The system focuses on improving the safety and intelligent, unmanned operation of energy storage power stations. It addresses key challenges such as equipment safety risks, insufficient operational reliability, difficult maintenance, and complex decision-making . Here we introduce an inconsistency-driven O&M paradigm for large-scale BESSs that systematically transforms routine monitoring data into explainable, decision-oriented guid-ance. The proposed framework integrates multi-dimensional inconsistency evaluation with large language model–based semantic. Energy storage stations feature diverse equipment types, narrow complex paths, multiple monitoring blind spots, and strong electromagnetic interference environments, making traditional safety operation and maintenance methods inadequate for rapid detection and handling of safety hazards. Utilizing IoT, digitalization, and AI. The integration of artificial intelligence (AI) and machine learning (ML) technologies in energy storage systems has emerged as a transformative approach in addressing the complex challenges of modern energy infrastructure. By analyzing real-time data (like battery temperature and usage patterns) alongside electricity prices and grid demand, AI can schedule charging during low-cost. An Energy Storage Management System is an intelligent software platform that optimizes the charging/discharging cycles, safety protocols, and performance analytics of battery storage systems.