Autonomous task planning method and device for satellite group collaborative observation of space target

Abstract

The invention provides an autonomous task planning method and device for satellite group collaborative observation of a space target. The method comprises the following steps: constructing a comprehensive constraint index based on acquired input parameters, and determining an observable constraint set of resources to targets and an observable target number of the resources based on the comprehensive constraint index; wherein the input parameters comprise a satellite working duration constraint set, a satellite minimum working duration constraint set and a target visibility identifier of the satellite; according to the observable constraint set of the resources to the targets and a preset optimization target, calculating a revenue coefficient of each satellite for observing each target; wherein the preset optimization target comprises load balancing, resource redundancy, tracking duration, earliest tracking time and resource switching times; and on the basis of the income coefficient and the observable target number of the resources, sequentially searching an optimal resource planning scheme under different observation target numbers by using a heuristic algorithm. According to the scheme, on-satellite real-time rapid optimal solution can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous task planning for constellation resources, and in particular to a method and device for autonomous task planning for collaborative observation of space targets by a constellation. Background Art

[0002] Continuous multi-target tracking in space is a prerequisite for achieving space situational awareness. Due to factors such as the relatively high-speed motion of satellites, limited sensor observation capabilities, the need for single-satellite surveillance, dual-satellite stereoscopic observation, and the dynamic changes in tracking missions, efficient, real-time autonomous mission planning algorithms are required onboard to dynamically schedule and allocate sensor resources for the collaborative observation constellation.

[0003] Autonomous mission planning for collaborative observation of space targets by a constellation is a multi-objective optimization problem under complex constraints. Related technologies have 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 require real-time resource scheduling. However, onboard computing resources are severely limited. Given the large number of tracking resources and targets, how to achieve rapid onboard optimization solutions is a challenge facing autonomous mission planning for collaborative observation of space targets by a constellation.

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

[0005] In order to solve the problem that traditional satellite cluster collaborative mission planning methods cannot meet the requirements of rapid optimization solutions on board, the present invention provides an autonomous mission planning method and device for satellite cluster collaborative observation of space targets.

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

[0007] A comprehensive constraint index is constructed based on the obtained input parameters, and an observable constraint set of the resource on the target is determined based on the comprehensive constraint index; wherein the input parameters include a satellite available working time constraint set, a satellite minimum working time constraint set, and a satellite visibility indicator for the target;

[0008] 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 goals; wherein the preset optimization goals include load balancing, resource redundancy, tracking time, earliest tracking time, and number of resource switching times;

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

[0010] In a second aspect, the present invention further provides an autonomous task planning device for collaborative observation of space targets by a constellation, the device comprising:

[0011] A determination unit is configured to construct a comprehensive constraint index based on the acquired input parameters, and determine an observable constraint set of the resource on the target based on the comprehensive constraint index; wherein the input parameters include a satellite available operating time constraint set, a satellite minimum operating time constraint set, and a satellite visibility indicator for the target;

[0012] A calculation unit, configured to calculate the benefit coefficient of each satellite observing each target based on the observable constraint set of the resource on the target and a preset optimization goal; wherein the preset optimization goal 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 profit coefficient and the number of observable targets of the resource by using a heuristic algorithm.

[0014] In a third aspect, the present invention further provides a computing device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method described in any embodiment of this specification.

[0016] On the other hand, the present application also provides a computer program product, which includes a computer program. The processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes any of the methods described in the first aspect above.

[0017] The present invention provides an autonomous task planning method for collaborative observation of space targets by a satellite cluster. First, a set of satellite operating time constraints, a set of satellite minimum operating time constraints, and a satellite visibility identifier for a target are simplified into a single comprehensive constraint index. Based on the 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 of the resource are determined. Then, based on the observability constraint of resources on the target and by considering multiple optimization objectives such as load balancing tracking time, earliest tracking time, resource redundancy, and number of resource switching, the benefit coefficient of each satellite observing each target is calculated respectively. Finally, based on the benefit coefficient, a heuristic algorithm is used to realize a rapid optimization allocation solution for the star cluster observation resources under different numbers of observed targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a flow chart of an autonomous task planning method for collaborative observation of space targets by a constellation, provided by one embodiment of the present invention;

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

[0021] Figure 3 This is a structural diagram of an autonomous task planning device for collaborative observation of space targets by a star cluster, provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] The specific implementation of the above concept is described below.

