## 1 Introduction  
Many breakthroughs have been achieved in service robotics in various fields due to technological advances in computer vision and robotics. A mobile service robot is usually a man-machine environment system. In this system, human participation and coordination are critical to improve system performance. Service robots have broad application prospects, especially in the home environment [1]. The service robot's perception of the home environment is typically based on the recognition and detection of indoor scenes, objects, and various tools. Reference [2] introduced a visual attention mechanism to detect salient objects in the environment, improving the efficiency, intelligence, and robustness of the 3D modeling of the robot environment. It manually annotated the functional parts of objects and identified scene categories using public datasets to ensure that the robot could quickly find key areas of objects in the before research [3]. An indoor functional area is an area with a specific function, such as the living room, study, bedroom, dining room, bathroom, and other spaces. Various functional tools with functional and operational attributes are used in these spaces. Both perception and classification of the indoor environment are required by robots for analysis, decision making, and human-computer interaction [4]. There is a lack of research on knowledge representation and management models of tools and functional areas. Current research has mainly focused on the semantics of tool names and spatial relationships. Problems in this area include a large number of labels and difficulties in labeling some tools. Functional semantic features are critical in human cognition. Functionality refers to the potential uses of a tool or place. It is a better semantic description than the relationship between tool names and spatial semantics. The latest research by [PERSON] et al. [5] shows that the integration of functionality into environmental cognition substantially improves the cognitive ability of service robots. It is expected that using knowledge representation of tools and functional areas through machine learning, especially deep learning, will improve the autonomous knowledge of service robots. Due to their natural interaction and humanized services, service robots already can recognize basic semantic information such as tool names and categories. As an important part of semantic knowledge, utility connects the real world and the task world and has attracted the attention of experts and scholars. Service robots typically use passive cognition to obtain the functional semantics of tools from semantic tags. Researchers are also exploring active cognitive methods based on inference learning [6; 7]. Intelligent control is an important development direction for people-centered home service intelligent robots. The development of artificial neural networks has greatly promoted the development of intelligent control theory, making robots more humanized and more able to think from the perspective of people. Developing human-centered, natural, and efficient processes will become the main goal of the new generation of human-computer interaction technology. The main contributions of this study are as follows:  
1. We use the theory of ontology and knowledge graphs to establish a knowledge graph system suitable for the home environment. 2. An analysis of the characteristics and determinants of robot service is conducted to establish a decision-making template for service tasks by correlating the three types of knowledge in the home environment domain ontology. Knowledge acquisition is based on visual, physical, category, functional, and other attributes. 3. A framework for the rapid classification of scenarios and tools for service robots is proposed because robots require timely information on indoor scenes using relatively small datasets. The rest of the manuscript is divided into three sections. Section 2 presents the related work.