92ec910a13
Shared backend + frontend for multi-customer EMS deployments. - 12 enterprise modules: quota, cost, charging, maintenance, analysis, etc. - 120+ API endpoints, 37 database tables - Customer config mechanism (CUSTOMER env var + YAML config) - Collectors: Modbus TCP, MQTT, HTTP API, Sungrow iSolarCloud - Frontend: React 19 + Ant Design + ECharts + Three.js - Infrastructure: Redis cache, rate limiting, aggregation engine Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
400 lines
16 KiB
Python
400 lines
16 KiB
Python
"""回填历史模拟能耗数据 - 过去30天逐小时数据，含碳排放记录
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Uses the shared weather_model for physics-based solar, temperature, and load
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generation. Deterministic seed (42) ensures reproducible output across runs.
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"""
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import asyncio
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import math
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import os
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import random
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import sys
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from datetime import datetime, timedelta, timezone
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "backend"))
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DATABASE_URL = os.environ.get(
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"DATABASE_URL",
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"postgresql+asyncpg://tianpu:tianpu2026@localhost:5432/tianpu_ems",
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)
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from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession
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from sqlalchemy import text, select
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from app.services.weather_model import (
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set_seed, reset_cloud_model,
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pv_power, pv_electrical_at, get_pv_orientation,
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heat_pump_data, building_load, indoor_sensor,
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heat_meter_data, outdoor_temperature, outdoor_humidity,
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get_hvac_mode,
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)
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from app.models.device import Device
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# ---------------------------------------------------------------------------
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# Device definitions — will be populated from DB at runtime
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# ---------------------------------------------------------------------------
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PV_IDS = []
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PV_CODES = ["INV-01", "INV-02", "INV-03"]
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HP_IDS = []
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HP_CODES = ["HP-01", "HP-02", "HP-03", "HP-04"]
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METER_IDS = []
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METER_CODES = ["METER-GRID", "METER-PV", "METER-HP", "METER-PUMP"]
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HEAT_METER_ID = None
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SENSOR_IDS = []
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SENSOR_CODES = ["TH-01", "TH-02", "TH-03", "TH-04", "TH-05"]
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async def _load_device_ids(session: AsyncSession):
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"""Load actual device IDs from DB by code."""
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global PV_IDS, HP_IDS, METER_IDS, HEAT_METER_ID, SENSOR_IDS
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result = await session.execute(select(Device.id, Device.code).order_by(Device.id))
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code_to_id = {row[1]: row[0] for row in result.all()}
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PV_IDS = [code_to_id[c] for c in PV_CODES if c in code_to_id]
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HP_IDS = [code_to_id[c] for c in HP_CODES if c in code_to_id]
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METER_IDS = [code_to_id[c] for c in METER_CODES if c in code_to_id]
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HEAT_METER_ID = code_to_id.get("HM-01")
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SENSOR_IDS = [code_to_id[c] for c in SENSOR_CODES if c in code_to_id]
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print(f" Loaded device IDs: PV={PV_IDS}, HP={HP_IDS}, Meters={METER_IDS}, HeatMeter={HEAT_METER_ID}, Sensors={SENSOR_IDS}")
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EMISSION_FACTOR = 0.8843 # kgCO2/kWh - North China grid
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DAYS = 30
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HOURS_PER_DAY = 24
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TOTAL_HOURS = DAYS * HOURS_PER_DAY
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# ---------------------------------------------------------------------------
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# Main backfill
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# ---------------------------------------------------------------------------
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async def backfill():
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# Set deterministic seed for reproducibility
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set_seed(42)
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engine = create_async_engine(DATABASE_URL, echo=False, pool_size=5)
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session_factory = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
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# Load actual device IDs from DB
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async with session_factory() as session:
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await _load_device_ids(session)
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now = datetime.now(timezone.utc).replace(minute=0, second=0, microsecond=0)
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start = now - timedelta(days=DAYS)
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print(f"Backfill range: {start.isoformat()} -> {now.isoformat()}")
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print(f"Total hours: {TOTAL_HOURS}")
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# ---- Collect rows ----
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energy_rows = []
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carbon_rows = []
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daily_buckets: dict[int, dict[str, dict]] = {}
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all_power_ids = PV_IDS + HP_IDS + METER_IDS
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for did in all_power_ids:
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daily_buckets[did] = {}
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print("Generating hourly energy_data rows (realistic models) ...")
