Adaptive Multi-Tier AI System with Hierarchical Tiny LLM Architecture, Hybrid AI Mesh Network Integrating Bluetooth and Multi-Protocol Communication, Offline-to-Online AI Learning, Dual-Mode Operations, and AI-Optimized Industrial Control for Scalable Applications

Smart overview of the Invention

Many AI-driven control systems face challenges due to high computational demands, limited real-time adaptability, fragmented communication protocols, energy inefficiency, and restricted offline processing capabilities. These issues hinder the deployment of efficient, scalable AI solutions, particularly in autonomous or resource-constrained environments. The invention introduces a new approach that combines hierarchical tiny LLM architecture, a hybrid AI mesh network, robust offline-to-online AI learning, dual-mode operations, and AI-optimized industrial control to address these limitations. This results in a versatile solution that enhances real-time responsiveness, seamless connectivity, and operational reach in industrial and critical applications.

Summary of the Invention

An adaptive AI-driven industrial control system is introduced, leveraging a hierarchical tiny LLM architecture and a hybrid AI mesh network supporting Bluetooth, Thread, Wi-Fi, and industrial protocols like Modbus and MQTT. AI models deployed at embedded, edge, and cloud levels optimize real-time decision-making and efficiency. Offline learning through autonomous local model updates ensures continuous adaptation, synchronizing improvements when connectivity is restored. Dual-mode operations intelligently switch between energy-saving and AI-driven modes, enhancing performance based on operational needs. This approach enables advanced industrial control, predicting maintenance requirements and improving scalability for mission-critical applications.

Brief Description of Drawings

• FIG. 1: System Architecture Overview
• FIG. 2: Multi-Tier AI Processing Flowchart
• FIG. 3: Hybrid AI Mesh Network Structure
• FIG. 4: Adaptive Offline-to-Online Learning Process
• FIG. 5: Industrial Control Integration Diagram

Detailed Description of the Invention

The AI-driven control system integrates hierarchical architecture, a hybrid mesh network, an offline-to-online learning framework, dual-mode operations, and AI-based industrial control. FIG. 1 shows the system architecture, with hierarchical tiny LLM processing working alongside the mesh network and dual-mode operations for efficient real-time control. FIG. 2 illustrates multi-tier AI processing, coordinating tasks across cloud, edge, and embedded levels to reduce latency and resource consumption. FIG. 3 demonstrates the hybrid AI mesh network's integration of Bluetooth, Thread, Wi-Fi, and industrial protocols, designed to self-heal and maintain connectivity.

FIG. 4 depicts the adaptive offline-to-online AI learning process, allowing uninterrupted local learning during limited connectivity and synchronizing improvements upon reestablishment. FIG. 5 shows AI-optimized industrial control integrating with process control, energy management, predictive maintenance, and real-time monitoring. The AI decision system refines parameters, detects anomalies, and schedules maintenance to reduce downtime. Dual-mode operations maximize efficiency by toggling between energy-saving and resource-intensive AI modes based on operational demands, conserving energy while maintaining advanced decision-making capabilities in mission-critical scenarios.