The Semantic-level Knowledge Graph – Daza Matrix is divided into two major functional zones: the Regular Data Zone and the Super Data Zone.
Regular Data Zone includes six functional sub-zones: Semantic-level Word Frequency, Species Name Recognition, Supplement Name Recognition, Disease Name Recognition, Keyword Context, and Relationship Graph Generation. For any keyword combination, the Regular Data Zone extracts and analyzes the top 2000 most relevant literatures retrieved from PubMed. The first four of these six functional sub-zones can be used independently, while the latter two are used in conjunction with the first four.
Super Data Zone includes seven functional sub-zones: Super Semantic Analysis, Super Species Recognition, Super Supplement Recognition, Super Disease Recognition, Super Keyword Context, Super Formula Design, and My Super Formulas (Custom Formula Pool). For any keyword combination, the Super Data Zone extracts and analyzes all literatures retrieved from PubMed in batches. The first four of these seven functional sub-zones can be used independently, while the latter three are used in conjunction with the first four.
Super Data breaks through the 10,000-record limit of the PubMed API through a batch retrieval and acquisition mechanism. Regardless of whether the number of eligible retrieved literatures is tens of thousands, hundreds of thousands, or even millions, all can be fully acquired. Combined with Super Data's batch retrieval and acquisition mechanism, Super Analysis can efficiently generate super semantic-level knowledge graphs (Super Semantic Analysis, Super Species Recognition, Super Supplement Recognition, Super Disease Recognition, Super Keyword Context) for hundreds of thousands of literatures through a batch processing mechanism.
When Daza Matrix completes all data batches, based on FoodWake's curated Global Species Library, Global Supplement Library, and Convolutional Neural Networks (CNN), combined with Correlation Coloring implemented by LLM/LSTM+SHA, it can find the most effective Super Species and Super Supplements for your current demands and diseases. Based on the Global Disease Name Library and Convolutional Neural Networks, you can view a complete graph of which diseases the current species or supplement is most effective at treating.
Using My Super Formulas (Custom Formula Pool) in Daza Matrix's Super Data, you can further select the ingredients you most want to use from Super Species Recognition and Super Supplement Recognition to build your own formula pool. By using Correlation Coloring to clearly view the correlation between species/supplements and diseases or demands, and consulting literature titles and abstracts as needed, you can easily select the most effective species and supplements and add them to your Custom Formula Pool. Then, let the Large Language Model generate top-tier formulas directly based on your formula pool. Compared with directly using Super Formula Design, My Super Formulas (Custom Formula Pool) allows you to more precisely construct formula ingredients and generate top-tier formulas with the ingredients you most want to use.
Four Large Language Models are currently supported: DeepSeek-R1, DeepSeek-V3, Doubao-1.5-pro, and DeepSeek-R1 (Volcano). When using My Super Formulas (Custom Formula Pool), you can not only specify which species/supplements are added to the formula pool but also fully control the final generated formula by setting Mandatory Ingredients, Number of Ingredients, Target Species, and Special Instructions.
