Multi-level CEP rules automatic extraction approach for air quality detection and energy conservation decision based on AI technologies

Energy conservation is a fundamental requirement in achieving sustainable development, enhancing environmental protection and improving economic and social wellbeing by reducing energy consumption. Carbon emissions are a significant manifestation of energy consumption. Real-time monitoring and forecasting of air quality plays a crucial role in effectively controlling carbon emissions and improving energy conservation measures. Complex Event Processing (CEP) is a technical framework for streaming data processing based on predefined rules and is widely used for real-time detection. Yet as the diversity of data increases, it becomes exceptionally difficult for experts to formulate the CEP rules. Rule-based algorithms can address this issue to a certain extent. However, the CEP rules extracted through these methods are obtained from static data and are often simple and independent of each other. Here, we regard such rules as direct rules, which ignore possible connections between events that usually imply deeper rules. In contrast, we refer to rules that reflect interconnections between events and reveal higher-level principles as indirect rules. In this paper, we propose a methodology fusing interpretable machine learning and decision mining to extract direct and indirect multi-level CEP rules from air pollution datasets for real-time detection, prediction, and early warning. First, we utilize the decision tree algorithm to extract direct rules to detect air quality in real-time. Next, we utilize the process mining algorithm to model the changes in air states and extract the Petri net model from it that reflects the changes in air quality. Then, we utilize our proposed decision mining algorithm to extract indirect CEP rules. Indirect rules can help to understand how pollutants in the air change over time. In addition, they can reveal underlying trends and patterns of air quality change for prediction and early warning. Finally, we validate the accuracy and effectiveness of our approach using a real air pollution dataset. This work contributes to the field of air pollution detection and can help promote the optimization of energy conservation, while it can also support the development of regulatory strategies.