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for h_offset in range(TOTAL_HOURS):
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ts = start + timedelta(hours=h_offset)
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beijing_dt = ts + timedelta(hours=8)
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date_str = beijing_dt.strftime("%Y-%m-%d")
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# Reset cloud model each day for variety
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if h_offset % 24 == 0:
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reset_cloud_model()
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# Re-seed per day for reproducibility but day-to-day variation
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set_seed(42 + h_offset // 24)
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# --- PV inverters ---
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for i, did in enumerate(PV_IDS):
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code = PV_CODES[i]
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orientation = get_pv_orientation(code)
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val = pv_power(ts, rated_power=110.0, orientation=orientation,
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device_code=code)
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val = round(val, 2)
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "power", "value": val, "unit": "kW", "quality": 0,
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})
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# Also generate electrical details for richer data
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elec = pv_electrical_at(val, ts, rated_power=110.0)
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "dc_voltage", "value": elec["dc_voltage"], "unit": "V", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "ac_voltage", "value": elec["ac_voltage"], "unit": "V", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "temperature", "value": elec["temperature"], "unit": "℃", "quality": 0,
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})
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daily_buckets[did].setdefault(date_str, {"values": [], "cops": []})
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daily_buckets[did][date_str]["values"].append(val)
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# --- Heat pumps ---
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hp_total_power = 0.0
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hp_cop_sum = 0.0
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hp_count = 0
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for i, did in enumerate(HP_IDS):
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code = HP_CODES[i]
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data = heat_pump_data(ts, rated_power=35.0, device_code=code)
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val = data["power"]
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cop = data["cop"]
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hp_total_power += val
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if cop > 0:
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hp_cop_sum += cop
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hp_count += 1
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "power", "value": val, "unit": "kW", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "cop", "value": cop, "unit": "", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "inlet_temp", "value": data["inlet_temp"], "unit": "℃", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "outlet_temp", "value": data["outlet_temp"], "unit": "℃", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "flow_rate", "value": data["flow_rate"], "unit": "m³/h", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "outdoor_temp", "value": data["outdoor_temp"], "unit": "℃", "quality": 0,
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})
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daily_buckets[did].setdefault(date_str, {"values": [], "cops": []})
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daily_buckets[did][date_str]["values"].append(val)
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daily_buckets[did][date_str]["cops"].append(cop)
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# --- Meters ---
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for i, did in enumerate(METER_IDS):
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code = METER_CODES[i]
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data = building_load(ts, base_power=50.0, meter_code=code)
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val = data["power"]
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "power", "value": val, "unit": "kW", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "voltage", "value": data["voltage"], "unit": "V", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "current", "value": data["current"], "unit": "A", "quality": 0,
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})
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energy_rows.append({
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"device_id": did, "timestamp": ts,
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"data_type": "power_factor", "value": data["power_factor"], "unit": "", "quality": 0,
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})
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daily_buckets[did].setdefault(date_str, {"values": [], "cops": []})
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daily_buckets[did][date_str]["values"].append(val)
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# --- Heat meter (correlated with heat pump totals) ---
