Similarity visual analysis method of momordica grosvenori raw material fingerprint spectrum

By using curvature energy constraint-based map alignment and 3D topographic map analysis, the quality control challenge in the analysis of large-scale data of monk fruit raw materials was solved, enabling rapid identification of abnormal batches and deviations in process parameters, and improving the intelligence and traceability of quality control.

CN122449047APending Publication Date: 2026-07-24GUILIN SANLENG BIOTECH CO LTD
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Patent Information

Application Number
CN202610426746.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-02
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies struggle to retain the rich information in fingerprint patterns during large-scale data analysis of monk fruit raw materials. Furthermore, traditional similarity evaluation and overlay methods cannot quickly identify abnormal batches or subtle evolutionary patterns between batches, leading to difficulties in quality control.

Method used

By introducing rigid weight constraints based on curvature energy for elastic alignment of the map, a ring-shaped elastic mesh model is constructed and a three-dimensional terrain map is generated. Combined with Laplace field analysis, the visualization of quality differences and the diagnosis of process faults are realized.

Benefits of technology

It significantly improves the intelligence and traceability of the quality control of monk fruit raw materials, and can quickly identify abnormal batches and deviations in specific process parameters, forming a closed-loop mechanism from data analysis to process decision-making.

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Abstract

The invention relates to the technical field of visual analysis, in particular to a siraitia grosvenorii raw material fingerprint similarity visual analysis method, which comprises the following steps of: carrying out smoothing treatment and second derivative operation on a standard fingerprint, constructing a rigid weight vector based on curvature energy, and introducing the rigid weight vector into a dynamic time warping algorithm to obtain a siraitia grosvenorii fingerprint similarity visual analysis result. And limited elastic alignment of the to-be-detected map and the standard map is realized. On the basis, map differences are mapped into centripetal loads and normal loads of an annular elastic grid through projection and residual decomposition, grid deformation is solved to generate a single-batch topological curved surface, and stacking interpolation is carried out on multi-batch results to construct a three-dimensional terrain map. Further performing Laplacian field analysis on the three-dimensional topographic map, extracting an abnormal extreme value, performing mode matching with a process fault knowledge base, and outputting a corresponding quality abnormality batch and a process diagnosis conclusion, thereby realizing visual expression of the similarity of the momordica grosvenori raw material fingerprint and intelligent analysis of process abnormality.
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