Real-Time Computer Vision Integration & Logistics System

نظرة عامة (Overview):

Developed a real-time, event-driven .NET application to fully automate operational reporting and logistics workflows. The system consumes validated data from external AI and Computer Vision models via message queues, routing it directly into the core enterprise system to eliminate manual data entry.

التخطيط وهيكلة النظام (Planning & Architecture):

Utilized a "plan-first" event-driven architecture approach. Before development, the integration contract across the engineering and operations teams was strictly defined. The architecture was designed to operate via independent background services (IHostedService), ensuring the messaging lifecycle and API polling had zero dependency on HTTP traffic.

الحل التقني (Technical Solution):

Engineered an Apache ActiveMQ consumer to ingest AI-triggered events.

Developed a dual-path flow: valid data is pushed automatically to the database, while failures are surfaced via a SignalR hub.

Built a real-time operator dashboard that receives live alerts and captured images via SignalR for fast exception resolution.

النتائج (Impact):

Fully automated the operational reporting cycle, entirely eliminating manual data entry, reducing human error, and cutting processing delays across a 24/7 production environment.