Specification core: first, data collection, real-time collect 56 key data such as operating current, voltage, temperature, vibration, noise, switch times, fault code, sampling frequency ≥10Hz, edge computing local preprocessing, reducing transmission load; second, AI algorithm, adopting deep learning, neural network, fault map, establishing component health model, life prediction model, fault diagnosis model, identifying early hidden faults, accuracy ≥95%; third, early warning classification, divided into blue (concern), yellow (early warning), orange (rectification), red (shutdown), orange maintenance within 7 days, red immediately stop use, automatically generate maintenance work order, spare parts list; fourth, platform connection, interconnection with elevator IoT, maintenance management, emergency rescue platform, data encrypted upload, cloud analysis, mobile push; fifth, effect requirement, fault early warning ≥30 days in advance, unplanned downtime reduced by 70%, maintenance cost reduced by 30%, equipment life extended by 20%. 500,000 elevators nationwide completed AI maintenance renovation in 2026.