Multi-task planning method and system for low-orbit communication-remote integrated satellite network

Abstract

The invention relates to the technical field of satellite network communication, and particularly discloses a multi-task planning method and system for a low-orbit communication-remote integrated satellite network, and the method comprises the steps: constructing a system model which comprises a communication-remote integrated satellite, a data relay satellite, a ground station and a task set, and carrying out the multi-task planning of the low-orbit communication-remote integrated satellite network based on the system model. Establishing an optimization problem aiming at maximizing the weighted task completion amount; solving the task acquisition planning sub-problem by using a positive semidefinite relaxation algorithm, and solving the task transmission planning sub-problem by using a column generation algorithm; and feeding back a solving result of the task transmission planning sub-problem to the task acquisition planning sub-problem to update a task weight, and iteratively executing the step of solving the task acquisition planning sub-problem and the task transmission planning sub-problem until a convergence condition is met, and aiming at a complex scene in which heterogeneous businesses coexist, solving the task transmission planning sub-problem and solving the task transmission planning sub-problem. An efficient global optimization solution is provided, the task income is maximized, a dynamic feedback iteration mechanism is introduced, and the overall collaboration and adaptability of the system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite network communication, in particular to a low-orbit integrated satellite network multi-task planning method and system. BACKGROUND

[0002] Since the 21st century, China's space industry has entered a period of rapid development, and the scale of satellites has grown "explosively". Satellite task scheduling is crucial. Different tasks may conflict in the use of satellite resources, and the cost of different satellites performing the same task may be different. The multi-satellite task planning problem, i.e., how to coordinate multi-satellite resources to maximize the benefits of multi-satellite multi-task scheduling, is a current research hotspot. Its performance directly affects whether satellite resources can be maximally utilized and whether users can be better served with more effective services. Integrated satellite communication and remote sensing satellites have attracted widespread attention from domestic and foreign institutions and scholars. China is actively promoting the construction of space infrastructure that realizes one satellite with multiple functions, networked multiple satellites, and coordinated processing between networks. Domestic enterprises have made great progress in the research of integrated satellite communication and remote sensing satellites with multiple payloads. Integrated satellite communication and remote sensing satellites, as an important research hotspot in the field of future satellite communication, have the advantage of saving satellite orbit resources. They can provide communication and remote sensing services on a single satellite, breaking the current status of separate communication and remote sensing. In the scenario of integrated satellite communication and remote sensing satellites, the competing resources are power and storage resources. The multi-task planning problem of integrated satellite communication and remote sensing satellites is usually a combination optimization problem with NP-hard characteristics. It often has the dual characteristics of sequence optimization and resource optimization, i.e., deciding the order of task execution and reasonably allocating satellite and payload resources to tasks. SUMMARY

[0003] The present application aims to provide a low-orbit integrated satellite network multi-task planning method and system to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A low-orbit integrated satellite network multi-task planning method, the method comprising: constructing a system model containing integrated satellite communication and remote sensing satellites, data relay satellites, ground stations, and a task set, and initializing system parameters, wherein the task set contains heterogeneous tasks with priorities, data volumes, and time constraints; based on the system model, establishing an optimization problem with the goal of maximizing weighted task completion; decomposing the optimization problem into a task acquisition planning sub-problem and a task transmission planning sub-problem; The semi-positive relaxation algorithm is used to solve the task collection planning sub-problem, and the column generation algorithm is used to solve the task transmission planning sub-problem. The solution result of the task transmission planning sub-problem is fed back to the task collection planning sub-problem to update the task weight, and the steps of solving the task collection planning sub-problem and the task transmission planning sub-problem are iteratively executed until a convergence condition is met.

[0005] As a further scheme of the present application, in the step of constructing a system model comprising an integrated communication and remote sensing satellite, a data relay satellite, a ground station and a task set, the integrated communication and remote sensing satellite can simultaneously perform a communication task and a remote sensing task, and is subject to constraints of on-board power resources and storage resources.