[0024] Please refer to Figure 1 The embodiment of the present invention provides an autonomous task planning method for collaborative observation of space targets by a constellation, the method comprising:

[0025] Step 100: constructing a comprehensive constraint index based on the acquired input parameters, and determining an observable constraint set of the resource on the target and the number of observable targets of the resource based on the comprehensive constraint index; wherein the input parameters include a satellite operational duration constraint set, a satellite minimum operational duration constraint set, and a satellite visibility indicator for the target;

[0026] Step 102: 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 goal; wherein the preset optimization goal includes load balancing, resource redundancy, tracking time, earliest tracking time, and number of resource switching times;

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

[0028] In an embodiment of the present invention, a set of satellite operating time constraints, a set of satellite minimum operating time constraints, and a satellite-to-target visibility indicator are first simplified into a single comprehensive constraint indicator. Based on the comprehensive constraint indicator, the observability constraint of the resource on the target (i.e., whether the satellite can observe the target) and the number of observable targets of the resource are determined. Then, based on the observability constraint of the resource on the target and by considering multiple optimization objectives such as load balancing tracking time, earliest tracking time, resource redundancy, and number of resource switching, the benefit coefficient of each satellite observing each target is calculated respectively. Finally, based on the benefit coefficient, a heuristic algorithm is used to achieve a rapid optimal allocation solution for the star cluster observation resources under different numbers of observed targets.

[0029] For step 100:

[0030] Assume that 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 embodiments, the input parameters further include a satellite observation resource set S = {s1, s2, ..., s m}、Target observation requirement set DS={ds1,ds2,...,ds n},ds j ∈{1,2}, 1 means single-star observation is required, 2 means dual-star stereo observation is required, and the target observation priority sorting set D = {d1,d2,...,d n}, d1 has the highest priority, d n The mission priority is the lowest, and satellite i is observing target j. The total duration of the visible window of satellite i to target j The earliest start time of the visibility window of satellite i to target j Position vector of satellite i pointing to target j at the planning moment Among them, r i is the satellite position vector, r j is the target position vector.

[0032] In the embodiment of the present invention, by acquiring the above parameters and defining each parameter, efficient task planning and resource allocation are facilitated.

[0033] In some implementations, the set of observable constraints of the resource on the target is determined as follows:

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

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

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

[0037] If the comprehensive constraint index is satisfied within the planning time domain, the current satellite can observe the current target (i.e., V i j =1);

[0038] If the comprehensive constraint index is not satisfied within the planning time domain, the current satellite cannot observe the current target (i.e., V i j =0); wherein, the comprehensive constraint index is: the current satellite is visible to the current target (i.e. ), and the current satellite's working time is not less than the total visible window time of the satellite to the target (i.e. ), and the minimum working time of the current satellite is not greater than the earliest starting time of the satellite's visible window to the target

[0039] In an embodiment of the present invention, the visibility of the satellite to the target, the satellite's operating time, and the satellite's minimum operating time are constructed into a single comprehensive constraint indicator to determine the observability constraint of the resource (i.e., the satellite) on the target, that is, whether the current satellite can observe the current target. This simplifies the constraint conditions, which is conducive to achieving rapid optimization solutions on the satellite.

[0040] Regarding step 102:

[0041] In some embodiments, step 102 includes:

[0042] For each targeti , i=1,2,...,m, all execute:

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

[0044] If the current satellite cannot observe the current target (i.e. V i j =0), then the benefit coefficient of the current satellite observing the current target is set to 0

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

[0046] Calculate the earliest tracking time benefit, the longest tracking time benefit, the resource switching number benefit, the load balancing benefit and the resource redundancy benefit respectively;

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

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

[0049]

[0050] The maximum tracking time benefit f2 is calculated by the following formula:

[0051]

[0052] The resource switching benefit f3 is calculated by the following formula:

[0053]

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

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

[0056] Where, sq i is the load balancing deviation, st i is the operating time of the i-th satellite, M(ST) is the mean of the satellite operating time constraint set ST;

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

[0058]

[0059] In the formula, sd i is the number of observable targets of satellite i, n is the total number of targets, V i j is the observability of satellite i to target j;

[0060] The benefit coefficient of the current satellite observing the current target Calculated by the following formula:

[0061]

[0062] Where η1, η2, η3, η4 and η5 are weights.

[0063] In an embodiment of the present invention, the benefit coefficient of the satellite resource observation target is calculated by comprehensively considering multiple optimization objectives such as load balancing, tracking time, earliest tracking time, number of resource switching times and resource redundancy. This is not only conducive to maximizing the task completion rate, but also can improve the stereoscopic observation accuracy of the target while ensuring the task priority. At the same time, through the comprehensive consideration of multiple indicators, it is possible to avoid the suboptimal solution caused by optimizing only a single indicator, so that the global optimal solution is taken into account when solving the local optimal solution.

