Satellite Scheduling Method, Device and Computer Equipment Based on Interval Revolution Model

Abstract

The present application relates to a satellite scheduling method, device and computer equipment based on an interval number of orbits model. The method includes: obtaining a target observation task, constructing a satellite observation model, constructing an interval number of orbits benefit function for each target to be observed, constructing an objective function and constraint conditions for maximizing the benefit according to the satellite observation model and the interval number of orbits benefit function, and solving the objective function to determine a satellite scheduling scheme. Using this method can improve the satellite scheduling efficiency of satellite scheduling that requires multiple observations.

Description

Technical Field

[0001] The present application relates to the technical field of satellite mission scheduling, and particularly to a satellite scheduling method, device, and computer device based on an interval revolution model. Background Art

[0002] Optical earth observation satellites are a type of satellite platform that detects the earth's surface and lower atmosphere through carried optical remote sensors (payloads) to obtain relevant information. Optical earth observation satellites have advantages such as a wide coverage range, high information acquisition accuracy, being unrestricted by airspace and national boundaries, and not involving personnel safety, and are widely used in fields such as situation reconnaissance, target recognition, earth resource exploration, natural disaster monitoring, urban planning, and crop monitoring.

[0003] Satellites operate in a certain orbit. There are many targets that may be observed within the same orbit revolution. Each target has a corresponding observation time window. In order to make full use of precious satellite resources and obtain ground target image data with the largest quantity and optimal observation effect, it is necessary to reasonably plan and schedule what time to observe and which targets to observe. Therefore, studying the scheduling problem of earth observation satellites is of great significance for improving the utilization efficiency of satellite resources and meeting user needs.

[0004] Traditional research on the scheduling problem of earth observation satellites assumes that each target only needs to be observed once to complete the task and meet user needs. Therefore, when modeling the problem, it is assumed that each target is observed at most once. Although this assumption is convenient for problem modeling and description, it is obviously not applicable to real application scenarios where targets have multiple observation requirements and the multiple observations are coupled and dependent on each other. Summary of the Invention

[0005] Based on this, it is necessary to provide a satellite scheduling method, device, and computer device based on an interval revolution model for the above technical problems.

[0006] A satellite scheduling method based on an interval revolution model, the method includes:

[0007] Obtain target observation tasks; the target observation tasks include: ordinary observation tasks and precise observation tasks; the precise observation tasks are after the ordinary observation tasks, and the time interval between the execution of the ordinary observation tasks and the precise observation tasks is related to the success rate of completing the precise observation tasks;

[0008] Construct a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: targets to be observed, a virtual starting point, and a virtual ending point;

[0009] Construct the benefit function of the number of laps between each target to be observed; the benefit function of the number of laps between observations represents the benefit of completing two observations of the target to be observed in the number of laps between observations.

[0010] According to the satellite observation model and the benefit function of the number of laps between observations, construct an objective function and constraints for maximizing the benefit, and solve the objective function to determine the satellite scheduling plan.

[0011] In one embodiment, it further includes: constructing a satellite observation model including a directed graph G=(N,A), where the node set is N={0,1,…,n + 1}, and the arc set is A={(i,j)|i,j∈N,i≠j}, and nodes 0 and n + 1 correspond to virtual starting and ending points with no benefits. Represents the set of nodes with benefits;

[0012] In the satellite observation model, each target i corresponds to a continuous observation time d i , and the set of laps is set as T={1,…,t}; where each lap starts from the virtual starting point and returns to the virtual ending point; the observable time window of each lap t for target i is TW it =[ws it ,we it , and the conversion time between passing through targets i and j in each lap is t ij .

[0013] In one embodiment, it further includes: dividing the difference between the targets to be observed in two observations into two targets, including: target i and target i + n, Represents the set of targets for the first observation, Represents the set of targets for the second observation;

[0014] Set the number of laps M between two observations of each target, where M={0,1,2,…,m};

[0015] Set the benefit obtained after target i completes two observations after an interval of m laps as:

[0016]

[0017] Among them, p im Represents the benefit obtained after target i completes two observations after an interval of m laps.

