Hyper Fleet, the fleet operation management platform, realizes real-time collection of vehicle and status data. Through continuous data analysis and in-depth excavation of data value, it continuously improves operational efficiency. Leveraging this foundation, AI enables intelligent decision-making and prediction, driving the intellectualization of management. Furthermore, relying on platform-based capabilities, operational data can be transformed into strategic assets of enterprises. The integrated platform overcomes data silos, achieving comprehensive operational optimization as well as cost reduction and efficiency improvement.

Hyper Flow is an efficient vehicle-cloud integrated data collection and intelligent processing system developed by Carlinx. Centered on the "Vehicle-Cloud Super Data Bus", it builds a digital-intelligent solution for the entire vehicle lifecycle through in-depth collaboration between the vehicle-end and cloud-end. The vehicle-end adopts a lightweight architecture, compatible with multiple systems and hardware, filtering out 85% of invalid data. The cloud-end natively supports time-series databases and big data lakes, reducing database consumption by 50%. Leveraging storage-computing integrated compression technology, it saves 90% of traffic bandwidth, while the MQTT over QUIC protocol reduces transmission latency by 70%.Widely applied in scenarios such as active diagnosis, intelligent coc

Hyper Agent is the R & D achievement of the joint laboratory established by Carlinx and Zhejiang University. It is based on data - driven and AI vertical - domain agent technology, innovating around the data of the vehicle's full life cycle. It can quickly transform massive multi - source data into knowledge graphs, use the reasoning and learning capabilities of basic models, and through model fine - tuning and business logic concatenation, provide data - driven decision - making support for all aspects of vehicle R & D, production, sales, use, maintenance and recycling. For example, in R & D, it analyzes historical data and user feedback to predict market trends and optimize designs; in after - sales, it predicts faults in advance by analyzing vehicle status. It has intelligent work colla