As unmanned retail becomes a new trend, AI virtual assistants based on LED displays are building a 24/7 intelligent service system for stores through visual interaction and voice recognition. This configuration solution, which integrates hardware deployment and algorithm training, has already increased the consultation conversion rate by 58% in some brand stores.

Three-step hardware integration

Screen selection and sensor deployment: Prioritize LED displays with a resolution of 4K or higher to ensure a clear and smooth virtual assistant image. One beauty store saw customer stay time increase to 7 minutes after adopting a P1.8 fine-pitch screen. A Time of Flight (ToF) depth camera and microphone array are used to enable human detection and voice pickup within a 3-meter range. The camera should be mounted at a 15° angle above the display to ensure facial recognition accuracy.

Edge computing unit configuration: Deploy edge servers such as the NVIDIA Jetson AGX Orin to locally process image rendering and voice interaction data, avoiding cloud latency. A test at a 3C store showed that edge computing reduced the response time of virtual store assistants from 1.2 seconds compared to a cloud-based solution to 0.4 seconds, significantly improving interaction fluency.

Network and power supply assurance: A 5G+Wi-Fi 6 dual-link network ensures stable transmission of high-volume video streams; UPS uninterruptible power supplies prevent service interruptions caused by power outages. Thanks to this robust power supply, a convenience store chain's virtual store assistants average over 672 hours of service per month.

Core algorithm training module

Multimodal interaction model: A "vision-speech-text" trimodal network is built based on TensorFlow. The vision side uses YOLOv8 to recognize customer gestures and expressions, the voice side uses DeepSpeech for dialect recognition (supporting eight dialects), and the text side accesses a product knowledge base (containing information on over 2,000 SKUs). A clothing brand achieved a 91% problem-solving rate for its virtual store assistants by training them with 20,000 hours of real-world conversation data. Dynamic Expression Generation System: Utilizing Blendshape technology, 68 facial expressions were created for virtual store assistants. These expressions, combined with real-time customer emotion recognition, provide real-time feedback. When a customer smiles, the virtual assistant displays a cheerful expression and recommends a popular product. This emotional interaction increases recommendation acceptance by 37%.

Operational Optimization Strategy

Scenario-Based Script Library: Script templates are organized by category. For example, the beauty section features a "skin quality test - product recommendation - user guide" process, while the digital section incorporates a "parameter comparison - function demonstration - after-sales service" logic. An electronics retailer used 20 scenario-specific scripts to increase average customer spending by virtual store assistants by 22%.

Data Closed-Loop Iteration: High-frequency issues are analyzed through screen interaction logs, and algorithm models are updated monthly. A maternity and baby store discovered that inquiries regarding "milk powder mixing temperature" accounted for 34% of inquiries. They subsequently added a quick query entry to the virtual store assistant interface, improving problem-solving efficiency by 60%. From technical implementation to operational iteration, AI virtual assistants are transforming LED displays from information carriers into service providers. Driven by rising labor costs and changing consumer habits, this solution not only fills the service gap in offline storefronts but also creates new experience value and business possibilities for stores through data-driven intelligent interaction.