Autonomous task planning method and device for coordinated observation of space targets by star cluster

Abstract

The application provides a kind of constellation cooperative observation space target autonomous task planning method and device.Method includes: based on the input parameter of acquisition, construct comprehensive constraint index, and based on the comprehensive constraint index, determine the observable constraint set of target of resource to target and the observable target number of resource;Wherein, the input parameter includes satellite workable time constraint set, satellite minimum work time constraint set and satellite visibility identification to target;According to the observable constraint set of target of resource to target and preset optimization target, the benefit coefficient of each satellite observation each target is calculated respectively;Wherein, the preset optimization target includes load balancing, resource redundancy, tracking time, earliest tracking time, resource switching frequency;Based on the benefit coefficient and the observable target number of resource, the optimal resource planning scheme under different observation target number is searched in turn using heuristic algorithm.This scheme can realize real-time fast optimal solution on satellite.

Description

Autonomous mission planning method and device for constellation collaborative observation of space targets Technical Field

[0001] This invention relates to the field of autonomous mission planning technology for constellation resources, and in particular to an autonomous mission planning method and apparatus for coordinating the observation of space targets by a constellation. Background Technology

[0002] Continuous multi-target tracking in space is a prerequisite for achieving space situational awareness. Due to factors such as the relatively high speed of the stars and targets, the limited observation capabilities of sensors, the need for single-star monitoring, dual-star stereo observation, and the dynamic changes in tracking tasks, the satellite needs to adopt efficient and real-time autonomous task planning algorithms to dynamically schedule and allocate sensor resources for collaboratively observing the star cluster.

[0003] Autonomous mission planning for stellar collaborative observation of space targets is a multi-objective optimization problem under complex constraints. Among related technologies, stellar collaborative mission planning has many optimization objectives and constraints, including maximizing mission completion rate, resource utilization, target tracking accuracy, visibility, stereo tracking, single-satellite monitoring, and resource usage constraints. Task requirements such as rapid mission response and fleeting tracking windows necessitate real-time resource scheduling. However, on-board computing resources are severely limited. How to achieve rapid on-board optimization solutions when the number of tracking resources and targets is large is a major challenge for autonomous mission planning of stellar collaborative observation of space targets.

[0004] Therefore, there is an urgent need to provide an autonomous mission planning method and device for constellation-based collaborative observation of space targets. Summary of the Invention

[0005] To address the problem that traditional constellation collaborative mission planning methods cannot meet the requirements for rapid on-board optimization solutions, this invention provides an autonomous mission planning method and apparatus for constellation collaborative observation of space targets.

[0006] In a first aspect, the present invention provides an autonomous mission planning method for constellation-based collaborative observation of space targets, the method comprising:

[0007] A comprehensive constraint index is constructed based on the acquired input parameters, and the set of observable constraints of resources on the target is determined based on the comprehensive constraint index; wherein, the input parameters include a set of constraints on the satellite's available working time, a set of constraints on the satellite's minimum working time, and a satellite's visibility identifier on the target;

[0008] Based on the observable constraint set of resources on the target and the preset optimization objectives, the benefit coefficient of each satellite observing each target is calculated respectively; wherein, the preset optimization objectives include load balancing, resource redundancy, tracking duration, earliest tracking time, and number of resource switching.

[0009] Based on the aforementioned benefit coefficient and the number of observable resource targets, a heuristic algorithm is used to sequentially search for the optimal resource planning scheme under different numbers of observable targets.

[0010] Secondly, the present invention also provides an autonomous mission planning device for constellation-based collaborative observation of space targets, the device comprising:

[0011] The determination unit is used to construct a comprehensive constraint index based on the acquired input parameters, and to determine the set of observable constraints of resources on the target based on the comprehensive constraint index; wherein, the input parameters include a set of constraints on the satellite's operational duration, a set of constraints on the satellite's minimum operational duration, and a satellite's visibility identifier on the target;

[0012] The calculation unit is used to calculate the benefit coefficient of each satellite observing each target based on the observable constraint set of the resource on the target and the preset optimization target; wherein, the preset optimization target includes load balancing, resource redundancy, tracking duration, earliest tracking time, and number of resource switching times;

[0013] The planning unit is used to search for the optimal resource planning scheme under different numbers of observable targets based on the benefit coefficient and the number of observable targets of the resource using a heuristic algorithm.

