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app/processing/analyzers/device_analyzer.py

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+from __future__ import annotations
+
+from datetime import datetime
+
+from app.models.mongodb.device_log import DeviceLog
+
+
+async def analyze_device_status(
+    device_id: str, start: datetime, end: datetime
+) -> dict:
+    """Analyze device status changes over a period."""
+    logs = await (
+        DeviceLog.find(
+            DeviceLog.device_id == device_id,
+            DeviceLog.event_type == "status_change",
+            DeviceLog.timestamp >= start,
+            DeviceLog.timestamp <= end,
+        )
+        .sort("+timestamp")
+        .to_list()
+    )
+
+    status_counts: dict[str, int] = {}
+    for log in logs:
+        status = log.payload.get("status", "unknown")
+        status_counts[status] = status_counts.get(status, 0) + 1
+
+    total_events = len(logs)
+    uptime_events = status_counts.get("online", 0)
+    uptime_ratio = uptime_events / total_events if total_events > 0 else 0.0
+
+    return {
+        "total_events": total_events,
+        "status_counts": status_counts,
+        "uptime_ratio": round(uptime_ratio, 4),
+        "period": {"start": start.isoformat(), "end": end.isoformat()},
+    }

app/processing/analyzers/trend_analyzer.py

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+from __future__ import annotations
+
+from datetime import datetime
+
+import numpy as np
+
+from app.models.mongodb.telemetry import TelemetryData
+
+
+async def analyze_trend(
+    device_id: str, start: datetime, end: datetime
+) -> dict:
+    """Analyze telemetry data trends using linear regression."""
+    docs = await (
+        TelemetryData.find(
+            TelemetryData.device_id == device_id,
+            TelemetryData.timestamp >= start,
+            TelemetryData.timestamp <= end,
+        )
+        .sort("+timestamp")
+        .to_list()
+    )
+
+    if len(docs) < 2:
+        return {"status": "insufficient_data", "count": len(docs)}
+
+    timestamps = np.array([d.timestamp.timestamp() for d in docs])
+    values = np.array([d.metrics.get("value", 0) for d in docs], dtype=float)
+
+    # Normalize timestamps
+    t_norm = timestamps - timestamps[0]
+
+    # Linear regression
+    coeffs = np.polyfit(t_norm, values, 1)
+    slope = float(coeffs[0])
+
+    return {
+        "count": len(docs),
+        "mean": float(np.mean(values)),
+        "std": float(np.std(values)),
+        "min": float(np.min(values)),
+        "max": float(np.max(values)),
+        "slope": slope,
+        "trend": "increasing" if slope > 0.001 else "decreasing" if slope < -0.001 else "stable",
+    }