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avg_cop = hp_cop_sum / hp_count if hp_count > 0 else 3.0
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hm_data = heat_meter_data(ts, hp_power=hp_total_power, hp_cop=avg_cop)
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energy_rows.append({
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"device_id": HEAT_METER_ID, "timestamp": ts,
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"data_type": "heat_power", "value": hm_data["heat_power"], "unit": "kW", "quality": 0,
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})
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energy_rows.append({
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"device_id": HEAT_METER_ID, "timestamp": ts,
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"data_type": "flow_rate", "value": hm_data["flow_rate"], "unit": "m³/h", "quality": 0,
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})
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energy_rows.append({
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"device_id": HEAT_METER_ID, "timestamp": ts,
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"data_type": "supply_temp", "value": hm_data["supply_temp"], "unit": "℃", "quality": 0,
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})
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energy_rows.append({
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"device_id": HEAT_METER_ID, "timestamp": ts,
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"data_type": "return_temp", "value": hm_data["return_temp"], "unit": "℃", "quality": 0,
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})
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# --- Temperature/humidity sensors ---
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for i, sid in enumerate(SENSOR_IDS):
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code = SENSOR_CODES[i]
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is_outdoor = (code == "TH-05")
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data = indoor_sensor(ts, is_outdoor=is_outdoor, device_code=code)
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energy_rows.append({
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"device_id": sid, "timestamp": ts,
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"data_type": "temperature", "value": data["temperature"], "unit": "℃", "quality": 0,
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})
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energy_rows.append({
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"device_id": sid, "timestamp": ts,
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"data_type": "humidity", "value": data["humidity"], "unit": "%", "quality": 0,
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})
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print(f" Generated {len(energy_rows)} energy_data rows")
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# ---- Build daily summary rows ----
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print("Computing daily summaries ...")
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summary_rows = []
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for did, dates in daily_buckets.items():
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is_pv = did in PV_IDS
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for date_str, bucket in dates.items():
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values = bucket["values"]
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cops = bucket["cops"]
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total = round(sum(values), 2)
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peak = round(max(values), 2) if values else 0
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min_p = round(min(values), 2) if values else 0
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avg_p = round(sum(values) / len(values), 2) if values else 0
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op_hours = sum(1 for v in values if v > 0.5)
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cost = round(total * 0.85, 2)
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carbon = round(total * EMISSION_FACTOR, 2)
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avg_cop = round(sum(cops) / len(cops), 2) if cops else None
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summary_rows.append({
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"device_id": did,
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"date": datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=timezone.utc),
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"energy_type": "electricity",
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"total_consumption": 0.0 if is_pv else total,
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"total_generation": total if is_pv else 0.0,
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"peak_power": peak,
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"min_power": min_p,
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"avg_power": avg_p,
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"operating_hours": float(op_hours),
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"avg_cop": avg_cop,
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"cost": cost,
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"carbon_emission": carbon,
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})
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print(f" Generated {len(summary_rows)} daily summary rows")
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# ---- Build carbon emission daily rows ----
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print("Computing daily carbon emissions ...")
