## 5 Discussion  
By comparing the objective function value \(J\) for all methods mentioned in the previous section, OPH had better results under the conditions of having enough data transmission time, memory capacity, and electrical energy, but had poorer performance when there was a lack of data transmission time and electrical energy; the ratios of the resulting values of OPH to TS in the three groups of conditions (the conditions are mentioned in Table 3) were 96%, 88%, and 74%, respectively. DPH worked better when it had a lack of data transmission time, but worse with the other two groups of conditions as the ratios of the resulting \(J\) values to TS in the three groups of conditions were 88%, 98%, and 74%, respectively. Additionally, EPH had a more favorable performance when electrical energy was deficient, but not in the other two groups of conditions, as the proportions of its resulting \(J\) values to TS were 74%, 80%, and 92%, respectively. In contrast, RS outperformed the other three methods as its ratios of the resulted objective function value \(J\) to those of TS were 96%, 98%, and 92%, respectively, under the three groups of conditions. In short, RS worked well under all different conditions, whereas the heuristic methods only performed better under suitable conditions. Under appropriate conditions, the heuristic methods and RS performed similarly, as the ratio of their resulting \(J\) value to TS was about 96%. However, with unsuitable conditions, the results of RS were roughly 16% higher than those of the heuristic methods. Under all conditions, RS produced an objective function value of \(J\) that was nearly 11% higher than the heuristic scheduling methods. From the analysis of the results of a single condition of each group, the reason behind the poor results obtained by the heuristic methods is that the main scheduling limitation factors were different in different coupling states of the satellite. Therefore, targets with higher profit should be selected when data transmission resources and electrical energy are sufficient. The satellite should also try to obtain more data transmission resources and electrical charging energy when the data transmission resources or electrical energy are limited. These match the coupling analysis results in the paper. From the simulation results, even though group 2 was a more constrained scenario, the objective function values were slightly higher for group 2 than group 1. This is because the distribution area of targets in group 2 was larger than that in group 1, which provided more observation opportunities. Therefore, under the working conditions of group 2, even though the data transmission segments were insufficient, the amount of observation data that can be downloaded was higher than group 1, and the objective function value for group 2 was higher than that of group 1. In all cases, although TS surpassed all heuristic methods, the calculation time taken is long due to its iterative property. The average calculation time of the heuristic methods and RS were about 12 s, whilst the average calculation time of TS was around one hour. Figure 18: Charging electrical power chart of a single condition in the third group. With good scheduling results and high computational efficiency, the RS we proposed can be applied to scenarios with limited computing resources and real-time requirements such as satellite autonomous mission scheduling, emergency scheduling, and so on. The research in this paper can be further extended in the following aspects:  
1. A potential direction is to apply the method to more different conditions, with the purpose of supplementing the knowledge base in the reasoning process according to the simulation results, and modifying the parameters;
2. Simulations can be carried out on a finer-grained component level that is closer to a real satellite.