Printer Paper Positioning via ADC Waveform Analysis
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Solution Overview
Problem
Existing printer technologies have limited compatibility with various types of printer paper due to the use of a single reflective sensor and a narrow label learning method, leading to inefficient use of RAM space and increased complexity in printer configuration.
Innovation Solution
A learning-based printer paper positioning method that utilizes multiple ADC value crests and troughs to calculate left edge crest-trough difference values, determining measurement positioning points, and establishing an ADC reference value to accurately position printer paper units and connection areas during the printing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single reflective sensor and single slope label learning method are used, then the device complexity is reduced, but the adaptability with diverse printer paper types deteriorates
Solution Approach 1:
The patent changes the parameter of using multiple ADC value crests and troughs instead of single slope detection. By analyzing multiple crests and troughs in the ADC waveform, the system can identify paper characteristics more accurately, enabling compatibility with diverse paper types while maintaining a single sensor configuration.
Solution Approach 2:
The patent implements a preliminary learning process where the system scans the paper and identifies multiple ADC crests and troughs before actual printing. This preliminary action of characterizing the paper through multiple waveform features enables the system to adapt to different paper types in advance, improving versatility without increasing device complexity.
2Manufacturing precision
If label learning stores and judges large amounts of ADC value data, then the manufacturing precision of positioning is improved, but the quantity of substance (RAM space) increases
Solution Approach 1:
The patent extracts only the essential features from the ADC waveform data - specifically the multiple crests and troughs - rather than storing and processing all raw ADC values. By focusing on these key waveform characteristics, the system achieves accurate positioning while significantly reducing the amount of data that needs to be stored in RAM.
Solution Approach 2:
The patent creates a simplified representation of the paper characteristics by identifying and storing only the critical ADC crest and trough points. This copied essence of the waveform data captures the necessary positioning information without requiring storage of the complete ADC value dataset, optimizing RAM usage while maintaining positioning precision.
Data Source
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AI summary
The present application provides a learning-based printer paper positioning method, a printing method for printer paper, and a printer. The positioning method comprises the following steps: triggering a learning instruction, and searching for a plurality of adc value crests and a plurality of adc value troughs during the printing of printer paper; calculating a plurality of left edge crest-trough difference values of a plurality of measurement printing point positions corresponding to the plurality of adc value crests; determining a measurement positioning point according to the plurality of left edge crest-trough difference values, wherein the measurement positioning point corresponds to an adc value crest; and determining an adc reference value according to an adc value corresponding to the measurement positioning point, and then completing learning, so that printing units and connection areas in the printer paper are positioned during the printing of the printer paper according to the adc reference value. According to the learning-based printer paper positioning method and the printing method for printer paper of the present application, comprehensive learning can be performed on the positioning of printer paper, so that the printer can be compatible with more types of paper, the printing positioning accuracy is improved, and a storage space in the printer is optimized.