How userstr193/health_monitoring_for_nasa_competition works
This is a simulated astronaut health-monitoring and explainable expert-system application. The principal workflow is a client submitting crew, environmental-system, and log data to the API; the application persists readings and logs, derives alerts, and exposes status and history. Expert-analysis endpoints use the rule engine to produce explainable findings and retain decision records. The excerpts show API validation, SQLite operations, alert logic, and expert-engine functions; several endpoint details and the knowledge-base loading path are unsampled, so those links are left unverified.
Main entry points: main.py API and monitoring · expert_system.py Rule evaluation · knowledge_base.json Rule catalog
Components 5
Monitoring API
- API and monitoring
main.py
Expert Analysis
- Rule evaluation
expert_system.py - Rule catalog
knowledge_base.json
Persistent Data
- SQLite records
main.py
Other components
- Monitoring client
Connections 6
- Monitoring client → API and monitoring: submits data
main.py - API and monitoring → SQLite records: reads and writes
main.py - API and monitoring → Rule evaluation: requests analysis
main.py - Rule evaluation → Rule catalog: loads rules
- Rule evaluation → API and monitoring: returns findings
main.py - Monitoring client → API and monitoring: queries status
main.py