[0018] In one embodiment, it further includes: according to the satellite observation model and the benefit function of the number of laps between observations, the objective function for maximizing the benefit is constructed as:

[0019]

[0020] Among them, y imIndicates whether the number of laps between two observations of target i is m laps apart.

[0021] In one embodiment, the construction constraints include: observation start and end constraints, flow balance constraints, observation constraints, complete observation constraints, decision variable constraints, observation lap constraints, observation sorting constraints, time window constraints, and decision variable value range constraints.

[0022] In one embodiment, the observation start and end constraints are:

[0023]

[0024] Among them, the starting point is node 0, and the ending point is node 2n + 1;

[0025] The flow balance constraint is:

[0026]

[0027] Among them, x ijt and x jit respectively represent from target i to target j for each lap t and from target j to target i for each lap t;

[0028] The observation constraint is:

[0029]

[0030] The complete observation constraint is:

[0031]

[0032] The decision variable constraint is:

[0033]

[0034] The observation lap constraint is:

[0035]

[0036] The observation sorting constraint is:

[0037]

[0038] Among them, s it represents the start time of observing target i for each lap t, and L is a positive integer;

[0039] The time window constraint is:

[0040]

[0041]

[0042] The value range constraint of the decision variable is as follows:

[0043]

[0044]

[0045]

[0046] In one embodiment, it further includes: using a branch-and-price exact algorithm to solve the objective function to determine the satellite scheduling scheme.

[0047] A satellite scheduling device based on an interval revolution number model, the device includes:

[0048] A task acquisition module, configured to acquire a target observation task; the target observation task includes: a general observation task and a precise observation task; the precise observation task is after the general observation task, and the time interval between the execution of the general observation task and the precise observation task is related to the success rate of completing the precise observation task;

[0049] An observation model construction module, configured to construct a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: a target to be observed, a virtual starting point, and a virtual ending point;

[0050] A revenue function construction module, configured to construct an interval revolution number revenue function for each target to be observed; the interval revolution number revenue function represents the revenue of completing the observation of the target to be observed by performing two observations in the interval revolutions;

[0051] A solution module, configured to construct an objective function for maximizing revenue and constraint conditions according to the satellite observation model and the interval revolution number revenue function, and solve the objective function to determine the satellite scheduling scheme.

[0052] A computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0053] Acquire a target observation task; the target observation task includes: a general observation task and a precise observation task; the precise observation task is after the general observation task, and the time interval between the execution of the general observation task and the precise observation task is related to the success rate of completing the precise observation task;

[0054] Construct a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: a target to be observed, a virtual starting point, and a virtual ending point;

[0055] Construct the interval cycle number revenue function for each target to be observed; the interval cycle number revenue function represents the revenue of completing two observations of the target to be observed at interval cycles.

[0056] According to the satellite observation model and the interval cycle number revenue function, construct an objective function for maximizing revenue and constraint conditions, and solve the objective function to determine the satellite scheduling scheme.

[0057] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0058] Obtain the target observation task; the target observation task includes: a general observation task and a precise observation task; the precise observation task is after the general observation task, and the time interval between the execution of the general observation task and the precise observation task is related to the success rate of completing the precise observation task.

[0059] Construct a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: the target to be observed, a virtual starting point, and a virtual ending point.

[0060] Construct the interval cycle number revenue function for each target to be observed; the interval cycle number revenue function represents the revenue of completing two observations of the target to be observed at interval cycles.

[0061] According to the satellite observation model and the interval cycle number revenue function, construct an objective function for maximizing revenue and constraint conditions, and solve the objective function to determine the satellite scheduling scheme.

