Optimal resource allocation decision method and device for space target coordinated observation of star cluster

Abstract

The application provides a method and device for optimal resource allocation decision of star group cooperative observation of space targets. The method comprises: obtaining input parameters, state parameters, constraint parameters and optimization variable parameters; based on the input parameters, the state parameters and the constraint parameters, calculating a benefit coefficient of observation of each target by each satellite, and updating each benefit coefficient based on the optimization variable parameters; calculating a cost coefficient matrix of double-star cooperative observation of targets according to the updated benefit coefficients, and constructing an optimal resource allocation decision model based on the cost coefficient matrix, so as to allocate satellite resources by using the optimal resource allocation decision model. According to the scheme, real-time and rapid optimal solving on the star can be facilitated, so that optimal resource allocation is realized.

Description

Technical Field

[0001] This invention relates to the field of autonomous mission planning technology for constellation resources, and in particular to a method and apparatus for optimal resource allocation decision-making for collaborative observation of space targets by a constellation. Background Technology

[0002] Resource allocation for collaborative observation of space targets by constellations is a complex task planning problem under numerous constraints such as time, resources, and tasks. The solution methods can be divided into three categories: exact algorithms, heuristic algorithms, and intelligent algorithms.

[0003] In related technologies, the aforementioned algorithms face the following problems when autonomously solving problems on satellite: Although exact solution algorithms can find the optimal solution to a problem, their solution complexity is extremely high, and the solution time increases exponentially with the scale of the problem, which cannot meet the real-time solution requirements on satellite; heuristic algorithms rely on the designer's experience to search for rules, which can find a solution in a short time, but the solution is not optimal; intelligent algorithms (such as genetic algorithms) require multiple iterations to obtain a good solution, and their solution performance is poor under the limited computing resources on satellite.

[0004] Therefore, there is an urgent need to provide a method and apparatus for optimal resource allocation decision-making for constellation-based collaborative observation of space targets. Summary of the Invention

[0005] To address the problems of low efficiency and poor performance of traditional on-board autonomous solution algorithms, which cannot meet the requirements for real-time optimal solution of on-board resources, this invention provides an optimal resource allocation decision-making method and device for satellite-group collaborative observation of space targets.

[0006] In a first aspect, the present invention provides an optimal resource allocation decision-making method for constellation-based collaborative observation of space targets, the method comprising:

[0007] Obtain input parameters, state parameters, constraint parameters, and optimization variable parameters;

[0008] Based on the input parameters, the state parameters, and the constraint parameters, the benefit coefficient for each satellite observing each target is calculated, and each benefit coefficient is updated based on the optimization variable parameters.

[0009] The cost coefficient matrix of the dual-satellite collaborative observation target is calculated based on the updated benefit coefficient, and an optimal resource allocation decision model is constructed based on the cost coefficient matrix to allocate satellite resources.

[0010] Secondly, the present invention also provides an optimal resource allocation decision-making device for constellation-based collaborative observation of space targets, the device comprising:

[0011] The acquisition unit is used to acquire input parameters, state parameters, constraint parameters, and optimization variable parameters;

[0012] The calculation and update unit is used to calculate the benefit coefficient of each satellite observing each target based on the input parameters, the state parameters and the constraint parameters, and update each benefit coefficient based on the optimization variable parameters;

[0013] The resource allocation unit is used to calculate the cost coefficient matrix of the dual-satellite collaborative observation target based on the updated benefit coefficient, and to construct an optimal resource allocation decision model based on the cost coefficient matrix, so as to allocate satellite resources using the optimal resource allocation decision model.

[0014] Thirdly, the present invention also provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in any embodiment of this specification.

[0015] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.

[0016] On the other hand, this application also provides a computer program product comprising a computer program, wherein a processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, causing the computer device to perform any of the methods described in the first aspect above.

