Energy consumption accounting and prediction in the medium and thick plate rolling process are crucial for controlling costs, improving production efficiency, optimizing equipment management, and enhancing the market competitiveness of enterprises. Starting from the perspective of integrating process mechanism and industrial big data, we overcame the difficulties brought by complex and highly nonlinear coupling of process variables, proposed a rolling power consumption accounting algorithm based on time slicing method, and gave a calculation method for the additional power consumption of the main motor for rough rolling and finishing rolling (auxiliary system power consumption, power loss, main motor power consumption deviation); with the help of SIMS model, forward recursion, and reverse recursion pass rolling force estimation strategies are proposed, and the rated power consumption of the main motor was predicted. Furthermore, a random forest regression model of additional power consumption based on data was established, and then a prediction algorithm for the comprehensive power consumption of billet rolling was given. Experiments showed the effectiveness of the proposed method.

This paper studies the formulation for an optimal attack strategy, which aims to maximize the identification error of Hammerstein systems with binary measurements subject to data tampering attacks. First, the convergence of the parameter estimates under attack is analyzed, and the absolute error between the estimated value and the true value is used as the objective function. Second, an optimization model with constraints on the maximum data tampering rate and the average data tampering rate is established to maximize the objective function. Then, the sine-cosine optimization algorithm is used to search for the optimal solution that meets the constraints, and its performance is compared with other algorithms. Finally, the effectiveness of the proposed method is verified by a numerical simulation.