[0062] The above satellite scheduling method, device, computer device, and storage medium based on the interval cycle number model, when processing a target observation task including a general observation task and a precise observation task, the traditional method of completing the task with a single observation can no longer solve the problems of the present invention. On this basis, the present invention establishes a relationship between the time interval between the execution of the general observation task and the precise observation task and the success rate of completing the precise observation task, so as to correspond the time interval with the interval cycle number. In order to better study the satellite observation problem, a satellite observation model is constructed, and then based on the satellite observation model, the interval cycle number revenue function for each target to be observed is determined. Finally, an optimization problem is constructed through the revenue function and the interval time, so as to solve and obtain the task scheduling strategy. The present invention separately models the two observations, takes the interval cycle number and the time interval as the research directions, and greatly improves the efficiency of satellite scheduling. Description of the Drawings

[0063] Figure 1 It is a schematic flowchart of a satellite scheduling method based on the interval cycle number model in an embodiment;

[0064] Figure 2 The structural block diagram of a satellite scheduling device based on the interval circle number model in an embodiment;

[0065] Figure 3 The internal structure diagram of a computer device in an embodiment. Specific implementation manners

[0066] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application 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 only used to explain the present application and are not used to limit the present application.

[0067] In one embodiment, as Figure 1 shown, a satellite scheduling method based on the interval circle number model is provided, including the following steps:

[0068] Step 102, obtain a target observation task.

[0069] The target observation task includes: a general observation task and a precise observation task. The general observation task refers to a census of the target to be observed, which may include the position, quantity, etc. of the target, while the precise observation task refers to a detailed investigation of the target to be observed, which may include the category, size, etc. of the target. For example, when performing a target search task, an electromagnetic spectrum satellite can be used to conduct a census of the target, and then a guiding optical satellite can be used to conduct a detailed investigation of the target to obtain detailed information about the target. Therefore, the precise observation task comes after the general observation task. At this time, at least two observations are required to meet the user's needs, and the observation effect depends on the time interval between the two observations. If the observation time interval is too long, the observation may fail due to target loss. Therefore, the time interval between the execution of the general observation task and the precise observation task is related to the success rate of completing the precise observation task.

[0070] Step 104, construct a satellite observation model.

[0071] The satellite observation model includes: a directed graph, and the nodes in the directed graph include: the target to be observed, a virtual starting point, and a virtual ending point. Specifically, the satellite is an earth-orbiting satellite that returns to the starting point after orbiting the earth once. Therefore, within one circle, it is impossible to turn back to observe the target and can only observe the target in the next circle.

[0072] Step 106, construct an interval circle number benefit function for each target to be observed.

[0073] The interval circle number benefit function represents the benefit of completing the observation of the target to be observed by completing two observations at an interval of circle times.

[0074] Step 108: Construct an objective function for maximizing the benefit and constraints based on the satellite observation model and the benefit function of the interval number of orbits, and solve the objective function to determine the satellite scheduling plan.

[0075] In the above satellite scheduling method based on the interval number of orbits model, when dealing with the target observation tasks including ordinary observation tasks and precise observation tasks, the traditional method of completing the tasks with a single observation can no longer solve the problems of the present invention. On this basis, the present invention establishes a relationship between the time interval between the execution of the ordinary observation task and the precise observation task and the success rate of completing the precise observation task, so as to correspond the time interval with the interval number of orbits. In order to better study the satellite observation problem, a satellite observation model is constructed. Then, based on the satellite observation model, the benefit function of the interval number of orbits for each target to be observed is determined. Finally, an optimization problem is constructed through the benefit function and the interval time, and the task scheduling strategy is obtained by solving. The present invention models the two observations separately, taking the interval number of orbits and the time interval as the research directions, which greatly improves the efficiency of satellite scheduling.

[0076] In one embodiment, constructing the satellite observation model includes a directed graph G=(N, A), where the node set is N={0, 1, …, n + 1}, the arc set is A={(i, j)|i, j ∈ N, i ≠ j}, and nodes 0 and n + 1 correspond to the virtual starting point and virtual ending point without benefit. represents the set of nodes with benefit; in the satellite observation model, each target i corresponds to a continuous observation time d i , and the set of orbit numbers is set as T={1, …, t}; where each orbit starts from the virtual starting point and returns to the virtual ending point; the observable time window of each orbit t for target i is TW it =[ws it , we it , and the transition time between passing through targets i and j in each orbit is t ij .

