## 3 Research Methodology
### Construction of SRs Requirement Representation Knowledge Graph  
Requirement analysis for complex systems often involves intricate interactions and relationships between multiple entities. To better understand and manage these requirements, a knowledge graph serves as an effective visualization tool, depicting key elements and their interrelationships in system requirements with nodes and edges [18]. The knowledge graph not only visually represents the structure of the requirements, but also uncovers the underlying dependencies and functional interactions between them, aiding designers in gaining a comprehensive understanding of the overall SRs. Upon analyzing the structure of the five types of requirements, it was found that physical requirements define the components of the system and their interrelationships, exhibiting static structural characteristics. Therefore, physical requirements can serve as the foundational framework for constructing the requirement representation knowledge graph. By describing the relationships between subjects, objects, and their attributes, physical requirements provide a clear structural backbone for the knowledge graph. Based on this framework, other types of requirements (such as functional requirements, interface requirements, performance requirements, and design constraints) will gradually expand and refine the graph. Each requirement category further enriches the graph content based on its specific elements (e.g., functions in functional requirements, interface requirements in interface specifications, numerical indicators in performance requirements, etc. ), ensuring that the logical relationships between different types of requirements are fully represented. The relationships between various nodes are shown in Table 3, and the SRs representation knowledge graph is constructed based on these node relationships to form the domain knowledge graph. The framework for constructing the knowledge graph based on physical requirements is shown in Figure 3. The first step of the algorithm is to create an empty directed graph G to store all of the entities and their interrelationships within the physical requirements. As the requirement data are parsed, nodes and edges are incrementally added to the graph. In the second step, the algorithm iterates through each row of the physical requirement sheet, adding the values from the \"subject\" column as nodes to graph G. If the \"attribute\" column in the row has a value, it indicates that the subject has specific attributes. The algorithm adds these attributes as nodes to the graph and connects the subject node to the attribute node via directed edges. Next, if the \"object\" column in the row contains a value, the algorithm adds the object as a node to the graph and connects it to the corresponding subject or attribute node through directed edges. If the row contains the \"action\", \"value\", and \"condition\" columns, the corresponding text is attached to the connecting edges between the subject and the object, between the subject and the attribute, between the attribute and the object, or the self-loop edge of the subject, to describe the specific supplementary description of the connection between the data points. Finally, the graph G is output. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Node Relationship** & **Description** & **Example** & **Illustration** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Schematic representation of node relationships. The basic framework for constructing a domain-specific requirement knowledge graph based on physical requirement data is outlined, followed by the stepwise integration of functional requirements, performance requirements, design constraints, and interface requirements into the graph. The specific algorithmic steps are shown in Figure 4. The first step involves importing the knowledge graph G built from the physical requirements and traversing each row of the functional requirements sheet.