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rafaeldpsilva
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layers/business/analytics_service.py
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300
layers/business/analytics_service.py
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"""
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Analytics business logic service
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Business Layer - handles analytics calculations and data aggregations
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"""
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from datetime import datetime, timedelta
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from typing import Dict, Any, List, Optional
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import logging
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from ..infrastructure.repositories import SensorReadingRepository
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logger = logging.getLogger(__name__)
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class AnalyticsService:
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"""Service for analytics and reporting operations"""
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def __init__(self):
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self.sensor_reading_repo = SensorReadingRepository()
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async def get_analytics_summary(self, hours: int = 24) -> Dict[str, Any]:
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"""Get comprehensive analytics summary for the specified time period"""
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try:
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start_time = datetime.utcnow() - timedelta(hours=hours)
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# Sensor-level analytics pipeline
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sensor_pipeline = [
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{"$match": {"created_at": {"$gte": start_time}}},
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{"$group": {
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"_id": {
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"sensor_id": "$sensor_id",
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"room": "$room",
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"sensor_type": "$sensor_type"
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},
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"reading_count": {"$sum": 1},
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"avg_energy": {"$avg": "$energy.value"},
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"total_energy": {"$sum": "$energy.value"},
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"avg_co2": {"$avg": "$co2.value"},
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"max_co2": {"$max": "$co2.value"},
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"avg_temperature": {"$avg": "$temperature.value"},
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"latest_timestamp": {"$max": "$timestamp"}
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}},
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{"$sort": {"total_energy": -1}}
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]
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sensor_analytics = await self.sensor_reading_repo.aggregate(sensor_pipeline)
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# Room-level analytics pipeline
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room_pipeline = [
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{"$match": {"created_at": {"$gte": start_time}, "room": {"$ne": None}}},
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{"$group": {
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"_id": "$room",
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"sensor_count": {"$addToSet": "$sensor_id"},
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"total_energy": {"$sum": "$energy.value"},
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"avg_co2": {"$avg": "$co2.value"},
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"max_co2": {"$max": "$co2.value"},
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"reading_count": {"$sum": 1}
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}},
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{"$project": {
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"room": "$_id",
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"sensor_count": {"$size": "$sensor_count"},
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"total_energy": 1,
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"avg_co2": 1,
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"max_co2": 1,
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"reading_count": 1
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}},
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{"$sort": {"total_energy": -1}}
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]
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room_analytics = await self.sensor_reading_repo.aggregate(room_pipeline)
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# Calculate summary statistics
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summary_stats = self._calculate_summary_stats(sensor_analytics, room_analytics)
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return {
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"period_hours": hours,
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"start_time": start_time.isoformat(),
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"sensor_analytics": sensor_analytics,
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"room_analytics": room_analytics,
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"summary": summary_stats
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}
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except Exception as e:
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logger.error(f"Error getting analytics summary: {e}")
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return {
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"period_hours": hours,
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"start_time": None,
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"sensor_analytics": [],
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"room_analytics": [],
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"summary": {}
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}
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def _calculate_summary_stats(self, sensor_analytics: List[Dict],
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room_analytics: List[Dict]) -> Dict[str, Any]:
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"""Calculate summary statistics from analytics data"""
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total_readings = sum(item["reading_count"] for item in sensor_analytics)
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total_energy = sum(item.get("total_energy", 0) or 0 for item in sensor_analytics)
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# Energy consumption insights
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energy_insights = {
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"total_consumption_kwh": round(total_energy, 2),
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"average_consumption_per_sensor": (
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round(total_energy / len(sensor_analytics), 2)
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if sensor_analytics else 0
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),
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"top_energy_consumer": (
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sensor_analytics[0]["_id"]["sensor_id"]
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if sensor_analytics else None
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)
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}
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# CO2 insights
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co2_values = [item.get("avg_co2") for item in sensor_analytics if item.get("avg_co2")]
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co2_insights = {
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"average_co2_level": (
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round(sum(co2_values) / len(co2_values), 1)
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if co2_values else 0
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),
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"sensors_with_high_co2": len([
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co2 for co2 in co2_values if co2 and co2 > 1000
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]),
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"sensors_with_critical_co2": len([
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co2 for co2 in co2_values if co2 and co2 > 5000
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])
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}
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return {
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"total_sensors_analyzed": len(sensor_analytics),
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"total_rooms_analyzed": len(room_analytics),
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"total_readings": total_readings,
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"energy_insights": energy_insights,
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"co2_insights": co2_insights
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}
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async def get_energy_trends(self, hours: int = 168) -> Dict[str, Any]:
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"""Get energy consumption trends (default: last week)"""
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try:
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start_time = datetime.utcnow() - timedelta(hours=hours)
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# Hourly energy consumption pipeline
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pipeline = [
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{"$match": {
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"created_at": {"$gte": start_time},
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"energy.value": {"$exists": True}
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}},
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{"$group": {
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"_id": {
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"year": {"$year": "$created_at"},
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"month": {"$month": "$created_at"},
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"day": {"$dayOfMonth": "$created_at"},
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"hour": {"$hour": "$created_at"}
