# A Business-Model-Driven Approach to Task-Planning Knowledge Graph Construction
## 5 Experimentation and Verification of the Proposed Method
### Method Assessment  
#### 5.3.1 Space-Station Mission-Planning Knowledge Graph Capability Assessment  
In this section, we assess the capability of the knowledge graph for space-station mission planning. The capability assessment mainly refers to verifying the effectiveness of the knowledge graph in supporting mission planning, reasoning, and decision-making through practical application scenarios. By retrieving and reasoning about specific mission requirements, we can assess the capability of the knowledge graph in providing decision support, mission optimization, and simulation analysis to ensure that it can fully meet the needs of mission planning. In the area of mission planning, simulation analysis helps to assess and optimize the effectiveness and feasibility of mission planning by constructing virtual environments and models that simulate real-world conditions and scenarios. The Petri net model, as a graphical and mathematical tool, can effectively capture the complex dynamic behavior in mission planning and fully support simulation analysis [32]. Compared with other mathematical methods, Petri nets have unique advantages in dealing with dynamic behaviors such as concurrency, synchronization, and conflict and thus are more suitable for describing complex systems in task planning. Taking the construction of the Petri net model of rendezvous and docking events at the flight control level as a demand, the constructed knowledge graph of space-station mission planning is retrieved and reasoned, as shown in Figure 12 for the knowledge nodes of the Petri net model of the construction of rendezvous and docking events at the flight control level obtained by retrieval and reasoning. By modeling the knowledge nodes of the Petri net model obtained from the retrieval inference in Figure 12, the Petri net model of the rendezvous and docking event at the flight control event level constructed based on the retrieval results is obtained, as in Figure 13. The model contains eight resource repositories and four flight control event operation repositories, and there are multiple combinations between each flight control event repository and different resource repositories, which together describe the complex resource occupation and process logic relationship during the execution of rendezvous-docking events. This model meets the simulation requirements and proves that the constructed knowledge map of space-station mission planning in Figure 11 has good support for simulation modeling, solves the problems of Petri net modeling in traditional mission planning that relies on manual experience, is inefficient, has a high error rate, and provides reliable support for the subsequent space-station planning. Figure 11: Part of the visualization of the knowledge graph in the domain of space-station mission planning. Taking the construction of the Petri net model of rendezvous and docking events at the flight control level as a requirement, the constructed knowledge graph of space-station mission planning is retrieved and reasoned, and the knowledge nodes of constructing Petri net model of rendezvous and docking events at the flight control level obtained by retrieval and reasoning are shown in Figure 12. The Petri net model knowledge nodes obtained by retrieval and reasoning in Figure 12 are modeled. The Petri net model of the rendezvous and docking event at the flight control event level constructed based on the retrieval results is obtained as in Figure 13. The model contains eight resource repositories and four flight control event operation repositories, and there are various combinations between each flight control event repository and different resource repositories, which together describe the complex resource occupation and process logic relationship during the execution of the rendezvous and docking event.