[0006] As a further scheme of the present application, the task collection planning sub-problem is used to solve a decision variable X, wherein that is, to determine a starting time slot of collecting data of a task j in a time window k, the problem model is as follows: ; ; wherein the constraint C3 represents that the integrated communication and remote sensing satellite simultaneously performs at most one task, is an edge set of a resource conflict graph, represents a feasible starting time slot set of a collection task, a k and b k represent a starting time and an ending time of a visible time window, d j represents a time required for performing a task j, w j is a priority of the task j, st j and et j respectively represent an earliest starting execution time and a latest ending execution time.

[0007] As a further scheme of the present application, the task transmission planning sub-problem is used to solve a decision variable Y, wherein that is, to determine a starting time slot of unloading data of a task j in a time window k, the problem model is as follows: ; ; wherein, respectively represent that the task j starts to transmit data in a time slot t of a time window k, respectively represent that the integrated communication and remote sensing satellite s performs a transmission task in the time slot t, and respectively represent a transmission data amount and a collection data amount in the time slot t, and are a battery power and a cache capacity of the integrated communication and remote sensing satellite, is the initial power.

[0008] As a further scheme of the present application, the task acquisition planning sub-problem is solved by using a semi-positive relaxation algorithm, and the method specifically comprises the following steps: a resource conflict graph is constructed, wherein a vertex represents a candidate task acquisition scheme, and an edge represents a conflict between different schemes due to occupying the same time slot resource of the same satellite; the task acquisition planning sub-problem is modeled as a quadratic constraint quadratic programming model based on the resource conflict graph; the quadratic constraint quadratic programming model is semi-positive relaxed, and is converted into a convex standard semi-positive relaxation form for solving.

[0009] As a further scheme of the present application, the task transmission planning sub-problem is solved by using a column generation algorithm, and the method specifically comprises the following steps: a directed acyclic graph is constructed for each satellite, and a path in the graph represents a feasible task transmission sequence; the task transmission planning sub-problem is reconstructed as a path-based set packing model based on the directed acyclic graph; the set packing model is decomposed into a main problem and multiple sub-problems, and the main problem and the sub-problems are solved iteratively, and columns with negative inspection numbers are dynamically generated to optimize the objective function.

[0010] As a further scheme of the present application, the solving result of the task transmission planning sub-problem is fed back to the task acquisition planning sub-problem to update the task weight, and the steps of solving the task acquisition planning sub-problem and the task transmission planning sub-problem are iteratively executed until the convergence condition is met, and a variable neighborhood search algorithm is adopted, the priority weight of the task is dynamically adjusted according to the success of the task transmission planning in the current iteration, and is fed back to the task acquisition planning sub-problem in the next iteration.

[0011] The present application also provides a low-orbit integrated satellite network multi-task planning system for realizing the low-orbit integrated satellite network multi-task planning method, and the system comprises: a model construction module, which is used for constructing a system model comprising an integrated satellite, a data relay satellite, a ground station and a task set, and initializing system parameters, wherein the task set comprises heterogeneous tasks with priorities, data volumes and time constraints; an optimization problem establishment module, which is used for establishing an optimization problem with the objective of maximizing a weighted task completion amount based on the system model; an optimization decomposition module, which is used for decomposing the optimization problem into a task acquisition planning sub-problem and a task transmission planning sub-problem; a solving module configured to solve the task collection planning sub-problem by using a semi-definite relaxation algorithm and solve the task transmission planning sub-problem by using a column generation algorithm; an iteration module configured to feed back a solution result of the task transmission planning sub-problem to the task collection planning sub-problem to update task weights, and iteratively perform the steps of solving the task collection planning sub-problem and the task transmission planning sub-problem until a convergence condition is met.

[0012] Compared with the prior art, the present application has the following beneficial effects: the present application introduces a telecommunication and remote sensing integrated satellite, a single satellite can perform both communication tasks and remote sensing tasks, breaking the traditional status of separate satellite communication and remote sensing, saving orbit resources, and improving satellite resource utilization; for a complex scene of heterogeneous business coexistence, an efficient global optimization solution is proposed to maximize task benefits; a collaborative solving framework of "semi-definite relaxation + column generation" is innovatively applied to effectively solve the problem of large-scale problem solving; a dynamic feedback iteration mechanism is introduced to enhance the overall collaboration and adaptability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application.