[0077] In one embodiment, the targets to be observed in the two observations are divided into two targets, including: target i and target i + n, represents the set of targets for the first observation, represents the set of targets for the second observation; the number of orbits M between the two observations of each target is set, where M={0, 1, 2, …, m}; the benefit obtained after target i completes two observations after an interval of m orbits is set as:

[0078]

[0079] where, p im represents the benefit obtained after target i completes two observations after an interval of m orbits.

[0080] In this embodiment, it is required to complete two observations of the target within a certain interval time. If the interval time is too short or too long, the benefit will decrease. In some scenarios, the benefit only decreases as the interval time increases. Therefore, in order to consider the impact of the interval time on the benefit, an interval-time-dependent benefit function is defined. Each target i has a discrete step benefit function, and different interval times m correspond to different benefits.

[0081] In one of the embodiments, according to the satellite observation model and the interval-cycle benefit function, the objective function for maximizing the benefit is constructed as:

[0082]

[0083] where y im indicates whether the number of cycles between the two observations of target i is m cycles.

[0084] It should be noted that in order to maximize the total benefit, the following decisions need to be made: which targets to observe and sort them in each cycle; determine the interval time between two observations of each target; select the start time of observing each target in each cycle. The following decision variables are designed:

[0085] x ijt = 1 means from target i to target j in each cycle t, otherwise it is 0;

[0086] y im = 1 means the interval number of cycles between two observations of target i is m, otherwise it is 0;

[0087] s it is a real variable, representing the start time of observing target i in each cycle t.

[0088] In one of the embodiments, the designed constraint conditions include: observation start and end constraints, flow balance constraints, observation constraints, completion of observation constraints, decision variable constraints, observation cycle constraints, observation sorting constraints, time window constraints, and decision variable value range constraints.

[0089] Specifically, the observation start and end constraints are:

[0090]

[0091] where the start point is node 0 and the end point is node 2n + 1;

[0092] The flow balance constraints are:

[0093]

[0094] where x ijt and x jitrespectively represent the number of times from target i to target j in each cycle t and the number of times from target j to target i in each cycle t;

[0095] The observation constraints are as follows:

[0096]

[0097] The completion observation constraints are as follows:

[0098]

[0099] The decision variable constraints are as follows:

[0100]

[0101] The observation cycle constraints are as follows:

[0102]

[0103] The observation sorting constraints are as follows:

[0104]

[0105] where s it represents the start time of observing target i in each cycle t, and L is a positive integer;

[0106] The time window constraints are as follows:

[0107]

[0108]

[0109] The decision variable value range constraints are as follows:

[0110]

[0111]

[0112]

[0113] For the above constraints, the observation start and end constraints specify the start point of each cycle and the end point returned after observation. The flow balance constraint ensures that each target has both inflows and outflows and is observed in the same cycle. The observation constraint ensures that each split target is observed at most once. The complete observation constraint ensures that either both observations of target i are completed or neither is. The decision variable constraint represents the relationship between decision variables, indicating the number of cycles between two observations of a target. The observation cycle constraint ensures that at most one interval cycle number m is selected for the number of cycles between two observations of each target. The observation sequencing constraint specifies the sequential relationship between the observation times of two consecutive targets, i.e., the sum of the start time, duration, and transition time of the previous target's observation does not exceed the start time of the subsequent target's observation. L is a sufficiently large positive integer. The time window constraint ensures that the start time of a target's observation is not less than the earliest start time of the time window, and the end time of the observation is not greater than the latest start time of the time window. The decision variable value range constraint defines the value range of the decision variables.

