Abstract: An energy-saving prediction method for factory electricity consumption and an electronic apparatus are provided. The method includes the following steps. A reference electricity consumption amount in a unit period is predicted based on factory actual operation information in the unit period by using an electricity consumption prediction model. An actual electricity consumption amount in the unit period is acquired. The reference electricity consumption amount and the actual electricity consumption amount are displayed on an electricity consumption reference interface. A function is provided according to a first difference value between the reference electricity consumption amount and the actual electricity consumption amount.

Abstract: A blood pressure prediction method and an electronic device using the same are provided. The method includes the following steps. A training data set is collected. A first blood pressure prediction model is established according to the training data set. Hemodialysis parameter data of a target patient is received, wherein the hemodialysis parameter data includes a first hemodialysis parameter at a previous time point and a second hemodialysis parameter at a current time point. A hemodialysis parameter variation amount between the first hemodialysis parameter and the second hemodialysis parameter is calculated. The hemodialysis parameter variation amount is provided to the first blood pressure prediction model to generate a prediction blood pressure variation associated with a next time point. An operation is performed according to the prediction blood pressure variation of the target patient.

Abstract: A blood pressure prediction method and an electronic device using the same are provided. The method includes the following steps. A training data set is collected. A first blood pressure prediction model is established according to the training data set. Hemodialysis parameter data of a target patient is received, wherein the hemodialysis parameter data includes a first hemodialysis parameter at a previous time point and a second hemodialysis parameter at a current time point. A hemodialysis parameter variation amount between the first hemodialysis parameter and the second hemodialysis parameter is calculated. The hemodialysis parameter variation amount is provided to the first blood pressure prediction model to generate a prediction blood pressure variation associated with a next time point. An operation is performed according to the prediction blood pressure variation of the target patient.