Satellite mission planning method based on greedy adaptive annealing contract network algorithm

Abstract

The present invention provides a satellite mission planning method, system, storage medium, and electronic device based on a greedy adaptive annealing contract net algorithm, relating to the field of satellite scheduling. In the present invention, based on a preset slave satellite mathematical model, each slave satellite uses a greedy adaptive annealing contract net algorithm to generate a corresponding current bid proposal for a currently unscheduled task sequence. Based on a preset master satellite mathematical model, the master satellite uses a total observation benefit maximization evaluation strategy to evaluate bids, selecting a winning proposal from the bids, and ultimately outputting a globally optimal satellite mission planning proposal. By establishing a two-level decision-making mathematical model, the inconsistency between the primary objective and sub-objectives in distributed satellite mission allocation and scheduling problems, which is not considered in traditional contract nets, is resolved. A higher-quality proposal is obtained through multiple rounds of bidding and tendering.

Description

Technical Field

[0001] The present invention relates to the field of satellite scheduling, and in particular to a satellite mission planning method, system, storage medium and electronic equipment based on a greedy adaptive annealing contract network algorithm. Background Art

[0002] With the continuous development of satellite technology in my country, the role of satellites is growing. Remote sensing satellites, capable of quickly and accurately acquiring surface information and widely used, have become a key indicator of China's comprehensive national strength. They also play a vital role in environmental disaster prevention, urban planning, and weather forecasting.

[0003] Research on satellite scheduling, both domestically and internationally, focuses on multi-satellite, multi-task scheduling. Traditional multi-satellite, multi-task planning is often handled using centralized architectures based on ground-based offline planning. However, centralized planning suffers from numerous drawbacks, including poor task responsiveness and low execution efficiency. Distributed architectures, with their advantages of decentralized communication and robustness, have become the dominant architectural approach for multi-satellite mission planning. Contract networks are an effective approach to distributed task planning, widely used in distributed satellite planning due to their high allocation efficiency and adaptability to dynamic environments.

[0004] The Contract Net is a classic negotiation strategy for task and resource allocation. It divides system members into managers and contractors, mimicking the "bidding and tendering" mechanism of economic behavior to achieve task allocation. Agents collaborate and compete for tasks through bids, pursuing a global optimum within a locally optimal system configuration, ultimately completing tasks with the optimal system configuration and the lowest cost. The Contract Net protocol is a classic and effective approach for distributed satellite task allocation. Its main workflow is as follows: the master satellite issues a task, each slave satellite submits a bid for the task, and the master satellite selects a winning proposal from among the bids and allocates the task to the winning satellite.

[0005] However, in actual distributed satellite mission planning, the main goal and sub-goals are not always completely consistent, and the inconsistency of goals needs to be taken into account. Summary of the Invention

[0006] (1) Technical problems solved

[0007] In response to the shortcomings of the existing technology, the present invention provides a satellite mission planning method, system, storage medium and electronic device based on a greedy adaptive annealing contract network algorithm, which solves the technical problem that in actual distributed satellite mission planning, the main goal and sub-goals are not always completely consistent.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] A satellite mission planning method based on a greedy adaptive annealing contract network algorithm is characterized by including a plurality of slave satellites and a master satellite for decision-making. The method specifically includes:

[0011] S1, the main satellite obtains the task sequence to be observed and enters the cyclic bidding process, setting q=1 and setting the number of cycle terminations;

[0012] S2, the master satellite starts the qth bidding and obtains the currently unscheduled task sequence. If the currently unscheduled task sequence is empty, go to S5; otherwise, go to S3;

[0013] S3. The master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites. Based on a preset slave satellite mathematical model, each of the slave satellites generates a corresponding current bidding solution for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm. If all the bidding solutions are empty, the process proceeds to S5; otherwise, the process proceeds to S4.

[0014] S4. Based on a preset mathematical model of the master satellite, the master satellite uses a total observation benefit maximization bid evaluation strategy to evaluate bids, selects a winning bid from the bids, updates the planning scheme of the winning slave satellite, and updates the currently unscheduled task sequence;

[0015] S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding plan, or the total observation benefit reaches the same number of consecutive cycle terminations, then the planning ends and the globally optimal satellite task planning plan is output; otherwise, set q = q + 1 and go to S2.