[0014] Figure 1 A flowchart of a low-orbit telecommunication and remote sensing integrated satellite network multi-task planning method is provided.

[0015] Figure 2 A telecommunication and remote sensing integrated satellite task execution schematic diagram is provided. DETAILED DESCRIPTION

[0016] In order to make the technical problems to be solved by the present application, technical solutions and beneficial effects more clearly, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0017] Figure 1 A flowchart of a low-orbit telecommunication and remote sensing integrated satellite network multi-task planning method is provided. Figure 2 A telecommunication and remote sensing integrated satellite task execution schematic diagram is provided. In the embodiments of the present application, a low-orbit telecommunication and remote sensing integrated satellite network multi-task planning method comprises: a system model containing a telecommunication and remote sensing integrated satellite, a data relay satellite, a ground station and a task set is constructed, and system parameters are initialized, wherein the task set contains heterogeneous tasks with priorities, data volumes and time constraints; Based on the system model, an optimization problem is established to maximize the weighted task completion amount; The optimization problem is decomposed into a task collection planning sub-problem and a task transmission planning sub-problem; The task collection planning sub-problem is solved by using a semi-positive relaxation algorithm, and the task transmission planning sub-problem is solved by using a column generation algorithm; The solution result of the task transmission planning sub-problem is fed back to the task collection planning sub-problem to update the task weight, and the steps of solving the task collection planning sub-problem and the task transmission planning sub-problem are iteratively executed until a convergence condition is met.

[0018] In the embodiment, the system model comprising the integrated communication and remote sensing satellite, the data relay satellite, the ground station and the task set is constructed, and the system parameter initialization step specifically comprises: The integrated communication and remote sensing satellite network scenario is composed of the integrated communication and remote sensing satellite, the ground station and the data relay satellite. In the network, the integrated communication and remote sensing satellite is denoted as a set , wherein S represents the total number of integrated communication and remote sensing satellites; the task is denoted as a set , wherein J represents the total number of tasks, and each task is represented by a four-tuple , wherein w j is the priority of the task j, D j represents the data amount of the task j, st j and et j respectively represent the earliest start execution time and the latest end execution time; the data receiving antenna set is denoted as H . For the sake of clarity, the set H is further divided into H1 and H2, i.e. , wherein H1 and H2 respectively represent the receiving antenna of the data relay satellite and the receiving antenna of the ground station; K represents the visible time window, specifically, and respectively represent the visible time window set of the task j and the integrated communication and remote sensing satellite, and the visible time window set of the integrated communication and remote sensing satellite s(j) and the data receiving antenna H to which the task j belongs. In addition, the scheduling period of the integrated communication and remote sensing network is divided into fixed time slots with a length of 30s , denoted as . The integrated communication and remote sensing satellite first acquires differentiated data, including communication tasks and remote sensing tasks, and the life cycle and data amount of the two kinds of tasks obey different uniform distributions; then the task data acquired by the integrated communication and remote sensing satellite is stored in the integrated communication and remote sensing satellite; then the task is transmitted to the ground station or the data relay satellite through the satellite-ground link or the inter-satellite link, and finally the task is transmitted to the destination within the specified time, which is regarded as successful task transmission.

[0019] Suppose that the transmission power of all satellites in the system, the power consumed by collecting data, and the static power consumption of the satellite to maintain operation are the same and constant, the resource consumption of the satellite in performing a task can be divided into electric quantity resource and storage resource.

[0020] The step of establishing an optimization problem with the maximum weighted task completion as the target based on the system model specifically comprises: The key challenge of the integrated satellite in performing a task is that the remote sensing task and the communication task compete for the electric quantity and the storage resource on the satellite. The energy consumption on the satellite mainly consists of two parts, one is the constant power consumption for maintaining the normal operation of the satellite , wherein is the constant power; and the other is the energy consumption of the satellite in collecting data and the energy consumption brought by transmitting data , wherein P m and P s respectively represent the power of collecting data and the power of transmitting data, and respectively represent the case that the integrated satellite s performs the collecting task and the transmitting task in the time slot t, and if 1, it indicates that the integrated satellite is occupied. The total consumption . In addition, the energy that the satellite can obtain through solar energy in each time slot is , wherein and respectively represent the battery charging rate of the integrated satellite s in the time slot t and the time in the sunlight in the time slot t.

