Abstract: A method and apparatus for training an information adjustment model of a charging station, an electronic device, and a storage medium are provided. An implementation comprises: acquiring a battery charging request, and determining environment state information corresponding to each charging station in a charging station set; determining, through an initial policy network, target operational information of each charging station in the charging station set for the battery charging request, according to the environment state information; determining, through an initial value network, a cumulative reward expectation corresponding to the battery charging request according to the environment state information and the target operational information; training the initial policy network and the initial value network by using a deep deterministic policy gradient algorithm; and determining the trained policy network as an information adjustment model corresponding to each charging station.

Abstract: Provided are a training method and device for a heterogeneous generative adversarial network model, an equipment, a program and a storage medium. In the training method, measurement data of a heterogeneous station is acquired, the measurement data of the heterogeneous station is set as a training sample, and joint training is performed on the heterogeneous generative adversarial network model according to a total objective function. A 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. A 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; a total objective function includes a first objective function of the generator and a second objective function of the discriminator.

Abstract: Provided are a training method for an air quality prediction model, a prediction method and apparatus, a device, a program, and a medium. The method includes the steps described below. A target monitoring range is divided into a plurality of regions; the air quality prediction model is pre-trained by adopting a pre-training sample and a pre-training objective function, where the pre-training sample includes measurement values; and the pre-trained air quality prediction model is trained by adopting a formal training sample and a formal training objective function, where the formal training sample includes the measurement values. The air quality prediction model is configured to predict air quality of the plurality of regions according to spatial information, historical information and environmental information.

Abstract: A weather parameter prediction model training method, a weather parameter prediction method, an electronic device and a storage medium are provided, and relate to the technical field of artificial intelligence, such as deep learning and big data. The method includes: establishing a weather parameter prediction model according to spatial correlation information among a plurality of monitoring stations; and adjusting the weather parameter prediction model according to observation values of a weather parameter for the plurality of monitoring stations and prediction values of the weather parameter for the plurality of monitoring stations output by the weather parameter prediction model. The present disclosure can improve an accuracy of predicting weather parameters.

Abstract: An air quality prediction model training method, an air quality prediction method, an electronic device and a storage medium are provided, and relate to the technical field of artificial intelligence, such as deep learning and big data. The training method includes: establishing an air quality prediction model according to spatial correlation information among a plurality of regions; and adjusting the air quality prediction model according to air quality observation values for the plurality of regions and air quality prediction values for the plurality of regions output by the air quality prediction model. The accuracy of air quality prediction result can be improved.