[0016] Preferably, the slave satellite mathematical model in S3 includes an objective function for maximizing comprehensive benefits:

[0017]

[0018] And the first constraint:

[0019]

[0020]

[0021] OTS ij +dur j =OTE ij (4)

[0022]

[0023] eij =e i dur j , i∈{1,…,m} (6)

[0024]

[0025]

[0026] Among them, formula (2) represents the uniqueness constraint, that is, a task can be observed at most once; formula (3) indicates that the task must meet the time window requirement when performing observation; formula (4) represents the relationship between the actual observation end time of the task and the task observation duration; formulas (5-6) represent the energy constraint in satellite planning and the energy consumption calculation method; formula (7) represents the storage constraint; formula (8) represents the calculation method of the disturbance generated by the generated solution;

[0027] Master satellite S′, slave satellite set S = {S1, S2, ..., S i ,…,S m}, there are m slave satellites in total;

[0028] T={t1,t2,…,t j ,…,.t n} represents the task sequence to be observed, with a total of n tasks;

[0029] t j =<P j ,dur j , dt j >, that is, the attributes of the task are represented by triples, where P j Represents task t j The observed return, dur j Represents task t j The observation duration, dt j Represents task t j The task observation deadline;

[0030] represents the set of visible time windows, representing task t j From the satellite S i The set of visible time windows on NTM ij Indicates the number of time windows;

[0031] That is, the visible time window is represented by a two-tuple, Represents task t j From the satellite S i The kth visible time window on , and Respectively represent the start time and end time of the visible time window;

[0032] OTW ij =<OTS ij ,OTE ij >, that is, the observation time window is represented by a two-tuple, OTW ij Represents task t j From the satellite S i The actual observation time window on OTS ij and OTE ij Respectively represent the start time and end time of the observation time window;

[0033] W p and W r denote the weights of observed returns and disturbances respectively;

[0034] e i Indicates the satellite S i Unit energy consumption of the observation task;

[0035] e ij Indicates the satellite S i Observation mission t j Energy consumed;

[0036] E i Indicates the satellite S i Maximum energy limit;

[0037] c ij Indicates the satellite S i Observation mission t j Amount of storage consumed;

[0038] C i Indicates the satellite S i Maximum storage limit;

[0039] Represents task t j Is it from satellite S i Observe the kth visible time window, if observed, the value is 1, otherwise 0;

[0040] rd ij Indicates the satellite S i For task t j The disturbance caused by the observation.

[0041] Preferably, the main satellite mathematical model in S5 includes an objective function for maximizing the total observation benefit:

[0042]

[0043] And the second constraint:

[0044]

[0045] ET iq ≤PTE, i∈{1,…,m}, q∈{1,2,…} (11)

[0046] Among them, formula (9) represents the constraint between the number of tasks in the bidding process; formula (10) represents the planning time deadline constraint; formula (11) represents the planning time deadline constraint;

[0047] ET iq Indicates that in the qth bidding, from satellite S i For task sequence T q The observation end time of the generated scenario, and where lst i Indicates the satellite S i The observation end time of the last task; PTE represents the task planning deadline.

[0048] Preferably, the rd ij There are three types of disturbances:

[0049] The task is arranged into the idle time slice of the original task sequence. ij =0;

[0050] Task scheduling causes the task in the original task sequence to be replaced, at this time 0<rd ij ≤1;

[0051] Task scheduling causes the tasks in the original task sequence to be unobservable. At this time, rd ij >1.

[0052] Preferably, the greedy adaptive annealing contract net algorithm is used in S3 to generate the corresponding current bidding scheme, specifically including:

[0053] S10, each slave satellite attempts to insert tasks in a currently unscheduled task sequence into the observation plan of the slave satellite in sequence according to a greedy rule of maximizing benefit, and obtains an initial solution of each slave satellite for the currently unscheduled task sequence;

[0054] S20, setting the initial temperature, isothermal step length, and cooling rate, and taking the initial solution as the current optimal solution;

[0055] S30, entering a loop, the number of loops being equal to the size of the isothermal step; each time a preset operator is selected to generate a neighborhood solution, the Metropolis criterion is used to determine whether to accept the neighborhood solution, and the selection probability of each operator is updated based on the adaptive operator rule to update the current optimal solution;

[0056] S40, the iteration is completed at this temperature, and the temperature is lowered according to the cooling rate;

[0057] S50, loop S30 to S40 to iteratively update the solution, and stop the loop when the termination condition is met;

[0058] S60: Each of the slave satellites obtains a current optimal solution for the currently unscheduled task sequence, and obtains a corresponding current bidding solution according to the intersection of the current optimal solution and the currently unscheduled task sequence.