[0021] The multi-task planning optimization problem of the low-orbit integrated satellite network can be represented as: ; ; In the above formula, the constraint C1 defines the variable , , , is a 0-1 variable, and respectively represent the start of collecting / transmitting data of the task j in the time slot t of the time window k. The constraints C2 and C5 represent that the task is collected / transmitted at most once, wherein represents all feasible time slot sets that can start collecting the task j in the visible time window k, and is defined as , wherein a k and b k represent the start time and the end time of the visible time window, d j represents the time required for performing the task j; represents all feasible time slot sets that can start transmitting the task j in the visible time window k, and is defined as where is the earliest time to start transmission task j. Constraints C3 and C6 represent that the TT&C satellite can only collect / transmit one task at the same time. Constraints C4 and C7 define the occupation of the TT&C satellite, where s(k) represents the satellite to which time window k belongs; represents the time slot set occupied by the TT&C satellite when it starts collecting task j at time slot t, which is defined as ; represents the time slot set occupied by the TT&C satellite when it starts transmitting task j at time slot t, which is defined as Constraint C8 represents that the number of transmission tasks cannot exceed the number of collection tasks, where and represent the transmission data volume and the collection data volume in time slot t, respectively, which are defined as , Constraints C9 and C 10 represent that the battery capacity of the TT&C satellite does not exceed the battery capacity and the total amount of data stored in the cache of the TT&C satellite does not exceed the cache capacity, respectively, where is the initial battery capacity, and are the battery capacity and the cache capacity of the TT&C satellite, respectively.

[0022] The step of decomposing the optimization problem into a task collection planning sub-problem and a task transmission planning sub-problem specifically includes: The task planning sub-problem in the collection phase is used to solve the decision variable X, where i.e., to determine the start time slot of collecting data of task j in time window k, the problem model is as follows: ; ; Constraint C3 in the above formula represents that the TT&C satellite can only perform one task at the same time, where is the edge set of the resource conflict graph, represents the feasible start time slot set of the collection task.

[0023] The task planning sub-problem in the transmission phase is used to solve the decision variable Y, where i.e., to determine the start time slot of unloading data of task j in time window k, the problem model is as follows: ; ; The specific steps of solving the task planning sub-problem in the collection phase by using the semi-positive relaxation method are as follows: A resource conflict graph is introduced, which consists of a vertex set and an edge set: where each vertex in the vertex set represents a candidate task acquisition scheme, denoted as , and are the start time slot and the end time slot of the task planning respectively, in addition, s(v) is denoted as the telecommunication-remote sensing integrated satellite corresponding to the vertex v. If there is a conflict between two points, there is an edge, which represents the conflict between different schemes due to occupying the same time slot resource of the same satellite. The conflict refers to the overlap of the scheduling time slots of two task arrangements for the same telecommunication-remote sensing integrated satellite, that is, if and s(u)=s(v) are satisfied, the edge set is updated as .

[0024] The acquisition phase task planning sub-problem is constructed as a quadratic constraint quadratic programming problem, and is defined, where , let , where , let and represent the column vectors of which the nth andn'th elements are 1 respectively. In addition, C1 in P1 is rewritten as . Thus, the acquisition phase task planning sub-problem can be expressed as follows: ; ; Define , then the acquisition phase task planning sub-problem can be expressed as follows: ; ; Wherein: ; Define , and remove the rank constraint of , to obtain the new optimization problem as follows: ; ; At this time, the acquisition phase task planning sub-problem has been converted into a convex standard semi-definite relaxation form.

[0025] As a further technical solution of the present application, the specific steps for solving the transmission phase task planning sub-problem by using column generation algorithm are as follows: A directed acyclic graph is constructed, where represents a feasible transmission scheme of the task on the satellite, that is, each vertex represents the start of scheduling of the task j in the time window k at time slot t, is a two-tuple representing the start time and end time of a task transmission. There is a directed edge if two schemes can be executed sequentially. In addition, two virtual vertices v s and v e are added to the graph, representing the virtual source and sink, respectively. A path from v s to v e in the graph corresponds to a transmission path p. Define a transmission sequence for each satellite, which is a set of ordered tasks that can be sequentially transmitted by a satellite, where any adjacent elements satisfy , and let , where P k denotes the set of all feasible transmission paths on satellite k.

