This project aims to design an AI assistant that supports electricians during the maintenance of remote electrical substations. The system is built to streamline the entire repair workflow with the following core functionalities:\n\n🔧 Before Maintenance: Intelligent Preparation\nUpon receiving a fault alert, the AI provides technicians with a comprehensive pre-maintenance briefing, including:\n\nBasic information about the substation\n\nDevice specifications and status\n\nLocal weather conditions to help plan repair schedules\n\nRequired tools and recommended replacement parts\n\n🤖 During Maintenance: Interactive AI Support\nThe electrician can ask the AI questions such as:\n\n“Is A broken?” → “Yes, based on abnormal readings in relevant indicators...”\n\n“Should I check B?” → “Yes, according to historical cases and field conditions...”\n\n“Do I need to replace C?” → “C has exceeded its expected lifespan. Replacement is recommended.”\n\nThe AI responds based on real-time data, equipment age, fault patterns, and a connected knowledge base.\n\n📄 After Maintenance: One-Click Report Generation\nAfter the task is completed, the system generates a structured maintenance log, incorporating:\n\nKey repair steps\n\nParts used\n\nAI Q&A logs\n\nEnvironmental context (e.g., weather, conditions)\n\nKey Highlights\n\n💡 Utilizes a large language model (LLM) in combination with structured databases to answer technician queries in natural language\n\n🔌 Supports integration with various databases and is designed for high portability\n\n📋 Standardizes the maintenance process and reduces errors\n\n⏱️ Helps workers prepare efficiently before deployment, saving time and increasing on-site effectiveness
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