[0114] In one embodiment, a branch-and-price exact algorithm can be used to solve the objective function to determine the satellite scheduling scheme. Specifically,

[0115] It should be understood that although Figure 1 the steps in the flowchart of Figure 1 are shown sequentially in the direction of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps has no strict order restriction, and these steps can be executed in other orders. Moreover,

[0116] In one embodiment, as Figure 2 shown, a satellite scheduling device based on the interval cycle number model is provided, including: a task acquisition module 202, an observation model construction module 204, a revenue function construction module 206, and a solution module 208, where:

[0117] The task acquisition module 202 is configured to acquire target observation tasks; the target observation tasks include: general observation tasks and precise observation tasks; the precise observation tasks are after the general observation tasks, and the time interval between the execution of the general observation tasks and the precise observation tasks is related to the success rate of completing the precise observation tasks;

[0118] An observation model construction module 204 is configured to construct a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: a target to be observed, a virtual starting point, and a virtual ending point;

[0119] A revenue function construction module 206 is configured to construct an interval lap number revenue function for each target to be observed; the interval lap number revenue function represents the revenue obtained by completing the observation of the target to be observed twice at an interval lap.

[0120] A solution module 208 is configured to construct an objective function for maximizing revenue and a constraint condition according to the satellite observation model and the interval lap number revenue function, and solve the objective function to determine a satellite scheduling scheme.

[0121] In one embodiment, the observation model construction module 204 is configured to construct a satellite observation model including a directed graph G=(N, A), where the node set is N={0, 1, …, n+1}, the arc set is A={(i, j)|i, j∈N, i≠j}, the nodes 0 and n+1 correspond to a virtual starting point and a virtual ending point without revenue, which represents the set of nodes with revenue; in the satellite observation model, each target i corresponds to a continuous observation time d i , and the lap number set is set as T={1, …, t}; where each lap starts from the virtual starting point and returns to the virtual ending point; the observable time window of each lap t for the target i is TW it =[ws it , we it , and the transition time between passing through the targets i and j in each lap is t ij .

[0122] In one embodiment, the revenue function construction module 206 is configured to divide the targets to be observed for two observations into two targets, including: target i and target i+n, which represents the set of targets for the first observation, which represents the set of targets for the second observation; set the number of laps M between two observations of each target, where M={0, 1, 2, …, m}; set the revenue obtained after target i completes two observations after an interval of m laps as:

[0123]

[0124] where p im represents the revenue obtained after target i completes two observations after an interval of m laps.

[0125] In one embodiment, the solution module 208 is further configured to construct an objective function for maximizing revenue according to the satellite observation model and the interval lap number revenue function as:

[0126]

[0127] Among them, y im indicates whether the number of laps between two observations of target i is m laps apart.

[0128] In one embodiment, the construction of the constraint conditions includes: observation start and end point constraints, flow balance constraints, observation constraints, complete observation constraints, decision variable constraints, observation lap number constraints, observation sorting constraints, time window constraints, and decision variable value range constraints.

[0129] In one embodiment, the observation start and end point constraints are:

[0130]

[0131] Among them, the starting point is node 0, and the end point is node 2n + 1;

[0132] The flow balance constraint is:

[0133]

[0134] Among them, x ijt and x jit respectively represent from target i to target j for each lap t and from target j to target i for each lap t;

[0135] The observation constraint is:

[0136]

[0137] The complete observation constraint is:

[0138]

[0139] The decision variable constraint is:

[0140]

[0141] The observation lap number constraint is:

[0142]

[0143] The observation sorting constraint is:

[0144]

[0145] Among them, s it represents the start time of observing target i for each lap t, and L is a positive integer;

[0146] The time window constraint is:

[0147]

[0148]

[0149] The value range constraint of the decision variable is as follows:

[0150]

[0151]

[0152]

[0153] In one embodiment, the solving module 208 is further configured to solve the objective function by using a branch-and-price exact algorithm to determine a satellite scheduling scheme.

[0154] For the specific limitations on the satellite scheduling device based on the interval circle number model, reference can be made to the limitations on the satellite scheduling method based on the interval circle number model in the above text, which will not be elaborated here. Each module in the above satellite scheduling device based on the interval circle number model can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in the form of hardware or independent of it, or stored in the memory of the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0155] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a satellite scheduling method based on the interval circle number model. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0156] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some of the structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. Specifically, the computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0157] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method in the above embodiment are implemented.

