Intelligent Decision Model for Home Robot Based on Structured and Unstructured Data Processing In order to enhance the userxe2x80x99s service experience and help the robot to make more intimate service decisions, this paper proposes a service task cognition and decision model based on structured and unstructured data processing. Firstly, all kinds of information mentioned in user instructions are extracted through natural language processing, including object information, namely structured data and environment information, namely unstructured data. For structured data, it is mapped to the predefined ontology knowledge base to obtain its location attributes and state attributes, and then obtain service instructions. Adaptive fuzzy Petri net (AFPN) is constructed based on fuzzy rules of temperature, humidity and other environmental information. The unstructured data is taken as the input parameter of AFPN, and the service instruction deduced as the output. Then, according to the userxe2x80x99s needs, the network weight can be continuously adjusted. If the user does not mention the environmental information, the environment information is periodically detected by the sensor, and the service instruction reasoning of the unstructured data is performed. Finally, back propagation neural network (BPNN) is introduced to combine the service inference of two kinds of data to eliminate the heterogeneity of different service instructions. Experimental results show that the model can provide different personalized services for usersxe2x80x99 preferences.

A Reasoning Approach of Human Activity Based on Ontology Model in Home Intelligent Space To solve the problem of lack of representation and reasoning of knowledge in human activity understanding in home environment, the activity reasoning method based on ontology model in intelligent space is presented. Firstly, a knowledge model of human activity domain based on the ontology technology is established. Then, the dataconcept conversion mechanism centered on the humanobject interaction relationship is applied to realize the conceptual representation of activity knowledge in intelligent space. On this basis, in order to make the best of activity domain knowledge, we use the semantic network rule language to build a knowledge rule base of human activity domain. The complex human activities in the current state are inferred by matching the realtime ontology in home intelligent space and the knowledge in rule base. Finally, the experimental results show that the method can effectively realize the reasoning of human activity.