Proactive warning is an important capability for edge intelligent services, where the system predicts whether a subject will successfully complete an incoming task under strict latency and privacy constraints. Such prediction depends on both long-term static attributes and short-term dynamic states derived from historical interaction logs. Recent Large Language Models (LLMs) offer strong long-context reasoning for constructing structured profiles from these logs, but existing solutions face two challenges for edge deployment: (1) profiling methods are typically domain-specific and lack a reusable abstraction across service scenarios, and (2) fine-tuning alignment models on heterogeneous edge clusters incurs high synchronization overhead due to the variance in input sequence lengths. To address these challenges, we propose CogGuard, a proactive-warning framework for edge intelligent services. CogGuard decouples offline LLM-based profile construction from online Small Language Model (SLM)-based score prediction through a shared static-dynamic profile-to-score pipeline, and instantiates it in two representative scenarios: educational performance warning and operational task outcome warning. For efficient profile construction, we design scenario-specific profiling methods with prefix-aligned KV-cache reuse to reduce repeated encoding overhead. For edge-side model alignment, we propose a length-aware distributed fine-tuning strategy with contrastive regularization to mitigate workload imbalance on heterogeneous clusters. Experiments on education and operation datasets show that CogGuard reduces profile construction time by up to 48% and distributed fine-tuning time by 19%, while achieving MAEs of 13.4 and 5.9, respectively, on 100-point-scale warning tasks. In the largest educational setting, CogGuard reduces prediction error by 15.4% compared with the strongest baseline.

翻译：主动预警是边缘智能服务的重要能力，系统需在严格的延迟和隐私约束下预测主体能否成功完成即将到来的任务。这类预测既依赖长期静态属性，也依赖从历史交互日志中提取的短期动态状态。近年来，大型语言模型（LLMs）为此类日志的结构化画像构建提供了强大的长上下文推理能力，但现有解决方案在边缘部署时面临两大挑战：（1）画像方法通常具有领域特异性，缺乏跨服务场景的可复用抽象；（2）在异构边缘集群上微调对齐模型时，输入序列长度的差异会导致高同步开销。针对这些问题，本文提出CogGuard——一个面向边缘智能服务的主动预警框架。该框架通过共享的静态-动态画像-分数流水线，将基于LLM的离线画像构建与基于小型语言模型（SLM）的在线分数预测相解耦，并在两个代表性场景中实例化：教育表现预警与操作任务结果预警。为高效构建画像，我们设计了场景特定的画像方法，并采用前缀对齐的KV缓存复用技术减少重复编码开销。针对边缘端模型对齐，我们提出一种长度感知的分布式微调策略，结合对比正则化以缓解异构集群上的负载不均衡问题。在教育和操作数据集上的实验表明，CogGuard将画像构建时间降低最多48%，分布式微调时间降低19%，并在百分制预警任务上分别实现了13.4和5.9的平均绝对误差（MAE）。在最大规模的教育场景中，与最强基线相比，CogGuard将预测误差降低了15.4%。