Graph Convolutional Neural Network for Utility Network Repair Sequences

The system performs preliminary actions by training the GCN-DRL model offline on historical damage scenarios before actual emergencies occur. The model learns optimal repair sequences in advance through simulated training episodes, storing learned policies that can be rapidly deployed during real disasters without requiring complex real-time computations.

The patent replaces traditional mechanical optimization algorithms (genetic algorithms, simulated annealing, branch-and-bound) with an intelligent GCN-DRL system. The GCN processes network topology and damage states, while the DRL agent learns optimal repair policies through reinforcement learning, substituting computationally intensive mechanical optimization with a trained neural network that provides faster inference.

The system performs preliminary hydraulic simulations during the offline training phase to teach the GCN-DRL model accurate performance evaluations. The model learns to predict system performance outcomes from training data that includes detailed hydraulic modeling results, enabling it to make accurate predictions during deployment without performing full hydraulic simulations in real-time.

The patent creates a virtual copy of the complex hydraulic system within the GCN-DRL training environment. By simulating and training on replicated damage scenarios and repair sequences, the model learns accurate performance evaluations from these virtual copies, allowing fast inference on real systems without repeatedly executing time-consuming physical or detailed computational hydraulic models.