Heterogeneous multi-satellite dynamic task planning method based on meta-heuristic algorithm

Abstract

A heterogeneous multi-satellite dynamic task planning method based on a meta-heuristic algorithm mainly comprises the steps that firstly, according to a given timestamp and user information, all satellites visible to a user are simulated, optimization is carried out based on a discrete PSO algorithm, after optimal configuration is obtained, a next timestamp and new user information are obtained, and then the satellites are obtained; and users with continuous communication requirements are not required to be switched in the circulation. According to the method, a discrete PSO heuristic algorithm is adopted, different requirements of different user groups and different task priorities are fully considered during task planning, and the user satisfaction degree is improved; meanwhile, dynamic task planning can be carried out along with time, the visible relation of the satellite and the user in the moving process is fully and dynamically considered, and it is guaranteed that the user is not required to be switched for a long time.

Description

Technical Field

[0001] The invention relates to a heterogeneous multi-satellite dynamic mission planning method based on a meta-heuristic algorithm, and belongs to the technical field of satellite mission planning. Background Art

[0002] The throughput of the next generation of high-throughput satellites will reach hundreds of gigabits per second or even terabits per second, and the number of beams will grow from dozens to hundreds. In actual application scenarios, high-orbit satellites and medium-orbit satellites communicate with vehicle-mounted, airborne, ship-mounted terminals and fixed stations. The coordinated scheduling of resources of multiple satellites at different orbital altitudes brings difficulties to optimization. Traditional high-throughput satellites cannot meet user needs and waste resources.

[0003] In the flexible high-throughput satellite resource management and control system, multi-satellite mission planning is to reasonably arrange satellite resources for the mission and maximize user satisfaction under the constraints of satellite and payload usage. When planning communication tasks for heterogeneous multi-satellite systems, traditional methods have three important defects:

[0004] (1) Most of the current research on mission planning algorithms focuses on earth observation and communication, and does not involve routine communications of a large number of users in daily life;

[0005] (2) The current research on task planning algorithms has not considered the different needs of different user groups, such as the need for low latency for specific users and the communication guarantee for high-priority users;

[0006] (3) The current research on mission planning algorithms does not take into account the fact that satellites and users are moving and their mutual visibility changes over time. Summary of the invention

[0007] The technical problem to be solved by the present invention is: to overcome the deficiencies of the prior art, based on a meta-heuristic algorithm, and facing the dynamic changes of heterogeneous multi-satellite and multi-user, thereby improving the efficiency of satellite resource management.

[0008] The purpose of the present invention is achieved through the following technical solutions:

[0009] A heterogeneous multi-satellite dynamic mission planning method based on a meta-heuristic algorithm includes:

[0010] According to the given timestamp and user information, all satellites visible to the user are simulated, and the discrete PSO algorithm is used to optimize. After obtaining the best configuration, the next timestamp and new user information are obtained. In the loop, it is ensured that users with continuous communication needs do not switch unless necessary.

[0011] The specific steps include:

[0012] (1) Initialize the number of high-orbit satellites N_GEO and the number of medium-orbit satellites N_MEO as well as their total capacity and orbits, and generate n user information;

[0013] (2) According to the given timestamp, the orbit information and number of high-orbit satellites and medium-orbit satellites, and the user information, simulate all satellites visible to the user, and determine whether it is the first cycle. If so, directly go to step (6), otherwise go to step (3);

[0014] (3) Compare the user information of the previous round with the new user information and divide them into two groups: repeated users and newly added users. For repeated users, confirm whether the best link satellite assigned to them in the previous round is still within the coverage after the update and whether the user satisfaction in the previous round is 1. If the best link satellite assigned to them in the previous round is within the coverage after the update and the user satisfaction in the previous round is 1, go to step (4); otherwise, go to step (5);

[0015] (4) The user does not make any changes, and the capacity occupied by the user is deducted from the total capacity of the connected satellite, and the process goes to step (6);

[0016] (5) Add the user to the list of newly added users and proceed to step (6);

[0017] (6) For the newly added users and the remaining capacity of the satellite, the improved discrete PSO algorithm is used for optimization. The speed setting in the PSO algorithm is deleted, and the update mechanism of the local optimal and global optimal positions is combined in each iteration, and random factors are added.

[0018] (7) If the number of loops reaches the preset N times, the loop ends. If not, the timestamp increases by 1 and the process goes to step (2).

[0019] An electronic device, comprising:

[0020] Processor; and

[0021] a memory for storing computer program instructions;

[0022] When the computer program instructions are loaded and executed by the processor, the processor executes the above-mentioned heterogeneous multi-satellite dynamic mission planning method.

