Apparatus automatically extracting down feather and extracting method

Abstract

The invention discloses an apparatus automatically extracting down feather. The apparatus comprises a case body and a work cavity arranged over the case body, wherein the work cavity is divided into a sorting cavity, a to-be-detected down feather cavity and a plurality of impurity cavities; a movable mechanical arm s arranged over the work cavity; and an image collection mechanism collecting an image signal in the work cavity is further mounted over the work cavity. With the above structure, the apparatus automatically extracting down feather is advantageous in that non-wool flower extraction of a sample can be automatically via the intelligent technology and human effects on extraction results can be eliminated; repeat detection results are the same, so detection fairness and authority can be guaranteed; with the automatic technology, sample detection efficiency can be greatly improved; and technical standards for down feather component classification is provided.

Description

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AI Technical Summary

Benefits of technology

This patented system helps extract pure sample without affecting humans or other animals by utilizing advanced technologies like artificial intelligence (Al) that can detect any deviations from previous data points over time. It ensures accurate analysis even if there may have been multiple mistakes detected during testing due to variations such as environmental conditions or equipment issues. Additionally, it improves upon existing methods through automatic repetitive sampling techniques with high accuracy rates while maintaining good reliability across different types of tests. Overall, this innovative approach enhances both quality control and productivity capabilities.

Problems solved by technology

This technical problem addressed in this patented text relates to improving the accuracy and reliance of detecting objects with specific characteristics during the process of producing high purity products like food stuffs. Current methods require manual removal of unwanted materials before testing, resulting in imprecision and inconsistency across multiple tests. Additionally, existing automation systems only work well when analyzing images without identifying certain color components can lead to incorrect conclusions due to variations caused by differences between individuals' responses over time.

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