Intelligent prediction and correction method for water content target value at feeding process outlet

By constructing a quantitative relationship model between environmental temperature and humidity variables and target moisture values, and using random forest, XGBoost, and linear regression algorithms, the problem of moisture control in the feeding process relying on human experience was solved, and intelligent dynamic correction of moisture at the outlet of the feeding process was realized, thereby improving tobacco quality and processing efficiency.

CN122139995APending Publication Date: 2026-06-05BEIJING SPACEFLIGHT TUOPUGAO SCI & TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING SPACEFLIGHT TUOPUGAO SCI & TECH CO LTD
Filing Date
2026-03-19
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

The existing moisture control strategy in the feeding process relies on manual experience, which makes it difficult to achieve real-time adaptive adjustment to fluctuations in environmental parameters. This affects the stability of the physical morphology and chemical composition of tobacco leaves, and has a negative impact on the processing efficiency and product quality of subsequent processes.

Method used

By establishing a quantitative relationship model between environmental temperature and humidity variables and target moisture values, and using random forest, XGBoost, and linear regression algorithms to construct prediction and correction models, intelligent dynamic correction of moisture at the outlet of the feeding process is achieved.

Benefits of technology

It achieves precise control of moisture content at the outlet of the feeding process, improves the dynamic adaptability of moisture regulation, and enhances the stability of tobacco quality and the processing efficiency of subsequent processes.

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Abstract

The application discloses a kind of intelligent prediction and correction method of moisture target value at feeding process outlet, and feeding process receives moisture regain process, connects cut tobacco process, and correction method includes: S1, obtains historical production data to construct the initial prediction model of moisture at feeding process outlet, obtains the predicted value of moisture at feeding process outlet;S2, based on the environmental data of obtained historical production data, construct the workshop temperature and humidity prediction model before production of cut tobacco process, obtain the indoor temperature and indoor humidity of workshop at the moment when the next cut tobacco process starts production;S3, construct the correction value prediction model of moisture at feeding process outlet, obtain the correction value of moisture at feeding process outlet;S4, the predicted moisture value of the feeding process outlet is added to the correction value of moisture at feeding process outlet, and the corrected target moisture value of feeding process outlet is obtained;The application converts the moisture control problem of traditional dependence on artificial experience into engineering optimization problem based on environmental parameter feedback, realizes the intelligent dynamic correction of outlet moisture.
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