Commit d73bd72d authored by Kang's avatar Kang
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# HVAC sensor to Backend data migration development
# Room Sensor Data Backend Integration
## Overviews
Develop a system that collects and migrates HVAC sensor data into a backend for energy optimization and predictive maintenance.
## Overview
Develop a backend pipeline that integrates room sensor data stored in InfluxDB with Grafana for visualization. The system should query sensor data from InfluxDB, enable dashboards in Grafana, and provide insights into room usage. The project also includes assessing the pros and cons of this setup.
## Targets
- Collect real-time HVAC sensor data (temperature, humidity, CO₂, occupancy).
- Clean and standardize raw data.
- Design migration pipeline → backend database.
- Provide APIs for analytics and visualization.
- Scalable to multiple sensors/buildings.
- Connect InfluxDB (sensor data storage) with Grafana.
- Build backend queries and endpoints for sensor data retrieval.
- Configure Grafana dashboards to visualize room conditions and usage.
- Evaluate pros and cons of using InfluxDB + Grafana for real-time monitoring.
## Data Needs
- HVAC sensor data streams (LoRaWAN, MQTT, REST API).
- Sensor metadata (location, type).
- Backend schema for storage.
- Reference dataset: [Raumbelegung-Messung-bplaced](https://gitlab.rz.hft-stuttgart.de/11akmi1bif/raumbelegung-messung-bplaced/)
(`Python_ML_Algorithms/csv_files/final_data.csv`)
- Access to InfluxDB instance with room sensor data.
- Sensor parameters (temperature, humidity, motion, CO₂, etc.).
- Room metadata (location, ID, usage type).
## Acceptance Criteria
- Pipeline ingests and stores live data.
- Database contains clean, queryable sensor data.
- APIs return correct values with low latency.
- Works across multiple buildings.
- Can Grafana visualize real-time sensor data from InfluxDB?
- Are queries efficient and dashboards responsive?
- Are pros and cons of this architecture clearly documented?
- Can new sensors or parameters be added without major changes?
## Environment Setup
- Languages: Python (FastAPI, Flask) or Node.js (Express).
- Messaging: MQTT, REST API, WebSocket.
- Database: PostgreSQL, TimescaleDB, InfluxDB.
- Tools: Docker, Grafana (for visualization).
- OS: Mac / Windows / Linux.
- **Languages:** Python (backend scripting), Flux/SQL (InfluxDB queries).
- **Frameworks/Tools:** InfluxDB, Grafana, Flask/Django (optional API layer).
- **Tools:** Docker for running InfluxDB + Grafana locally.
- **OS:** Mac / Windows / Linux.
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