Abstract: Embodiments of this application disclose a method for solving a system of nonlinear equations. In the method, during an ith iteration of a first system of nonlinear equations, a neural network model can identify, based on input description information of the ith iteration, system of equations feature information of the first system of nonlinear equations during the ith iteration, to efficiently determine a first convergence policy that is appropriate for the first system of nonlinear equations in the current iteration process. In this way, a target convergence policy is determined according to the first convergence policy, and the first system of nonlinear equations is iteratively solved efficiently and accurately according to the target convergence policy, so that performance of iterative solving on a system of nonlinear equations is improved (for example, a solving speed is improved and solving time consumption is reduced).

Abstract: Disclosed are a magnetic auxiliary support structure for a stirring shaft and an intelligent monitoring method. The magnetic auxiliary support structure includes an inner magnetic member and an outer magnetic member. The inner magnetic member is fixedly connected to the stirring shaft, and the outer magnetic member is sleeved around an outer side of the inner magnetic member and fixedly mounted. The inner magnetic member and the outer magnetic member are arranged in a magnetically repulsive manner, and a repulsive force generated therebetween is uniformly distributed in a circumferential direction of the stirring shaft. The magnetic auxiliary support structure is adopted for the intelligent monitoring method, and an actual deflection of the stirring shaft is calculated according to a variation in a motion field formed by the relative motion of the inner magnetic member and the outer magnetic member captured through the sensor.