Research on intelligent modeling and data optimization methods for quantum current sensor measurement system

Aiming at the problems of high modeling complexity and large fluctuations in data quality in quantum current sensor measurement systems, a research scheme integrating intelligent modeling and data optimization is proposed. Firstly, the nonlinear characteristics and high-dimensional structure of quantum measurement data are analyzed, and then an adaptive deep modeling framework is established. Transfer learning methods and data augmentation methods are used to improve the universality of the model. The second step is to compare the predictive performance and robustness of the model with various optimization measures through experiments, and to compare and analyze them with traditional methods. The results show that the new method has significant advantages in measurement accuracy, real-time responsiveness, response efficiency, and system stability. Provide the theoretical basis and implementation path for a quantum sensor intelligent system solution.