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Issues with data flow & consistency

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This commit is contained in:
Marcelo
2025-12-22 14:36:40 +00:00
parent ffc39a5c90
commit 945ff2dc09
4 changed files with 414 additions and 121 deletions
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150
app/api/machines/[machineId]/route.ts
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@@ -4,9 +4,26 @@ import { prisma } from "@/lib/prisma";
import { requireSession } from "@/lib/auth/requireSession";
function normalizeEvent(row: any) {
// data can be object OR [object]
// -----------------------------
// 1) Parse row.data safely
// data may be:
// - object
// - array of objects
// - JSON string of either
// -----------------------------
const raw = row.data;
const blob = Array.isArray(raw) ? raw[0] : raw;
let parsed: any = raw;
if (typeof raw === "string") {
try {
parsed = JSON.parse(raw);
} catch {
parsed = raw; // keep as string if not JSON
}
}
// data can be object OR [object]
const blob = Array.isArray(parsed) ? parsed[0] : parsed;
// some payloads nest details under blob.data
const inner = blob?.data ?? blob ?? {};
@@ -17,21 +34,71 @@ function normalizeEvent(row: any) {
.toLowerCase()
.replace(/_/g, "-");
// Prefer the DB columns if they are meaningful
const fromDbType = row.eventType && row.eventType !== "unknown" ? row.eventType : null;
const fromBlobType = blob?.anomaly_type ?? blob?.eventType ?? blob?.topic ?? inner?.anomaly_type ?? inner?.eventType ?? null;
// -----------------------------
// 2) Alias mapping (canonical types)
// -----------------------------
const ALIAS: Record<string, string> = {
// Spanish / synonyms
macroparo: "macrostop",
"macro-stop": "macrostop",
macro_stop: "macrostop",
// infer slow-cycle if the signature exists
microparo: "microstop",
"micro-paro": "microstop",
micro_stop: "microstop",
// Node-RED types
"production-stopped": "stop", // we'll classify to micro/macro below
// legacy / generic
down: "stop",
};
// -----------------------------
// 3) Determine event type from DB or blob
// -----------------------------
const fromDbType =
row.eventType && row.eventType !== "unknown" ? row.eventType : null;
const fromBlobType =
blob?.anomaly_type ??
blob?.eventType ??
blob?.topic ??
inner?.anomaly_type ??
inner?.eventType ??
null;
// infer slow-cycle if signature exists
const inferredType =
fromDbType ??
fromBlobType ??
((inner?.actual_cycle_time && inner?.theoretical_cycle_time) || (blob?.actual_cycle_time && blob?.theoretical_cycle_time)
((inner?.actual_cycle_time && inner?.theoretical_cycle_time) ||
(blob?.actual_cycle_time && blob?.theoretical_cycle_time)
? "slow-cycle"
: "unknown");
const eventTypeRaw = normalizeType(inferredType);
let eventType = ALIAS[eventTypeRaw] ?? eventTypeRaw;
const eventType = normalizeType(inferredType);
// -----------------------------
// 4) Optional: classify "stop" into micro/macro based on duration if present
// (keeps old rows usable even if they stored production-stopped)
// -----------------------------
if (eventType === "stop") {
const stopSec =
(typeof inner?.stoppage_duration_seconds === "number" && inner.stoppage_duration_seconds) ||
(typeof blob?.stoppage_duration_seconds === "number" && blob.stoppage_duration_seconds) ||
(typeof inner?.stop_duration_seconds === "number" && inner.stop_duration_seconds) ||
null;
// tune these thresholds to match your MES spec
const MACROSTOP_SEC = 300; // 5 min
eventType = stopSec != null && stopSec >= MACROSTOP_SEC ? "macrostop" : "microstop";
}
// -----------------------------
// 5) Severity, title, description, timestamp
// -----------------------------
const severity =
String(
(row.severity && row.severity !== "info" ? row.severity : null) ??
@@ -55,10 +122,10 @@ function normalizeEvent(row: any) {
blob?.description ??
inner?.description ??