[0059] Preferably, the operators preset in S30 include insert, delete and shift operators; updating the selection probability of each operator based on the adaptive operator rule specifically includes:

[0060] S100: First, the three operators are assigned the same probability of being selected, and then enter the annealing process;

[0061] S200, select neighborhood operator Q i , i = {1, 2, 3}, generating a neighborhood solution of the initial solution;

[0062] S300, compare the comprehensive benefits of the initial solution and the neighborhood solution. If the comprehensive benefit value of the neighborhood solution is less than the comprehensive benefit value of the initial solution, reduce the operator Q i The probability of being selected; if the comprehensive benefit values ​​of the two are equal, the probability will not be changed; if the comprehensive benefit value of the neighborhood solution is greater than the comprehensive benefit value of the initial solution, the operator Q will be increased. i The probability of being selected.

[0063] Preferably, after the main satellite in S1 obtains the sequence of tasks to be observed, the sequence of tasks to be observed is re-sorted from large to small according to the benefits; if there are multiple tasks with the same benefits, they are sorted again from small to large according to the task observation duration, and then enter the circular bidding process.

[0064] A satellite mission planning system based on a greedy adaptive annealing contract network algorithm includes a plurality of slave satellites and a master satellite for decision-making. The system specifically includes:

[0065] The task acquisition module is used to execute S1, wherein the main satellite acquires the task sequence to be observed and enters the cyclic bidding process, setting q=1 and setting the number of cycle terminations;

[0066] The bidding start module is used to execute S2, the master satellite starts the qth bidding, obtains the task sequence that is not currently scheduled, and if the task sequence that is not currently scheduled is empty, the program confirmation module executes S5; otherwise, the program generation module executes S3;

[0067] The scheme generation module is configured to execute S3, wherein the master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites; and each of the slave satellites generates a corresponding current bidding scheme for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm according to a preset slave satellite mathematical model. If all the bidding schemes are empty, the scheme confirmation module executes S5; otherwise, the scheme evaluation module executes S4.

[0068] a proposal evaluation module, configured to execute S4, evaluate bids using a total observation benefit maximization evaluation strategy based on a preset master satellite mathematical model, select a winning proposal from the bidding proposals, update the planning proposal of the winning slave satellite, and update the currently unscheduled task sequence;

[0069] The scheme confirmation module is used to execute S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding scheme, or the total observation benefit reaches the same number of consecutive cycle terminations, the planning ends and the globally optimal satellite mission planning scheme is output; otherwise, set q = q + 1 and switch to the bidding start module to execute S2.

[0070] A storage medium stores a computer program for satellite mission planning based on a greedy adaptive annealing contract net algorithm, wherein the computer program enables a computer to execute the satellite mission planning method described above.

[0071] An electronic device, comprising:

[0072] one or more processors;

[0073] Memory; and

[0074] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for executing the satellite mission planning method as described above.

[0075] (3) Beneficial effects

[0076] The present invention provides a satellite mission planning method, system, storage medium, and electronic device based on a greedy adaptive annealing contract network algorithm. Compared with the existing technology, it has the following advantages:

[0077] In this invention, based on a preset mathematical model for slave satellites, each slave satellite uses a greedy adaptive annealing contract net algorithm to generate a corresponding current bid for a currently unscheduled task sequence. Based on a preset mathematical model for the master satellite, the master satellite uses a total observation benefit maximization strategy to evaluate bids, selecting a winning bid from the bids. Finally, a globally optimal satellite mission planning solution is output. This two-tier decision-making mathematical model addresses the inconsistency between primary and sub-goals in distributed satellite task allocation and scheduling, a problem not addressed by traditional contract nets. Through multiple rounds of bidding and tendering, higher-quality solutions are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] 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 only 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.

