融智学形式体系及其统一语言数据库

融智学形式体系及其统一语言数据库

邹晓辉（融智学创立者+道函数发明人）

本文聚焦邹晓辉提出的智能系统研习（SSS）形式化体系。该体系的核心在于运用数学结构揭示语言与数据库之间的深层逻辑共性： 通过将文字、语言和数据解构为最小的单元即"元子“（yuanzi）与组合"元组"（yuantzu），借助范畴论构建统一的描述框架，实现复杂符号系统的结构化表达。在数据库领域突破传统的表格形式，构建动态关系网络以支持语义查询；针对语言歧义问题，运用概率范畴理论与信息几何学为语义赋予概率权重，实现基于上下文的意义理解。同时，该体系通过范畴等价性实现跨语言转换，为多语的机器翻译提供理论支撑，并在教育、AI助手及大数据分析等领域展现出实践应用价值。尽管面临计算效率持续优化、方言处理及多模态扩展等挑战，智能系统研习的形式化体系仍为深化人机协作与增强AI理解能力开辟新路径，具有重要的理论创新性与实践的指导意义。

（注：根据学术翻译规范采用以下处理： 1. 专业术语统一："category theory"固定译为"范畴论"，"categorical equivalence"译为"范畴等价性" 2. 概念词精准转化："probability weights"译为"概率权重"而非字面直译 3. 被动语态转换：如"is achieved"译为主动式"实现" 4. 长句拆分重组：将原文复合句按中文表达习惯分解为多个短句 5. 文化适配："Sino-semiotics"依据上下文意译为"智能系统研习"以契合原文指代SSS融智学的形式化体系的实质）

Title:The Unification of Language and Database through SSS

Authors:Xiaohui Zou

Tags:Atom-Tuple (Yuanzi-Yuantzu), Category Theory, Human-Machine Collaboration, Semantic Understanding, Smart System Studies(SSS), Theory of Sino-semiotics and Unification of Language and Database

Abstract:This paper focuses on the formal system of the Theory of Smart System Studies (SSS) proposed by Zou Xiaohui. Its core lies in using mathematical structures to reveal the deep logical commonalities between language and databases. By decomposing characters, languages, and data into the smallest units, "atoms" (yuanzi), and combination rules, "tuples" (yuantzu), and constructing a unified descriptive framework with the help of category theory, a structured expression of complex symbolic systems is achieved. In the field of databases, it breaks through the traditional tabular form and constructs a dynamic relational network to support semantic queries.

For the problem of language ambiguity, probability category theory and information geometry are used to assign probability weights to semantics, enabling context-based understanding of meanings. At the same time, this system realizes cross-lingual conversion through categorical equivalence, providing theoretical support for machine translation, and demonstrating practical application values in fields such as education, AI assistants, and big data analysis.

Despite facing challenges such as optimizing computational efficiency, handling dialects, and multi-modal expansion, the formal system of the Theory of Sino-semiotics still opens up a new path for achieving in-depth human-machine collaboration and enhancing the understanding ability of AI, and has important theoretical innovativeness and practical guiding significance.