ISW-Agent：LLM 对话结合 n8n 工作流处理内孤立波遥感图像
ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing
把 GPT-5.6 Sol 或 DeepSeek 接到 n8n 上做遥感科学计算，中文指令匹配率 94.2%，故障自恢复设计也写得挺细，做智能体工作流的可以看看。
研究团队发布 ISW-Agent，将 LLM 解读、n8n 工作流与独立科学模型结合，通过结构化任务约束、地理空间转换和分阶段输出验证来处理内孤立波遥感任务。在每种后端各 120 条中文指令的测试中，GPT-5.6 Sol 和 DeepSeek 均达到 94.2% 的严格任务匹配率。Sentinel-1 案例实现了从对话请求到波前预测的连续处理，50 个计时场景中 39 个完成检测、提取和预测，中位耗时 48.04 秒。在 3,408 次受控故障注入运行中，重试机制恢复了 234 次瞬态故障，输出验证拦截了 234 个结构缺陷。
ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing
Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and propagation prediction. Here, we present ISW-Agent, which integrates large language model (LLM) interpretation with n8n workflows and independent scientific models through structured task constraints, geospatial transformations, and stage-specific output validation. In tests of 120 Chinese instructions per backend, GPT-5.6 Sol and DeepSeek both achieved a strict task-match rate of 94.2%, with all intents and execution decisions matching the reference. Sentinel-1 cases demonstrated continuous processing from dialogue requests to wavefront prediction for the specified scene. Of 50 timed scenes, 39 completed detection, extraction, and prediction with a median turnaround time of 48.04 s; 11 stopped after empty extraction. Across 3,408 controlled fault-injection runs, retries recovered 234 transient-failure conditions and output validation rejected 234 structural defects, while 61 provenance or completeness errors indicated the need for content-consistency checks. Overall, ISW-Agent thus connects natural-language requests to traceable spatial processing chains for multimodel ISW analysis.