Infection control robot based on artificial intelligence

Abstract

The invention discloses an infection control robot based on artificial intelligence. The robot comprises a robot body, a data acquisition module, a data analysis and processing module and an intelligent decision-making module; when the infection probability reaches a certain threshold value, the robot can automatically send an alarm through an audible and visual alarm, furthermore, the intelligentdecision-making module can be used for automatically planning a route for the robot according to the analysis of air quality and infection probability in different areas, so that the robot can intelligently move toward areas with poor air quality and high infection probability, and infection control management is carried out on the areas; and meanwhile, distributed storage and calculation can becarried out on infection control monitoring data, so that medical staff can master the environment and infection control conditions in a hospital anytime and anywhere, and the real-time performance, effectiveness and accuracy of the infection controlling work can be realized; and according to the infection control robot based on the artificial intelligence, a data analysis result can be intelligently decided, potential infectious diseases in the hospital can be effectively controlled and warned in time, so that huge medical loss and casualties caused by epidemic situation outbreak are avoided.

Description

technical field [0001] The present invention relates to the field of computer technology and intelligent medical treatment, in particular to a sensory control robot based on artificial intelligence. Background technique [0002] With the sharp increase in the flow of people in major hospitals across the country, nosocomial infection has become one of the important hidden dangers of medical quality and safety, which has a huge impact on the efficiency and quality of medical activities. According to the survey results of the prevalence rate of the national hospital infection control monitoring network, the prevalence rate of nosocomial infection among hospitalized patients nationwide is 5% to 6%. At present, the average daily outpatient visits of a tertiary hospital is about 6,000. It is difficult to effectively detect and control infectious diseases in such an overcrowded environment. [0003] The current hospital infection control work has a small coverage, complex workflow...

AI Technical Summary

Problems solved by technology

Traditional data analysis and statistical methods cannot meet the requirements of efficient analysis and decision-making. With the development of information-based hospitals, analysis is not limited to the data level, but also requires forward-looking predictions, and timely intelligent, automated processing

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