[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 autonomous mission planning method for co-observing space targets by a constellation. First, the constraints on satellite working time, minimum satellite working time, and satellite visibility of targets are simplified into a single comprehensive constraint index. Based on this comprehensive constraint index, the observability constraints of resources on targets (i.e., whether satellites can observe targets) and the number of observable targets for resources are determined. Then, based on the observability constraints of resources on targets and considering multiple optimization objectives such as load balancing tracking time, earliest tracking time, resource redundancy, and resource switching times, the benefit coefficient for each satellite to observe each target is calculated. Finally, based on this benefit coefficient, a heuristic algorithm is used to achieve rapid optimization allocation of constellation observation resources for targets with different numbers of observed targets. 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 is a flowchart of an autonomous mission planning method for constellation cooperative observation of space targets provided in an embodiment of the present invention;

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

[0021] Figure 3 is a structural diagram of an autonomous mission planning device for co-observing space targets by a constellation according to 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 embodiment of the invention provides an autonomous mission planning method for constellation-based collaborative observation of space targets. The method includes:

[0025] Step 100: Construct a comprehensive constraint index based on the acquired input parameters, and determine the set of observable constraints of resources on targets and the number of observable targets of resources based on the comprehensive constraint index; wherein, the input parameters include the set of satellite working duration constraints, the set of satellite minimum working duration constraints, and the satellite visibility identifier of targets;

[0026] Step 102: Based on the observable constraint set of resources on the target and the preset optimization target, calculate the benefit coefficient of each satellite observing each target; wherein, the preset optimization target includes load balancing, resource redundancy, tracking duration, earliest tracking time, and number of resource switching.

[0027] Step 104: Based on the aforementioned benefit coefficient and the number of observable resource targets, a heuristic algorithm is used to sequentially search for the optimal resource planning scheme under different numbers of observable targets.

[0028] In this embodiment of the invention, the satellite's operational duration constraint set, the satellite's minimum operational duration constraint set, and the satellite's visibility identifier for the target are first simplified into a single comprehensive constraint index. Based on this comprehensive constraint index, the observability constraint of resources on the target (i.e., whether the satellite can observe the target) and the number of observable targets for the resources are determined. Then, based on the observability constraint of resources on the target and by considering multiple optimization objectives such as load balancing tracking duration, earliest tracking time, resource redundancy, and resource switching times, the benefit coefficient for each satellite to observe each target is calculated. Finally, based on this benefit coefficient, a heuristic algorithm is used to achieve a fast and optimized allocation of satellite constellation observation resources for the target under different numbers of observed targets.

[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 further include a satellite observation resource set S = {s1, s2, ..., s...} m The target observation requirement set DS = {ds1, ds2, ..., ds} n},ds j ∈{1,2}, where 1 represents the need for single-satellite observation and 2 represents the need for two-satellite stereo observation. The target observation priority ranking set D = {d1,d2,...,d...} n}, task d1 has the highest priority, d n The task has the lowest priority; satellite i is currently observing 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 The position vector of satellite i pointing to target j at the planned time. Where, r i Let r be the satellite's position vector. j The position vector of the target.

[0032] 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.

[0033] In some implementations, the set of observable constraints of the resource on the target is determined in the following manner:

[0034] Define and initialize the observable constraints of resources on the objective, V i j =0; i=1,2,...,m; j=1,2,...,n;

[0035] For each satellite s i For i = 1, 2, ..., m, execute:

[0036] For each target d j For j = 1, 2, ..., n, execute:

[0037] If the comprehensive constraint indicators are met within the planning time domain, then the current satellite can observe the current target (i.e., V). i j =1);

[0038] If the comprehensive constraint index is not met within the planning time domain, then the current satellite is unobservable of the current target (i.e., V). i j =0); where the comprehensive constraint index is: the current satellite is visible to the current target (i.e., the target is visible to the current satellite). And the current operational time of the satellite is not less than the total visible window duration of the satellite to the target (i.e., Furthermore, the current minimum operational duration of the satellite is not greater than the earliest start time of the satellite's visible window to the target.

