# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 6 Simulation  
In this paper, STK11.3 software is used to build a scenario of space networks. Tensorflow1.15 is used to build a neural network, and Python3.7 is used to build the algorithm main program. The space network scenario configuration is as follows:  
1. A total of 150 low-orbit satellites are located in 150 different orbits. The orbital altitudes are 500 km, 650 km, and 800 km. Inclinations are evenly distributed within the range of 96 to 100 degrees. 2. There are two ground stations (Xi'an Station and Kashi Station in China), each of which has three sets of TT&C equipment. The task-planning horizon is from 2022-8-27 04:00:00 to 2022-8-28 04:00:00. During the experiment, each TT&C task has two to six different TT&C window requests of the orbit ascending or descending. A total of 150 groups of tasks are randomly generated, of which 120 are used as training sets and 30 as test sets. The time fragment length threshold Th is set to 5 min during the experiment. Figure 6 shows the convergence of training the function Q network at different learning rates. It can be seen that the larger the learning rate, the faster the Q-function converges. When the learning rate is 0.05, the loss function converges the fastest, but the result quickly falls into the local optimum. When the learning efficiency is 0.0005, the convergence speed of the loss function is too slow, resulting in too long a training time to achieve a good training effect. Therefore, this paper uses a learning rate of 0.005 to train the Q evaluation function. Because this can not only prevent the loss function from converging too slowly but also prevent the loss function from falling into a local optimum to a certain extent. Finally, the best training result is achieved. In order to verify the performance of the algorithm proposed in this paper, this paper compares the LTFIT&C algorithm with the randomized adaptive search procedure (GRASP) [22] and genetic algorithm (GA) [23]. It mainly conduct performance comparisons in terms of the total time fragments in different TT&C task scenarios, average response time of emergency tasks, and total task revenue. Figure 7 compares the total time fragments of the TT&C equipment of the three TT&C task-planning algorithms under different task numbers. It can be seen that, compared with the other two algorithms, the LTFIT&C algorithm significantly reduced the total amount of time fragments of equipment under different task numbers. Compared with the GRASP algorithm, LTFIT&C reduced the total time fragments of TT&C equipment by 48%. Compared with the genetic algorithm, LTFIT&C reduced the total time fragments of TT&C equipment by 32%. To further verify the LTFIT&C algorithm to reduce the gain of time fragmentation on emergency task planning, the emergency task test response time (i.e., the difference between the emergency task completion time and arrival time) was inserted based on the three algorithms for common TT&C tasks. As shown in Figure 8, with the increase in the number of emergency tasks, the average response time of the emergency task is 0.0005. The average response time of the emergency task is 0.  
tasks of the three algorithms increases to varying degrees, and the LTFTT&C algorithm is obviously better than the other two algorithms. LTFTT&C is 43% faster than the GRASP algorithm and 36% faster than the genetic algorithm in the average response time of the emergency tasks. This is mainly because the LTFTT&C algorithm effectively reduces the amount of time fragments, making it easier for emergency tasks to insert into suitable visible windows. Figure 9 shows the performance of the three algorithms regarding the total task revenue. It can be seen from the figure that when the number of tasks is small, the LTFTT&C algorithm is significantly better than the other two comparison algorithms in terms of task revenue. It is 21% and 15% higher than the GRASP and genetic algorithms. When the number of tasks is large, the LTFTT&C algorithm improves the total task revenue by 28% compared with the GRASP algorithm.