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daily_consumption: dict[str, float] = {}
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daily_pv_gen: dict[str, float] = {}
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daily_hp_consumption: dict[str, float] = {}
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for did, dates in daily_buckets.items():
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for date_str, bucket in dates.items():
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total = sum(bucket["values"])
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if did in PV_IDS:
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daily_pv_gen[date_str] = daily_pv_gen.get(date_str, 0) + total
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elif did in HP_IDS:
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daily_hp_consumption[date_str] = daily_hp_consumption.get(date_str, 0) + total
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daily_consumption[date_str] = daily_consumption.get(date_str, 0) + total
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else:
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daily_consumption[date_str] = daily_consumption.get(date_str, 0) + total
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all_dates = sorted(set(list(daily_consumption.keys()) + list(daily_pv_gen.keys())))
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for date_str in all_dates:
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dt = datetime.strptime(date_str, "%Y-%m-%d").replace(tzinfo=timezone.utc)
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# Grid electricity emission (Scope 2)
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grid_kwh = daily_consumption.get(date_str, 0)
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carbon_rows.append({
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"date": dt, "scope": 2, "category": "electricity",
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"emission": round(grid_kwh * EMISSION_FACTOR, 2),
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"reduction": 0.0,
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"energy_consumption": round(grid_kwh, 2),
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"energy_unit": "kWh",
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"note": "园区用电碳排放",
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})
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# PV generation reduction (Scope 2 avoided)
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pv_kwh = daily_pv_gen.get(date_str, 0)
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if pv_kwh > 0:
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carbon_rows.append({
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"date": dt, "scope": 2, "category": "pv_generation",
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"emission": 0.0,
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"reduction": round(pv_kwh * EMISSION_FACTOR, 2),
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"energy_consumption": round(pv_kwh, 2),
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"energy_unit": "kWh",
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"note": "光伏发电碳减排",
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})
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# Heat pump saving (COP-based reduction vs electric heating)
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hp_kwh = daily_hp_consumption.get(date_str, 0)
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if hp_kwh > 0:
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avg_cop_day = 3.2
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heat_delivered = hp_kwh * avg_cop_day
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electric_heating_kwh = heat_delivered # COP=1 for electric heating
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saved_kwh = electric_heating_kwh - hp_kwh
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carbon_rows.append({
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"date": dt, "scope": 2, "category": "heat_pump_saving",
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"emission": 0.0,
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"reduction": round(saved_kwh * EMISSION_FACTOR, 2),
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"energy_consumption": round(saved_kwh, 2),
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"energy_unit": "kWh",
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"note": "热泵节能碳减排(相比电加热)",
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})
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print(f" Generated {len(carbon_rows)} carbon emission rows")
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# ---- Bulk insert ----
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BATCH = 2000
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async with session_factory() as session:
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# Insert energy_data
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print("Inserting energy_data ...")
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insert_energy = text("""
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INSERT INTO energy_data (device_id, timestamp, data_type, value, unit, quality)
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VALUES (:device_id, :timestamp, :data_type, :value, :unit, :quality)
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""")
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for i in range(0, len(energy_rows), BATCH):
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batch = energy_rows[i : i + BATCH]
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await session.execute(insert_energy, batch)
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done = min(i + BATCH, len(energy_rows))
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if done % 10000 < BATCH:
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print(f" energy_data: {done}/{len(energy_rows)}")
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await session.commit()
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print(" energy_data done.")
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# Insert daily summaries
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print("Inserting energy_daily_summary ...")
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insert_summary = text("""
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INSERT INTO energy_daily_summary
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(device_id, date, energy_type, total_consumption, total_generation,
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peak_power, min_power, avg_power, operating_hours, avg_cop, cost, carbon_emission)
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VALUES
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(:device_id, :date, :energy_type, :total_consumption, :total_generation,
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:peak_power, :min_power, :avg_power, :operating_hours, :avg_cop, :cost, :carbon_emission)
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""")
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for i in range(0, len(summary_rows), BATCH):
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batch = summary_rows[i : i + BATCH]
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await session.execute(insert_summary, batch)
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await session.commit()
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print(f" daily_summary done. ({len(summary_rows)} rows)")
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# Insert carbon emissions
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print("Inserting carbon_emissions ...")
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insert_carbon = text("""
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INSERT INTO carbon_emissions
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(date, scope, category, emission, reduction,
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energy_consumption, energy_unit, note)
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VALUES
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(:date, :scope, :category, :emission, :reduction,
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:energy_consumption, :energy_unit, :note)
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""")
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for i in range(0, len(carbon_rows), BATCH):
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batch = carbon_rows[i : i + BATCH]
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await session.execute(insert_carbon, batch)
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await session.commit()
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print(f" carbon_emissions done. ({len(carbon_rows)} rows)")
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await engine.dispose()
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print("=" * 60)
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print("Backfill complete!")
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print("=" * 60)
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if __name__ == "__main__":
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asyncio.run(backfill())
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