[0023] A computer-readable storage medium stores computer program instructions, which, when loaded and executed by a processor, enable the processor to execute the above-mentioned heterogeneous multi-satellite dynamic mission planning method.

[0024] A computer program product stored on a non-transitory computer-readable medium, the computer program product comprising program codes for implementing the heterogeneous multi-satellite dynamic mission planning method.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) The present invention proposes a task planning method for dynamic changes in heterogeneous multi-satellite and multi-user environments. In order to improve the efficiency of satellite resource management, based on a meta-heuristic algorithm, the method improves resource utilization in actual application scenarios for daily communications between high-orbit satellites and medium-orbit satellites and a large number of vehicle-mounted, airborne, ship-mounted terminals and fixed stations.

[0027] (2) The present invention proposes a meta-heuristic algorithm based on discrete PSO, which fully considers the different needs and different task priorities of different user groups during task planning, thereby improving user satisfaction.

[0028] (3) The present invention proposes a method for dynamic mission planning over time, which can fully and dynamically take into account the visible relationship between the satellite and the user, each of which is in motion, and ensure that long-term connected users do not switch unless necessary. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Flow chart of the method of the present invention.

[0030] Figure 2 It is the average user satisfaction and total user demand diagram at each moment. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] A heterogeneous multi-satellite dynamic mission planning method based on a meta-heuristic algorithm mainly includes first simulating all satellites visible to the user according to a given timestamp and user information, optimizing based on a discrete PSO algorithm, obtaining the next timestamp and new user information after obtaining the best configuration, and ensuring that users with continuous communication needs do not switch unless necessary in the cycle. Figure 1 As shown, the following steps are included:

[0033] (1) Initialize the number of high-orbit satellites N_GEO and the number of medium-orbit satellites N_MEO as well as their total capacity and orbits, and generate n user information, including mobile users and fixed users (randomly generate the moving direction and speed of mobile users); user priority P u = {1, 2, 3, 4, 5}, indicating the priority of user u. This priority can be used as a weight factor to adjust the allocation of satellite resources.u The larger the value, the higher the priority of user u, and the needs of u need to be met first. u Indicates that the user's preference for different satellite types can be represented by a number between 0 and 1. u =1, indicating that the user completely prefers GEO satellites and requires a wider coverage area. u = 0, indicating that the user completely prefers MEO satellites and requires lower latency; capacity matching rate, the capacity required by the user C R , providing capacity C O , then the capacity matching rate is Link budget SNR;

[0034] (2) According to the given timestamp, the orbit information and number of high-orbit satellites and medium-orbit satellites, and the user information, simulate all satellites visible to the user, and determine whether it is the first cycle. If so, directly go to step (6); otherwise, go to step (3);

[0035] (3) Compare the user information of the previous round with the new user information and divide them into two groups: repeated users and newly added users. For repeated users, confirm whether the best link satellite assigned to them in the previous round is still within the coverage after the update and whether the user satisfaction in the previous round is 1. If the best link satellite assigned to them in the previous round is within the coverage after the update and the user satisfaction in the previous round is 1, go to step (4); otherwise, go to step (5);

[0036] (4) The user does not make any changes, and the capacity occupied by the user is deducted from the total capacity of the connected satellite, and the process goes to step (6);

[0037] (5) Add the user to the newly added users;

[0038] (6) For the newly added users and the remaining capacity of the satellite, the improved discrete PSO algorithm is used for optimization. The speed setting in the PSO algorithm is deleted, and the update mechanism of the local optimal and global optimal positions is combined in each iteration. At the same time, a random factor is added. Specifically, in the initialization phase, an initial position is generated for each particle, where each particle represents an overall solution, and the local optimal position and fitness of each user are recorded. In the iterative update phase, each particle updates its position based on the current local optimal position and the global optimal position, and updates and evaluates the fitness of the particle according to the following fitness function:

[0039]

[0040] The optimization goal of this algorithm is to obtain the maximum value of the fitness function. In the formula, S has two values, GEO and MEO, which represent the satellites assigned to the user. Different S will change the corresponding fitness function. GEO and SNR MEO They represent the link budgets of GEO and MEO respectively when assigning them to users. The differences between them are mainly caused by the satellite height and antenna gain, which are set as fixed values ​​in the calculation. Z represents the penalty. In order to better meet the needs of users, when the user capacity satisfaction is 1, Z is 0, otherwise a penalty will be imposed, that is, Z takes a constant greater than zero. When the capacity satisfaction of high-priority users is not met, a more severe penalty will be imposed, that is, Z will take a constant with a larger value.

