Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts· 融合自优化提示的高原旅游天气预警智能代理
To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integrating intent recognition, hazard prediction, and reasoning-enhanced generation. The system fuses rule-based methods with large language models to parse queries at multiple granularities and employs a LightGBM model enriched with highland-specific features (e.g., wind speed abruptness rate), achieving an F1-Macro score of 0.605 with 1.60 ms latency on high-wind, precipitation, and low-temperature events. A 12-round micro-step prompt self-optimization loop boosts the composite warning quality score S_final from 4.2 (B01) to 8.9 (B12, +112%). Key improvements include a sharp rise in B08 from data
融合规则与大模型的智能代理，提升高原旅游天气预警效果。
- 核心方法
- 提出SmartWeatherAgent，整合意图识别、灾害预测和增强推理生成，使用规则方法与大语言模型解析多粒度查询，并通过特定于高原的LightGBM模型提高预警准确性和速度。
- 适合谁读
- 研究者、工程师、旅游气象服务提供者
- 要解决的问题
- 解决现有旅游气象服务中情境不足、泛化能力弱和场景适应性差的问题。
- 关键实验
- 实验在高风速、降水和低温事件上达到0.605的F1-Macro得分，12轮微步骤提示自优化将综合预警质量评分从4.2提升到8.9。
- 主要贡献
- 实现了高原气象事件的高性能预警，通过自优化提示机制显著提升预警质量。
- 意义与局限
- 该系统能够更好地支持高原地区旅游气象服务，但可能受限于特定区域的特征和模型的泛化能力。