With the rapid development of advanced sensing technology, there are more and more ways to sense the health status of engineering equipment, which provides more possibilities for the acquisition of big data of equipment running FAHP-FAST combination. Therefore, the development of data-driven home service robot technology has ushered in a new opportunity, and the problem of home service robot with random degradation equipment for big data processing has attracted the attention of a large number of scholars. Firstly, this paper summarizes the research background, main methods and ideas, and core issues of home service robot based on the characteristics of FAHP-FAST. This paper analyzes the basic research ideas and development trends of home service robot technology based on hybrid model and data driven, home service robot technology based on FAHP-FAST, home service robot technology based on statistical data driven, and home service robot technology based on combination of FAHP-FAST and statistical data driven, at the same time, combined with the characteristics of random degradation equipment FAHP-FAST combined big data and the core problem of uncertainty quantification of home service robot, the limitations and common problems of current research are deeply analyzed. The research shows that the number of services of home service robots based on FAHP-FAST combination method is 36% more than that of machine learning discrete robots, and the mapping relationship between FAHP-FAST combination data and failure time of home service robots is established to realize home service robots, which provides a more efficient service mode for home service robots with random degraded devices under the background of big data.

Jie Zhang and Bo Zhang. Functional Design of Home Service Robot Based on FAHP-FAST Combination Method. Machine Learning Theory and Practice (2021), Vol. 2, Issue 2: 1-8. https://doi.org/10.38007/ML.2021.020201.

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