A Petri Net-based Conflict Detection Method of Trigger-Action Programming in Smart Home Environments

In the smart home environment, users develop a variety of automation rules to meet their needs in different scenarios. These rules can automatically trigger the operation of smart devices under specific conditions, thereby realizing intelligent home management. However, with the increasing number of intelligent devices, the automation rules formulated by users have become more complex and diverse, leading to potential conflicts between rules that can affect the stability of the smart home system. Therefore, conflict detection of rules is essential. Currently, rule conflict detection methods are primarily divided into static detection and dynamic detection. However, relying on only one of these methods leads to insufficiently comprehensive detection and hinders the user's ability to effectively modify conflicting rules. Thus, this paper defines in detail the various types of conflicts in Trigger-Action Programming rules and constructs a Petri net-based conflict detection model. This model applies to both the static and dynamic detection of Trigger-Action Programming rule conflicts proposed in this paper, while also considering device-shaking in dynamic detection. The experimental results demonstrate that the proposed method offers significant advantages in terms of comprehensiveness and effectiveness in detecting conflicts. This not only enhances the stability of the smart home system but also provides a solid guarantee for users when modifying conflict rules at a later stage.