In response to the problems of sparse temperature measurement point distribution and poor cross-interval continuity in the hot continuous rolling process, a full-process temperature field prediction and reconstruction method based on the combination of a physics-informed neural network (PINN) and a Bayesian-XGBoost surrogate model is proposed in this paper. First, a PINN model across five process areas (from the reheating furnace to the coiler) is established, taking time nodes as input and directly outputting discrete points of the temperature-time curve along the strip in each process area. Subsequently, a Bayesian-XGBoost surrogate model is used; at this time, hierarchical surrogate decision-making determines the number of prediction points automatically by the maximum error principle and thereby improves the prediction accuracy. Finally, a piecewise cubic spline interpolation algorithm is designed, based on curvature detection to achieve a precise high-precision temperature curve reconstruction of discrete prediction results. The experimental results show that this method has high accuracy in temperature curve reconstruction and good reliability in mechanical property prediction; it verifies the effect and practicability of the proposed framework in real hot continuous rolling processes.

This paper explores the use of differential privacy encryption to protect against data tampering attacks in the context of finite impulse response (FIR) system identification under binary observation conditions. The study begins by introducing the core principles of differential privacy and discussing the current security challenges faced by FIR systems. It highlights the risks of data tampering and privacy leakage during the system identification process. To address these challenges, two distinct differential privacy algorithms are proposed, providing dual encryption protection for the system parameters. By integrating differential privacy mechanisms into the FIR system, the proposed approach ensures the security and privacy of both data and parameters during transmission and processing. Experimental results demonstrate that the dual differential privacy protection effectively safeguards data while providing accurate parameter estimation, validating the effectiveness of the proposed scheme.