[0017] This invention provides an optimal resource allocation decision-making method for constellation-based collaborative observation of space targets. By considering input parameters such as satellite visibility to the target and the number of satellite working duration constraint parameters, the benefit coefficient of each satellite observing each target is calculated. Each benefit coefficient is updated based on the parameters of the aggregateable targets, and the benefit coefficient is further converted into a cost coefficient, thus constructing an optimal resource allocation decision-making model. This transforms the task planning problem of constellation-based collaborative observation of space targets into a simplified optimal decision-making problem of task assignment, which is convenient for real-time and rapid optimal solution on the satellite, thereby achieving optimal resource allocation. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of an optimal resource allocation decision-making method for constellation collaborative observation of space targets provided in an embodiment of the present invention;

[0020] Figure 2 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention;

[0021] Figure 3 This is a structural diagram of an optimal resource allocation decision-making device for constellation collaborative observation of space targets provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] The following describes the specific implementation of the above concept.

[0024] Please refer to Figure 1 This invention provides an optimal resource allocation decision-making method for constellation-based collaborative observation of space targets, the method comprising:

[0025] Step 100: Obtain the input parameters, state parameters, constraint parameters, and optimization variable parameters;

[0026] Step 102: Based on the input parameters, the state parameters, and the constraint parameters, calculate the benefit coefficient for each satellite observing each target, and update each benefit coefficient based on the optimization variable parameters;

[0027] Step 104: Calculate the cost coefficient matrix of the dual-satellite collaborative observation target based on the updated benefit coefficient, and construct an optimal resource allocation decision model based on the cost coefficient matrix to allocate satellite resources using the optimal resource allocation decision model.

[0028] In this embodiment of the invention, the benefit coefficient of each satellite observing each target is calculated by considering input parameters such as the satellite's visibility to the target and the number of satellite working time constraint parameters. Each benefit coefficient is updated based on the parameters of the aggregateable targets, and the benefit coefficient is further converted into a cost coefficient. An optimal resource allocation decision model is constructed, thereby transforming the task planning problem of satellite constellation collaborative observation of space targets into a simplified optimal decision problem of task assignment, which is convenient for real-time and fast optimal solution on the satellite, thereby achieving optimal resource allocation.

[0029] For step 100:

[0030] Assume the duration to be planned is L, the current time is 0, and the planning time domain is [0, L]. Define the following symbols based on the input:

[0031] In some implementations, the input parameters include a set of satellite observation resources S = {s1, s2, ..., s...} m The set of targets to be observed is D = {d1, d2, ..., dn}. n The set of satellites requiring stereo observation is DS = {ds1, ds2, ..., ds...} q}∈D, the set of target observation priority weight coefficients DP={dp1,dp2,...,dp n},dp j ∈(0,1], Visibility identifier of satellite i to target j Total duration of the visible window for satellite i to target j The earliest start time of the visible window of satellite i to target j

[0032] The constraint parameters include a set of satellite operational time constraints ST = {st1, st2, ..., st...} m The minimum operational duration constraint set for satellites is SA = {sa1, sa2, ..., sa...} m};

[0033] The state parameters include the current observation status of satellite i on target j.

[0034] The optimization variable parameters include the set of targets that can be aggregated when observing targets from the satellite.

[0035] In this embodiment of the invention, by obtaining the above parameters and defining each parameter, it is beneficial to achieve efficient task planning and resource allocation.

[0036] Regarding step 102:

[0037] In some implementations, step 102 includes:

[0038] For each satellite s i For i = 1, 2, ..., m, execute:

[0039] For each target d j For j = 1, 2, ..., n, execute:

[0040] If the preset constraints are met, the following operations will be performed:

[0041] Calculate the benefits of the earliest tracking time, the longest tracking time, the number of resource switching times, and the load balancing.

[0042] Based on the earliest tracking time benefit, longest tracking time benefit, resource switching number benefit, load balancing benefit, and target observation priority weight coefficient, the benefit coefficient for the current satellite observing the current target is obtained.

[0043] If the preset constraints are not met, the benefit coefficient for the current satellite to observe the current target will be set to 0; wherein, the constraint is: the current satellite is visible to the current target. Furthermore, the current operational duration of the satellite is not less than the total duration of the satellite's visible window to the target. Furthermore, the current minimum operational duration of the satellite is no greater than the total duration of the satellite's visible window to the target.

