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Research Article
QingE Wu
Zhengzhou University of Light Industry
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
In order to reduce the number of defective products caused by the unreasonable baking time during the tobacco production process, this paper proposes a method for establishing a multi-model reasoning tobacco baking quality prediction model. Conduct data mining and analysis on the data of various indicators of the original tobacco, and screen out the data that have an impact on the quality of tobacco baking. In order to reduce the complexity of the model and eliminate the influence between different dimensions, the data are carried out and standardized processing. Next, the normalized data is explored for the multi-input and multi-output mapping relationship. Finally, a mapping matrix is given for the multi-input and multi-output mapping relationship so as to establish a tobacco baking quality prediction model. The test results show that the predicted value of this model is basically the actual value, and the prediction accuracy rate is more than 90%. It has a high prediction accuracy rate. The cured tobacco leaves are basically the same as the actual cured yellow expected value. This model provides a practical guide method for tobacco baking, which has certain practical value in actual tobacco baking.
Keywords
Tobacco baking, Multi-modal reasoning, Data mining, Standardization, Relationship matrix
This preprint is available for download as a PDF.
No competing interests reported.
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Electrical Engineering
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
QingE Wu
Zhengzhou University of Light Industry
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 02 Nov, 2021
No community comments so far
Reviews received
Received 02 Dec, 2021
Reviewers agreed
On 18 Nov, 2021
Reviewers agreed
On 18 Nov, 2021
Reviewers invited
Invitations sent on 18 Nov, 2021
Editor assigned
On 18 Nov, 2021
Editor invited
On 01 Nov, 2021
Submission checks complete
On 01 Nov, 2021
First submitted
On 23 Oct, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Electrical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In order to reduce the number of defective products caused by the unreasonable baking time during the tobacco production process, this paper proposes a method for establishing a multi-model reasoning tobacco baking quality prediction model. Conduct data mining and analysis on the data of various indicators of the original tobacco, and screen out the data that have an impact on the quality of tobacco baking. In order to reduce the complexity of the model and eliminate the influence between different dimensions, the data are carried out and standardized processing. Next, the normalized data is explored for the multi-input and multi-output mapping relationship. Finally, a mapping matrix is given for the multi-input and multi-output mapping relationship so as to establish a tobacco baking quality prediction model. The test results show that the predicted value of this model is basically the actual value, and the prediction accuracy rate is more than 90%. It has a high prediction accuracy rate. The cured tobacco leaves are basically the same as the actual cured yellow expected value. This model provides a practical guide method for tobacco baking, which has certain practical value in actual tobacco baking.
This preprint is available for download as a PDF.
No competing interests reported.
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