demo seed change 5
This commit is contained in:
Urtzi Alfaro
@@ -58,6 +58,107 @@ def verify_internal_api_key(x_internal_api_key: str = Header(...)):
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return True
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async def load_fixture_data_for_tenant(
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db: AsyncSession,
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tenant_uuid: UUID,
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demo_account_type: str,
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reference_time: datetime
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) -> int:
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"""
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Load orchestration run data from JSON fixture directly into the virtual tenant.
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Returns the number of runs created.
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"""
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from shared.utils.seed_data_paths import get_seed_data_path
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from shared.utils.demo_dates import resolve_time_marker, adjust_date_for_demo
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# Load fixture data
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try:
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json_file = get_seed_data_path(demo_account_type, "11-orchestrator.json")
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except ImportError:
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# Fallback to original path
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seed_data_dir = Path(__file__).parent.parent.parent.parent / "shared" / "demo" / "fixtures"
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json_file = seed_data_dir / demo_account_type / "11-orchestrator.json"
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if not json_file.exists():
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logger.warning("Orchestrator fixture file not found", file=str(json_file))
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return 0
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with open(json_file, 'r', encoding='utf-8') as f:
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fixture_data = json.load(f)
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orchestration_run_data = fixture_data.get("orchestration_run")
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if not orchestration_run_data:
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logger.warning("No orchestration_run data in fixture")
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return 0
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# Parse and adjust dates from fixture to reference_time
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base_started_at = resolve_time_marker(orchestration_run_data.get("started_at"))
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base_completed_at = resolve_time_marker(orchestration_run_data.get("completed_at"))
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# Adjust dates to make them appear recent relative to session creation
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started_at = adjust_date_for_demo(base_started_at, reference_time) if base_started_at else reference_time - timedelta(hours=2)
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completed_at = adjust_date_for_demo(base_completed_at, reference_time) if base_completed_at else started_at + timedelta(minutes=15)
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# Generate unique run number with session context
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current_year = reference_time.year
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unique_suffix = str(uuid.uuid4())[:8].upper()
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run_number = f"ORCH-DEMO-PROF-{current_year}-001-{unique_suffix}"
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# Create orchestration run for virtual tenant
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new_run = OrchestrationRun(
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id=uuid.uuid4(), # Generate new UUID
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tenant_id=tenant_uuid,
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run_number=run_number,
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status=OrchestrationStatus[orchestration_run_data["status"]],
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run_type=orchestration_run_data.get("run_type", "daily"),
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priority="normal",
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started_at=started_at,
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completed_at=completed_at,
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duration_seconds=orchestration_run_data.get("duration_seconds", 900),
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# Step statuses from orchestration_results
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forecasting_status="success",
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forecasting_started_at=started_at,
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forecasting_completed_at=started_at + timedelta(minutes=2),
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production_status="success",
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production_started_at=started_at + timedelta(minutes=2),
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production_completed_at=started_at + timedelta(minutes=5),
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procurement_status="success",
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procurement_started_at=started_at + timedelta(minutes=5),
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procurement_completed_at=started_at + timedelta(minutes=8),
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notification_status="success",
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notification_started_at=started_at + timedelta(minutes=8),
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notification_completed_at=completed_at,
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# Results from orchestration_results
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forecasts_generated=fixture_data.get("orchestration_results", {}).get("forecasts_generated", 10),
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production_batches_created=fixture_data.get("orchestration_results", {}).get("production_batches_created", 18),
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procurement_plans_created=0,
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purchase_orders_created=fixture_data.get("orchestration_results", {}).get("purchase_orders_created", 6),
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notifications_sent=fixture_data.get("orchestration_results", {}).get("notifications_sent", 8),
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# Metadata
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triggered_by="system",
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created_at=started_at,
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updated_at=completed_at
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)
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db.add(new_run)
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await db.flush()
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logger.info(
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"Loaded orchestration run from fixture",
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tenant_id=str(tenant_uuid),
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run_number=new_run.run_number,
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started_at=started_at.isoformat()
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)
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return 1
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@router.post("/internal/demo/clone")
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async def clone_demo_data(
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base_tenant_id: str,
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@@ -73,6 +174,8 @@ async def clone_demo_data(
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This endpoint is called by the demo_session service during session initialization.
