Data processing method and device

An electronic device, comprising at least one processor and a memory;

• the memory stores computer-executable instructions;
• the at least one processor execute the computer-executable instructions stored by the memory to cause the at least one processor to:
• acquire exercise data of a user in a preset time period and a physical parameter preset by the user, wherein the exercise data comprises exercise data of a head, a hand and a leg of the user;
• and
• input the exercise data and the physical parameter into a pre-trained energy determination model for processing to obtain energy consumed by the user in the preset time period,
• wherein the pre-trained energy determination model comprises a pre-trained multivariate linear regression submodel, and
• the computer-executable instructions, when executed by a computer processor, further cause the computer processor to:
• train a multivariate linear regression submodel to obtain the pre-trained multivariate linear regression submodel by:
• acquiring a training sample and label data corresponding to the training sample, wherein the training sample is used as the exercise data of a sample, and the label data is the exercise coefficient corresponding to the training sample;
• inputting the training sample into a multiple linear regression submodel for processing to obtain a predicted exercise coefficient;
• and
• adjusting a model parameter of the multivariate linear regression submodel based on the predicted exercise coefficient and a loss value of the label data to obtain the pre-trained multivariate linear regression submodel.

A non-transitory computer readable storage medium having stored thereon computer executable instructions which, when executed by a processor, cause the computer processor to:

• acquire exercise data of a user in a preset time period and a physical parameter preset by the user, wherein the exercise data comprises exercise data of a head, a hand and a leg of the user;
• and
• input the exercise data and the physical parameter into a pre-trained energy determination model for processing to obtain energy consumed by the user in the preset time period,
• wherein the pre-trained energy determination model comprises a pre-trained multivariate linear regression submodel, and
• the computer-executable instructions, when executed by a computer processor, further cause the computer processor to train a multivariate linear regression submodel to obtain the pre-trained multivariate linear regression submodel by:
• acquiring a training sample and label data corresponding to the training sample, wherein the training sample is used as the exercise data of a sample, and the label data is the exercise coefficient corresponding to the training sample;
• inputting the training sample into a multiple linear regression submodel for processing to obtain a predicted exercise coefficient;
• and
• adjusting a model parameter of the multivariate linear regression submodel based on the predicted exercise coefficient and a loss value of the label data to obtain the pre-trained multivariate linear regression submodel.