Auxiliary judgment method based on deep learning and virtual reality

Abstract

The invention provides an auxiliary judgment method based on deep learning and virtual reality, and belongs to the technical field of auxiliary detection. The method comprises the following steps: constructing a virtual reality environment, and selecting a virtual dialogue character and scene; wearing a virtual reality VR device and an intelligent wearable sensor to realize interaction with a virtual reality environment and data acquisition; in combination with the data, using a model from the cloud data center for prediction and making auxiliary prompt; realizing real-time rendering of a virtual reality scene at an edge computing node or a workstation through voice recognition, character conversion, voice synthesis and expression generation; and assisting to finish the real-time interaction and judgment.

Description

technical field [0001] The invention relates to the technical field of auxiliary detection, in particular to an auxiliary judgment method based on deep learning and virtual reality. Background technique [0002] With the rapid development of deep learning technology, as well as the support of massive data and efficient computing capabilities in the Internet and cloud computing era, deep learning technology represented by CNN convolutional neural network can be trained to construct a large-scale neural network similar to the structure of the human brain. , has made breakthroughs in the fields of computer vision, speech recognition, and natural language understanding, and is bringing subversive changes to the entire society. [0003] Virtual reality technology (Virtual Reality, abbreviated as VR) is a brand-new practical technology formed by the combination of computer, electronic information and simulation technology, involving computer graphics, human-computer interaction te...

AI Technical Summary

Problems solved by technology

[0004] The diagnosis of some traditional diseases, such as depression, is mainly based on medical history, clinical symptoms, course of disease, physical examination and laboratory examination, with relatively high subjective factors and a high rate of misjudgment

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