[0039] In this embodiment of the invention, the observability constraint of resources (i.e., satellites) on the target is determined by constructing the satellite's visibility to the target, the satellite's operational duration, and the satellite's minimum operational duration into a single comprehensive constraint index. This simplifies the constraint conditions and facilitates rapid on-board optimization solutions.

[0040] Regarding step 102:

[0041] In some implementations, step 102 includes:

[0042] For each target si For i = 1, 2, ..., m, execute:

[0043] For each satellite d j For j = 1, 2, ..., n, execute:

[0044] If the current satellite cannot observe the current target (i.e., V) i j If the value is 0, then the benefit coefficient for the current satellite observation of the current target will be set to 0.

[0045] If the current satellite can observe the current target (V) i j If = 1), then perform the following operation:

[0046] Calculate the benefits of the earliest tracking time, the longest tracking time, the number of resource switching times, the load balancing, and the resource redundancy separately;

[0047] Based on the earliest tracking time benefit, longest tracking time benefit, resource switching number benefit, load balancing benefit, and resource redundancy benefit, the benefit coefficient for the current satellite observation of the current target is obtained.

[0048] In some specific implementations, the earliest tracking time gain f1 is calculated using the following formula:

[0049]

[0050] The longest tracking time gain f2 is calculated using the following formula:

[0051]

[0052] The benefit f3 from resource switching times is calculated using the following formula:

[0053]

[0054] The load balancing benefit f4 is calculated using the following formula:

[0055] sq i =st i -M(ST)

[0056] 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.

[0057] The resource redundancy benefit f5 is calculated using the following formula:

[0058]

[0059] In the formula, sd i V represents the number of observable targets for satellite i, n represents the total number of targets, and V represents the number of observable targets for satellite i. i j For the observability of satellite i for target j;

[0060] The current satellite observation of the current target's benefit coefficient It is calculated using the following formula:

[0061]

[0062] In the formula, η1, η2, η3, η4 and η5 are all weights.

[0063] In this embodiment of the invention, the benefit coefficient of the satellite resource observation target is calculated by comprehensively considering multiple optimization objectives such as load balancing, tracking duration, earliest tracking time, number of resource switching and resource redundancy. This not only helps to maximize the task completion rate, but also improves the stereo observation accuracy of the target while ensuring task priority. At the same time, by comprehensively considering multiple indicators, it is possible to avoid suboptimal solutions caused by optimizing only a single indicator, so that the solution of the local optimum is taken into account while considering the global optimum.

[0064] Regarding step 104:

[0065] In some implementations, the number of observable targets of the resource is determined by the following formula:

[0066]

[0067] In the formula, sd i Let n be the number of observable targets for the resource, and n be the total number of targets. Let be the observability of satellite i for target j, where sd i It is 1 or 2, when sd i When sd = 1, the target requires single-satellite observation; when sd = 1, the target requires single-satellite observation. i When the value is 2, the target requires two-satellite stereo observation.

[0068] In this embodiment of the invention, the number of observable targets of the satellite is first determined based on the observability of the target by the satellite, and then the observation requirements of the current target are further determined based on the number of observable targets. Then, a heuristic algorithm is used to realize the rapid optimization allocation of satellite constellation observation resources under different observation requirements.

[0069] In some implementations, step 104 includes:

[0070] If the number of observable targets of the resource is 2(ds) j =2), meaning the current target requires stereoscopic observation by two satellites, then perform the following operations:

[0071] Initialize the first maximum profit g max =0, the first decision variable The first resource index to be allocated is k1 = k2 = 0, where k1 and k2 are two observation satellites;

[0072] Each satellite in the satellite set is paired with each of the other satellites, and for each pair of paired satellites, the following is executed:

[0073] Determine the current target d j Can it be used by two satellites that are currently paired up? i s k Observation;