[0044] In some specific implementations, the earliest tracking time gain f1 is calculated using the following formula:

[0045]

[0046] The longest tracking time gain f2 is calculated using the following formula:

[0047]

[0048] The benefit f3 from resource switching times is calculated using the following formula:

[0049]

[0050] The load balancing benefit f4 is calculated using the following formula:

[0051] sq i =st i -M(ST)

[0052] In the formula, sq i For load balancing deviation, st i Let M(ST) be the operational duration of the i-th satellite, and let M(ST) be the mean of the set of constraints on the operational duration of satellites, ST.

[0053] The current satellite observation of the current target's benefit coefficient It is calculated using the following formula:

[0054]

[0055] In the formula, dp j η1, η2, η3, and η4 are priority weight coefficients for satellite observation target j, where η1, η2, η3, and η4 are all weights.

[0056] In this embodiment of the invention, by comprehensively considering visibility, working duration constraints, and optimization indicators such as earliest tracking time, longest tracking time, number of resource switching times, and load balancing to calculate the benefit coefficient of satellite resource observation targets, it is not only beneficial to improve the mission completion rate, but also to optimize resource utilization efficiency. At the same time, by comprehensively considering multiple indicators, it is possible to avoid suboptimal solutions caused by optimizing only a single indicator, thereby achieving a better overall resource allocation effect.

[0057] In some implementations, updating each revenue coefficient based on the optimization variable parameters includes: adding the current revenue coefficient to the revenue coefficients of the aggregateable targets to update the current revenue coefficient.

[0058]

[0059] In the formula, The updated profit coefficient, The profit coefficients were calculated before the update. The benefit coefficient of targets that can be aggregated when satellite i observes target j.

[0060] In this embodiment of the invention, by increasing the benefit coefficient of the target that can be aggregated when observing satellite targets to update the benefit coefficient of the current satellite observation target, the planning model is modeled with consideration of task aggregation. This not only enables the planning of aggregated tasks, but also helps to reduce the number of satellite observations or resource consumption.

[0061] Regarding step 104:

[0062] In some implementations, step 104 includes:

[0063] The maximum value c in the updated set of profit coefficients max Each with the updated profit coefficient Perform the difference to obtain the cost coefficient.

[0064]

[0065] For each satellite, perform the following:

[0066] For each objective, execute:

[0067] For each target requiring dual-satellite stereo observation, the following is executed:

[0068] in, Let i be the combined cost coefficient for target j and target k requiring dual-satellite stereo observation. is the cost coefficient of satellite i for target k that requires dual-satellite stereo observation.

[0069] In this embodiment of the invention, the above-described transformation process can transform a complex task planning problem into a simple assignment problem, thereby improving computational efficiency and making it easier to solve quickly and in real time on the satellite.

[0070] In some implementations, the optimal resource allocation decision model is as follows:

[0071]

[0072] In the formula, i represents the i-th satellite, m represents the total number of satellites, j represents the j-th target, n represents the total number of targets, and k represents the target requiring dual-satellite stereo observation. Let be the cost coefficient of satellite i for target j. Let be the decision variable and st be the constraint.

[0073] In this embodiment of the invention, by considering practical engineering application constraints such as resource availability duration, minimum availability duration, and visibility, the optimal resource allocation decision model is constructed with minimizing the total cost as the objective function. This transforms the task planning problem of constellation collaborative observation of space targets into the optimal decision problem of task assignment, thereby enabling real-time and optimal on-board solution.

