Intelligent Grading and Quantitative Non-destructive Testing Method for Deer Antler Slices
By combining image recognition and infrared spectroscopy with non-destructive testing technology and utilizing machine learning models to construct multi-dimensional feature relationships, the subjective and destructive problems of traditional deer antler grading methods have been solved, enabling rapid and accurate grading of deer antler slicing.
CN121280681BActive Publication Date: 2026-05-26DALIAN INSTITUTE OF CHEMICAL PHYSICS CHINESE ACADEMY OF SCIENCES +1
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Patent Information
- Application Number
- CN202510488860.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-18
- Publication Date
- 2026-05-26
- Estimated Expiration
- 2045-04-18
AI Technical Summary
Technical Problem
Traditional methods for grading deer antler slices are highly subjective, prone to misjudgment, and can damage the appearance, making it impossible to achieve accurate and efficient objective grading.
Method used
By employing non-destructive testing technology that combines image recognition and infrared spectroscopy, and training an artificial intelligence model through machine learning, a multi-dimensional feature relationship model of "property-component-spectrum" is constructed to achieve rapid and high-throughput quality grading of deer antler slices.
Benefits of technology
It enables rapid, accurate, and non-destructive grading of deer antler slices, overcomes the subjectivity of traditional methods, and provides a scientifically based quality evaluation.
✦ Generated by Eureka AI based on patent content.
Abstract
This invention relates to a method for intelligent grading and quantitative non-destructive testing of deer antler slices. It utilizes machine learning and other methods to train an artificial intelligence recognition model, enabling rapid and high-throughput quality grading of deer antler. Simultaneously, it extracts the spectral characteristics of deer antler slices at each grade using bioinformatics and chemometrics analysis, establishing a correlation between the characteristics, physicochemical properties, and spectral features of deer antler slices. This provides a scientific basis for quantitatively predicting the quality of deer antler slices using infrared spectroscopy, thereby achieving non-destructive intelligent grading and quantitative classification of deer antler slices. This invention overcomes the problem of traditional deer antler slice quality grading relying on subjective experience through multi-data fusion, enabling rapid and accurate evaluation of deer antler slice quality.
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