[0074] If so, then calculate the temporary gain g based on the current gain coefficients and geometric precision factors of the two satellites observing the current target, and compare the current temporary gain g with the current first maximum gain g. max In comparison, if the current temporary profit is greater than the current first maximum profit, then the current temporary profit is assigned to the current first maximum profit to update the current first maximum profit g. max =g, and record the index of the first resource to be allocated: k1 = i; k2 = k;

[0075] Based on the current maximum return, determine whether a binary star stereo observation scheme has been found. If the current maximum return is greater than zero g... max If the value is greater than 0, then a dual-star three-dimensional tracking scheme exists, and the first decision variable is updated. If it is less than or equal to 0, then the number of observable targets for the resource will be changed to 1 (ds j =1).

[0076] In some specific implementations, if both currently paired satellites are visible relative to target j, that is... And currently neither of the two satellites has been allocated, that is Then the current target d j Two satellites that can be paired up at present i s k Observation;

[0077] In some specific implementations, the temporary revenue is calculated as follows:

[0078] Calculate the absolute value L of the difference between the position vectors of the two satellites pointing towards the target in the pairwise combination at the current planning time: in, and These are the position vectors of satellite i pointing to target j and satellite k pointing to target j, respectively;

[0079] The position vectors of two satellites based on pairwise pairings The absolute values ​​of two position vectors And the absolute value L of the difference between the position vectors of the two satellites pointing towards the target, and the observation angles θ1 and θ2 of the two satellites are calculated respectively: where θ1 and θ2 are calculated by the following formulas respectively:

[0080]

[0081] Based on the absolute value L of the difference between the position vectors of the two satellites pointing towards the target, the observation angles θ1 and θ2 of the two satellites, and the observation accuracy, the geometric precision factor when the two satellites observe the target together is obtained.

[0082]

[0083] In the formula, σ represents the observation accuracy;

[0084] Based on the current benefit coefficients of the two satellites observing the current target Geometric precision factor when observing targets using a combination of two satellites Calculate the temporary gain g from observing the current target using the two current satellites:

[0085]

[0086] In the formula, η6 is the weight.

[0087] In some implementations, if the number of observable targets of the resource is 1 (ds) j =1), meaning the current target requires single-satellite observation, then perform the following operation:

[0088] Initialize the second maximum return g max =0, decision variable The second resource to be allocated index k1 = 0;

[0089] For each satellite d j For j = 1, 2, ..., n, execute:

[0090] Determine the current target d j Can it be detected by the current satellite? i The observation is performed to determine whether the current satellite's gain coefficient for observing the current target is greater than the second maximum gain, i.e., whether V is satisfied.i j =1 and

[0091] If so, then assign the current satellite observation target's gain coefficient to the current second-maximum gain to update the current second-maximum gain. And record the index k1 = i of the second resource to be allocated;

[0092] Based on the current second-maximum return, determine whether a single-star observation scheme has been found. If the current second-maximum return is greater than zero g... max If '>0, then a single-star observation scheme exists, and the second decision variable is updated. If the current second maximum return is not greater than zero, then a single-star observation scheme does not exist.

[0093] In this embodiment of the invention, the observation requirements of each target are determined based on the number of observable targets on the satellite. First, observation resources are allocated to targets with higher observation priority (i.e., targets that require dual-satellite stereo observation), ensuring that high-priority tasks are observed first. Furthermore, when allocating a single resource, all feasible solutions are traversed to find the best one, and local optimal solutions can be obtained while taking into account the global optimal solution.

[0094] In summary, this embodiment of the invention, under the constraints of resource availability duration, minimum availability duration, visibility, and number of observation targets, rapidly iteratively searches to obtain a superior resource scheduling scheme. It also considers multiple optimization objectives such as maximizing task completion rate, load balancing, task priority, stereo observation accuracy, tracking duration, earliest tracking time, and resource switching frequency, achieving rapid optimization and allocation of constellation observation resources. This method possesses global optimization performance, a simple autonomous task planning algorithm, a small solution scale, and can quickly obtain the optimal solution.

