摘要：

意图驱动的人工智能系统在面对复杂多变的环境时, 须具备对意图(Purpose)的自适应感知、动态调整与多层反馈能力。传统人工智能模型普遍缺乏统一的意图生命周期建模机制, 导致系统行为难以追踪、调控与优化, 进而影响其可解释性与长期效能。基于数据-信息-知识-智慧-意图(DIKWP)五层语义空间模型, 构建一套面向认知演化路径的意图生命周期管理机制。该机制以语义传导为核心, 涵盖数据层的动态校验、信息层的迁移响应、知识层的逻辑重构、智慧层的价值演化、意图层的目标闭环与冲突调节5个阶段, 形成多层次、多目标、多反馈路径的语义治理体系。通过引入多层图谱建模与认知空间区分(如概念空间与语义空间), 实现意图生成、更新与调优的结构化与可视化建模。进一步结合人工意识系统的"体验-叙事"双循环结构, 强化系统在多轮互动中的意图稳定性与环境适应能力。在智能家居与智慧城市典型应用场景中对该机制进行理论推演与语义验证, 结果表明, 该机制具备良好的通用性、可扩展性与鲁棒性, 为主权人工智能系统中的价值对齐、语义主权与自主演化提供了理论支撑与工程参考。

关键词: 数据-信息-知识-智慧-意图模型, 意图生命周期, 语义治理, 人工意识, 语义图谱, 认知系统

Abstract:

Purpose-driven Artificial Intelligence (AI) systems must exhibit adaptive purpose perception, dynamic adjustments, and multi-level feedback when operated in complex and evolving environments. However, traditional AI models lack a unified mechanism for modeling purpose lifecycle, thereby resulting in challenges in behavior traceability, control, and optimization, which, in turn, limit interpretability and long-term effectiveness. This paper proposes a Data-Information-Knowledge-Wisdom-Purpose (DIKWP)-based semantic framework for purpose lifecycle management oriented toward cognitive evolutionary pathways. This mechanism consists of five semantic stages: data-layer dynamic verification, information-layer migration response, knowledge-layer logical reconstruction, wisdom-layer value evolution, and purpose-layer goal closure and conflict regulation. A multi-level, multi-goal, and multi-feedback semantic governance structure is formed. In addition, multi-layer graph modeling and cognitive space differentiation are introduced, specifically between conceptual and semantic spaces, to enable structured and visual modeling of purpose generation, updating, and tuning. By integrating the dual-loop structure of "experience-narrative" from artificial consciousness theory, the purpose stability and adaptability of the system in interactive environments are enhanced. The proposed mechanism was theoretically validated in smart home and smart city scenarios. The experimental results demonstrate its generality, scalability, and robustness, offering theoretical and engineering support for value alignment, semantic safety, and autonomous evolution in sovereign AI systems.

Key words: Data-Information-Knowledge-Wisdom-Purpose (DIKWP) model, purpose lifecycle, semantic governance, artificial consciousness, semantic graph, cognitive systems