[0158] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0159] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application may include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0160] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0161] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A satellite scheduling method based on an interval turn number model, characterized in that The method includes: Obtaining a target observation task; the target observation task includes: a general observation task and a precise observation task; the precise observation task is after the general observation task, and the time interval between the execution of the general observation task and the precise observation task is related to the success rate of completing the precise observation task; Constructing a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: a target to be observed, a virtual starting point, and a virtual ending point; Constructing an interval cycle number benefit function for each target to be observed; the interval cycle number benefit function represents the benefit of completing two observations of the target to be observed in interval cycles; According to the satellite observation model and the interval cycle number benefit function, constructing an objective function for maximizing the benefit and constraint conditions, and solving the objective function to determine a satellite scheduling scheme; The constructing of the satellite observation model includes: Constructing a satellite observation model includes a directed graph , where the node set is , and the arc set is . Nodes 0 and correspond to a virtual start point and a virtual end point without benefits, representing the set of nodes with benefits; In the satellite observation model, each target i corresponds to a continuous observation time , and the set of loop times is ; where each loop starts from a virtual starting point and returns to a virtual ending point; for each loop t the observable time window for the target i is , and the transition time for each loop passing through the target i and j is ; The constructing of the interval cycle number benefit function for each target to be observed includes: Split the target to be observed in two observations into two targets, including: target i and target i + n , , represents the target set of the first observation, represents the target set of the second observation; Set the number of turns M of the observation interval between two observations for each target, where, ; Setting the benefit achieved after completing two observations of target i after m intervals as: Among them, represents the benefit achieved by target i after completing two observations after m laps of interval.

2. The method according to claim 1, wherein According to the satellite observation model and the interval cycle number benefit function, constructing an objective function for maximizing the benefit, including: According to the satellite observation model and the interval cycle number benefit function, the objective function for maximizing the benefit is: Among them, represents the target i whether the laps where the two observations are completed are separated by m lap.

3. The method according to claim 2, characterized in that, The constructing of the constraint conditions includes: observation start and end constraints, flow balance constraints, observation constraints, completion of observation constraints, decision variable constraints, observation cycle number constraints, observation sorting constraints, time window constraints, and decision variable value range constraints.

4. The method according to claim 3, wherein The observation start and end constraints are: ； Among them, the starting point is node 0 and the ending point is node 2 n +1; The flow balance constraints are: Among them, and respectively represent that for each cycle t from target i to target j and for each cycle t from target j to target i ; The observation constraints are: The completion of observation constraints are: The decision variable constraints are: The observation cycle number constraints are: The observation sorting constraints are: Among them, represents the start time of each cycle t observation target i where L is a positive integer; The time window constraints are: The decision variable value range constraints are: 。 5. The method according to any one of claims 1 to 3, characterized in that, Solving the objective function to determine a satellite scheduling scheme includes: Using a branch-and-price exact algorithm to solve the objective function to determine a satellite scheduling scheme.

6. A satellite scheduling device based on an interval revolution number model, which is used to implement the satellite scheduling method based on the interval revolution number model according to any one of claims 1 to 5, and is characterized in that, The device includes: A task acquisition module for obtaining a target observation task; the target observation task includes: a general observation task and a precise observation task; the precise observation task is after the general observation task, and the time interval between the execution of the general observation task and the precise observation task is related to the success rate of completing the precise observation task; An observation model construction module for constructing a satellite observation model; the satellite observation model includes: a directed graph, and the nodes in the directed graph include: a target to be observed, a virtual starting point, and a virtual ending point; A benefit function construction module for constructing an interval cycle number benefit function for each target to be observed; the interval cycle number benefit function represents the benefit of completing two observations of the target to be observed in interval cycles; A solving module for constructing an objective function for maximizing the benefit and constraint conditions according to the satellite observation model and the interval cycle number benefit function, and solving the objective function to determine a satellite scheduling scheme.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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