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It clones orchestration runs with date adjustments to make them appear recent.
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If the base tenant has no orchestration runs, it will first seed them from the fixture.
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"""
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start_time = datetime.now(timezone.utc)
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@@ -96,150 +199,24 @@ async def clone_demo_data(
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)
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try:
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base_uuid = uuid.UUID(base_tenant_id)
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virtual_uuid = uuid.UUID(virtual_tenant_id)
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# Fetch base tenant orchestration runs
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# Get all completed and partial_success runs from the base tenant
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result = await db.execute(
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select(OrchestrationRun)
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.where(OrchestrationRun.tenant_id == base_uuid)
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.order_by(OrchestrationRun.started_at.desc())
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.limit(10) # Clone last 10 runs for demo
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# Load fixture data directly into virtual tenant (no base tenant cloning)
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runs_created = await load_fixture_data_for_tenant(
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db,
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virtual_uuid,
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demo_account_type,
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reference_time
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)
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base_runs = list(result.scalars().all())
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runs_cloned = 0
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# Clone each orchestration run with date adjustment
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for base_run in base_runs:
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# Use the shared date adjustment utility to ensure dates are always in the past
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# This calculates the offset from BASE_REFERENCE_DATE and applies it to session creation time
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if base_run.started_at:
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new_started_at = adjust_date_for_demo(
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base_run.started_at, reference_time
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)
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else:
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new_started_at = reference_time - timedelta(hours=2)
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# Adjust completed_at using the same utility
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if base_run.completed_at:
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new_completed_at = adjust_date_for_demo(
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base_run.completed_at, reference_time
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)
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# Ensure completion is after start (in case of edge cases)
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if new_completed_at and new_started_at and new_completed_at < new_started_at:
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# Preserve original duration
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duration = base_run.completed_at - base_run.started_at
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new_completed_at = new_started_at + duration
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else:
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new_completed_at = None
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# Adjust all step timestamps using the shared utility
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def adjust_timestamp(original_timestamp):
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if not original_timestamp:
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return None
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return adjust_date_for_demo(original_timestamp, reference_time)
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# Create new orchestration run for virtual tenant
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# Update run_number to have current year instead of original year, and make it unique
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current_year = reference_time.year
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# Extract type from original run number and create new format
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parts = base_run.run_number.split('-')
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if len(parts) >= 4:
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tenant_prefix = parts[1] if len(parts) > 1 else "DEMO"
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type_code = parts[2] if len(parts) > 2 else "TST"
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original_index = parts[3] if len(parts) > 3 else "001"
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# Generate a more robust unique suffix to avoid collisions
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# Use UUID instead of just session_id substring to ensure uniqueness
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unique_suffix = str(uuid.uuid4())[:8].upper()
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proposed_run_number = f"ORCH-{tenant_prefix}-{type_code}-{current_year}-{original_index}-{unique_suffix}"
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else:
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unique_suffix = str(uuid.uuid4())[:12].upper()
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proposed_run_number = f"{base_run.run_number}-{unique_suffix}"
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# Ensure the run number is truly unique by checking against existing entries
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# This prevents collisions especially in high-concurrency scenarios
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run_number = await ensure_unique_run_number(db, proposed_run_number)
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new_run = OrchestrationRun(