[0095] As shown in Figures 2 and 3, this embodiment of the invention provides an autonomous mission planning device for constellation-based collaborative observation of space targets. The device can be implemented through software, hardware, or a combination of both. From a hardware perspective, Figure 2 shows a hardware architecture diagram of the computing device housing the autonomous mission planning device for constellation-based collaborative observation of space targets provided in this embodiment. Besides the processor, memory, network interface, and non-volatile memory shown in Figure 2, the computing device in this embodiment typically includes other hardware, such as a forwarding chip responsible for processing messages. Taking software implementation as an example, as shown in Figure 3, as a logically defined device, it is formed by the CPU of its computing device reading the corresponding computer program from the non-volatile memory into memory and running it.

[0096] This embodiment provides an autonomous mission planning device for constellation-based collaborative observation of space targets. The device includes:

[0097] The determining unit 301 is used to construct a comprehensive constraint index based on the acquired input parameters, and to determine the set of observable constraints of resources on targets and the number of observable targets of resources based on the comprehensive constraint index; wherein, the input parameters include the set of satellite working duration constraints, the set of satellite minimum working duration constraints, and the satellite visibility identifier of targets;

[0098] The calculation unit 302 is used to calculate the benefit coefficient of each satellite observing each target based on the observable constraint set of the resource on the target and the preset optimization target; wherein, the preset optimization target includes load balancing, resource redundancy, tracking duration, earliest tracking time, and number of resource switching times;

[0099] Planning unit 303 is used to search for the optimal resource planning scheme under different numbers of observable targets based on the benefit coefficient and the number of observable targets of resources using a heuristic algorithm.

[0100] In this embodiment of the invention, the determining unit 301 can be used to execute step 100 in the above method embodiment, the calculating unit 302 can be used to execute step 102 in the above method embodiment, and the planning unit 303 can be used to execute step 104 in the above method embodiment.

[0101] In one embodiment of the present invention, the input parameters further include a satellite observation resource set, a target observation demand set, a target observation priority ranking set, the satellite's current observation status of the target, the total duration of the satellite's visible window of the target, the earliest start time and the planned time of the satellite's visible window of the target, and the position vector of the satellite pointing to the target; wherein, the target observation demand set includes a target set that requires single-satellite observation and a target set that requires dual-satellite stereo observation.

[0102] In one embodiment of the present invention, in the determining unit 301, the set of observable constraints of the resource on the target is determined in the following manner:

[0103] For each satellite, perform the following:

[0104] For each objective, execute:

[0105] If the comprehensive constraint indicators are met within the planning time domain, then the current satellite can observe the current target;

[0106] If the comprehensive constraint index is not met within the planning time domain, the current satellite is not observable of the current target; wherein the comprehensive constraint index is: the current satellite is visible to the current target, and the working duration 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 duration of the current satellite is not greater than the earliest start time of the visible window of the satellite to the target.

[0107] In one embodiment of the present invention, in the determining unit 301, the number of observable targets of the resource is determined by the following formula:

[0108]

[0109] In the formula, sd i Let n be the number of observable targets for the resource, and n be the total number of targets. Let be the observability of satellite i for target j, where sd i It is 1 or 2, when sd i When sd = 1, the target requires single-satellite observation; when sd = 1, the target requires single-satellite observation. i When the value is 2, the target requires two-satellite stereo observation.

[0110] In one embodiment of the present invention, when the computing unit 302 calculates the benefit coefficient of each satellite observing each target based on the observable constraint set of the resource on the target and the preset optimization target, it performs the following operations:

[0111] For each objective, execute:

[0112] For each satellite, perform the following:

[0113] If the current satellite cannot observe the current target, then the benefit coefficient for the current satellite to observe the current target is set to 0;

[0114] If the current satellite can observe the current target, then perform the following operation:

[0115] Calculate the benefits of the earliest tracking time, the longest tracking time, the number of resource switching times, the load balancing, and the resource redundancy separately;

[0116] Based on the earliest tracking time benefit, longest tracking time benefit, resource switching number benefit, load balancing benefit, and resource redundancy benefit, the benefit coefficient for the current satellite observation of the current target is obtained.