[0079] Figure 1 A schematic flow chart of a satellite mission planning method based on a greedy adaptive annealing contract network algorithm provided in an embodiment of the present invention;

[0080] Figures 2-4 A schematic diagram of the insertion, deletion, and shift operators provided in an embodiment of the present invention;

[0081] Figure 5 A schematic diagram of a bidding scheme screening method provided by an embodiment of the present invention;

[0082] Figure 6 This is a structural block diagram of a satellite mission planning system based on a greedy adaptive annealing contract network algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0083] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0084] The present invention provides a satellite mission planning method, system, storage medium, and electronic device based on a greedy adaptive annealing contract network algorithm. This solves the technical problem of not always fully aligning primary and secondary objectives in actual distributed satellite mission planning.

[0085] The technical solution in the embodiments of the present application is to solve the above technical problems, and the overall idea is as follows:

[0086] In this embodiment of the present invention, based on a preset mathematical model for slave satellites, each slave satellite uses a greedy adaptive annealing contract net algorithm to generate a corresponding current bid for a currently unscheduled task sequence. Based on a preset mathematical model for the master satellite, the master satellite uses a total observation benefit maximization evaluation strategy to evaluate bids, selecting a winning bid from the bids. Finally, a globally optimal satellite mission planning solution is output. This approach addresses the inconsistency between primary and sub-goals in distributed satellite task allocation and scheduling, a problem not considered in traditional contract nets. A multi-round bidding process results in higher-quality solutions.

[0087] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0088] Example:

[0089] First, as Figure 1 As shown, an embodiment of the present invention provides a satellite mission planning method based on a greedy adaptive annealing contract network algorithm, including a plurality of slave satellites and a master satellite for decision-making. The method specifically includes:

[0090] S1, the main satellite obtains the task sequence to be observed and enters the cyclic bidding process, setting q=1 and setting the number of cycle terminations;

[0091] S2, the master satellite starts the qth bidding and obtains the currently unscheduled task sequence. If the currently unscheduled task sequence is empty, go to S5; otherwise, go to S3;

[0092] S3. The master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites. Based on a preset slave satellite mathematical model, each of the slave satellites generates a corresponding current bidding solution for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm. If all the bidding solutions are empty, the process proceeds to S5; otherwise, the process proceeds to S4.

[0093] S4. Based on a preset mathematical model of the master satellite, the master satellite uses a total observation benefit maximization bid evaluation strategy to evaluate bids, selects a winning bid from the bids, updates the planning scheme of the winning slave satellite, and updates the currently unscheduled task sequence;

[0094] S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding plan, or the total observation benefit reaches the same number of consecutive cycle terminations, then the planning ends and the globally optimal satellite task planning plan is output; otherwise, set q = q + 1 and go to S2.

[0095] The embodiment of the present invention solves the problem of inconsistency between main objectives and sub-objectives in distributed satellite task allocation and scheduling problems that is not considered in traditional contract networks by establishing a two-level decision-making mathematical model; and obtains higher quality solutions through a multi-round bidding and tendering process.

[0096] The following will introduce each step of the above technical solution in detail with specific content:

[0097] First, it should be noted that the "single-task bidding, single-agent winning" model adopted by the traditional contract network generates a large amount of negotiation communication volume. The embodiment of the present invention designs a full-task bidding strategy to reduce the number of negotiations. In each bidding round, the master satellite updates the task sequence that is not currently scheduled for observation and uses it as bidding information for bidding. Each slave satellite screens the bid proposals to decide whether to bid. The main steps are as follows:

[0098] S1, the main satellite obtains the task sequence to be observed and enters the cyclic bidding process, setting q = 1 and setting the number of cycle terminations N te .

[0099] After the main satellite obtains the sequence of tasks to be observed, it re-sorts the sequence of tasks to be observed from large to small according to the benefits; if there are multiple tasks with the same benefits, they are sorted again from small to large according to the mission observation duration, and then enter the circular bidding process.

[0100] S2. The master satellite starts the qth bidding and obtains the currently unscheduled task sequence. If the currently unscheduled task sequence is empty, go to S5; otherwise, go to S3.