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id=uuid.uuid4(),
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tenant_id=virtual_uuid,
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run_number=run_number,
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status=base_run.status,
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run_type=base_run.run_type,
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priority=base_run.priority,
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started_at=new_started_at,
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completed_at=new_completed_at,
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duration_seconds=base_run.duration_seconds,
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# Forecasting step
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forecasting_started_at=adjust_timestamp(base_run.forecasting_started_at),
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forecasting_completed_at=adjust_timestamp(base_run.forecasting_completed_at),
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forecasting_status=base_run.forecasting_status,
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forecasting_error=base_run.forecasting_error,
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# Production step
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production_started_at=adjust_timestamp(base_run.production_started_at),
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production_completed_at=adjust_timestamp(base_run.production_completed_at),
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production_status=base_run.production_status,
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production_error=base_run.production_error,
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# Procurement step
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procurement_started_at=adjust_timestamp(base_run.procurement_started_at),
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procurement_completed_at=adjust_timestamp(base_run.procurement_completed_at),
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procurement_status=base_run.procurement_status,
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procurement_error=base_run.procurement_error,
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# Notification step
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notification_started_at=adjust_timestamp(base_run.notification_started_at),
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notification_completed_at=adjust_timestamp(base_run.notification_completed_at),
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notification_status=base_run.notification_status,
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notification_error=base_run.notification_error,
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# AI Insights (if exists)
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ai_insights_started_at=adjust_timestamp(base_run.ai_insights_started_at) if hasattr(base_run, 'ai_insights_started_at') else None,
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ai_insights_completed_at=adjust_timestamp(base_run.ai_insights_completed_at) if hasattr(base_run, 'ai_insights_completed_at') else None,
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ai_insights_status=base_run.ai_insights_status if hasattr(base_run, 'ai_insights_status') else None,
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ai_insights_generated=base_run.ai_insights_generated if hasattr(base_run, 'ai_insights_generated') else None,
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ai_insights_posted=base_run.ai_insights_posted if hasattr(base_run, 'ai_insights_posted') else None,
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# Results summary
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forecasts_generated=base_run.forecasts_generated,
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production_batches_created=base_run.production_batches_created,
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procurement_plans_created=base_run.procurement_plans_created,
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purchase_orders_created=base_run.purchase_orders_created,
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notifications_sent=base_run.notifications_sent,
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# Performance metrics
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fulfillment_rate=base_run.fulfillment_rate,
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on_time_delivery_rate=base_run.on_time_delivery_rate,
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cost_accuracy=base_run.cost_accuracy,
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quality_score=base_run.quality_score,
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# Data
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forecast_data=base_run.forecast_data,
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run_metadata=base_run.run_metadata,
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# Metadata
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triggered_by=base_run.triggered_by,
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created_at=reference_time,
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updated_at=reference_time
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)
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db.add(new_run)
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await db.flush()
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runs_cloned += 1
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await db.commit()
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duration_ms = int((datetime.now(timezone.utc) - start_time).total_seconds() * 1000)
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logger.info(
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"Orchestration runs cloned successfully",
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"Orchestration runs loaded from fixture successfully",
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virtual_tenant_id=str(virtual_tenant_id),
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runs_cloned=runs_cloned,
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runs_created=runs_created,
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duration_ms=duration_ms
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)
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@@ -247,8 +224,8 @@ async def clone_demo_data(
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"service": "orchestrator",
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"status": "completed",
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"success": True,
