Improvement of the Public Health Service Platform System Based on the Big Data-Driven System

Area of Science:

• Medical Informatics
• Control Systems Engineering
• Public Health Technology

Background:

• Complex discrete dynamic systems are integral to modern medicine, with a trend towards intelligent control.
• Traditional discrete modeling is insufficient for current societal and technological demands.
• Improving medical platform systems requires advanced modeling strategies.

Purpose of the Study:

• To enhance public health service platform systems using complex discrete dynamic systems.
• To address limitations in traditional discrete modeling for public platforms.
• To improve data handling and system stability in healthcare networks.

Main Methods:

• Implementation of a time-driven control strategy for output feedback with random sampling.
• Analysis of system stability concerning random parameters.
• Integration of dynamic scheduling strategies for enhanced performance.
• Addition of controllable and observable performance metrics.

Main Results:

• The optimized system significantly improves data transmission across input and output channels.
• A notable reduction in data transmission problems was observed compared to traditional systems.
• Enhanced system stability was achieved through the incorporation of controllable and observable features.

Conclusions:

• The improved public health service platform system effectively manages large-scale user data in network environments.
• The system demonstrates automatic stability maintenance, crucial for reliable healthcare data management.
• The research offers significant social value through enhanced public health service delivery.