Wave peak detection method and sorting machine
By converting multiple sequence data into line graphs for parallel processing, utilizing a unified coordinate axis and initialization information, and combining contour extraction and adaptive algorithms, the problem of low accuracy and efficiency in multi-sequence peak detection is solved, achieving efficient and accurate peak detection.
CN122265692APending Publication Date: 2026-06-23BEIJING HONEST TECHNOLOGY CO LTD
Patent Information
- Authority / Receiving Office
- CN ยท China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING HONEST TECHNOLOGY CO LTD
- Filing Date
- 2025-08-15
- Publication Date
- 2026-06-23
AI Technical Summary
Technical Problem
Existing technologies are prone to mutual interference in the detection of peaks in multiple different data sequences, resulting in poor detection accuracy and low efficiency, and making parallel processing impossible.
Method used
By converting multiple sequence data into line charts, normalizing them using a unified coordinate axis and initialization information, and combining contour extraction and edge detection with adaptive algorithms and machine learning filtering, synchronous peak detection is achieved.
Benefits of technology
It improves the accuracy and efficiency of peak detection for multiple sequence data, avoids mutual data interference and noise interference, and achieves precise peak localization and parallel processing.
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Smart Images
Figure CN122265692A_ABST
Abstract
This disclosure relates to the field of data processing technology, specifically to a peak detection method and a sorting machine. The peak detection method includes: acquiring multiple sequence data; determining a line graph of the multiple sequence data, wherein the line graph includes a line corresponding to each sequence data; synchronously determining the peak corresponding to each line based on the line graph; and outputting all peaks of the multiple sequence data. By converting the data of multiple sequences into an image and plotting the data of multiple sequences as line graphs on the same graph, parallel processing of multiple sequence data can be achieved, effectively improving the efficiency of peak detection. Simultaneously, it can effectively improve the accuracy of peak detection for multiple sequence data, avoid mutual interference between data sequences and the influence of external noise, achieve precise peak localization, and has high detection accuracy and data processing stability.
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