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"records_cloned": runs_cloned,
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"runs_cloned": runs_cloned,
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"records_cloned": runs_created,
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"runs_cloned": runs_created,
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"duration_ms": duration_ms
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}
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@@ -187,7 +187,7 @@ async def clone_demo_data(
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logger.info(
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"Loaded orders seed data",
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customers=len(seed_data.get('customers', [])),
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orders=len(seed_data.get('orders', []))
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orders=len(seed_data.get('customer_orders', []))
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)
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# Load Customers from seed data
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@@ -249,7 +249,7 @@ async def clone_demo_data(
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# Load Customer Orders from seed data
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order_id_map = {}
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for order_data in seed_data.get('orders', []):
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for order_data in seed_data.get('customer_orders', []):
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# Transform IDs using XOR
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from shared.utils.demo_id_transformer import transform_id
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try:
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@@ -246,47 +246,60 @@ async def clone_demo_data(
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# Flush to get equipment IDs
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await db.flush()
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# Clone Quality Check Templates
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# Note: Quality check templates are not included in seed data
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# They would need to be added to the production seed data if needed
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# Clone Quality Check Templates from seed data
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template_id_map = {}
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base_templates = []
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logger.info(
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"No quality check templates to clone (not in seed data)",
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count=len(base_templates)
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)
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for template_data in seed_data.get('quality_check_templates', []):
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# Transform template ID using XOR
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from shared.utils.demo_id_transformer import transform_id
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try:
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template_uuid = UUID(template_data['id'])
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transformed_id = transform_id(template_data['id'], virtual_uuid)
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except ValueError as e:
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logger.error("Failed to parse template UUID",
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template_id=template_data['id'],
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error=str(e))
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continue
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# Only create templates if they exist in base templates
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for template in base_templates:
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new_template_id = uuid.uuid4()
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template_id_map[template.id] = new_template_id
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template_id_map[UUID(template_data['id'])] = transformed_id
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# Parse date fields (supports BASE_TS markers and ISO timestamps)
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adjusted_created_at = parse_date_field(
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template_data.get('created_at'),
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session_time,
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"created_at"
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) or session_time
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adjusted_updated_at = parse_date_field(
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template_data.get('updated_at'),
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session_time,
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"updated_at"
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) or adjusted_created_at
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new_template = QualityCheckTemplate(
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id=new_template_id,
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id=str(transformed_id),
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tenant_id=virtual_uuid,
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name=template.name,
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template_code=template.template_code,
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check_type=template.check_type,
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category=template.category,
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description=template.description,
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instructions=template.instructions,
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parameters=template.parameters,
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thresholds=template.thresholds,
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scoring_criteria=template.scoring_criteria,
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is_active=template.is_active,
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is_required=template.is_required,
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is_critical=template.is_critical,
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weight=template.weight,
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min_value=template.min_value,