[0074] like Figure 2 , Figure 3 As shown, this invention provides an optimal resource allocation decision-making device for constellation-based collaborative observation of space targets. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of a computing device for an optimal resource allocation decision-making device for constellation collaborative observation of space targets, provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the computing device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0075] This embodiment provides an optimal resource allocation decision-making device for constellation-based collaborative observation of space targets. The device includes:

[0076] The acquisition unit 301 is used to acquire input parameters, state parameters, constraint parameters, and optimization variable parameters;

[0077] The calculation and update unit 302 is used to calculate the benefit coefficient of each satellite observing each target based on the input parameters, the state parameters and the constraint parameters, and update each benefit coefficient based on the optimization variable parameters;

[0078] Resource allocation unit 303 is used to calculate the cost coefficient matrix of the dual-satellite collaborative observation target based on the updated benefit coefficient, and to construct an optimal resource allocation decision model based on the cost coefficient matrix, so as to allocate satellite resources using the optimal resource allocation decision model.

[0079] In this embodiment of the invention, the storage unit 301 can be used to execute step 100 in the above method embodiment, the calculation and update unit 302 can be used to execute step 102 in the above method embodiment, and the resource allocation unit 303 can be used to execute step 104 in the above method embodiment.

[0080] In one embodiment of the present invention,

[0081] The input parameters include a set of satellite observation resources, a set of targets to be observed, a set of satellites requiring dual-satellite stereo observation, a set of target observation priority weight coefficients, satellite visibility indicators of targets, the total duration of satellite visibility windows of targets, and the earliest start time of satellite visibility windows of targets.

[0082] The constraint parameters include a set of constraints on the satellite's operational duration and a set of constraints on the satellite's minimum operational duration.

[0083] The status parameters include the satellite's ongoing observation status of the target;

[0084] The optimization variable parameters include the set of targets that can be aggregated when observing targets from the satellite.

[0085] In one embodiment of the present invention, when the update calculation unit 302 calculates the benefit coefficient for each satellite observing each target based on the input parameters, the state parameters, and the constraint parameters, it performs the following operations:

[0086] For each satellite, perform the following:

[0087] For each objective, execute:

[0088] If the preset constraints are met, the following operations will be performed:

[0089] Calculate the benefits of the earliest tracking time, the longest tracking time, the number of resource switching times, and the load balancing.

[0090] Based on the earliest tracking time benefit, longest tracking time benefit, resource switching number benefit, load balancing benefit, and target observation priority weight coefficient, the benefit coefficient for the current satellite observing the current target is obtained.

[0091] If the preset constraints are not met, the benefit coefficient of the current satellite observing the current target will be set to 0; wherein the constraints are: the current satellite is visible to the current target, and the working time of the current satellite is not less than the total duration of the visible window of the satellite to the target, and the minimum working time of the current satellite is not greater than the total duration of the visible window of the satellite to the target.

[0092] In one embodiment of the present invention, when updating each profit coefficient based on the optimization variable parameters, the update calculation unit 302 performs the following operation: adding the current profit coefficient to the profit coefficient of the aggregateable target to update the current profit coefficient.

[0093] In one embodiment of the present invention, the resource allocation unit calculates the cost coefficient matrix of the dual-satellite collaborative observation target based on the updated benefit coefficient, and performs the following operations:

[0094] The cost coefficient is obtained by subtracting the maximum value in the updated set of profit coefficients from each updated profit coefficient.

[0095] For each satellite, perform the following:

[0096] For each objective, execute:

[0097] For each target requiring dual-satellite stereo observation, the following is executed:

[0098] in, Let i be the combined cost coefficient for target j and target k requiring dual-satellite stereo observation. is the cost coefficient of satellite i for target k that requires dual-satellite stereo observation.

[0099] In one embodiment of the present invention, the optimal resource allocation decision model in the resource allocation unit 303 is as follows:

[0100]

[0101] In the formula, i represents the i-th satellite, m represents the total number of satellites, j represents the j-th target, n represents the total number of targets, and k represents the target requiring dual-satellite stereo observation. Let be the cost coefficient of satellite i for target j. Let be the decision variable and st be the constraint.

[0102] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on an optimal resource allocation decision-making device for constellation-based collaborative observation of space targets. In other embodiments of the present invention, an optimal resource allocation decision-making device for constellation-based collaborative observation of space targets may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0103] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0104] This invention also provides a computing device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements an optimal resource allocation decision method for co-observing space targets of a constellation according to any embodiment of this invention.

