Research on performance evaluation and optimization algorithm of PLC control system based on graph neural network

Traditional evaluation and optimization methods have limitations in dealing with complex and nonlinear industrial systems. As a new technology, graph neural network (GNN) can effectively process graph structure data, which provides a new idea for the performance evaluation and optimization of PLC control system. This paper first introduces the basic principle of GNN and its applicability in PLC control system, and then constructs a performance evaluation model based on graph-convolution network (GCN), which can comprehensively evaluate the stability, accuracy, real-time performance and reliability of PLC control system by aggregating node information. Experimental results show that the model can accurately reflect the performance state of the system. Furthermore, this article proposes an optimization algorithm based on GNN, which optimizes the energy consumption of the actuator as the objective function and updates the model weights using stochastic gradient descent (SGD) algorithm, achieving real-time optimization of the PLC control system. Compared with the traditional PID algorithm, GNN optimization algorithm performs well in reducing energy consumption and system stability, and meets the real-time requirements. The performance evaluation and optimization algorithm of PLC control system based on GNN can effectively improve the system performance and provide new methods and theoretical support for the optimization of complex industrial control system.