Method for establishing data analysis model

Abstract

The invention provides a method for establishing a data analysis model, and the method comprises the steps: learning and pre-training an initial cloud model at a server side through employing a deep neural network based on the current user data, and transmitting the initial cloud model to different users; the user constructs a user model of the user based on the received cloud model according to the data characteristics of the user, learns and trains the user model by adopting a deep neural network, and returns the trained user model to the server; the server fuses the received user model based on a preset period to obtain a new cloud model; and the user adjusts the own user model based on the received new cloud model, learns and trains by adopting the deep neural network at the same time,and returns the trained user model to the server. According to the model framework constructed based on the method, the problems of data islands and individuation can be solved by combining federatedlearning and homomorphic encryption technologies, a powerful machine learning model is constructed by summarizing data from different organizations, and the privacy of the user can also be protected.

Description

Technical field [0001] The present invention relates to the field of data analysis, in particular to the field of wearable health monitoring, specifically, to a method for establishing data sharing models for different users, and more specifically, to a method for establishing data analysis models. Background technique [0002] In recent years, with the rapid development of computing technology, wearable technology based on wearable devices such as smart phones, wristbands and smart glasses can help people better understand their health. Daily behavioral activities are closely related to people's health and may be an early signal of certain cognitive diseases. For example, changes in gait may lead to small vessel disease or stroke. Research has found that by monitoring user activities by wearing sensors, daily behaviors can be identified. These wearable devices can easily access people's health information, including behavior, sleep, exercise, etc. Existing studies have shown ...

AI Technical Summary

Problems solved by technology

However, different users have different physical characteristics and daily behavior patterns, so a common model cannot achieve personalized health monitoring

[0007] At present, in the field of wearable health monitoring, there is no effective way to solve the above challenges

Images