Graph Neural Network for Infrastructure Structural Change Optimization
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Solution Overview
Problem
Current solutions for capital investment planning in social infrastructure systems, such as electric power systems, fail to adapt to changes in configurations and do not effectively manage risk costs, installation costs, and maintenance costs, leading to inefficiencies in facility management.
Innovation Solution
An information processing device and method that utilize a graph neural network to evaluate structural changes in social infrastructure systems by defining a convolution function associated with a model representing data of a graph structure, performing reinforcement learning to optimize structural changes based on cost and system state, and proposing facility change plans that minimize cumulative costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional capital investment planning methods are used for social infrastructure systems, then planning can be performed with existing frameworks, but the plans cannot adapt to changes in system configurations and fail to effectively manage risk costs, installation costs, and maintenance costs
Solution Approach 1:
The patent implements dynamic adaptation by allowing the graph neural network to process varying system configurations and time horizons. The reinforcement learning component continuously optimizes planning decisions based on feedback from system state changes, enabling the planning system to adapt to configuration changes while maintaining cost and risk management effectiveness.
Solution Approach 2:
The patent changes the approach by using graph neural networks that can process different graph structures representing system configurations. The model adjusts its analysis based on varying parameters such as system state, time horizon, and cost parameters, enabling both adaptability to configuration changes and effective cost management through learned optimization policies.
2Adaptability or versatility
If graph neural networks with reinforcement learning are implemented to optimize structural changes, then adaptability to configuration changes and cost management improve, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex planning problem into distinct components: graph representation of system structure, node and edge feature extraction, convolutional processing for pattern recognition, and reinforcement learning for optimization. This segmentation allows each component to handle specific aspects of the problem, managing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The graph neural network acts as an intermediary between the raw system configuration data and the optimization decisions. It processes and transforms the complex relationships in infrastructure systems into meaningful representations that the reinforcement learning algorithm can effectively use for cost and risk optimization, managing complexity through this intermediate processing layer.
3Reliability
If comprehensive cost and risk management is implemented in facility planning, then planning effectiveness improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing system data into graph structures with extracted features before the main optimization process. The graph neural network can process these pre-prepared representations efficiently, and the reinforcement learning algorithm uses these prepared inputs to generate optimized plans, reducing overall processing time while maintaining comprehensive cost and risk management.
Solution Approach 2:
The patent replaces traditional mechanical/computational planning methods with graph neural networks and reinforcement learning. This substitution enables more efficient processing of complex planning problems by using learned patterns and optimizations, reducing computation time while improving the effectiveness of cost and risk management through data-driven decision making.
Data Source
AI summary
An information processing device includes a definer, a determiner, and a reinforcement learner. The definer is configured to associate a node and an edge with attributes and to define a convolution function associated with a model representing data of a graph structure representing a system structure on the basis of data regarding the graph structure. The evaluator is configured to input a state of the system into the model. The evaluator is configured to obtain, for each time step, a policy function as a probability distribution of a structural change and a state value function for reinforcement learning for a system of one or more structurally changed models which have been changed with assumable structural changes from the model for each time step. The evaluator is configured to evaluate the structural changes in the system on the basis of the policy function. The reinforcement learner is configured to perform reinforcement learning by using a reward value as a cost generated when the structural change is applied to the system, the state value function, and the model, to optimize the structural change in the system.