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max_value=template.max_value,
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target_value=template.target_value,
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unit=template.unit,
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tolerance_percentage=template.tolerance_percentage,
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applicable_stages=template.applicable_stages,
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created_by=template.created_by,
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created_at=session_time,
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updated_at=session_time
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name=template_data.get('name'),
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template_code=template_data.get('template_code'),
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check_type=template_data.get('check_type'),
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category=template_data.get('category'),
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description=template_data.get('description'),
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instructions=template_data.get('instructions'),
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parameters=template_data.get('parameters'),
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thresholds=template_data.get('thresholds'),
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scoring_criteria=template_data.get('scoring_criteria'),
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is_active=template_data.get('is_active', True),
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is_required=template_data.get('is_required', False),
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is_critical=template_data.get('is_critical', False),
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weight=template_data.get('weight', 1.0),
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min_value=template_data.get('min_value'),
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max_value=template_data.get('max_value'),
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target_value=template_data.get('target_value'),
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unit=template_data.get('unit'),
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tolerance_percentage=template_data.get('tolerance_percentage'),
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applicable_stages=template_data.get('applicable_stages'),
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created_by=template_data.get('created_by'),
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created_at=adjusted_created_at,
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updated_at=adjusted_updated_at
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)
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db.add(new_template)
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stats["quality_check_templates"] += 1
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@@ -1731,5 +1731,304 @@
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"created_at": "BASE_TS",
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"updated_at": "BASE_TS"
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}
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],
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"quality_check_templates": [
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{
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"id": "80000000-0000-0000-0000-000000000001",
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"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
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"name": "Inspección Visual de Cocción",
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"template_code": "QC-VISUAL-001",
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"check_type": "visual",
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"category": "Apariencia",
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"description": "Inspección visual del color, dorado y apariencia general del producto horneado",
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"instructions": "Verificar que el producto tenga un color uniforme, dorado apropiado y sin quemaduras. Revisar grietas, corteza y estructura general.",
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"parameters": {
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"color_uniformity": true,
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"golden_brown": true,
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"no_burns": true,
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"proper_crust": true
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},
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"thresholds": {
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"min_score": 7.0,
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"critical_defects": ["burnt", "raw", "collapsed"]
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},
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"scoring_criteria": {
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"color": 3.0,
|
||||
"texture": 3.0,
|
||||
"appearance": 2.0,
|
||||
"structure": 2.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": true,
|
||||
"is_critical": false,
|
||||
"weight": 1.0,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": null,
|
||||
"tolerance_percentage": null,
|
||||
"applicable_stages": ["baking", "cooling", "packaging"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000002",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Control de Peso Unitario",
|
||||
"template_code": "QC-WEIGHT-001",
|
||||
"check_type": "measurement",
|
||||
"category": "Dimensiones",
|
||||
"description": "Verificación del peso unitario del producto contra los estándares definidos",
|
||||
"instructions": "Pesar una muestra representativa de 5 unidades y verificar que el peso promedio esté dentro de la tolerancia permitida.",
|
||||
"parameters": {
|
||||
"sample_size": 5,
|
||||
"unit": "grams"
|
||||
},
|
||||
"thresholds": {
|
||||
"min_weight": null,
|
||||
"max_weight": null,
|
||||
"tolerance": 5.0
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"weight_accuracy": 5.0,
|
||||
"consistency": 5.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": true,
|
||||
"is_critical": false,
|
||||
"weight": 1.5,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": "g",
|
||||
"tolerance_percentage": 5.0,
|
||||
"applicable_stages": ["packaging"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000003",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Control de Temperatura Interna",
|
||||
"template_code": "QC-TEMP-001",
|
||||