[0117] In one embodiment of the present invention, when the planning unit 303 searches for the optimal resource planning scheme under different numbers of observable targets based on the revenue coefficient and the number of observable targets of resources using a heuristic algorithm, it performs the following operations:

[0118] If the number of observable targets for the resource is 2, then perform the following operation:

[0119] Initialize the first maximum profit, the first decision variable, and the first resource index to be allocated;

[0120] Each satellite in the satellite set is paired with each of the other satellites, and for each pair of paired satellites, the following is executed:

[0121] Determine whether the current target can be observed by the two satellites currently paired together;

[0122] If so, calculate the temporary benefit based on the benefit coefficient and geometric precision factor of the current target observed by the two satellites, and compare the current temporary benefit with the current maximum first benefit. If the current temporary benefit is greater than the current maximum first benefit, assign the current temporary benefit to the current maximum first benefit to update the current maximum first benefit, and record the index of the current first resource to be allocated.

[0123] Based on the current maximum benefit, determine whether a dual-star stereo observation scheme has been found. If the current maximum benefit is greater than zero, then a dual-star stereo tracking scheme exists, and the first decision variable is updated. If it is less than or equal to zero, then the number of observable targets of the resource is changed to 1.

[0124] In one embodiment of the present invention, when the planning unit 303 searches for the optimal resource planning scheme under different numbers of observable targets based on the revenue coefficient and the number of observable targets of resources using a heuristic algorithm, it performs the following operations:

[0125] If the number of observable targets for the resource is 1, then perform the following operation:

[0126] Initialize the second maximum profit, the second decision variable, and the second resource index to be allocated;

[0127] For each satellite, perform the following:

[0128] Determine whether the current target can be observed by the current satellite, and whether the payoff coefficient for the current satellite to observe the current target is greater than the second maximum payoff;

[0129] If so, the current satellite observation target's benefit coefficient is assigned to the current second maximum benefit to update the current second maximum benefit, and the current second resource index to be allocated is recorded;

[0130] Based on the current second maximum profit, determine whether a single-star observation scheme has been found. If the current second profit is greater than zero, then a single-star observation scheme exists, and the second decision variable is updated.

[0131] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on an autonomous mission planning device for constellation-based collaborative observation of space targets. In other embodiments of the present invention, an autonomous mission planning 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.

[0132] 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.

[0133] 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 autonomous mission planning method for co-observing space targets by a constellation according to any embodiment of this invention.

[0134] 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 autonomous mission planning method for co-observing space targets based on a constellation in any embodiment of this invention.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 above embodiments.

[0140] 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 autonomous mission planning method for constellation collaborative observation of space targets provided in the above-described method embodiments.

[0141] 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.

[0142] 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.

[0143] 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 autonomous mission planning of space targets through stellar cluster cooperative observation, characterized in that, include: A comprehensive constraint index is constructed based on the acquired input parameters, and the set of observable constraints of resources on targets and the number of observable targets of resources are determined based on this comprehensive constraint index; wherein, the input parameters include the set of satellite operational duration constraints, the set of satellite minimum operational duration constraints, and the satellite visibility identifier of targets; the number of observable targets of resources is determined by the following formula: In the formula, Let n be the number of observable targets for the resource, and n be the total number of targets. Let be the observability of satellite i for target j, where It is 1 or 2, when At that time, the target needs to be observed as a single satellite. At this time, the target requires two-satellite stereo observation. Based on the observable constraint set of the resources on the target and the preset optimization objectives, the benefit coefficient for each satellite observing each target is calculated. The preset optimization objectives include load balancing, resource redundancy, tracking duration, earliest tracking time, and resource switching count. Based on the benefit coefficients and the number of observable targets for the resources, a heuristic algorithm is used to sequentially search for the optimal resource planning scheme under different numbers of observable targets. If the number of observable targets for the resources is 1, the following operations are performed: initialize the second maximum benefit, the second decision variable, and the second resource index to be allocated. For each satellite, the following is performed: determine whether the current target can be observed by the current satellite, and whether the benefit coefficient of the current satellite observing the current target is greater than the second maximum benefit. If so, assign the benefit coefficient of the current satellite observing the current target to the current second maximum benefit to update the current second maximum benefit, and record the current second resource index to be allocated. Based on the current second maximum benefit, determine whether a single-satellite observation scheme is found. If the current second benefit is greater than zero, a single-satellite observation scheme exists, and the second decision variable is updated.