[0041] Update the local optimal position and the global optimal position according to the new fitness, and finally return to the best global position found after reaching the preset number of iterations, and save the obtained best configuration and the user information of this round;

[0042] (7) If the number of loops reaches the preset N times, the loop ends. If not, the timestamp increases by 1 and the process goes to step (2).

[0043] Example:

[0044] In the embodiment, the scenario is set to three high-orbit satellites and five medium-orbit satellites, that is, N_GEO is 3, N_MEO is 5, the number of cycles N is 10, the number of users n is 1000, and the cycle is set to be ten times, each time increasing by one hour.

[0045] According to the method proposed in the present invention, the average user satisfaction and total user demand at each moment are calculated as follows: Figure 2 As shown, the average user satisfaction fluctuates around 0.999.

[0046] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

[0047] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A heterogeneous multi-satellite dynamic mission planning method based on meta-heuristic algorithm, characterized in that: include: According to the given timestamp and user information, all satellites visible to the user are simulated, and the discrete PSO algorithm is used to optimize. After obtaining the best configuration, the next timestamp and new user information are obtained. In the loop, it is ensured that users with continuous communication needs do not switch unless necessary.

2. The heterogeneous multi-satellite dynamic mission planning method according to claim 1, characterized in that: It also includes specific steps: (1) Initialize the number of high-orbit satellites N_GEO and the number of medium-orbit satellites N_MEO as well as their total capacity and orbits, and generate n user information; (2) According to the given timestamp, the orbit information and number of high-orbit satellites and medium-orbit satellites, and the user information, simulate all satellites visible to the user, and determine whether it is the first cycle. If so, directly go to step (6), otherwise go to step (3); (3) Compare the user information of the previous round with the new user information and divide them into two groups: repeated users and newly added users. For repeated users, confirm whether the best link satellite assigned to them in the previous round is still within the coverage after the update and whether the user satisfaction in the previous round is 1. If the best link satellite assigned to them in the previous round is within the coverage after the update and the user satisfaction in the previous round is 1, go to step (4); otherwise, go to step (5); (4) The user does not make any changes, and the capacity occupied by the user is deducted from the total capacity of the connected satellite, and the process goes to step (6); (5) Add the user to the list of newly added users and proceed to step (6); (6) For the newly added users and the remaining capacity of the satellite, the improved discrete PSO algorithm is used for optimization. The speed setting in the PSO algorithm is deleted, and the update mechanism of the local optimal and global optimal positions is combined in each iteration, and random factors are added. (7) If the number of loops reaches the preset N times, the loop ends. If not, the timestamp increases by 1 and the process goes to step (2).

3. The heterogeneous multi-satellite dynamic mission planning method according to claim 1, characterized in that: n user information, including mobile users and fixed users; also includes user priority P u 、User Preference H u , Capacity Matching Rate Link budget SNR.

4. The heterogeneous multi-satellite dynamic mission planning method according to claim 3 is characterized in that: User Priority P u ={1,2,3,4,5}, if P u The larger the value is, the higher the priority of user u is, and the needs of u need to be met first.

5. The heterogeneous multi-satellite dynamic mission planning method according to claim 3, characterized in that: User Preferences u Indicates the user's preference for different satellite types, represented by a number between 0 and 1. u =1, indicating that the user completely prefers GEO satellites and requires a wider coverage area. u When =0, it means that the user completely prefers MEO satellites and requires lower latency.

6. The heterogeneous multi-satellite dynamic mission planning method according to claim 3 is characterized in that: Step (6) also includes: generating an initial position for each particle in the initialization phase, wherein each particle represents an overall solution, and recording the local optimal position and fitness of each user; in the iterative update phase, each particle updates its position based on the current local optimal position and the global optimal position, and updates and evaluates the fitness of the particle according to the following fitness function: The optimization goal of this algorithm is to obtain the maximum value of the fitness function, where S has two values, GEO and MEO, representing the satellites assigned to the user; SNR GEO and SNR MEO They represent the link budgets of GEO and MEO respectively when allocating them to users; Z represents penalty. When the user capacity satisfaction is 1, Z is 0, otherwise a penalty will be imposed.

7. The heterogeneous multi-satellite dynamic mission planning method according to claim 6 is characterized in that , ,When the capacity satisfaction of high priority users is not met, a more severe penalty will be imposed.

8. An electronic device comprising: processor; as well as a memory for storing computer program instructions; When the computer program instructions are loaded and executed by the processor, the processor performs the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having computer program instructions stored thereon, wherein when the computer program instructions are loaded and executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product stored on a non-transitory computer readable medium, the computer program product comprising a program code for carrying out the method according to any one of claims 1 to 7.

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