A runtime modeling and executing method for a smart home context awareness service

Abstract

The invention relates to a runtime modeling and executing method for a smart home context awareness service. The method comprises the steps of establishing a smart home situation awareness service knowledge graph concept model, namely a unified model for describing abstract elements including concepts and relations in the smart home scene; providing a smart home situation awareness service knowledge graph instance model runtime modeling method, wherein the invention discloses the smart home situation awareness service knowledge graph instance model represents the context knowledge of the smarthome context awareness service by the concept instance and the relationship instance; constructing runtime models of different types of concept instances and relationship instances, and establishinga bidirectional synchronization mechanism of the knowledge graph instance model of the smart home context awareness service and the smart home scene. The invention provides an intelligent home situation awareness service execution method based on knowledge reasoning. According to the invention, the difficulty and complexity of developing the smart home context awareness service can be greatly reduced.

Description

technical field [0001] The invention belongs to the field of smart home, and in particular relates to a runtime modeling and execution method for context-aware services of smart home. Background technique [0002] With the continuous development of smart home infrastructure, smart home has gradually entered a new era characterized by smart services. A large number of complex and heterogeneous new devices such as smart robots, smart wearable devices, and smart home appliances will be connected to the network for interaction. and collaboration to realize massive applications such as intelligent identification and monitoring. Among them, context-aware services such as temperature adjustment and air conditioning are typical representatives of smart home applications. They provide accurate temperature control, air purification and other services to service objects according to changes in their situations. At present, context-aware services are often built for scenarios, which ma...

AI Technical Summary

Problems solved by technology

One is the diversity of devices: smart devices have differences in their functions, brands, and models, and often provide different data reading and function calling methods, which brings great complexity to the interaction and collaboration of devices

The second is the on-demand nature of services: there are different types of service requirements, and the relationship between contextual knowledge and smart devices needs to be established, which brings great complexity to the writing of service management logic

[0005] On the one hand, it is difficult for knowledge graphs to represent changes in the context of service objects

Knowledge graphs can organize structured data and represent knowledge with entities and relationships. However, contextual knowledge representation requires knowledge graphs to be able to perceive real-time scene information, and existing knowledge graph technologies are difficult to reflect real-time changes in data.

[0006] On the other hand, building inference rules for different service requirements requires a lot of work

Knowledge graphs are used to represent contextual information, and realize the automatic execution of context-aware services through inference rules. However, smart home services are open, dynamic, and changeable, which brings great complexity to the construction of inference rules.

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