Intelligent tea making method based on openmv machine vision

By combining OpenMV machine vision and multiple linear regression models with PID control, the accurate estimation and dynamic adjustment of tea infusion concentration were achieved, solving the problems of unstable quality and sensory judgment in automatic tea brewing machines, and improving the intelligence level of intelligent tea brewing machines.

CN122368985APending Publication Date: 2026-07-10YANSHAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
YANSHAN UNIV
Filing Date
2026-04-10
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing automatic tea brewing machines lack a real-time feedback mechanism, resulting in unstable tea quality. Relying on human sensory judgment makes it difficult to quantify, and image processing has poor adaptability in complex environments, making it impossible to analyze the color and concentration of tea in detail.

Method used

The OpenMV machine vision system acquires tea infusion images in real time. Through adaptive threshold segmentation and multidimensional color feature extraction, combined with a multivariate linear regression model and PID control algorithm, the heating power and water outlet valve opening are dynamically adjusted to achieve precise control of the tea infusion concentration.

Benefits of technology

It achieves precise estimation and dynamic adjustment of tea infusion concentration, improves the intelligence level of tea brewing machines, adapts to different tea varieties and environmental changes, and solves the problem of unstable quality.

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Abstract

This invention discloses an intelligent tea brewing method based on OpenMV machine vision, belonging to the field of intelligent tea brewing technology. It includes acquiring real-time image data of tea infusion in a brewing container using an OpenMV camera at a preset frequency. The image data includes RGB color channel information. This invention acquires tea infusion images in real time using OpenMV, utilizes adaptive threshold segmentation and multi-dimensional color feature extraction, and combines a multiple linear regression model to transform visual information into accurate concentration estimates, replacing traditional human sensory judgment and solving the quality instability problem caused by open-loop control. It introduces a PID control algorithm combined with a water temperature compensation factor to dynamically adjust the heating power according to the concentration change trend. By dynamically time-warping and comparing the real-time curve with the standard leaching curve, it automatically adapts to different tea varieties and abnormal conditions, achieving a leap from fixed programs to data models, significantly improving the intelligence of home tea brewing machines.
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