Training method and device for generative adversarial network model, equipment, program and storage medium

An electronic equipment, comprising:

• at least one processor;
• and
• a memory communicatively connected to the at least one processor;
• wherein
• the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a training method for a heterogeneous generative adversarial network model, wherein the heterogeneous generative adversarial network model comprises a generator and a discriminator, and the method comprises:
• acquiring measurement data of a heterogeneous station, wherein the heterogeneous station comprises at least two types of stations, and each type of station among the at least two types of stations is configured to measure and obtain environment data corresponding to the each type as measurement data of the each type of station;
• and
• setting the measurement data of the heterogeneous station as a training sample, and performing joint training on the heterogeneous generative adversarial network model according to a total objective function;
• wherein the generator is configured to predict environment data at a future occasion according to environment data of the heterogeneous station at a historical occasion so as to output predicted data;
• the discriminator is configured to be input the predicted data output by the generator and corresponding measurement data, and discriminate a similarity between the measurement data and the predicted data;
• and
• the total objective function comprises a first objective function of the generator and a second objective function of the discriminator.

A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a training method for a heterogeneous generative adversarial network model, wherein the heterogeneous generative adversarial network model comprises a generator and a discriminator, and the method comprises:

• acquiring measurement data of a heterogeneous station, wherein the heterogeneous station comprises at least two types of stations, and each type of station among the at least two types of stations is configured to measure and obtain environment data corresponding to the each type as measurement data of the each type of station;
• and
• setting the measurement data of the heterogeneous station as a training sample, and performing joint training on the heterogeneous generative adversarial network model according to a total objective function;
• wherein the generator is configured to predict environment data at a future occasion according to environment data of the heterogeneous station at a historical occasion so as to output predicted data;
• the discriminator is configured to be input the predicted data output by the generator and corresponding measurement data, and discriminate a similarity between the measurement data and the predicted data;
• and
• the total objective function comprises a first objective function of the generator and a second objective function of the discriminator.