Method and system for intelligently predicting residual amount of printer consumables based on deep learning

Through a deep learning-based method, dynamic convolution and medium-sensing fusion mechanism are used to extract long and short-term features, solving the problem of insufficient prediction accuracy of consumable margin in multi-material mixed printing scenarios by traditional prediction methods, and achieving high-precision and robust consumable prediction.

CN119806449AActive Publication Date: 2025-04-11HUNAN ZHIJIAN COPIER REMANUFACTURING CO
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Patent Information

Application Number
CN202510300512.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-14
Publication Date
2025-04-11
Estimated Expiration
2045-03-14

AI Technical Summary

Technical Problem

In the multi-material hybrid printing scenario, the consumable margin prediction accuracy of traditional printer consumable prediction methods is insufficient, and it is impossible to effectively deal with the dynamic changes of different printing media.

Method used

Using a deep learning-based method, multi-modal input vectors are constructed by collecting and preprocessing printing consumable data, and dynamic convolution and medium-sensing fusion mechanisms are used to extract long and short-term features to achieve accurate prediction of inkjet consumption.

Benefits of technology

It significantly improves the accuracy of consumable margin prediction in multi-material mixed printing scenarios, enhances the generalization ability and robustness of the model, and is suitable for high-frequency, multi-media mixed industrial-grade printing equipment consumable management scenarios.

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Abstract

The invention discloses a deep learning-based printer consumable allowance intelligent prediction method and system, and the method comprises the steps: S1, collecting printing consumable data, and obtaining to-be-processed printing consumable data; s2, integrity verification and abnormal value detection are carried out on the to-be-processed printing consumable data, and the cleaned printing consumable data are output; s3, extracting short-time features, long-time features and long-time and short-time fusion features based on the cleaned printing consumable data; s4, extracting steady state features and transient state features according to the long and short time fusion features, and then calculating ink jet consumption features and an ink jet consumption prediction value; and S5, according to the ink jet consumption prediction value, calculating a residual ink amount and an ink amount consumption rate prediction value, then constructing an ink amount early warning classifier, and outputting an ink amount warning level according to probability distribution. The problem that the consumable allowance prediction precision of a traditional printer consumable prediction method in a multi-material mixed printing scene is insufficient can be solved.
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