[0026] Define to represent that transmission path p is assigned to satellite s; to represent the priority of all tasks contained in transmission path p, i.e. , where if satellite k's transmission path p contains task j. The transmission phase task planning subproblem can be reconstructed as a path-based set packing model: ; ; Decompose the problem into a master problem and S generated problems, each corresponding to a TT&C satellite. The specific mathematical model is as follows: ; ; ; ; Solve the transmission phase task planning subproblem. First, initialize the transmission path and substitute it into the original column generation model, and relax the variable to continuous type. Solve the master problem to obtain the corresponding dual variable value . According to the generated problem, determine whether a new column can be added to the master problem to increase the value of the objective function. Iterate until no new column can be added to the master problem, dynamically generate a column with a negative check number to optimize the objective function, and convert the generated problem into a multi-constraint path optimization problem.

[0027] As a further technical solution of the present application, the task priority is adjusted by using the variable neighborhood search algorithm in the iteration step and fed back to the acquisition phase. In this way, the acquisition phase task planning scheme is adjusted according to the transmission phase task planning result, and the iteration is continuously performed until the task scheduling success rate has no obvious gain.

[0028] The application introduces a communication and remote sensing integrated satellite, which can perform communication tasks and remote sensing tasks, breaks the traditional satellite communication and remote sensing separation status, saves the orbit resources, and improves the satellite resource utilization rate. The application constructs a communication and remote sensing integrated satellite network model, constructs an optimization problem with the maximum weighted task completion amount as the target, analyzes the optimization problem, and decomposes it into a collection stage task planning sub-problem and a transmission stage task planning sub-problem. The collection stage task planning sub-problem is converted into a convex standard semi-definite relaxation form, and the semi-definite relaxation method is used for solving; the transmission stage task planning sub-problem is converted by introducing a directed acyclic graph, and the column generation algorithm is used for solving, and finally repeated iteration is performed until convergence, so that the best task planning result is obtained. The method provided in the application fully utilizes the resources of the communication and remote sensing integrated satellite while meeting the needs of heterogeneous tasks.

[0029] In the embodiment of the application, a low-orbit communication and remote sensing integrated satellite network multi-task planning system is also provided, which comprises: A model construction module is configured to construct a system model comprising a communication and remote sensing integrated satellite, a data relay satellite, a ground station and a task set, and initialize system parameters, wherein the task set comprises heterogeneous tasks with priorities, data volumes and time constraints. An optimization problem establishment module is configured to establish an optimization problem with the maximum weighted task completion amount as the target based on the system model. An optimization decomposition module is configured to decompose the optimization problem into a task collection planning sub-problem and a task transmission planning sub-problem. A solving module is configured to solve the task collection planning sub-problem by using a semi-definite relaxation algorithm, and solve the task transmission planning sub-problem by using a column generation algorithm. An iteration module is configured to feed back the solving result of the task transmission planning sub-problem to the task collection planning sub-problem to update the task weight, and iteratively perform the steps of solving the task collection planning sub-problem and the task transmission planning sub-problem until the convergence condition is met.

[0030] The above only describes the preferred embodiments of the application and is not intended to limit the application. Any modification, equivalent replacement and improvement within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network, characterized in that, The method includes: Construct a system model that includes a communication and remote sensing integrated satellite, a data relay satellite, a ground station, and a set of missions, and initialize the system parameters. The set of missions includes heterogeneous missions with priority, data volume, and time constraints. Based on the system model, an optimization problem is established with the objective of maximizing the weighted task completion amount. The optimization problem is decomposed into a task acquisition planning sub-problem and a task transmission planning sub-problem; The task acquisition planning subproblem is solved using a positive semidefinite relaxation algorithm, and the task transmission planning subproblem is solved using a column generation algorithm. The solution result of the task transport planning subproblem is fed back to the task acquisition planning subproblem to update the task weights, and the steps of solving the task acquisition planning subproblem and the task transport planning subproblem are iteratively executed until the convergence condition is met.