"check_type": "temperature",
|
||||
"category": "Cocción",
|
||||
"description": "Medición de la temperatura interna del producto para verificar cocción completa",
|
||||
"instructions": "Insertar termómetro en el centro del producto y verificar que alcance la temperatura mínima de seguridad alimentaria.",
|
||||
"parameters": {
|
||||
"measurement_location": "center",
|
||||
"thermometer_type": "digital"
|
||||
},
|
||||
"thresholds": {
|
||||
"min_temp": 88.0,
|
||||
"max_temp": 98.0,
|
||||
"critical_min": 75.0
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"temperature_range": 10.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": true,
|
||||
"is_critical": true,
|
||||
"weight": 2.0,
|
||||
"min_value": 88.0,
|
||||
"max_value": 98.0,
|
||||
"target_value": 93.0,
|
||||
"unit": "°C",
|
||||
"tolerance_percentage": 5.0,
|
||||
"applicable_stages": ["baking"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000004",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Evaluación de Textura y Estructura",
|
||||
"template_code": "QC-TEXTURE-001",
|
||||
"check_type": "visual",
|
||||
"category": "Estructura",
|
||||
"description": "Evaluación de la textura interna, alveolado y estructura de la miga",
|
||||
"instructions": "Cortar el producto por la mitad y evaluar: alveolado uniforme, miga suave y húmeda, estructura adecuada sin zonas densas.",
|
||||
"parameters": {
|
||||
"crumb_structure": true,
|
||||
"moisture_level": true,
|
||||
"alveoli_distribution": true
|
||||
},
|
||||
"thresholds": {
|
||||
"min_score": 7.0,
|
||||
"critical_defects": ["dense", "dry", "gummy"]
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"crumb_openness": 3.0,
|
||||
"moisture": 3.0,
|
||||
"consistency": 2.0,
|
||||
"mouthfeel": 2.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": false,
|
||||
"is_critical": false,
|
||||
"weight": 1.0,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": null,
|
||||
"tolerance_percentage": null,
|
||||
"applicable_stages": ["cooling", "packaging"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000005",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Control de Dimensiones",
|
||||
"template_code": "QC-DIM-001",
|
||||
"check_type": "measurement",
|
||||
"category": "Dimensiones",
|
||||
"description": "Verificación de las dimensiones (largo, ancho, alto) del producto terminado",
|
||||
"instructions": "Medir una muestra de 5 unidades con calibrador o regla. Verificar que las dimensiones estén dentro del rango especificado.",
|
||||
"parameters": {
|
||||
"sample_size": 5,
|
||||
"dimensions": ["length", "width", "height"]
|
||||
},
|
||||
"thresholds": {
|
||||
"tolerance": 10.0
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"dimensional_accuracy": 5.0,
|
||||
"uniformity": 5.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": false,
|
||||
"is_critical": false,
|
||||
"weight": 0.8,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": "cm",
|
||||
"tolerance_percentage": 10.0,
|
||||
"applicable_stages": ["shaping", "packaging"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000006",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Prueba Organoléptica (Sabor y Aroma)",
|
||||
"template_code": "QC-TASTE-001",
|
||||
"check_type": "visual",
|
||||
"category": "Calidad Sensorial",
|
||||
"description": "Evaluación del sabor, aroma y características organolépticas del producto",
|
||||
"instructions": "Probar una muestra del producto. Evaluar sabor apropiado, aroma característico, sin sabores extraños u oxidación.",
|
||||
"parameters": {
|
||||
"flavor_profile": true,
|
||||
"aroma": true,
|
||||
"off_flavors": false,
|
||||
"freshness": true
|
||||
},
|
||||
"thresholds": {
|
||||
"min_score": 8.0,
|
||||
"critical_defects": ["off_taste", "rancid", "bitter"]
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"flavor": 4.0,
|
||||
"aroma": 3.0,
|
||||
"freshness": 2.0,
|
||||
"overall_quality": 1.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": false,
|
||||
"is_critical": true,
|
||||
"weight": 1.5,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": null,
|
||||
"tolerance_percentage": null,
|
||||
"applicable_stages": ["cooling", "packaging"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000007",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Control de Fermentación",
|
||||
"template_code": "QC-PROOF-001",
|
||||
"check_type": "visual",
|
||||
"category": "Fermentación",
|
||||
"description": "Evaluación del nivel de fermentación adecuado antes del horneado",
|
||||
"instructions": "Verificar volumen de la masa, elasticidad al tacto y señales de fermentación apropiada. Prueba del dedo para verificar punto óptimo.",
|
||||
"parameters": {
|
||||
"volume_increase": true,
|
||||
"finger_test": true,
|
||||
"structure": true
|
||||
},
|
||||
"thresholds": {
|
||||
"min_score": 7.0,
|
||||
"critical_defects": ["underproofed", "overproofed", "collapsed"]
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"volume": 4.0,
|
||||
"elasticity": 3.0,
|
||||
"structure": 3.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": true,
|
||||
"is_critical": false,
|
||||
"weight": 1.2,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": null,
|
||||
"tolerance_percentage": null,
|
||||
"applicable_stages": ["proofing"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
},
|
||||
{
|
||||
"id": "80000000-0000-0000-0000-000000000008",
|
||||
"tenant_id": "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"name": "Control de Laminado (Hojaldre)",
|
||||
"template_code": "QC-LAMINATE-001",
|
||||
"check_type": "visual",
|
||||
"category": "Técnica",
|
||||
"description": "Evaluación de la calidad del laminado en productos de hojaldre (croissants, napolitanas)",
|
||||
"instructions": "Verificar número de capas visibles, separación entre capas, sin mantequilla derramada. Cortar producto para inspeccionar estructura interna.",
|
||||
"parameters": {
|
||||
"layer_count": true,
|
||||
"layer_separation": true,
|
||||
"butter_distribution": true,
|
||||
"no_leakage": true
|
||||
},
|
||||
"thresholds": {
|
||||
"min_score": 8.0,
|
||||
"critical_defects": ["butter_leakage", "collapsed_layers", "uneven_distribution"]
|
||||
},
|
||||
"scoring_criteria": {
|
||||
"layer_definition": 4.0,
|
||||
"butter_incorporation": 3.0,
|
||||
"structure": 2.0,
|
||||
"appearance": 1.0
|
||||
},
|
||||
"is_active": true,
|
||||
"is_required": false,
|
||||
"is_critical": false,
|
||||
"weight": 1.3,
|
||||
"min_value": null,
|
||||
"max_value": null,
|
||||
"target_value": null,
|
||||
"unit": null,
|
||||
"tolerance_percentage": null,
|
||||
"applicable_stages": ["shaping", "baking"],
|
||||
"created_by": "c1a2b3c4-d5e6-47a8-b9c0-d1e2f3a4b5c6",
|
||||
"created_at": "BASE_TS",
|
||||
"updated_at": "BASE_TS"
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user