血府逐瘀浸膏质量智能评价体系构建:基于物理指纹图谱、机器视觉与大语言模型的联合应用OA

In order to solve the limitation of relying on a single index or experience to judge the con-centration end point in the production of traditional Chinese medicine extract,this study took Xuefu Zhuyu extract as the object,and innovatively proposes an intelligent quality control system of"physi-cal fingerprint+machine vision+large language model"multi-modal fusion strategy.Seven core physical indexes such as density and pH value were screened and standardized to construct physical fingerprint.Then,the abnormal samples were eliminated by Mahalanobis distance,and the compre-hensive score was calculated by"CRITIC+entropy weight+TOPSIS"method.With 0.6 as the quali-fied threshold,100 batches of samples were divided into 62 batches of qualified samples and 38 batches of unqualified samples.At the same time,the extract image features were collected and ex-tracted,and six machine learning models were constructed.Among them,the XGBoost model had the best performance,with accuracy,precision,recall,F1 score,and AUC reaching 0.933 3,1.000 0,0.833 3,0.909 1,and 0.963 0,respectively.Based on this,an intelligent assessment platform integrating large language models can be further developed,which can complete the analy-sis and generate actionable process suggestions within about ten seconds.This system provides an in-tegrated quality control scheme of"objective grading+rapid prediction+intelligent"suggestion for Xuefu Zhuyu extract,and also provides a reusable technical paradigm for multi-dimensional quality evaluation of Chinese medicine extract intermediates,which effectively promotes the transformation of Chinese medicine production to"data-driven".