[0064] Regarding step 104:

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

[0066]

[0067] In the formula, sd i is the number of observable targets of the resource, n is the total number of targets, is the observability of satellite i to target j, where sd i is 1 or 2, when sd i = 1, the target needs to be observed as a single star. When sd i =2, the target requires two-star stereo observation.

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

[0069] In some embodiments, step 104 includes:

[0070] If the number of observable targets of the resource is 2 (ds j=2), that is, the current target requires dual-satellite stereo observation, then perform the following operations:

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

[0072] Pair each satellite in the satellite set with the remaining satellites in pairs, and for each pair of satellites in the pair, execute:

[0073] Determine the current target d j Can the two satellites currently paired with each other be i s k observation;

[0074] If so, calculate the temporary benefit g based on the benefit coefficient and geometric precision dilution of the current two satellites observing the current target, and add the current temporary benefit g to the current first maximum benefit g max In comparison, if the current temporary benefit is greater than the current first maximum benefit, the current temporary benefit is assigned to the current first maximum benefit to update the current first maximum benefit g max =g, and record the current first resource index to be allocated k1=i; k2=k;

[0075] Based on the current first maximum benefit, it is determined whether a dual-star stereo observation scheme has been found. If the current first maximum benefit is greater than zero g max >0, then there is a dual-satellite stereo tracking solution, and the first decision variable is updated. If it is less than or equal to 0, the observable target quantity of 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 both satellites are not assigned, that is The current target d j The two satellites s that can be currently paired i s k observation;

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

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

[0079] Position vectors of two satellites based on pairwise combination The absolute value of two position vectors And the absolute value L of the difference between the position vectors of the two satellites pointing to the target, calculate the observation angles θ1 and θ2 of the two satellites respectively: Among them, θ1 and θ2 are calculated by the following formulas:

[0080]

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

[0082]

[0083] Where, σ is the observation accuracy;

[0084] The benefit coefficient of the current target observed by the two current satellites Geometric Dilution of Precision when observing a target with two satellites Calculate the temporary benefit g of the current two satellites observing the current target:

[0085]

[0086] Where η6 is the weight.

[0087] In some embodiments, if the number of observable targets of the resource is 1 (ds j =1), that is, the current target requires single-star observation, then perform the following operations:

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

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

[0090] Determine the current target d j Can it be seen by the current satellite i Observation, and whether the profit coefficient of the current satellite observing the current target is greater than the second maximum profit, that is, satisfying V i j =1 and

[0091] 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 k1=i;

[0092] Based on the current second maximum benefit, determine whether a single star observation plan has been found. If the current second maximum benefit is greater than zero g max '>0, then there is a single-star observation plan, and the second decision variable is updated. If the current second maximum benefit is not greater than zero, then there is no single-star observation plan.

[0093] In an embodiment of the present invention, the observation requirements of each target are determined based on the number of observable targets of the satellite. Observation resources are first allocated to targets with higher priority in observation requirements (i.e., targets requiring dual-satellite stereoscopic observation), thereby ensuring priority observation of high-priority tasks. Moreover, when allocating a single resource, all feasible solutions are traversed and optimized, and a local optimal solution can be obtained while taking into account the global optimal solution.

[0094] In summary, the embodiments of the present invention achieve a rapid, iterative search to obtain an optimal resource scheduling solution while satisfying various constraints, including resource availability, minimum availability, visibility, and the number of observed targets. Furthermore, they consider multiple optimization objectives, including maximizing task completion rate, load balancing, task priority, stereoscopic observation accuracy, tracking duration, earliest tracking time, and the number of resource switching times. This method achieves a rapid, optimal allocation of constellation observation resources. This method exhibits global optimization performance, a simple autonomous task planning algorithm, and a small solution scale, enabling rapid optimal solution acquisition.

[0095] like Figure 2 、 Figure 3 As shown, the embodiment of the present invention provides an autonomous mission planning device for a constellation to collaboratively observe a space target. The device embodiment can be implemented by software, hardware, or a combination of software and hardware. From the hardware level, Figure 2 As shown in FIG, a hardware architecture diagram of a computing device where an autonomous task planning device for collaborative observation of space targets by a star cluster provided by an embodiment of the present invention is located, 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 messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, it is formed by the CPU of the computing device in which it is located reading the corresponding computer program in the non-volatile memory into the internal memory and running it.

