Congrong Great Model Through our human-machine cooperative operating system, Cloudwalk Operating System (CWOS), Cloudwalk Technology has created a multi-modal series of large-scale models that aim to create an agent that thinks and acts like a human, integrating core technologies in multiple fields such as vision, voice, and NLP based on data elements, that is Congrong Great Model. Congrong Great Model includes but is not limited to language models, visual models, voice models, code generation models, image generation models, etc. It can address the pain points of many AI applications through real-time learning and synchronized feedback, which promotes the rapid adoption of personalized applications. At the same time, it has contextual learning ability and can be applied to finance, security, government affairs, transportation, energy, education, medical care, entertainment and other industries with better interactive performance.

Training and Reasoning All-In-One Machine Based on Congrong Great Model Combined with the traditional advantages of cloud technology, the All-In-One Machine offers three types of basic model reasoning and training capabilities: language, vision, and multi-modality. At present, Congrong Great Model provides travel advice for citizens in smart government affairs, helps improve the quality of digital human life broadcasts in the media industry, reduces the workload of teachers in the education industry, and standardizes DevOps flow in the manufacturing industry.

Self-Service Boarding Booth One of the supporting products for smart airport construction is self-service boarding booth. This product integrates face recognition technology, identity recognition technology, sensor technology, and 5G communication technology, which can quickly and accurately identify airport passengers’ identities and provide self-service boarding. Compared with other self-boarding equipment, its cost performance and traffic efficiency are higher.

Urban Traffic Great Model Based on the Congrong Great Model platform continuously trains and self-learns knowledge about the urban transportation industry, forming industry thinking. Improving traffic control efficiency and solving business problems is more intelligent. It can perform comprehensive and in-depth analysis and predictions of the urban transportation system, as well as monitor the utilization of various transportation facilities such as roads, public transportation, and lanes in real-time. It can efficiently analyze traffic flow, congestion, and travel demand, and provide accurate decision-making for urban planners, policy makers, and citizens.