Abstract: Provided are a method and a device for acquiring a temperature and a computer-readable storage medium. The method for acquiring a temperature includes: building a temperature acquisition model, wherein the temperature acquisition model is configured to acquire, based on an operating parameter of the radioactive substance treatment system input to the temperature acquisition model, a temperature of different parts of the radioactive reactant in the radioactive substance treatment system under a condition of the parameter; inputting a current operating parameter of the radioactive substance treatment system into the temperature acquisition model during the treatment for the radioactive substance; and acquiring a current temperature of different parts of the radioactive reactant in the radioactive substance treatment system output by the temperature acquisition model.

Abstract: Provided are a method and a device for acquiring a temperature and a computer-readable storage medium. The method for acquiring a temperature includes: building a temperature acquisition model, wherein the temperature acquisition model is configured to acquire, based on an operating parameter of the radioactive substance treatment system input to the temperature acquisition model, a temperature of different parts of the radioactive reactant in the radioactive substance treatment system under a condition of the parameter; inputting a current operating parameter of the radioactive substance treatment system into the temperature acquisition model during the treatment for the radioactive substance; and acquiring a current temperature of different parts of the radioactive reactant in the radioactive substance treatment system output by the temperature acquisition model.

Abstract: A parallel analog circuit automatic optimization method based on genetic algorithm and machine learning comprises global optimization based on genetic algorithm and local optimization based on machine learning, with the global optimization and the local optimization performed alternately. The global optimization based on genetic algorithm utilizes parallel SPICE simulations to improve the optimization efficiency while guaranteeing the optimization accuracy, combined with parallel computing. The local optimization based on machine learning establishes a machine learning model near the global optimal point obtained by the global optimization, and uses the machine learning model to replace the SPICE simulator, thus reducing the time costs brought by a large number of simulations.

Abstract: A parallel analog circuit automatic optimization method based on genetic algorithm and machine learning comprises global optimization based on genetic algorithm and local optimization based on machine learning, with the global optimization and the local optimization performed alternately. The global optimization based on genetic algorithm utilizes parallel SPICE simulations to improve the optimization efficiency while guaranteeing the optimization accuracy, combined with parallel computing. The local optimization based on machine learning establishes a machine learning model near the global optimal point obtained by the global optimization, and uses the machine learning model to replace the SPICE simulator, thus reducing the time costs brought by a large number of simulations.