Massive data processing technology
Streaming data real-time computing technology
AI video monitoring technology
核心技术
With a distributed computing framework, data flow processing technology, and a big data storage system, the platform processes massive data with high efficiency to ensure high data quality and timeliness.
A streaming data processing engine, a real-time computing model, and distributed data storage enable the platform to do real-time analysis and processing of data flow to ensure timely warning and response.
Computer vision, deep learning model, and recurrent neural network, plus a real-time data processing system, enable the platform to recognize, analyze, and give warnings, keeping a close watch on people of concern.
Health Code Visualization Subsystem
Centered on the health code data, the subsystem keep track of people’s entry and exit and their whereabouts.
Big data analysis plus visualization enables the subsystem to monitor people’s health conditions so as to inform government decisions regarding disease control.
The subsystem supports functions such as personal health code check, code scanning data analysis, and health code-based tracking.
Disease Control Supervision Visualization Subsystem
Real-time monitoring and control over key personnel, isolated people, lockdown residential complexes, and traffic check points.
A grid-based map and monitoring system ensure real-time monitoring and supervision on targets.
The subsystem supports functions such as key personnel management, lockdown residential complexes, and traffic check points.
Disease Control Warning Visualization Subsystem
Build a disease control data middle office based on data from multiple sources and use various modules to do comprehensive monitoring and give warnings.
The subsystem supports functions such as real-time data broadcast, comprehensive analysis and warning, city-wide data monitoring, close contact tracking and risk warning.
Public health and disease control platform for the integrated use of data in healthcare and disease control
Promote information sharing between disease control authorities, medical institutions and other relevant departments as a way to explore integrated use of public health big data.
Build a working mechanism centered on infectious disease monitoring and virus tracing, where warnings can be triggered by multiple factors and monitoring can be done via multiple channels.
Build a lab test information system that adopts open standards and features interconnectivity and sharing so as to share lab test results and information across disease control authorities at the provincial, municipal and district levels.
Comprehensively promote innovations in public health emergency response management and service models and provide technological support for fast and accurate response to public health emergencies, targeted implementation of disease control measures, and integration and collaboration between healthcare and public health forces.
Disease control in a city—public health and disease control platform
The platform plays important roles in the city’s disease control efforts in terms of information transmission, resource allocation, and management coordination, providing timely and accurate information for both the government and the residents, and contributing to the disease control work on the whole.
