Power grid control model training method, system, computer device, and storage medium

A computer device, comprising a memory and a processor, wherein the processor is configured to:

• acquire and group power grid control model training data to obtain a plurality of training data sets, wherein the power grid control model training data is acquired from control big data based on different control service requirements, and the control big data big data is a collection of data associated with power grid operation, and comprises power grid model data, monitoring data, alarm data, and operation records;
• train, according to one-to-one correspondences between the training data sets and learning bodies, a plurality of preset learning bodies in parallel through the plurality of training data sets to obtain a plurality of execution bodies;
• wherein when training the learning bodies through the training data sets, a current training data set is divided into a plurality of training data units, a current learning body is trained through the plurality of training data units in sequence, and training effects of the plurality of training data units on the current learning body are acquired;
• and a training data unit with a training effect meeting a preset training effect requirement is taken as a shared training data unit, and the shared training data unit is shared with training data sets except a training data set comprising the shared training data unit, wherein the plurality of learning bodies are a plurality of Artificial Intelligence (AI) models for a power grid control service constructed according to a power grid control requirement;
• and
• combine the plurality of execution bodies to obtain a power grid control model, wherein the combining comprises taking inputs to the plurality of execution bodies together as an input to the power grid control model, and fusing outputs of the plurality of execution bodies into an output of the power grid control model,
• wherein the power grid control service is bus load prediction or power grid device fault prediction.

A computer-readable storage medium, having stored thereon a computer program that, when executed by a processor, implements following operations:

• acquiring and grouping power grid control model training data to obtain a plurality of training data sets, wherein the power grid control model training data is acquired from control big data based on different control service requirements, and the control big data big data is a collection of data associated with power grid operation, and comprises power grid model data. monitoring data, alarm data, and operation records;
• training, according to one-to-one correspondences between the training data sets and learning bodies, a plurality of preset learning bodies in parallel through the plurality of training data sets to obtain a plurality of execution bodies;
• wherein when training the learning bodies through the training data sets, a current training data set is divided into a plurality of training data units, a current learning body is trained through the plurality of training data units in sequence, and training effects of the plurality of training data units on the current learning body are acquired;
• and a training data unit with a training effect meeting a preset training effect requirement is taken as a shared training data unit, and the shared training data unit is shared with training data sets except a training data set comprising the shared training data unit, wherein the plurality of learning bodies are a plurality of Artificial Intelligence (AI) models for a power grid control service constructed according to a power grid control requirement;
• and
• combining the plurality of execution bodies to obtain a power grid control model, wherein the combining comprises taking inputs to the plurality of execution bodies together as an input to the power grid control model, and fusing outputs of the plurality of execution bodies into an output of the power grid control model,
• wherein the power grid control service is bus load prediction or power grid device fault prediction.