(eventType === "slow-cycle" &&
inner?.actual_cycle_time &&
inner?.theoretical_cycle_time &&
inner?.delta_percent != null
? `Cycle took ${Number(inner.actual_cycle_time).toFixed(1)}s (+${inner.delta_percent}% vs ${Number(inner.theoretical_cycle_time).toFixed(1)}s objetivo)`
(inner?.actual_cycle_time ?? blob?.actual_cycle_time) &&
(inner?.theoretical_cycle_time ?? blob?.theoretical_cycle_time) &&
(inner?.delta_percent ?? blob?.delta_percent) != null
? `Cycle took ${Number(inner?.actual_cycle_time ?? blob?.actual_cycle_time).toFixed(1)}s (+${Number(inner?.delta_percent ?? blob?.delta_percent)}% vs ${Number(inner?.theoretical_cycle_time ?? blob?.theoretical_cycle_time).toFixed(1)}s objetivo)`
: null);
const ts =
@@ -161,24 +228,54 @@ export async function GET(
const ALLOWED_TYPES = new Set([
"slow-cycle",
"anomaly-detected",
"performance-degradation",
"scrap-spike",
"down",
"microstop",
"macrostop",
"oee-drop",
"quality-spike",
"performance-degradation",
"predictive-oee-decline",
]);
const events = normalized
.filter((e) => ALLOWED_TYPES.has(e.eventType))
// keep slow-cycle even if severity is info, otherwise require warning/critical/error
.filter((e) => e.eventType === "slow-cycle" || ["warning", "critical", "error"].includes(e.severity))
.filter((e) =>
["slow-cycle", "microstop", "macrostop"].includes(e.eventType) ||
["warning", "critical", "error"].includes(e.severity)
)
.slice(0, 30);
// ---- cycles window ----
const url = new URL(_req.url);
const windowSec = Number(url.searchParams.get("windowSec") ?? "10800"); // default 3h
const latestKpi = machine.kpiSnapshots[0] ?? null;
// If KPI cycleTime missing, fallback to DB cycles (we fetch 1 first)
const latestCycleForIdeal = await prisma.machineCycle.findFirst({
where: { orgId: session.orgId, machineId },
orderBy: { ts: "desc" },
select: { theoreticalCycleTime: true },
});
const effectiveCycleTime =
latestKpi?.cycleTime ??
latestCycleForIdeal?.theoreticalCycleTime ??
null;
// Estimate how many cycles we need to cover the window.
// Add buffer so the chart doesn’t look “tight”.
const estCycleSec = Math.max(1, Number(effectiveCycleTime ?? 14));
const needed = Math.ceil(windowSec / estCycleSec) + 50;
// Safety cap to avoid crazy payloads
const takeCycles = Math.min(5000, Math.max(200, needed));
const rawCycles = await prisma.machineCycle.findMany({
where: { orgId: session.orgId, machineId },
orderBy: { ts: "desc" },
take: 200,
take: takeCycles,
select: {
ts: true,
cycleCount: true,
@@ -194,23 +291,14 @@ const cycles = rawCycles
.slice()
.reverse()
.map((c) => ({
ts: c.ts, // keep Date for “time ago” UI
t: c.ts.getTime(), // numeric x-axis for charts
ts: c.ts,
t: c.ts.getTime(),
cycleCount: c.cycleCount ?? null,
actual: c.actualCycleTime, // rename to what chart expects
actual: c.actualCycleTime,
ideal: c.theoreticalCycleTime ?? null,
workOrderId: c.workOrderId ?? null,
sku: c.sku ?? null,
}
));
const latestKpi = machine.kpiSnapshots[0] ?? null;
// rawCycles is ordered DESC, so [0] is the most recent cycle row
const latestCycleIdeal = rawCycles[0]?.theoreticalCycleTime ?? null;
// REAL effective value (not mock): prefer KPI if present, else fallback to cycles table
const effectiveCycleTime = latestKpi?.cycleTime ?? latestCycleIdeal ?? null;
}));