[0101] S3. The master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites. Based on a preset slave satellite mathematical model, each of the slave satellites generates a corresponding current bidding scheme for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm. If all the bidding schemes are empty, go to S5; otherwise, go to S4.

[0102] After each satellite obtains the bidding information, it is necessary to generate a corresponding bidding plan. In this step, a greedy adaptive annealing contract network algorithm is designed to generate the bidding plan from the satellite. The algorithm encoding method uses integer encoding. The specific content is as follows:

[0103] The mathematical model from the satellite includes an objective function to maximize the comprehensive benefits:

[0104]

[0105] And the first constraint:

[0106]

[0107]

[0108] OTS ij +dur j =OTE ij (4)

[0109]

[0110] e ij =e i dur j , i∈{1,…,m} (6)

[0111]

[0112]

[0113] Among them, formula (2) represents the uniqueness constraint, that is, a task can be observed at most once; formula (3) indicates that the task must meet the time window requirement when performing observation; formula (4) represents the relationship between the actual observation end time of the task and the task observation duration; formulas (5-6) represent the energy constraint in satellite planning and the energy consumption calculation method; formula (7) represents the storage constraint; formula (8) represents the calculation method of the disturbance generated by the generated solution;

[0114] Master satellite S′, slave satellite set S = {S1, S2, ..., S i ,…,S m}, there are m slave satellites in total;

[0115] T={t1,t2,…,t j ,…,.t n} represents the task sequence to be observed, with a total of n tasks;

[0116] t j =<P j ,dur j , dt j >, that is, the attributes of the task are represented by triples, where P j Represents task t j The observed return, dur j Represents task t j The observation duration, dtj Represents task t j The task observation deadline;

[0117] represents the set of visible time windows, representing task t j From the satellite S i The set of visible time windows on NTM ij Indicates the number of time windows;

[0118] That is, the visible time window is represented by a two-tuple, Represents task t j From the satellite S i The kth visible time window on , and Respectively represent the start time and end time of the visible time window;

[0119] OTW ij =<OTS ij ,OTE ij >, that is, the observation time window is represented by a two-tuple, OTW ij Represents task t j From the satellite S i The actual observation time window on OTS ij and OTE ij Respectively represent the start time and end time of the observation time window;

[0120] W p and W r denote the weights of observed returns and disturbances respectively;

[0121] e i Indicates the satellite S i Unit energy consumption of the observation task;

[0122] e ij Indicates the satellite S i Observation mission t j Energy consumed;

[0123] E i Indicates the satellite S i Maximum energy limit;

[0124] c ij Indicates the satellite S i Observation mission t j Amount of storage consumed;

[0125] C i Indicates the satellite S i Maximum storage limit;

[0126] Represents task t j Is it from satellite S i Observe the kth visible time window, if observed, the value is 1, otherwise 0;

[0127] rd ij Indicates the satellite S i For task t j The disturbance caused by the observation.

[0128] Specifically, the rd ij There are three types of disturbances:

[0129] The task is arranged into the idle time slice of the original task sequence. ij =0;

[0130] Task scheduling causes the task in the original task sequence to be replaced, at this time 0<rd ij ≤1, for example, rd ij =1;

[0131] Task scheduling causes the tasks in the original task sequence to be unobservable. At this time, rd ij >1, for example, you can take rd ij =2.

[0132] In addition, the greedy adaptive annealing contract network algorithm is used in S3 to generate the corresponding current bidding scheme, which specifically includes:

[0133] S10. Each slave satellite attempts to insert tasks in the currently unscheduled task sequence into the observation plan of the slave satellite in sequence according to the greedy rule of maximizing benefit, and obtains an initial solution of each slave satellite for the currently unscheduled task sequence.

[0134] S20, setting the initial temperature, isothermal step length, and cooling rate, and taking the initial solution as the current optimal solution.

[0135] S30, entering a loop, the number of loops being the size of the isothermal step; each time a preset operator is selected to generate a neighborhood solution, the Metropolis criterion is used to determine whether to accept the neighborhood solution, and the selection probability of each operator is updated based on the adaptive operator rule to update the current optimal solution.

