Skip to content

Latest commit

 

History

History
102 lines (80 loc) · 6.78 KB

SYSTEM_ARCHITECTURE.md

File metadata and controls

102 lines (80 loc) · 6.78 KB

System Architecture

1. Overview of System Components:

  1. Drone Platform:

    • Frame and Propulsion System: Provides the physical structure and movement capabilities of the drone.
    • Power System: Powers all onboard components, including the flight system, sensors, and computing units.
    • Flight Controller: Manages the drone’s flight dynamics, stabilization, and navigation.

  2. Sensors:

    • Imaging Sensors:
      • RGB Camera: Captures high-resolution images for visual analysis.
      • Thermal Camera: Detects temperature variations, useful for identifying heat signatures or water accumulation.
    • Environmental Sensors:
      • Humidity Sensor: Measures the moisture level in the air.
      • Temperature Sensor: Monitors ambient temperature.
      • Air Pressure Sensor: Detects changes in atmospheric pressure, which can indicate weather changes.
      • Soil Moisture Sensor: Measures moisture levels in the soil, important for drought detection.

  3. Edge AI Processing Unit:

    • FPGA (e.g., Xilinx PYNQ-Z2 or UltraScale+ MPSoC): Handles real-time data processing, including image and sensor data analysis, using pre-trained AI models.
    • Companion Processor (e.g., ARM Cortex-A9): Runs the higher-level software stack, including the operating system, drone control logic, and communication protocols.

  4. AI Models and Data Processing:

    • Image Processing Algorithms: Analyze images in real-time to detect signs of natural disasters, such as flooding or drought.
    • Sensor Data Fusion: Combines inputs from multiple sensors to enhance detection accuracy and provide contextual information.
    • Anomaly Detection Models: AI models trained to recognize environmental patterns that indicate potential disasters.

  5. Communication System:

    • Telemetry and Control Link: Provides real-time communication between the drone and the ground control station, allowing for remote monitoring and control.
    • Data Transmission Module: Sends critical data and alerts to the cloud or ground stations. Can operate over various communication channels (e.g., 4G/5G, LoRa, or satellite) depending on the deployment environment.

  6. Ground Control Station (GCS) / Cloud Integration:

    • Real-Time Monitoring Dashboard: Displays live data from the drone, including sensor readings, captured images, and detected anomalies.
    • Data Storage and Analysis: Stores collected data for post-mission analysis and long-term monitoring.
    • Alert and Notification System: Automatically sends alerts to relevant authorities when a potential disaster is detected.

2. System Architecture Diagram:

Here’s a conceptual layout of how these components interact:

+---------------------------------------------------------------------+
|                        DisasterShield AI System                     |
| +------------------+   +-------------------------------------------+|
| |   Drone Platform |   |              Ground Station / Cloud       ||
| |   (Flight System)|   |   +-----------------------------------+   ||
| |   +-------------+|   |   |   Real-Time Monitoring Dashboard   |  ||
| |   |  Sensors    ||<----->|    (Live Data & Alerts)            |  ||
| |   | (Image &    ||       |   +----------------------------------+||
| |   | Environment)||       |                                        |
| |   +-------------+|   |   +-----------------------------------+   ||
| |   +-------------+|   |   |    Data Storage & Analysis        |   ||
| |   |  Edge AI    ||<----->|    (Post-Processing, Reports)     |   ||
| |   | Processing  ||       |   +----------------------------------+||
| |   | Unit (FPGA) ||       |                                        |
| |   +-------------+|   |   +-----------------------------------+   ||
| |   +-------------+|   |   |  Alert & Notification System      |   ||
| |   |Communication||<----->|    (SMS, Email, API Integration)  |   ||
| |   | System      ||       |   +----------------------------------+||
| |   +-------------+|   +-------------------------------------------+|
+---------------------------------------------------------------------+

3. Workflow and Data Flow:

  1. Data Acquisition:

    • The drone navigates a predefined flight path, collecting data via onboard cameras and environmental sensors.

  2. Real-Time Processing:

    • The captured images and sensor data are sent to the FPGA-based processing unit.
    • AI models running on the FPGA analyze the data in real-time to detect potential signs of disasters.
    • If an anomaly is detected, the processing unit flags it for further action.

  3. Data Transmission:

    • The drone transmits critical data (e.g., detected anomalies, images) to the ground control station or cloud in real-time.
    • The data transmission system ensures that even in low-connectivity environments, essential data is stored locally and transmitted once a connection is available.

  4. Ground Station / Cloud Analysis:

    • The ground control station receives and visualizes the real-time data.
    • Collected data is stored in the cloud for further analysis, post-mission assessment, and long-term monitoring.
    • Alerts are generated and sent to relevant authorities when a potential disaster is detected.

  5. Decision Making:

    • Authorities or automated systems can take immediate actions based on the alerts, such as evacuating areas, deploying resources, or adjusting the drone’s flight path for more detailed analysis.

4. System Effectiveness:

  • High-Performance Processing: Leveraging FPGA technology ensures real-time, high-accuracy analysis of complex data.
  • Robust Communication: Multi-modal communication systems ensure data integrity and availability, even in remote or disaster-prone areas.
  • Scalability: The system can be scaled with multiple drones, integrated with additional sensors, or expanded to cover larger areas.
  • Adaptability: AI models can be continuously updated with new data, improving their effectiveness over time.

5. Future Enhancements:

  • Swarm Drones: Expanding the system to include multiple drones working in coordination to cover larger areas.
  • Advanced AI Models: Integrating predictive analytics to forecast potential disasters based on detected trends.
  • Cloud-Based AI Training: Continuous improvement of AI models using cloud resources for data storage, training, and model refinement.

Conclusion:

The DisasterShield AI system architecture is designed to be robust, efficient, and scalable, ensuring that it can effectively monitor environments, detect early signs of disasters, and provide timely alerts. By integrating edge AI processing with real-time data analysis, this system is poised to make a significant impact in disaster prevention and environmental monitoring.