2. The multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network according to claim 1, characterized in that, In the step of constructing a system model that includes an integrated communication and remote sensing satellite, a data relay satellite, a ground station, and a set of missions, the integrated communication and remote sensing satellite can simultaneously perform communication and remote sensing missions, and is constrained by onboard power and storage resources.

3. The multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network according to claim 1, characterized in that, The task acquisition planning subproblem is used to solve for the decision variable X, where That is, to determine the starting time slot for task j to collect data in time window k. The problem model is as follows: ； ； Among them, constraint C3 indicates that the integrated communication and remote sensing satellite can perform a maximum of one mission at a time. It is the edge set of the resource conflict graph. a represents the set of feasible start slots for the data acquisition task. k and b k d represents the start and end times of the visible time window. j w represents the time required to execute task j. j st is the priority of task j j and et j These represent the earliest start time and the latest end time of execution, respectively.

4. The multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network according to claim 1, characterized in that, The task transfer planning subproblem is used to solve for the decision variable Y, where That is, to determine the starting time slot for task j to unload data in time window k. The problem model is as follows: ； ； in, These represent that task j begins transmitting data in time slot t within time window k. These represent the situations where the integrated communication and remote sensing satellite s performs a transmission mission in time slot t. and These represent the amount of data transmitted and the amount of data acquired within time slot t, respectively. and These are the battery capacity and cache capacity of the integrated communication and remote sensing satellite, respectively. This is the initial charge level.

5. A multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network according to claim 1 or 3, characterized in that, The method of using a semidefinite relaxation algorithm to solve the task acquisition planning subproblem specifically includes: Construct a resource conflict graph, where vertices represent candidate task acquisition schemes and edges represent conflicts between different schemes due to occupying the same time slot resources of the same satellite; Based on the resource conflict graph, the task acquisition planning subproblem is modeled as a quadratic constraint quadratic programming model. The quadratic constrained quadratic programming model is subjected to positive semidefinite relaxation, which transforms it into a convex standard semidefinite relaxation form for solution.

6. A multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network according to claim 1 or 4, characterized in that, The method of using a column generation algorithm to solve the task transport planning sub-problem specifically includes: For each satellite, construct a directed acyclic graph, where the paths represent a feasible mission delivery sequence; Based on the directed acyclic graph, the task transmission planning subproblem is reconstructed into a path-based set wrapping model; The set-wrapping model is decomposed into a main problem and multiple subproblems. By iteratively solving the main problem and subproblems, columns with negative test numbers are dynamically generated to optimize the objective function.

7. The multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network according to claim 1, characterized in that, In the step of feeding back the solution result of the task transport planning subproblem to the task acquisition planning subproblem to update the task weights, and iteratively executing the steps of solving the task acquisition planning subproblem and the task transport planning subproblem until the convergence condition is met, a variable neighborhood search algorithm is used to dynamically adjust the priority weights of the tasks based on the success of the task transport planning in the current iteration, and feed them back to the task acquisition planning subproblem in the next iteration.

8. A multi-mission planning system for a low-Earth orbit (LEO) integrated communication and remote sensing satellite network, used to implement the multi-mission planning method for a LEO integrated communication and remote sensing satellite network as described in any one of claims 1-7, characterized in that, The system includes: The model building module is used to build a system model that includes a communication and remote sensing integrated satellite, a data relay satellite, a ground station, and a set of tasks, and to initialize system parameters. The set of tasks includes heterogeneous tasks with priority, data volume, and time constraints. The optimization problem establishment module is used to establish an optimization problem based on the system model, with the objective of maximizing the weighted task completion amount. The optimization decomposition module is used to decompose the optimization problem into task acquisition planning sub-problems and task transmission planning sub-problems; The solution module is used to solve the task acquisition planning subproblem using a positive semidefinite relaxation algorithm and to solve the task transmission planning subproblem using a column generation algorithm. The iterative module is used to feed back the solution results of the task transport planning subproblem to the task acquisition planning subproblem to update the task weights, and iteratively execute the steps of solving the task acquisition planning subproblem and the task transport planning subproblem until the convergence condition is met.

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