.

Benefits of technology

It achieves high-precision oxygen concentration control in dynamic environments, reduces response lag and overshoot, and improves the accuracy and stability of oxygen supply.

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Abstract

The invention discloses an oxygen concentration regulation and control method based on a hybrid intelligent model. The method comprises the following steps: collecting multi-modal environment parameters; performing optimization estimation on the multi-modal environmental parameters by using a Kalman filter, and performing feature extraction on the optimized and estimated multi-modal environmental parameters; constructing an oxygen concentration prediction model based on a bidirectional LSTM network and a Transform model, and training the oxygen concentration prediction model by using a near-end strategy optimization algorithm and the multi-modal feature data; acquiring an oxygen concentration prediction value by using the real-time multi-mode environmental parameters and the trained oxygen concentration prediction model; and constructing a self-adaptive proportional-integral-derivative controller, and generating a ventilation equipment control instruction by using the oxygen concentration predicted value and the self-adaptive proportional-integral-derivative controller so as to regulate and control the oxygen concentration. The system can accurately control the oxygen concentration, is suitable for various scenes such as mines, medical treatment and industry, and provides an intelligent solution for high-precision oxygen concentration control.
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Citation Information

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