[0096] This embodiment provides an autonomous task planning device for collaborative observation of space targets by a constellation, the device comprising:

[0097] A determination unit 301 is configured to construct a comprehensive constraint index based on the acquired input parameters, and determine an observable constraint set of the resource on the target and the number of observable targets of the resource based on the comprehensive constraint index; wherein the input parameters include a satellite operational duration constraint set, a satellite minimum operational duration constraint set, and a satellite visibility indicator for the target;

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

[0099] The planning unit 303 is configured to sequentially search for optimal resource planning solutions under different numbers of observable targets using a heuristic algorithm based on the profit coefficient and the number of observable targets of the resource.

[0100] In an embodiment of the present invention, the determination unit 301 may be used to execute step 100 in the above method embodiment, the calculation unit 302 may be used to execute step 102 in the above method embodiment, and the planning unit 303 may be used to execute step 104 in the above method embodiment.

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

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

[0103] For each satellite, execute:

[0104] For each target, perform:

[0105] If the comprehensive constraint index is met within the planning time domain, 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 cannot observe the current target; wherein, the comprehensive constraint index is: the current satellite is visible to the current target, and the operating time of the current satellite is not less than the total duration of the satellite's visible window for the target, and the minimum operating time of the current satellite is not greater than the earliest start time of the satellite's visible window for 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 is the number of observable targets of the resource, n is the total number of targets, is the observability of satellite i to target j, where sd i is 1 or 2, when sd i = 1, the target needs to be observed as a single star. When sd i =2, the target requires two-star stereo observation.

[0110] In one embodiment of the present invention, the calculation unit 302 is configured to perform the following operations when 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:

[0111] For each target, perform:

[0112] For each satellite, execute:

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

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

[0115] Calculate the earliest tracking time benefit, the longest tracking time benefit, the resource switching number benefit, the load balancing benefit and the resource redundancy benefit respectively;

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

[0117] In one embodiment of the present invention, the planning unit 303 is configured to perform the following operations when searching for the optimal resource planning scheme under different numbers of observable targets using a heuristic algorithm based on the profit coefficient and the number of observable targets of the resource:

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

[0119] Initializing a first maximum benefit, a first decision variable, and a first index of resources to be allocated;

[0120] Pair each satellite in the satellite set with the remaining satellites in pairs, and for each pair of satellites in the pair, execute:

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

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

[0123] Based on the current first maximum benefit, determine whether a dual-satellite stereo observation solution has been found. If the current first maximum benefit is greater than zero, a dual-satellite stereo tracking solution exists, and the first decision variable is updated. If it is less than or equal to 0, the number of observable targets of the resource is changed to 1.

[0124] In one embodiment of the present invention, the planning unit 303 is configured to perform the following operations when searching for the optimal resource planning scheme under different numbers of observable targets using a heuristic algorithm based on the profit coefficient and the number of observable targets of the resource:

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

[0126] Initialize the second maximum benefit, the second decision variable, and the second index of resources to be allocated;

[0127] For each satellite, execute:

[0128] Determining 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;

[0129] If yes, 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 to be allocated index;

[0130] Based on the current second maximum benefit, it is determined whether a single-star observation solution has been found. If the current second benefit is greater than zero, a single-star observation solution exists, and the second decision variable is updated.

[0131] It should be understood that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on the autonomous mission planning device for collaborative observation of space targets by a constellation. In other embodiments of the present invention, the autonomous mission planning device for collaborative observation of space targets by a constellation may include more or fewer components than illustrated, or may combine or separate certain components, or employ different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of both.

[0132] The information interaction, execution process, etc. between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For specific contents, please refer to the description in the embodiment of the method of the present invention and will not be repeated here.

[0133] An embodiment of the present invention also provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements an autonomous task planning method for collaborative observation of space targets by a star cluster in any embodiment of the present invention.

[0134] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes an autonomous task planning method for collaborative observation of space targets by a star cluster in any embodiment of the present invention.

[0135] Specifically, a system or device equipped with a storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program codes stored in the storage medium.

[0136] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute part of the present invention.

[0137] Examples of storage media for providing 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, the program code can be downloaded from a server computer via a communication network.

[0138] In addition, it should be clear that the functions of any of the above embodiments can be achieved not only by executing the program code read by the computer, but also by enabling the operating system operating on the computer to complete part or all of the actual operations based on the instructions of the program code.

[0139] In addition, it can be understood that the program code read from the storage medium is written into a memory provided in an expansion board inserted into the computer or into a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above embodiments.

