Industrial Computer Vision & Edge AI: Architecting Real-Time Quality Inspection Pipelines Integrated with Enterprise ERP Systems

The 1-10-100 Rule of Quality Cost Escalation

This manufacturing economic law encapsulates the compounding financial penalty of delayed defect detection:

import json
import paho.mqtt.client as mqtt

# Configure industrial MQTT broker gateway
mqtt_client = mqtt.Client(client_id="Edge_Vision_Station_04")
mqtt_client.connect("192.168.10.50", 1883, 60)

def evaluate_inspection_frame(frame, work_order_id, part_serial):
    """
    Executes real-time defect inference on NVIDIA Jetson using TensorRT.
    Emits physical PLC trigger and dispatches asynchronous enterprise ERP payload.
    """
    # Run TensorRT FP16 Anomaly Model
    is_defective, defect_type, confidence = run_tensorrt_inference(frame)

    if is_defective and confidence > 0.88:
        # A. Instant physical PLC trigger to divert piece (< 5ms)
        trigger_plc_ejector_pulse(line_pin=12)

        # B. Construct enterprise telemetry packet
        payload = {
            "event": "QUALITY_DEFECT_DETECTED",
            "work_order_id": work_order_id,
            "part_serial": part_serial,
            "work_center": "CNC-Milling-02",
            "defect_type": defect_type,
            "confidence_score": round(confidence, 4),
            "action_taken": "PHYSICAL_REJECTED"
        }
        
        # Asynchronously dispatch to ERP broker without stalling next frame
        mqtt_client.publish(
            "factory/line04/quality/defects",
            json.dumps(payload),
            qos=1
        )
        return False

    return True

from django.db import transaction
from rest_framework.views import APIView
from rest_framework.response import Response
from cv.models import WorkOrder, StockEntry, MaintenanceTicket, DefectLog

class EdgeQualityWebhookView(APIView):
    """
    Ingests Edge AI defect telemetry, executing quarantine transfers and maintenance tickets.
    """
    @transaction.atomic
    def post(self, request):
        data = request.data
        work_order = WorkOrder.objects.select_for_update().get(id=data["work_order_id"])

        # 1. Persist audit-grade defect telemetry log
        defect = DefectLog.objects.create(
            work_order=work_order,
            part_serial=data["part_serial"],
            work_center=data["work_center"],
            defect_type=data["defect_type"],
            confidence=data["confidence_score"]
        )

        # 2. Reallocate physical inventory to Quarantine Warehouse
        StockEntry.objects.create(
            entry_type="Quarantine Transfer",
            from_warehouse="WIP-Floor",
            to_warehouse="Quarantine-Storage",
            part_serial=data["part_serial"],
            remarks=f"Edge AI automated rejection: {data['defect_type']}"
        )

        # 3. Increment Work Order scrap line item
        work_order.scrap_count += 1
        work_order.save(update_fields=['scrap_count'])

        # 4. Check defect repetition threshold for automated maintenance dispatch
        recent_defects = DefectLog.objects.filter(
            work_center=data["work_center"],
            defect_type=data["defect_type"],
            created_at__gte=timezone.now() - timedelta(minutes=15)
        ).count()

        if recent_defects >= 3:
            MaintenanceTicket.objects.get_or_create(
                work_center=data["work_center"],
                status="Urgent",
                defaults={
                    "title": f"Preventive Maintenance Alert: 3 Consecutive {data['defect_type']} Defects",
                    "assigned_team": "Tooling & Mechanical Engineering",
                    "auto_generated": True
                }
            )

        return Response({"status": "SUCCESS", "message": "Quarantine & Scrap Processed"}, status=200)