The increasing use of smart devices has emphasized the critical role of maintenance in production activities. Interactive Electronic Technical Manuals (IETMs) are vital tools that support the maintenance of smart equipment. However, traditional IETMs face challenges such as transitioning from Graphical User Interfaces (GUIs) to natural Language User Interfaces (LUIs) and managing complex logical relationships. Additionally, they must meet the current demands for higher intelligence. This paper proposes a Maintenance Scheme Generation Method based on Large Language Models (LLM-R). The proposed method includes several key innovations: We propose the Low Rank Adaptation-Knowledge Retention (LORA-KR) loss technology to proportionally adjust mixed maintenance data for fine-tuning the LLM. This method prevents knowledge conflicts caused by mixed data, improving the model's adaptability and reasoning ability in specific maintenance domains, Besides, Hierarchical Task-Based Agent and Instruction-level Retrieval-Augmented Generation (RAG) technologies are adopted to optimize the generation steps and mitigate the phenomenon of hallucination caused by the model's Inability to access contextual information. This enhancement improves the model's flexibility and accuracy in handling known or unknown maintenance objects and maintenance scheme scenarios. To validate the proposed method's effectiveness in maintenance tasks, a maintenance scheme dataset was constructed using objects from different fields. The experimental results show that the accuracy of the maintenance schemes generated by the proposed method reached 91.59%, indicating which improvement enhances the intelligence of maintenance schemes and introduces novel technical approaches for equipment maintenance.
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Southeast University（东南大学）；Hello World(Shanghai) Technology Co., Ltd.（Hello World（上海）科技有限公司）
Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery
Pritzker School of Molecular Engineering, University of Chicago（芝加哥大学普利兹克分子工程学院）；Chemical Sciences and Engineering Division, Argonne National Laboratory（阿贡国家实验室化学科学与工程 division）；Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）
Smart Commander: A Hierarchical Reinforcement Learning Framework for Fleet-Level PHM Decision Optimization
School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）；College of Information and Control Engineering, Xi'an University of Architecture and Technology（西安建筑科技大学信息与控制工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；First Aircraft Institute of Aviation Industry Corporation of China（中国航空工业集团公司第一飞机设计研究院）；Science and Technology on Complex Aviation System Simulation Laboratory（复杂航空系统仿真重点实验室）
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue
Hong Kong University of Science and Technology（香港科技大学）；Huazhong University of Science and Technology（华中科技大学）；University of California San Diego（加州大学圣地亚哥分校）；University of Science and Technology of China（中国科学技术大学）；Beihang University（北京航空航天大学）
From PDF to RAG-Ready: Evaluating Document Conversion Frameworks for Domain-Specific Question Answering
Faculty of Engineering, University of Porto（葡萄牙波尔图大学工程学院）；Department of Business Administration, University of Maia（马亚大学商业管理系）；LIACC—Artificial Intelligence and Computer Science Laboratory, University of Porto（葡萄牙波尔图大学人工智能与计算机科学实验室）；Department of Communication Sciences and Information Technologies, University of Maia（马亚大学通讯科学与信息科技系）；School of Health, Polytechnic of Porto（波尔图理工学院健康学院）；School of Technology and Management, Polytechnic Institute of Maia（马亚理工学院技术与管理学院）
