Low-orbit satellite network multi-task planning method and system

Abstract

The application relates to the technical field of satellite network communication, and particularly discloses a low-orbit integrated satellite network multi-task planning method and system, which comprises the following steps: a system model containing an integrated satellite, a data relay satellite, a ground station and a task set is constructed; based on the system model, an optimization problem with the target of maximizing the weighted task completion amount is established; a semi-positive definite relaxation algorithm is used to solve the task collection planning sub-problem, and a column generation algorithm is used to solve the task transmission planning sub-problem; the solving 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 the convergence condition is met; for a complex scene where heterogeneous services coexist, an efficient global optimization solution is provided, the task benefit is maximized, a dynamic feedback iteration mechanism is introduced, and the overall coordination and adaptability of the system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite network communication, and 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 with one satellite serving multiple functions, multiple satellites networking, and networked processing. Domestic enterprises have made great progress in the research of integrated satellite communication and remote sensing satellites. 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 integrated satellite communication and remote sensing satellite scenario, the competing resources are power and storage resources when there are heterogeneous services. 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:

[0005] A low-orbit integrated satellite network multi-task planning method, the method comprising:

[0006] constructing a system model comprising an integrated satellite communication and remote sensing 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;

[0007] based on the system model, establishing an optimization problem with the objective of maximizing the weighted task completion amount;

[0008] The optimization problem is decomposed into a task acquisition planning sub-problem and a task transmission planning sub-problem;

[0009] 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.

[0010] 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.

[0011] As a further embodiment of the present invention, 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 mission set, the integrated communication and remote sensing satellite is capable of simultaneously performing communication and remote sensing missions, and is constrained by onboard power and storage resources.

[0012] As a further aspect of the present invention, the task acquisition planning subproblem is used to solve for the decision variable X, wherein... That is, to determine the starting time slot for task j to collect data in time window k. The problem model is as follows:

[0013] ;

[0014] ;

[0015] 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.

[0016] As a further aspect of the present invention, the task transfer planning subproblem is used to solve for the decision variable Y, wherein That is, to determine the starting time slot for task j to unload data in time window k. The problem model is as follows:

[0017] ;

[0018] ;

[0019] 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 battery level.

[0020] As a further aspect of the present invention, the method of using a semidefinite relaxation algorithm to solve the task acquisition planning sub-problem specifically includes:

[0021] 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;

[0022] Based on the resource conflict graph, the task acquisition planning subproblem is modeled as a quadratic constraint quadratic programming model.

[0023] The quadratic constrained quadratic programming model is subjected to positive semidefinite relaxation, which transforms it into a convex standard semidefinite relaxation form for solution.

[0024] As a further aspect of the present invention, the method of using a column generation algorithm to solve the task transmission planning sub-problem specifically includes:

[0025] For each satellite, construct a directed acyclic graph, where the paths represent a feasible mission delivery sequence;

[0026] Based on the directed acyclic graph, the task transmission planning subproblem is reconstructed into a path-based set wrapping model;

[0027] 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.

[0028] As a further embodiment of the present invention, in the step of feeding back the solution result of the task transmission planning subproblem to the task acquisition planning subproblem to update the task weight, and iteratively executing the steps of solving the task acquisition planning subproblem and the task transmission planning subproblem until the convergence condition is met, a variable neighborhood search algorithm is used to dynamically adjust the priority weight of the task based on the success of the task transmission planning in the current iteration, and feed it back to the task acquisition planning subproblem in the next iteration.

[0029] This invention also provides a multi-mission planning system for an integrated low-Earth orbit (LEO) communication and remote sensing satellite network, used to implement a multi-mission planning method for an integrated LEO communication and remote sensing satellite network, characterized in that the system comprises:

[0030] 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.

[0031] 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.

[0032] The optimization decomposition module is used to decompose the optimization problem into task acquisition planning sub-problems and task transmission planning sub-problems;

[0033] 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.

