刘江川
Founder

Jiangxingai provides an edge‑cloud AI platform for the energy sector that connects IoT sensors, cameras, drones and industrial equipment to deliver real‑time monitoring, automated inspections, and predictive analytics for substations, transmission lines, distribution rooms, charging stations and power plants. Its solution combines container‑based edge inference with cloud‑orchestrated model training and a virtual power‑plant management module, enabling utilities and large energy users to improve grid reliability, reduce manual patrols, and optimize carbon‑neutral operations.
Founder
Power grid operators and energy producers face challenges in monitoring vast, distributed assets such as substations, transmission lines, distribution rooms, and generation facilities. Limited visibility, manual inspections, and fragmented data hinder real-time fault detection, efficient maintenance, and integration of renewable resources, impacting reliability and carbon‑reduction goals.
Jiangxingai delivers an edge‑cloud AI platform that unifies IoT device connectivity, containerized services, and advanced analytics for the energy sector. The solution ingests data from sensors, cameras, drones and specialized equipment across smart substations, transmission corridors, distribution rooms, charging stations, and power plants. Edge nodes perform low‑latency inference using pre‑trained industry algorithms, while the cloud orchestrates model training, multi‑layer reasoning and centralized dashboards. Operators receive automated inspections, anomaly alerts, and predictive insights that support autonomous patrols, safety monitoring, and optimized dispatch of virtual power‑plant resources, thereby enhancing grid reliability and supporting carbon‑neutral objectives.
Primary customers are electric utilities, independent power producers, and large industrial energy consumers that require automated asset monitoring, virtual power‑plant coordination, and AI‑enhanced operational efficiency.