## 4 Main Research Content
### Research Methodology  
(1) Literature Review Analysis Method  
In response to the current domestic and international research landscape in the field of the intelligent operation and maintenance of an WSHP energy station, this study employed a literature review analysis method. It involved a thorough review and analysis of both domestic and international literature to fully understand the latest research progress in integrating WSHP systems with the energy internet field. This process clarified the main content and research framework of the study, identified solutions that integrate related technologies such as \"digital twin\", \"knowledge graph\", and \"situational awareness\", and provided a theoretical and methodological basis for the intelligent operation of an WSHP energy station. (2) Digital Twin Model Construction Method  
The method combined GIS information, IoT information, point cloud data, and other data based on the operation and maintenance BIM model of the WSHP energy station to construct a \"BIM+\" physical entity DT model. This achieved the integration of multiple sources of heterogeneous data in the twin body, including structured, unstructured, and semi-structured data. It also utilized methods such as finite state machines or process flow diagram theory to logically decouple energy flow information and generate a digital twin model that integrates static physical equipment parameters with dynamic energy information flow in a non-parameterized manner. (3) Operation and Maintenance Monitoring Big Data Mining Method  
The processing and analysis of energy station operation data are crucial for the achieving intelligent operation of energy stations. Through big data mining algorithms, research objectives can be effectively achieved. According to the operation and maintenance characteristics of energy stations, this method primarily consists of principal component analysis, association rule analysis, and attribute reduction feature extraction. Principal component  
Figure 2: Technical roadmap of research content. (**a**) Graph of digital twin construction and integration technical path. (**b**) Graph of feature extraction and construction of knowledge graph technical path. (**c**) Graph of predicting the operation situation and intelligent optimization technical path. analysis, a classic method for feature extraction from high-dimensional data, helps to eliminate the correlation between data used for monitoring the statuses of energy station equipment, making the transformed factors mathematically independent of each other. Association rule analysis mines the relationships between state indicator variables from a large amount of equipment status monitoring data, identifying possible correlations hidden in different state indicators. Attribute reduction feature extraction extracts the simplest subset of attributes from numerous monitoring state attributes. By simplifying the attributes of energy station equipment status indicators, unnecessary attributes of energy equipment status are removed to reduce the difficulty and cost of handling energy system status monitoring data, decrease the storage volume of monitoring data, and improve the utilization rate of equipment monitoring data. Operation and Maintenance Monitoring Data Knowledge Graph Construction Method  
This study combined text data such as daily operation and maintenance fault handling management regulations, fault handling plans, scheduling regulations, and abnormal monitoring manuals, as well as operational status data collected from equipment. Through concept extraction and relationship extraction methods, automated identification was carried out. The identification results were abstractly verified by business experts to form a bottom-up category system. Then, this system was integrated with the basic ontology architecture to build a knowledge ontology architecture for energy station equipment operation monitoring and fault handling.