A hybrid self-evolving edge-AI framework for scalable digital process twins in enterprise systems

Edge-centric digital process twins struggle with slow adaptation and real-time compliance. We propose a self-evolving Edge-AI architecture with five components. The Hierarchical Neuro-Symbolic Verification Graph integrates symbolic rules and neural graphs, reducing latency by 35% while maintaining over 97% compliance. Federated Evolutionary Drift Adaptation improves drift response by 28% and F1 score by 15% using evolutionary operators. The Multi-Resolution Spatio-Temporal Causal Inference Network enables 40% earlier fault detection via causal separation. The Quantum-Inspired Edge Reinforcement Optimiser speeds convergence by 30% and boosts effectiveness by 12%. The Cross-Layer Digital Twin Consistency Ledger ensures tamper-proof state integrity with <0.5% violations at 10k+ transactions/s, enabling secure, adaptive automation.