[0034] 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.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention introduces an integrated communication and remote sensing satellite, in which a single satellite can perform both communication and remote sensing tasks, breaking the traditional separation of satellite communication and remote sensing, saving orbital resources, improving satellite resource utilization, proposing an efficient global optimization solution for complex scenarios with heterogeneous services coexisting, maximizing task benefits, innovatively applying a collaborative solution framework of "semi-positive definite relaxation + column generation", effectively overcoming the problem of solving large-scale problems, and introducing a dynamic feedback iteration mechanism to enhance the overall synergy and adaptability of the system. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0037] Figure 1 This is a flowchart illustrating a multi-task planning method for an integrated low-orbit communication and remote sensing satellite network, as provided in an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram illustrating the execution of an integrated communication and remote sensing satellite mission, as provided in an embodiment of the present invention. Detailed Implementation

[0039] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0040] Figure 1 This is a flowchart of a multi-mission planning method for an integrated low-Earth orbit communication and remote sensing satellite network. Figure 2 This is a schematic diagram illustrating the execution of an integrated communication and remote sensing satellite mission. In this embodiment of the invention, a multi-mission planning method for a low-Earth orbit integrated communication and remote sensing satellite network is provided, the method comprising:

[0041] 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.

[0042] Based on the system model, an optimization problem is established with the objective of maximizing the weighted task completion amount.

[0043] The optimization problem is decomposed into a task acquisition planning sub-problem and a task transmission planning sub-problem;

[0044] 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.

[0045] 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.

[0046] In this embodiment, the steps of constructing a system model including an integrated communication and remote sensing satellite, a data relay satellite, a ground station, and a mission set, and initializing system parameters specifically include:

[0047] The integrated communication and remote sensing satellite network scenario consists of integrated communication and remote sensing satellites, ground stations, and data relay satellites. In this network, the integrated communication and remote sensing satellites are denoted as a set. Where S represents the total number of integrated communication and remote sensing satellites; the mission is denoted as set. Where J represents the total number of tasks, and each task is represented by a quadruple. It means that w j Let D be the priority of task j. j st represents the amount of data for task j. j and et j These represent the earliest start time and the latest end time of execution, respectively; using This represents the set of data receiving antennas. For clarity, the set H is further divided into H1 and H2, i.e. Where H1 and H2 represent the receiving antenna of the data relay satellite and the receiving antenna of the ground station, respectively; K represents the visible time window, specifically, and Let represent the set of visible time windows for mission j and the integrated communication and remote sensing satellite, and the set of visible time windows for the integrated communication and remote sensing satellite s(j) to which mission j belongs and the data receiving antenna H, respectively. Furthermore, the scheduling cycle of the integrated communication and remote sensing network is divided into fixed time slots of 30 seconds each. , recorded as The integrated communication and remote sensing satellite first acquires differentiated data, including communication and remote sensing tasks, with the lifecycles and data volumes of the two tasks following 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. Afterward, the task is transmitted to a ground station or data relay satellite via a satellite-to-ground link or an inter-satellite link. Finally, the task is considered successfully transmitted if it is transmitted to its destination within the specified time.

[0048] Assuming that the transmission power, data acquisition power, and static power consumption for maintaining operation of all satellites in the system are the same and constant, the resource consumption of satellites in performing their missions can be divided into electrical power resources and storage resources.

[0049] The steps for establishing an optimization problem based on the system model, with the objective of maximizing the weighted task completion amount, specifically include:

[0050] A key challenge for integrated remote sensing and communication satellites during missions lies in the competition between remote sensing and communication tasks for onboard power and storage resources. Onboard energy consumption consists primarily of two parts: one is the constant power consumption required to maintain normal satellite operation. ,in The first is constant power; the second is the energy consumption of satellite data acquisition. And the energy consumption caused by data transmission , where P m and P s These represent the power for acquiring data and the power for transmitting data, respectively. and These represent the acquisition and transmission tasks performed by the integrated communication and remote sensing satellite s in time slot t, respectively. A value of 1 indicates that the integrated communication and remote sensing satellite is occupied. Total consumption Furthermore, each time-slot satellite can obtain energy from solar power. ,in and These represent the battery charging rate of the integrated communication and remote sensing satellite s in time slot t and the time it is exposed to sunlight within time slot t, respectively.