[0140] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set, or instruction set, and the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement an autonomous task planning method for collaborative observation of space targets by a star cluster provided in the above-mentioned 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 entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0142] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk, etc. Various media that can store program codes.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An autonomous mission planning method for collaborative observation of space targets by a constellation, characterized in that: include: A comprehensive constraint index is constructed based on the obtained input parameters, and an observable constraint set of the resource on the target and the number of observable targets of the resource are determined based on the comprehensive constraint index; wherein the input parameters include a satellite operational duration constraint set, a satellite minimum operational duration constraint set, and a satellite visibility identifier for the target; 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 goals; wherein the preset optimization goals include load balancing, resource redundancy, tracking time, earliest tracking time, and number of resource switching times; Based on the profit coefficient and the number of observable targets of resources, a heuristic algorithm is used to sequentially search for optimal resource planning schemes under different numbers of observable targets.

2. The method according to claim 1, characterized in that The input parameters also include a satellite observation resource set, a target observation requirement set, a target observation priority ranking set, the satellite's observation status of the target, the total duration of the satellite's visible window of the target, the earliest starting time of the satellite's visible window of the target, and the position vector of the satellite pointing to the target at the planned time; wherein the target observation requirement 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 that the resource imposes on the target is determined as follows: For each satellite, execute: For each target, perform: If the comprehensive constraint index is met within the planning time domain, the current satellite can observe the current target; If the comprehensive constraint index is not met within the planning time domain, the current satellite cannot observe the current target; wherein, the comprehensive constraint index is: the current satellite is visible to the current target, and the operating time of the current satellite is not less than the total duration of the satellite's visible window for the target, and the minimum operating time of the current satellite is not greater than the earliest start time of the satellite's visible window for the target.

4. The method according to claim 1, wherein The method 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, perform: For each satellite, execute: If the current satellite cannot observe the current target, the benefit coefficient of the current satellite observing the current target is set to 0; If the current satellite can observe the current target, perform the following operations: Calculate the earliest tracking time benefit, the longest tracking time benefit, the resource switching number benefit, the load balancing benefit and the resource redundancy benefit respectively; Based on the earliest tracking time benefit, the longest tracking time benefit, the resource switching times benefit, the load balancing benefit and the resource redundancy benefit, a benefit coefficient of the current satellite observing the current target is obtained.

5. The method according to claim 1, wherein The observable target quantity of the resource is determined by the following formula: In the formula, sd i is the number of observable targets of the resource, n is the total number of targets, is the observability of satellite i to target j, where sd i is 1 or 2, when sd i = 1, the target needs to be observed as a single star. When sd i =2, the target requires two-star stereo observation.

6. The method according to claim 5, characterized in that The method of searching for the optimal resource planning scheme under different numbers of observable targets based on the profit coefficient and the number of observable targets of the resource by using a heuristic algorithm in sequence includes: If the number of observable targets for the resource is 2, perform the following operations: Initializing a first maximum benefit, a first decision variable, and a first index of resources to be allocated; Pair each satellite in the satellite set with the remaining satellites in pairs, and for each pair of satellites in the pair, execute: 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 dilution of precision of the current two satellites observing the current target, and compare the current temporary benefit with the current maximum first benefit. If the current temporary benefit is greater than the current first maximum benefit, assign the current temporary benefit to the current first maximum benefit to update the current first maximum benefit, and record the current first resource index to be allocated; Based on the current first maximum benefit, determine whether a dual-satellite stereo observation solution has been found. If the current first maximum benefit is greater than zero, a dual-satellite stereo tracking solution exists, and the first decision variable is updated. If it is less than or equal to 0, the number of observable targets of the resource is changed to 1.

7. The method according to claim 5, characterized in that The method of searching for the optimal resource planning scheme under different numbers of observable targets based on the profit coefficient and the number of observable targets of the resource by using a heuristic algorithm in sequence includes: If the number of observable targets for the resource is 1, perform the following operations: Initialize the second maximum benefit, the second decision variable, and the second index of resources to be allocated; For each satellite, execute: Determining 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 yes, 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 to be allocated index; Based on the current second maximum benefit, it is determined whether a single-star observation solution has been found. If the current second benefit is greater than zero, a single-star observation solution exists, and the second decision variable is updated.

8. An autonomous mission planning device for collaborative observation of space targets by a constellation, characterized in that: include: A determination unit is configured to construct a comprehensive constraint index based on the acquired input parameters, and determine an observable constraint set of the resource on the target based on the comprehensive constraint index; wherein the input parameters include a satellite available operating time constraint set, a satellite minimum operating time constraint set, and a satellite visibility indicator for the target; A calculation unit, configured to calculate the benefit coefficient of each satellite observing each target based on the observable constraint set of the resource on the target and a preset optimization goal; wherein the preset optimization goal includes load balancing, resource redundancy, tracking duration, earliest tracking time, and number of resource switching times; The planning unit is used to search for the optimal resource planning scheme under different numbers of observable targets based on the profit coefficient and the number of observable targets of the resource by using a heuristic algorithm.

9. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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

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