## 1 Introduction  
As a pivotal component of the electrical power system, substations' reliable and efficient operations are crucial to ensuring the electricity industry's safety and stability. Substations' primary equipment manages power generation, transmission, distribution, and consumption. Meanwhile, secondary equipment focuses on measuring, monitoring, controlling, and protecting the primary apparatus. In the smart grid era, substations have significantly advanced by integrating advanced sensing and artificial intelligence technologies beyond traditional infrastructure. These advancements facilitate functionalities such as automatic data collection, intelligent measurement, remote control, and real-time monitoring, further enabling the adaptation of control strategies within the station, based on the power grid's operational status. Aligning with the dual-carbon objectives, the national grid has initiated proposals to enhance the development of green and low-carbon technologies across all grid planning, construction, operation, and maintenance aspects, aiming to boost the energy sector's digital economy growth [1]. Constructing, inspecting, and maintaining secondary screen cabinets in substations require a thorough understanding of the diagrams and physical terminal blocks' elements and interconnections. Despite this, manufacturing, design, construction, and inspection data carriers have remained in unstructured forms like diagrams and documents for decades, leading to significant repetitive and inefficient manual data entry and duplication across various stages. The rapid advancement of computer and communication technologies and artificial intelligence techniques, especially deep learning (DL), has reached new heights. Thesetechnologies have attracted widespread attention and application in sectors such as transportation, electrical power, and communications [2; 3; 4]. As a result, digitally modeling secondary systems in substations, particularly protective screen cabinets, and rapidly comparing diagrams with their physical counterparts offer broad development prospects in the current construction, operation, and maintenance of substations. Digital modeling's essence for secondary system screen cabinet diagrams and physical entities lies in object detection technology. In the last decade, DL-based object detection technologies have stood out, leading to the development of numerous remarkable models, such as the region-based convolutional neural network (R-CNN) [5], Fast-RCNN [6], and Faster-RCNN [7]. Notably, the You Only Look Once (YOLO) approach [8; 9], treating object detection as a regression problem, has significantly advanced the field by segmenting images into grid units for regression and classification, thus outputting the categories and locations of entities [10]. Following extensive research, the model has evolved into YOLO version 7 (YOLOv7) [11], integrating efficient aggregation networks, auxiliary head detection, and model scaling mechanisms, thereby enhancing the YOLO series algorithms' speed and accuracy. However, the level of object recognition for engineering diagrams and complex construction sites remains less than ideal. This challenge is due to the small size of micro-object targets and the complexity of effectively extracting their feature information, especially in terms of processing speed, noise resistance, and recognizing various characters and curves. Moreover, intelligently operating and maintaining substations requires comparing digitally modeled diagrams and physical entities. This comparison, focusing primarily on node information, becomes increasingly complex as the scale of nodes expands, requiring significant computational resources. Identifying similar parts between two topological graphs is known as the maximum common subgraph (MCS) partitioning problem in graph theory [12], a challenge that has applications in software analysis, graph database systems, cloud platforms, and chemical drug synthesis [13; 14; 15; 16].