[0105] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform an optimal resource allocation decision method for co-observing space targets in any embodiment of this invention.

[0106] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0107] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0108] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0109] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0110] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0111] The embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the optimal resource allocation decision method for constellation collaborative observation of space targets provided in the above-described method embodiments.

[0112] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0113] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimal resource allocation decision-making in constellation-based collaborative observation of space targets, characterized in that, include: The system acquires input parameters, state parameters, constraint parameters, and optimization variable parameters. The input parameters include a set of satellite observation resources, a set of targets to be observed, a set of satellites requiring dual-satellite stereo observation, a set of target observation priority weight coefficients, satellite visibility indicators for targets, the total duration of satellite visibility windows for targets, and the earliest start time of satellite visibility windows for targets. The constraint parameters include a set of satellite operational duration constraints and a set of minimum operational duration constraints for satellites. The state parameters include the satellite's current observation status of the target. The optimization variable parameters include the set of targets that can be aggregated when the satellite observes the target. Based on the input parameters, the state parameters, and the constraint parameters, the benefit coefficient for each satellite observing each target is calculated, and each benefit coefficient is updated based on the optimization variable parameters. For each satellite, perform the following: For each objective, execute: If the preset constraints are met, the following operations will be performed: Calculate the benefits of the earliest tracking time, the longest tracking time, the number of resource switching times, and the load balancing. Based on the earliest tracking time benefit, longest tracking time benefit, resource switching number benefit, load balancing benefit, and target observation priority weight coefficient, the benefit coefficient for the current satellite observing the current target is obtained. If the preset constraints are not met, the benefit coefficient of the current satellite observing the current target will be set to 0; wherein the constraints are: the current satellite is visible to the current target, and the working time of the current satellite is not less than the total duration of the visible window of the satellite to the target, and the minimum working time of the current satellite is not greater than the total duration of the visible window of the satellite to the target. The cost coefficient matrix of the dual-satellite collaborative observation target is calculated based on the updated benefit coefficient, and an optimal resource allocation decision model is constructed based on the cost coefficient matrix to allocate satellite resources.

2. The method according to claim 1, characterized in that, The step of updating each revenue coefficient based on the optimized variable parameters includes: adding the current revenue coefficient to the revenue coefficient of the aggregateable target to update the current revenue coefficient.

3. The method according to claim 1, characterized in that, The calculation of the cost coefficient matrix for the dual-satellite collaborative observation target based on the updated benefit coefficient includes: The cost coefficient is obtained by subtracting the maximum value in the updated set of profit coefficients from each updated profit coefficient. For each satellite, perform the following: For each objective, execute: For each target requiring dual-satellite stereo observation, the following is executed: ,in, Let i be the combined cost coefficient for target j and target k requiring dual-satellite stereo observation. is the cost coefficient of satellite i for target k that requires dual-satellite stereo observation.

4. The method according to any one of claims 1 to 3, characterized in that, The optimal resource allocation decision model is as follows: In the formula, i represents the i-th satellite, m represents the total number of satellites, N = n + q, N represents the total number of tasks to be assigned, n represents the number of ordinary targets, and q represents the number of targets requiring dual-satellite stereo observation. Let be the cost coefficient of satellite i for target j. Let be the decision variable and st be the constraint.

5. An optimal resource allocation decision-making device for constellation-based collaborative observation of space targets, used to implement the method as described in any one of claims 1 to 4, characterized in that, include: The acquisition unit is used to acquire input parameters, state parameters, constraint parameters, and optimization variable parameters; The calculation and update unit is used to calculate the benefit coefficient of each satellite observing each target based on the input parameters, the state parameters and the constraint parameters, and update each benefit coefficient based on the optimization variable parameters; The resource allocation unit is used to calculate the cost coefficient matrix of the dual-satellite collaborative observation target based on the updated benefit coefficient, and to construct an optimal resource allocation decision model based on the cost coefficient matrix, so as to allocate satellite resources using the optimal resource allocation decision model.

6. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method of any one of claims 1-4.

8. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-4.

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