Infectious disease collaborative prediction method based on multi-source big data deep learning, and robot

Abstract

The invention discloses an infectious disease collaborative prediction method based on multi-source big data deep learning, and a robot. The method comprises the following steps: determining a target infectious disease; initializing an infectious disease prediction model; constructing an optimal infectious disease prediction model; and using the optimal infectious disease prediction model. According to the method, a system and the robot, the infectious disease prediction models of different regions are obtained through different combinations of the multi-source input data, the credibility of the models is evaluated by calculating the accuracy of the different infectious disease prediction models, and then the model with the highest credibility is selected as the optimal model of the region; and meanwhile, the credible error range can be given, so that the optimal combination of multi-source data can be fully utilized, the acquisition cost of data sources which do not act or even react on prediction is saved, and meanwhile, the accuracy of regional prediction is improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a collaborative prediction method and robot for infectious diseases based on multi-source big data deep learning. Background technique [0002] In the process of realizing the present invention, the inventors found at least the following problems in the prior art: a big problem with multi-source big data is that some data sources are not very stable and reliable, so there are often cases of missing data. When there are many sources of infectious disease big data, how to coordinate the data from various sources so that they do not conflict and can complement each other? And sometimes when multiple data conflicts, you don't know which is right and which is wrong, so what should you do? The traditional method is to compare the multi-source data with each other for correction, but in this example, the multi-source data are mostly heterogeneous, for example, the data ...

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to address the defects or insufficiencies of existing technologies, and provide collaborative prediction methods and robots for infectious diseases based on multi-source big data deep learning, so as to solve the problem of insufficient data in the initial stage of new major infectious diseases in existing technologies. Improving the effectiveness of spatio-temporal prediction models in the early stage of major emerging infectious diseases

Images