[0051] The multi-mission planning and optimization problem for a low-Earth orbit integrated communication and remote sensing satellite network can be expressed as:

[0052] ;

[0053] ;

[0054] In the above formula, constraint C1 defines the variable. , , , For 0-1 variables, and These represent tasks j starting to collect / transmit data in time slot t within time window k. Constraints C2 and C5 indicate that a task is collected / transmitted at most once. This represents the set of all feasible time slots within the visible time window k where task j can begin acquisition, defined as... , where a k and b k d represents the start and end times of the visible time window. j This represents the time required to execute task j; The set of all feasible time slots within the visible time window k that can begin transmitting task j is defined as follows: ,in This represents the earliest time to start transmitting task j. Constraints C3 and C6 indicate that the integrated communication and remote sensing satellite can acquire / transmit at most one task simultaneously. Constraints C4 and C7 define the occupancy status of the integrated communication and remote sensing satellite, where s(k) represents the satellite to which time window k belongs; This represents the set of time slots occupied when the integrated communication and remote sensing satellite begins data acquisition task j in time slot t, and it is defined as follows: ; This represents the set of time slots occupied when the integrated communication and remote sensing satellite begins transmitting mission j in time slot t, defined as... Constraint C8 indicates that the number of transmission tasks must not exceed the number of acquisition tasks, where and Let them represent the amount of data transmitted and the amount of data acquired within time slot t, respectively, defined as follows: , Constraints C9 and C 10 These respectively indicate that the battery power of the integrated communication and remote sensing satellite does not exceed the battery capacity, and the total amount of data stored in the integrated communication and remote sensing satellite's cache does not exceed the cache capacity. This is the initial charge level. and These are the battery capacity and cache capacity of the integrated communication and remote sensing satellite, respectively.

[0055] The steps of decomposing the optimization problem into task acquisition planning sub-problems and task transmission planning sub-problems specifically include:

[0056] The task planning subproblem in the data collection phase 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:

[0057] ;

[0058] ;

[0059] The constraint C3 in the above formula indicates that the integrated communication and remote sensing satellite can perform at most one mission at a time, where It is the edge set of the resource conflict graph. This represents the set of feasible start time slots for the data acquisition task.

[0060] The task planning subproblem in the transmission phase 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:

[0061] ;

[0062] ;

[0063] The specific steps for solving the task planning subproblem in the data acquisition phase using the semidefinite relaxation method are as follows:

[0064] Introducing a resource conflict graph, which consists of a vertex set and an edge set: each vertex in the vertex set represents a candidate task acquisition scheme, denoted as . , and These represent the start and end time slots of the mission planning, respectively. Furthermore, s(v) is denoted as the integrated communication and remote sensing satellite corresponding to vertex v. If a conflict exists between two points, an edge exists, representing a conflict arising from different schemes occupying the same time slot resource of the same satellite. A conflict refers to the overlap of scheduling time slots for two missions arranging the same integrated communication and remote sensing satellite; that is, if the following conditions are met... And since s(u) = s(v), the edge set is updated to... .

[0065] The task planning subproblem of the data collection phase is constructed as a quadratic constraint quadratic programming problem, defined as follows: ,in ,make ,in ,make and These represent column vectors whose nth and n'th elements are 1, respectively. Furthermore, C1 in P1 is rewritten as... Therefore, the task planning sub-problem of the data collection phase can be expressed as follows:

[0066] ;

[0067] ;

[0068] definition The task planning subproblem of the data collection phase can then be expressed as follows:

[0069] ;

[0070] ;

[0071] in:

[0072] ;

[0073] definition and remove With the rank constraint, a new optimization problem is obtained as follows:

[0074] ;

[0075] ;

[0076] At this point, the task planning subproblem of the data acquisition phase has been transformed into a convex standard semidefinite relaxation form.

[0077] As a further technical solution of the present invention, the specific steps for solving the task planning sub-problem of the transmission phase using the column generation algorithm are as follows:

[0078] Construct a directed acyclic graph ,in A feasible transmission scheme for a mission on a satellite, i.e., each vertex Both indicate that task j begins scheduling in time slot t of time window k. A tuple represents the start and end times of the task transfer. A directed edge exists if both schemes can be executed sequentially. Additionally, two dummy vertices v are added to the graph. s and v e Let v represent the virtual source and destination points, respectively. Then, in the diagram, from v... s to v e Each path corresponds to a transmission path p. A transmission sequence is defined for each satellite, which is an ordered set of tasks that can be transmitted sequentially by a single satellite, where any adjacent elements satisfy... ,make , where P k This represents the set of all feasible transmission paths on the integrated communication and remote sensing satellite k.

