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Next.js-App zur Visualisierung des Stromverbrauchs aus InfluxDB (Bucket strom, measurement vzlogger, Feld arbeit = kumulativer Zaehlerstand in Wh). - 4 Balken-Diagramme: 24h (stuendlich), Woche (stuendlich), 31 Tage (taeglich), Jahr (pro Monat, immer 12 Monate) - Verbrauch via difference des Zaehlerstands, zeitzonen-korrekt - 24h liest Rohdaten (aktuell), 7d/31d/365d den stuendlichen Downsampling-Rollup (arbeit_hourly) -> sub-sekunde - Gesamtverbrauch je Zeitraum unter dem Chart - Design/Layout angelehnt an Werte-Log "Verlauf" Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
85 lines
3.4 KiB
TypeScript
85 lines
3.4 KiB
TypeScript
import { InfluxDB } from '@influxdata/influxdb-client';
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import { ResolvedRange, windowStartBefore } from '@/lib/ranges';
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import { VerbrauchPoint } from '@/types/strom';
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const URL = process.env.INFLUX_URL || 'http://nuccy:8086';
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const TOKEN = process.env.INFLUX_TOKEN || '';
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const ORG = process.env.INFLUX_ORG || 'citysensor';
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const BUCKET = process.env.INFLUX_BUCKET || 'strom';
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const FIELD = process.env.INFLUX_FIELD || 'arbeit';
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// Rohdaten (1-Sekunden-Zaehlerstand) — fuer 24h, immer aktuell
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const MEAS_RAW = process.env.INFLUX_MEASUREMENT || 'vzlogger';
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// Stuendliche Snapshots (Downsampling-Task) — fuer 7d/31d/365d, schnell
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const MEAS_ROLLUP = process.env.INFLUX_ROLLUP_MEASUREMENT || 'arbeit_hourly';
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const UNIT_FACTOR = parseFloat(process.env.INFLUX_UNIT_FACTOR || '0.001'); // *Faktor -> kWh (Wh)
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const TZ = process.env.INFLUX_TZ || 'Europe/Berlin';
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const influx = new InfluxDB({ url: URL, token: TOKEN, timeout: 90_000 });
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/**
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* Liefert den Verbrauch je Fenster aus dem kumulativen Zaehlerstand.
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*
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* Quelle 'raw' (24h): hoechstaufgeloeste Daten, aggregateWindow(last) holt den
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* Zaehlerstand am Fensterende, difference() bildet den Zuwachs = Verbrauch.
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*
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* Quelle 'rollup' (7d/31d/365d): stuendliche Snapshots des Zaehlerstands.
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* difference() ergibt den stuendlichen Verbrauch, der dann tages-/stundenweise
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* summiert wird. Tagesgrenzen sind ueber `location` zeitzonen-korrekt.
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*
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* timeShift(-every) verschiebt den Zeitstempel auf den Fenster-ANFANG, sodass ein
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* Balken den Zeitraum [T, T+every) repraesentiert. Die Abfrage startet ein Fenster
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* frueher, damit das erste sichtbare Fenster bereits einen Differenzwert hat.
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*/
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export async function fetchVerbrauch(r: ResolvedRange): Promise<VerbrauchPoint[]> {
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const queryStart = windowStartBefore(r.start, r.every); // ein Fenster Vorlauf
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const measurement = r.source === 'raw' ? MEAS_RAW : MEAS_ROLLUP;
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const aggregation =
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r.source === 'raw'
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? // Rohdaten: letzter Zaehlerstand je Fenster, dann Differenz
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`|> aggregateWindow(every: ${r.every}, fn: last, createEmpty: false)
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|> difference(nonNegative: true)`
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: // Rollup: stuendliche Differenz, dann je Fenster summieren
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`|> difference(nonNegative: true)
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|> aggregateWindow(every: ${r.every}, fn: sum, createEmpty: false)`;
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const flux = `
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import "timezone"
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option location = timezone.location(name: "${TZ}")
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from(bucket: "${BUCKET}")
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|> range(start: ${queryStart.toISOString()}, stop: ${r.stop.toISOString()})
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|> filter(fn: (r) => r._measurement == "${measurement}")
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|> filter(fn: (r) => r._field == "${FIELD}")
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${aggregation}
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|> timeShift(duration: -${r.every})
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|> sort(columns: ["_time"], desc: false)
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|> keep(columns: ["_time", "_value"])
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`;
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const queryApi = influx.getQueryApi(ORG);
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const points: VerbrauchPoint[] = [];
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const minTs = r.start.getTime();
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const maxTs = r.stop.getTime();
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return new Promise<VerbrauchPoint[]>((resolve, reject) => {
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queryApi.queryRows(flux, {
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next(row, tableMeta) {
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const o = tableMeta.toObject(row);
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const ts = new Date(o._time as string).getTime();
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if (ts < minTs || ts >= maxTs) return; // Vorlauf-/Ueberhangfenster verwerfen
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const value = Number(o._value);
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if (!Number.isFinite(value)) return;
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points.push([ts, value * UNIT_FACTOR]);
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},
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error(err) {
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reject(err);
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},
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complete() {
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resolve(points);
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},
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});
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});
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}
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