A device for predicting physiological health data

Abstract

The invention discloses a physiological health data prediction device, which includes an input unit, a storage unit, an output unit and a prediction processing unit, wherein the input unit is used to input the physiological health characteristic value sequence collected by the health data collection device and store it in the storage unit ; The prediction processing unit reads the corresponding physiological health feature value sequence three times from the storage unit, performs three gray predictions to obtain three prediction results, and merges the three prediction results in the form of equal-weighted mean value to obtain the final prediction result and store it in The storage unit, the sequences of three prediction processes are respectively: the first n-2, the middle n-2 and the last n-2, wherein n is the length of the physiological health characteristic value sequence; the output unit is used to read from the storage unit And output the final prediction result of the input physiological health feature value sequence. In the present invention, the errors of the final prediction results are corrected by three prediction methods, so as to reduce the errors caused by data mutations to the prediction results.

Description

technical field [0001] The invention belongs to the technical field of smart medical treatment, and in particular relates to a physiological health data prediction device based on a gray prediction model. Background technique [0002] Grey system theory was first proposed in 1982, and now it has been widely used in social systems, economic management, weather forecasting, and disease control. [0003] The grey forecasting method can be divided into the following four categories based on the forecasting characteristics: 1. Time series forecasting: By performing grey analysis on the event characteristics of a certain time series, it is possible to predict the event characteristic value at a certain time point in the future or to reach a certain point in time. Predict the time required for a certain feature value; 2. Distortion prediction: By performing gray analysis on the time series of the feature, it is possible to predict the occurrence interval of the future distortion of...

AI Technical Summary

Problems solved by technology

[0007] However, when there is a sudden change in the data, the existing gray prediction model will bring errors to the prediction results, resulting in insufficient prediction accuracy

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