2. The method according to claim 1, characterized in that, The input parameters also include a satellite observation resource set, a target observation demand set, a target observation priority ranking set, the satellite's current observation status of the target, the total duration of the satellite's visible window for the target, the earliest start time and planned time of the satellite's visible window for the target, and the satellite's position vector pointing to the target; wherein, the target observation demand set includes a target set that requires single-satellite observation and a target set that requires dual-satellite stereo observation.

3. The method according to claim 2, characterized in that, The set of observable constraints of the resources on the target is determined as follows: For each satellite, the following is executed: For each target, the following is executed: If the comprehensive constraint index is met within the planning time domain, then the current satellite is observable of the current target; if the comprehensive constraint index is not met within the planning time domain, then the current satellite is not observable of the current target; wherein, the comprehensive constraint index is: the current satellite is visible to the current target, and the working duration of the current satellite is not less than the total duration of the satellite's visible window to the target, and the minimum working duration of the current satellite is not greater than the earliest start time of the satellite's visible window to the target.

4. The method according to claim 1, characterized in that, The step of calculating the benefit coefficient of each satellite observing each target based on the observable constraint set of the resource on the target and the preset optimization target includes: for each target, performing the following operations: for each satellite, performing the following operations: if the current satellite cannot observe the current target, then setting the benefit coefficient of the current satellite observing the current target to 0; if the current satellite can observe the current target, then performing the following operations: calculating the benefit of the earliest tracking time, the benefit of the longest tracking time, the benefit of the number of resource switching times, the benefit of load balancing, and the benefit of resource redundancy; and obtaining the benefit coefficient of the current satellite observing the current target based on the benefit of the earliest tracking time, the benefit of the longest tracking time, the benefit of the number of resource switching times, the benefit of load balancing, and the benefit of resource redundancy.

5. The method according to claim 1, characterized in that, The step of using a heuristic algorithm to sequentially search for the optimal resource planning scheme under different numbers of observable targets based on the benefit coefficient and the number of observable targets of the resource includes: if the number of observable targets of the resource is 2, then the following operations are performed: initialize the first maximum benefit, the first decision variable, and the first resource index to be allocated; pair each satellite in the satellite set with the other satellites respectively, and for each pair of paired satellites, perform the following: determine whether the current target can be observed by the two satellites currently paired; if so, calculate the temporary benefit based on the benefit coefficient and geometric precision factor of the current target observed by the two satellites, and compare the current temporary benefit with the current maximum first benefit; if the current temporary benefit is greater than the current maximum first benefit, assign the current temporary benefit to the current maximum first benefit to update the current maximum first benefit, and record the current first resource index to be allocated; determine whether a two-satellite stereo observation scheme is found based on the current maximum first benefit; if the current maximum first benefit is greater than zero, a two-satellite stereo tracking scheme exists, and the first decision variable is updated; if it is less than or equal to 0, change the number of observable targets of the resource to 1.

6. An autonomous mission planning device for constellation-based collaborative observation of space targets, used to implement the method as described in claim 1, characterized in that, include: The system comprises: a determination unit, used to construct a comprehensive constraint index based on the acquired input parameters, and to determine the set of observable constraints of resources on the target based on the comprehensive constraint index; wherein the input parameters include a set of satellite operational duration constraints, a set of satellite minimum operational duration constraints, and a satellite visibility identifier for the target; a calculation unit, used to calculate the benefit coefficient for each satellite observing each target based on the set of observable constraints of resources on the target and preset optimization objectives; wherein the preset optimization objectives include load balancing, resource redundancy, tracking duration, earliest tracking time, and number of resource switching operations; and a planning unit, used to search for the optimal resource planning scheme under different numbers of observable targets using a heuristic algorithm based on the benefit coefficient and the number of observable targets of resources.

7. 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 1-5.

8. 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 1-5.

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