用可配置角色生成农业洪水报告，确保系统可回放可审计。
Persona-as-Configuration: Generative Stakeholder Reporting for Agricultural Floods
- 通过角色即配置方式，让大模型按不同用户需求生成报告。
- 生成层只读不写，保证边缘系统的确定性与可追溯性。
- 适合需要合规性与多角色报告的智能农业系统开发人员。
基于确定性边缘推理的网络物理系统（如车载农田洪水检测）会产生结构化决策日志，需针对不同利益相关方差异化解读。将此类系统与大语言模型（LLM）结合生成定制化报告时，存在生成层非确定性与边缘系统需可回放、可审计之间的矛盾。本文提出一种架构模式，包含两个不变性：单向消费（生成层仅为只读消费者，不反写）和角色即配置（用户适配以版本化提示模板实现，而非运行时动态调整）。该模式在已有边缘积水检测系统的基础上，构建上下文感知的仪表板层，利用其决策日志（JSON格式），并验证集成边界可作为配置或中间件级扩展点，支持标准生成可靠性缓解措施。结构化专家评审对模式在五个符合ISO/IEC 25010质量维度上的表现给予积极评价，尤其在关注分离方面达成最强共识。未来工作计划开展面向农业利益相关者的终端用户评估。
原文摘要 · Abstract (English)
Cyber-physical systems built on deterministic edge inference, such as on-vehicle flood detection for agricultural fields, produce structured decision logs that must be interpreted differently by heterogeneous stakeholders. Pairing such systems with large language models (LLMs) to generate stakeholder-specific reports introduces a tension: the generative layer is non-deterministic, while the edge plane must remain replayable and auditable. We propose an architectural pattern resting on two invariants: unidirectional consumption, in which the generative layer is a strict read-only consumer of the deterministic plane and never writes back, and persona-as-configuration, in which stakeholder adaptation is a versioned prompt-template artifact rather than runtime improvisation. We instantiate the pattern as a context-aware dashboard layer over the JSON decision logs of a previously published edge-based standing-water detection system, and analyse how the integration boundary admits standard generative-reliability mitigations as configuration- or middleware-level extension points. A structured expert review rated the pattern favourably across five ISO/IEC 25010-aligned quality dimensions, with strongest agreement on separation of concerns. End-user evaluation with agricultural stakeholders is planned for future work.
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