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},
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"total_energy": {"$sum": "$energy.value"},
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"sensor_count": {"$addToSet": "$sensor_id"},
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"reading_count": {"$sum": 1}
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}},
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{"$project": {
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"_id": 0,
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"timestamp": {
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"$dateFromParts": {
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"year": "$_id.year",
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"month": "$_id.month",
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"day": "$_id.day",
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"hour": "$_id.hour"
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}
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},
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"total_energy": {"$round": ["$total_energy", 2]},
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"sensor_count": {"$size": "$sensor_count"},
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"reading_count": 1
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}},
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{"$sort": {"timestamp": 1}}
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]
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trends = await self.sensor_reading_repo.aggregate(pipeline)
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# Calculate trend insights
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insights = self._calculate_trend_insights(trends)
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return {
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"period_hours": hours,
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"data_points": len(trends),
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"trends": trends,
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"insights": insights
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}
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except Exception as e:
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logger.error(f"Error getting energy trends: {e}")
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return {
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"period_hours": hours,
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"data_points": 0,
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"trends": [],
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"insights": {}
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}
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def _calculate_trend_insights(self, trends: List[Dict]) -> Dict[str, Any]:
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"""Calculate insights from trend data"""
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if not trends:
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return {}
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energy_values = [item["total_energy"] for item in trends]
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# Peak and low consumption
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max_consumption = max(energy_values)
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min_consumption = min(energy_values)
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avg_consumption = sum(energy_values) / len(energy_values)
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# Find peak time
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peak_item = max(trends, key=lambda x: x["total_energy"])
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peak_time = peak_item["timestamp"]
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return {
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"peak_consumption_kwh": max_consumption,
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"lowest_consumption_kwh": min_consumption,
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"average_consumption_kwh": round(avg_consumption, 2),
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"peak_time": peak_time.isoformat() if hasattr(peak_time, 'isoformat') else str(peak_time),
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"consumption_variance": round(max_consumption - min_consumption, 2)
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}
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async def get_room_comparison(self, hours: int = 24) -> Dict[str, Any]:
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"""Get room-by-room comparison analytics"""
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try:
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start_time = datetime.utcnow() - timedelta(hours=hours)
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pipeline = [
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{"$match": {
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"created_at": {"$gte": start_time},
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"room": {"$ne": None}
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}},
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{"$group": {
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"_id": "$room",
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"total_energy": {"$sum": "$energy.value"},
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"avg_energy": {"$avg": "$energy.value"},
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"avg_co2": {"$avg": "$co2.value"},
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"max_co2": {"$max": "$co2.value"},
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"avg_temperature": {"$avg": "$temperature.value"},
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"sensor_count": {"$addToSet": "$sensor_id"},
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"reading_count": {"$sum": 1}
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}},
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{"$project": {
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"room": "$_id",
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"_id": 0,
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"total_energy": {"$round": [{"$ifNull": ["$total_energy", 0]}, 2]},
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"avg_energy": {"$round": [{"$ifNull": ["$avg_energy", 0]}, 2]},
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"avg_co2": {"$round": [{"$ifNull": ["$avg_co2", 0]}, 1]},
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"max_co2": {"$round": [{"$ifNull": ["$max_co2", 0]}, 1]},
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"avg_temperature": {"$round": [{"$ifNull": ["$avg_temperature", 0]}, 1]},
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"sensor_count": {"$size": "$sensor_count"},
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"reading_count": 1
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}},
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{"$sort": {"total_energy": -1}}
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]
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room_comparison = await self.sensor_reading_repo.aggregate(pipeline)
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# Calculate comparison insights
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insights = self._calculate_room_insights(room_comparison)
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return {
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"period_hours": hours,
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"rooms_analyzed": len(room_comparison),
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"comparison": room_comparison,
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"insights": insights
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}
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except Exception as e:
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logger.error(f"Error getting room comparison: {e}")
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return {
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"period_hours": hours,
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"rooms_analyzed": 0,
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"comparison": [],
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"insights": {}
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}
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def _calculate_room_insights(self, room_data: List[Dict]) -> Dict[str, Any]:
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"""Calculate insights from room comparison data"""
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if not room_data:
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return {}
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# Energy insights
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total_energy = sum(room["total_energy"] for room in room_data)
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highest_consumer = room_data[0] if room_data else None
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lowest_consumer = min(room_data, key=lambda x: x["total_energy"]) if room_data else None
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# CO2 insights
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rooms_with_high_co2 = [
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room for room in room_data
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if room.get("avg_co2", 0) > 1000
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]
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# Temperature insights
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temp_values = [room.get("avg_temperature", 0) for room in room_data if room.get("avg_temperature")]
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avg_building_temp = sum(temp_values) / len(temp_values) if temp_values else 0
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return {
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"total_building_energy_kwh": round(total_energy, 2),
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"highest_energy_consumer": highest_consumer["room"] if highest_consumer else None,
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"lowest_energy_consumer": lowest_consumer["room"] if lowest_consumer else None,
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"rooms_with_high_co2": len(rooms_with_high_co2),
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"high_co2_rooms": [room["room"] for room in rooms_with_high_co2],
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"average_building_temperature": round(avg_building_temp, 1),
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"total_active_sensors": sum(room["sensor_count"] for room in room_data)
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}
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