## 6 Discussion  
Due to the huge number of satellites in the LEO constellation and the changeable operating environment, the probability of system faults is much higher than that of a single satellite. Therefore, the design and operation stages at both ends of the constellation system life cycle are significant and challenging. Traditional spacecraft O&M often relies on logic defined early in the design process as well as real-time telemetry data. Through the case study in the previous section, the effectiveness of the framework proposed in this paper is proved, and the enlightenment for the design and O&M stage of the constellation system is as follows:  
1. The multidomain characteristics of the satellite constellation system can be expressed through a unified language. The constellation model expressed in the unified language is more flexible and convenient for the design and verification of the constellation system and is more conducive to the knowledge accumulation of the design unit.
2. The model-data-hybrid-driven PHM method has more advantages than the traditional data-driven PHM method, and its advantages are reflected in the model's enhancement of data and the model's predictive function.
3. The data enhancement of the model is manifested in two aspects. One is the expansion of telemetry data from the time dimension and the number of variables in state monitoring. The other is that the model can generate a large amount of labeled data for training fault diagnosis algorithms, as a supplement to the experimental data.  
Figure 12: Simulation Results of PHM System.  
According to the existing research results, simulation prediction based on the mechanism model is more stable, reliable and generic than the data-based method. However, there are still several issues to be addressed in future work.  
1. The work efficiency of fault diagnosis, state monitoring or state prediction is closely related to the accuracy of the model. High-fidelity models help improve the performance of PHM systems. In this example, the orbital data and the calculation results of the satellite energy system are satisfactory, but there are still large errors in the calculation results of the information link, so the construction of a high-fidelity model of the satellite constellation system requires further in-depth research.
2. We hope to incorporate more efficient machine learning algorithms into the PHM framework proposed in this paper. Appropriate machine learning algorithms can play an active role in high-fidelity model construction, fault diagnosis and state prediction.
3. Due to the limitations of PC performance, we tested a constellation of up to 1000 satellites, and although the simulation worked, the efficiency was unacceptably low. In order to solve the problem of simulation, we plan to adopt the scheme of distributed simulation. Distributed simulation may not be a good choice for energy-based interfaces because it requires decoupling the variables coupled inside the energy-based interface into two causal-type interfaces, which is detrimental to the solution of the model. However, for the satellite constellation, there is no energy exchange between satellites, only information transfer, which is completely suitable for the distributed simulation scheme. We only need to arrange one master and dozens of high-performance slaves in a local area network to achieve real-time simulation.