Add AI-powered carbon emission anomaly detection and dashboard integration

[BRUH-on]

Summary

This PR introduces an AI-powered anomaly detection system for identifying sudden carbon emission spikes within ClimaCore.

The feature adds a backend anomaly detection API and integrates anomaly monitoring directly into the dashboard, enabling proactive detection of unusual emission events and improving environmental intelligence capabilities.

Changes Made

Backend

• Added new anomaly detection route:

  • backend/src/routes/anomaly.ts

• Implemented anomaly detection logic using:

  • Rolling mean
  • Rolling standard deviation
  • Z-score analysis
  • Sudden percentage increase detection

• Detects anomalies when:

  • Z-score > 2.5
  • CO₂ increase exceeds 40% compared to previous reading

• Returns:

  • Anomaly status
  • Severity level
  • Confidence score
  • Detection reason
  • Summary information

API

Added endpoint:

POST /api/v1/anomaly/detect

Example request:

{
  "readings": [
    {
      "timestamp": "2026-05-29T10:00:00Z",
      "co2": 120
    },
    {
      "timestamp": "2026-05-29T11:00:00Z",
      "co2": 125
    },
    {
      "timestamp": "2026-05-29T12:00:00Z",
      "co2": 260
    }
  ]
}

Frontend

• Integrated anomaly detection into the dashboard.

• Added AI anomaly scan capability.

• Displays:

  • Carbon spike alerts
  • Severity classification
  • Confidence percentage
  • Detection reason

• Maintains existing industrial/sketch-style UI.

• Preserves existing dashboard, compliance, reporting, and heatmap functionality.

Impact

This enhancement improves environmental monitoring by automatically identifying abnormal emission patterns and providing actionable alerts before compliance issues escalate.

Testing

• Verified backend route registration.
• Verified anomaly detection API responses.
• Tested emission spike scenarios.
• Confirmed dashboard integration and alert rendering.
• Confirmed no regressions to existing dashboard modules.

Future Improvements

• Replace mock data with live sensor streams.
• Add anomaly history tracking.
• Add predictive forecasting for emissions.
• Add automated compliance escalation workflows.