Design of Intelligent Education Service Robots for Big Data

Abstract

With the development of robot technology, service robots have gradually come into people's lives, which also makes human-computer interaction more and more frequent. However, most of the current robot control algorithms have low accuracy and difficult operation, and people can't wait to find more effective algorithms. In order to design the most convenient and accurate intelligent education service robot, this paper uses SF algorithm and improved PCNN to establish a hybrid model, and proposes an improved saliency region extraction algorithm based on education service robot. The algorithm compares the standard database with the real environment, and the PR curve of the proposed algorithm is improved by about 5%. It is 10% higher than the SF algorithm in the AUC index, and the comprehensive F value is improved by 3.4% ~ 7.4%. This paper fully demonstrates that the saliency area generated by the proposed algorithm is closer to the true value, which can effectively suppress the high-brightness background area in the detection results of the SF algorithm. It also verifies that the PCNN model with the neuron propagation stimulation mechanism as the core more effective simulation of biological vision systems. Combining compressed sensing technology, this paper proposes a speech recognition scheme that is easy to implement in hardware. The algorithm performance of the robot is verified, and the optimal effect parameters are selected through comparative experiments. The method uses Chinese phonetic phrase (sentence) test to obtain good recognition results, and it can be used as an effective improvement scheme for the speech input of the proposed robot voice interaction system.