Multi-sensor information fusion method for sterilization robot

Abstract

The invention relates to a multi-sensor information fusion method for a sterilization robot, and aims to solve the problems that when multi-sensor information fusion is carried out on the sterilization robot in the medical field, the uncertainty is relatively high, the operation parameters of the sterilization robot in different scenes cannot be accurately determined, the autonomy is relatively poor, and the like. The method comprises the following steps: generating a target classification training data set, obtaining feature information by processing a hybrid deep neural network fused with multi-source signals, establishing a decision fusion model based on a voting evidence theory, and performing training to obtain a sterilization robot operation parameter model. According to the invention, the deep neural network is combined with an improved evidence theory method; visual signals and sensor signals of the sterilization robot are organically fused together through deep learning in combination with an improved evidence theory method, possible redundancy and contradiction among multi-sensor information are eliminated, complementation is performed, the uncertainty is reduced, and then operating parameters of the sterilization robot are output more accurately.

Description

technical field [0001] The invention relates to an information fusion method, in particular to a hybrid deep neural network multi-sensor information fusion method for sterilization robots. Background technique [0002] The sterilizing robot uses the robot as the carrier and is used in the medical sterilization scene to automatically, efficiently and accurately sterilize the room according to the set route. The sterilizing robot can use the principle of high-voltage ionization and polarized light emission of pulsed xenon lamps to efficiently and quickly inactivate various microorganisms on the surface of objects and in the air through high-intensity ultraviolet light to achieve the purpose of disinfection and sterilization. [0003] At present, high-risk cross-infection areas such as infectious disease isolation rooms, wards, and operating rooms require regular sterilization and disinfection, but this part of the work is mainly done manually. Traditional indoor sterilization ...

AI Technical Summary

Problems solved by technology

[0005] The above-mentioned traditional fusion method, when dealing with the multi-sensor information fusion of the sterilization robot in the medical field, there are redundancy and contradictions among the multi-sensor information, the uncertainty is high, and the operation parameters of the sterilization robot in different scenarios cannot be accurately determined. The performance is poor and requires too much manual intervention and control, and the effect needs to be improved

Images