feat(application): integrate measured-load ingestion training and planner source

2026-10-02 21:00:05 +00:00
parent 44362e4bf5
commit fc024fcfba
96 changed files with 8933 additions and 0 deletions
@@ -0,0 +1,100 @@
+"""Read-only probe executed INSIDE the existing forecast-engine container.
+
+No get_configs() (it may migrate SQL), no training/prediction, no forecast writing,
+no manual /run_now request, and no user credentials in output. Reads one plant's
+stored raw 5-minute forecast values and the input frames used by the engine.
+"""
+import contextlib
+from datetime import datetime, timezone
+import hashlib
+import io
+import json
+import math
+import os
+from pathlib import Path
+import sqlite3
+from urllib.parse import quote
+from uuid import UUID
+
+class Discard(io.TextIOBase):
+    def write(self,text):return len(text)
+
+def clean_time(value):
+    if hasattr(value,'to_pydatetime'):value=value.to_pydatetime()
+    if value.tzinfo is None:value=value.replace(tzinfo=timezone.utc)
+    return value.astimezone(timezone.utc).isoformat()
+
+def run():
+    aid=str(UUID(os.environ['ENELIX_ACCEPTANCE_PLANT']))
+    os.environ['FORECAST_INFLUX_TIMEOUT_MS']='20000'
+    # These assignments affect only this diagnostic process, not the service.
+    with contextlib.redirect_stdout(Discard()),contextlib.redirect_stderr(Discard()):
+        import main
+        import numpy as np
+        import pandas as pd
+        path=Path(main.SQLITE_DB_PATH)
+        if not path.is_file():raise RuntimeError('Existing configuration database missing')
+        con=sqlite3.connect('file:'+quote(str(path))+'?mode=ro',uri=True)
+        con.row_factory=sqlite3.Row
+        try:row=con.execute('SELECT * FROM anlagen_meta WHERE anlagen_id=?',(aid,)).fetchone()
+        finally:con.close()
+        if row is None:raise RuntimeError('Installation not found')
+        cfg=dict(row)
+        cfg['daecher']=json.loads(cfg.get('daecher') or '[]')
+        for key,default in [('ac_leistung',10.),('batt_capacity_kwh',0.),('batt_power_kw',0.),('tarif_bezug_fest',.3),('tarif_einspeisung_fest',.1),('tarif_peak_fest',5.)]:
+            value=cfg.get(key);cfg[key]=float(default if value is None or value=='' else value)
+        data=main.build_data_object(cfg,training=False)
+        frames={}
+        for key in ('df_hist','df_recent_raw','df_load_training','df_fut'):
+            frame=data.get(key)
+            if frame is None:frames[key]={'available':False};continue
+            item={'rows':len(frame),'from':clean_time(frame.index.min()) if len(frame) else None,'until':clean_time(frame.index.max()) if len(frame) else None,'columns':{}}
+            for col in ('Hausverbrauch','PV','Netzleistung','SOC'):
+                if col not in frame.columns:continue
+                values=pd.to_numeric(frame[col],errors='coerce');valid=values[np.isfinite(values)]
+                item['columns'][col]={'finite':len(valid),'zeros':int((valid==0).sum()),'median':float(valid.median()) if len(valid) else None,'maximum':float(valid.max()) if len(valid) else None,'lastFiniteAt':clean_time(valid.index[-1]) if len(valid) else None,'recentValues':[{'time':clean_time(t),'value':float(v)} for t,v in valid.tail(12).items()]}
+            frames[key]=item
+        start=datetime.now(timezone.utc).replace(second=0,microsecond=0)
+        from datetime import timedelta
+        end=start+timedelta(hours=48)
+        fields=('prog_var_1','prog_var_2','prog_var_10','prog_var_11','prog_var_21','prog_var_22')
+        field_filter=' or '.join('r["_field"] == '+json.dumps(f) for f in fields)
+        query='''from(bucket: %s)
+          |> range(start: %s, stop: %s)
+          |> filter(fn: (r) => r["_measurement"] == "api_telemetry")
+          |> filter(fn: (r) => r["anlagen_id"] == %s)
+          |> filter(fn: (r) => r["data_type"] == "forecast")
+          |> filter(fn: (r) => %s)
+          |> keep(columns: ["_time", "_field", "_value"])
+        ''' % (json.dumps(main.INFLUX_BUCKET),start.isoformat(),end.isoformat(),json.dumps(aid),field_filter)
+        client=main.InfluxDBClient(url=main.INFLUX_URL,token=main.INFLUX_TOKEN,org=main.INFLUX_ORG,timeout=20000)
+        series={field:[] for field in fields}
+        try:
+            tables=client.query_api().query(org=main.INFLUX_ORG,query=query)
+            for table in tables:
+                for record in table.records:
+                    value=record.get_value();field=record.get_field()
+                    if field in series and isinstance(value,(int,float)) and math.isfinite(value):
+                        series[field].append({'time':clean_time(record.get_time()),'value':float(value)})
+        finally:client.close()
+        for field in series:series[field].sort(key=lambda p:p['time'])
+        versions={}
+        for relative in ('main.py','methods/var_1.py','methods/var_2.py','methods/var_10.py','methods/var_11.py','methods/var_21.py','methods/var_22.py'):
+            source=Path('/app')/relative
+            if source.is_file():versions[relative]=hashlib.sha256(source.read_bytes()).hexdigest()
+    summary={}
+    for field,points in series.items():
+        values=[p['value'] for p in points]
+        summary[field]={'points':len(values),'allZero':bool(values) and max(abs(v) for v in values)==0,'maximumW':max(values) if values else None,'from':points[0]['time'] if points else None,'until':points[-1]['time'] if points else None}
+    native=True
+    for points in series.values():
+        stamps=[datetime.fromisoformat(p['time']).timestamp() for p in points]
+        if not stamps or any(t%300 for t in stamps) or any(b-a!=300 for a,b in zip(stamps,stamps[1:])):native=False
+    return {'status':'read_only_acquired','installationId':aid,'observedAt':datetime.now(timezone.utc).isoformat(),'queryResolution':'raw_no_chart_resampling','nativeFiveMinuteForecast':native,'generationTimeVerified':False,'inputFrames':frames,'forecastSummary':summary,'forecastSeries':series,'runningSourceHashes':versions,'modelWrite':False,'liveControlChanged':False}
+
+try:
+    result=run()
+except Exception as error:
+    # Never echo exception details containing SQL payloads, credentials or URLs.
+    result={'status':'read_only_probe_failed','errorType':type(error).__name__,'liveControlChanged':False}
+print(json.dumps(result,allow_nan=False))