[0079] definition This indicates that transmission path p is assigned to satellite s; This represents the sum of priorities of all tasks contained in the transmission path p, i.e. If the transmission path p of satellite k contains mission j, then The task planning subproblem in the transmission phase can then be refactored into a path-based set wrapper model:

[0080] ;

[0081] ;

[0082] The problem is decomposed into a main problem and S generative problems, with each generative problem corresponding to an integrated communication and remote sensing satellite. The specific mathematical model is as follows:

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] To solve the task planning subproblem of the transmission phase, first initialize the transmission path and substitute it into the original column generation model, and then set the variables... The relaxation is continuous; the principal problem yields the values ​​of the corresponding dual variables. The generator problem determines whether a new column can be added to the main problem to increase the value of the objective function. This process iterates until no new column can be added to the main problem. Columns with negative test numbers are dynamically generated to optimize the objective function. The generator problem is then transformed into a multi-constraint path optimization problem.

[0088] As a further technical solution of the present invention, the task priority is adjusted and fed back to the acquisition stage by using the idea of ​​variable neighborhood search algorithm in the iterative execution step. In this way, the task planning scheme of the acquisition stage is adjusted according to the task planning results of the transmission stage, and the process is continuously iterated until there is no significant gain in the task scheduling success rate.

[0089] This invention introduces an integrated communication and remote sensing satellite, where a single satellite can perform both communication and remote sensing tasks, breaking the traditional separation of satellite communication and remote sensing, saving orbital resources, and improving satellite resource utilization. This invention constructs an integrated communication and remote sensing satellite network model, establishing an optimization problem with the objective of maximizing the weighted task completion rate. Through analysis of the optimization problem, it is decomposed into task planning subproblems for the acquisition phase and transmission phase. The acquisition phase task planning subproblem is transformed into a convex standard semidefinite relaxation form and solved using a positive semidefinite relaxation method. The transmission phase task planning subproblem is transformed by introducing a directed acyclic graph and solved using a column generation algorithm. Finally, it is iterated repeatedly until convergence, thus obtaining the optimal task planning result. The method proposed in this invention fully utilizes the resources of the integrated communication and remote sensing satellite while meeting the needs of heterogeneous tasks.

[0090] This invention also provides a low-Earth orbit integrated communication and remote sensing satellite network multi-mission planning system, the system comprising:

[0091] 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.

[0092] 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.

[0093] The optimization decomposition module is used to decompose the optimization problem into task acquisition planning sub-problems and task transmission planning sub-problems;

[0094] 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.

[0095] 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.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

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. The task set is denoted as Where J represents the total number of tasks, and each task is represented by a quadruple. It means that w j Let D be the priority of task j. j st represents the amount of data for task j. j and et j These represent the earliest start time and the latest end time of execution, respectively. Based on the system model, an optimization problem is established with the objective of maximizing the weighted task completion amount. The multi-mission planning and optimization problem of the low-orbit communication and remote sensing integrated satellite network can be expressed as: ； ； In the above formula, constraint C1 defines the variable. , , , For 0-1 variables, and Let C1 and C2 represent that task j starts collecting / transmitting data in time slot t of time window k, respectively. Constraints C2 and C5 indicate that the task is collected / transmitted at most once. This represents the set of all feasible time slots within the visible time window k where task j can begin acquisition, defined as... , where a k and b k d represents the start and end times of the visible time window. j This represents the time required to execute task j; The set of all feasible time slots within the visible time window k that can begin transmitting task j is defined as follows: ,in The earliest time to start transmitting task j is defined by constraints C3 and C6, which indicate that the integrated communication and remote sensing satellite can acquire / transmit at most one task at a time. Constraints C4 and C7 define the occupancy status of the integrated communication and remote sensing satellite, where s(k) represents the satellite to which time window k belongs. This represents the set of time slots occupied when the integrated communication and remote sensing satellite begins data acquisition task j in time slot t, and it is defined as follows: ; This represents the set of time slots occupied when the integrated communication and remote sensing satellite begins transmitting mission j in time slot t, defined as... Constraint C8 indicates that the number of transmission tasks must not exceed the number of acquisition tasks, where and Let them represent the amount of data transmitted and the amount of data acquired within time slot t, respectively, defined as follows: , Constrain C9 and C 10 These respectively indicate that the battery power of the integrated communication and remote sensing satellite does not exceed the battery capacity, and the total amount of data stored in the integrated communication and remote sensing satellite's cache does not exceed the cache capacity. This is the initial charge level. and These are the battery capacity and cache capacity of the integrated communication and remote sensing satellite, respectively. 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 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. 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; 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 mission set, 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 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.

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 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.

5. 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.

6. 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 1-5, 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 break down 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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