[0136] like Figures 2-4 As shown, the operators preset in S30 include insert, delete and shift operators; updating the selection probability of each operator based on the adaptive operator rule specifically includes:

[0137] S100: First, the three operators are assigned the same selection probability and enter the annealing process;

[0138] S200, select neighborhood operator Q i , i = {1, 2, 3}, generating a neighborhood solution of the initial solution;

[0139] S300, compare the comprehensive benefits of the initial solution and the neighborhood solution. If the comprehensive benefit value of the neighborhood solution is less than the comprehensive benefit value of the initial solution, reduce the operator Q i The probability of being selected; if the comprehensive benefit values ​​of the two are equal, the probability will not be changed; if the comprehensive benefit value of the neighborhood solution is greater than the comprehensive benefit value of the initial solution, the operator Q will be increased. i The probability of being selected.

[0140] S40, the iteration is completed at this temperature, and the temperature is lowered according to the cooling rate.

[0141] S50, loop S30 to S40 to iteratively update the solution, and stop the loop when the termination condition is met.

[0142] S60, each of the slave satellites obtains the current optimal solution for the currently unscheduled task sequence, such as Figure 5 As shown, according to the intersection of the current optimal solution and the currently unscheduled task sequence, the corresponding current bidding solution is obtained.

[0143] From the above, it can be seen that the embodiment of the present invention designs a greedy-based adaptive annealing contract network algorithm. First, a good initial solution can be obtained quickly through the greedy strategy. Secondly, the diversity of neighborhood solutions is enhanced by designing multiple operators. Finally, the rationality of neighborhood solution generation is further enhanced through the adaptive operator strategy.

[0144] S4. According to the preset mathematical model of the master satellite, the master satellite adopts the total observation benefit maximization evaluation strategy to evaluate bids, selects a winning bid from each bidding scheme, updates the planning scheme of the winning slave satellite, and updates the currently unscheduled task sequence.

[0145] The main satellite mathematical model includes an objective function to maximize the total observation benefit:

[0146]

[0147] And the second constraint:

[0148]

[0149] ET iq ≤PTE, i∈{1,…,m}, q∈{1,2,…} (11)

[0150] Among them, formula (9) represents the constraint between the number of tasks in the bidding process; formula (10) represents the planning time deadline constraint; formula (11) represents the planning time deadline constraint;

[0151] ET iq Indicates that in the qth bidding, from satellite S i For task sequence T q The observation end time of the generated scenario, and where lst i Indicates the satellite S i The observation end time of the last task; PTE represents the task planning deadline.

[0152] S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding plan, or the total observation benefit reaches the same number of consecutive cycle terminations, then the planning ends and the globally optimal satellite task planning plan is output; otherwise, set q = q + 1 and go to S2.

[0153] Second, as Figure 6 As shown, an embodiment of the present invention provides a satellite mission planning system based on a greedy adaptive annealing contract network algorithm, characterized in that it includes a plurality of slave satellites and a master satellite for decision-making. The system specifically includes:

[0154] The task acquisition module is used to execute S1, wherein the main satellite acquires the task sequence to be observed and enters the cyclic bidding process, setting q=1 and setting the number of cycle terminations;

[0155] The bidding start module is used to execute S2, the master satellite starts the qth bidding, obtains the task sequence that is not currently scheduled, and if the task sequence that is not currently scheduled is empty, the program confirmation module executes S5; otherwise, the program generation module executes S3;

[0156] The scheme generation module is configured to execute S3, wherein the master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites; and each of the slave satellites generates a corresponding current bidding scheme for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm according to a preset slave satellite mathematical model. If all the bidding schemes are empty, the scheme confirmation module executes S5; otherwise, the scheme evaluation module executes S4.

[0157] a proposal evaluation module, configured to execute S4, evaluate bids using a total observation benefit maximization evaluation strategy based on a preset master satellite mathematical model, select a winning proposal from the bidding proposals, update the planning proposal of the winning slave satellite, and update the currently unscheduled task sequence;

[0158] The scheme confirmation module is used to execute S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding scheme, or the total observation benefit reaches the same number of consecutive cycle terminations, the planning ends and the globally optimal satellite mission planning scheme is output; otherwise, set q = q + 1 and switch to the bidding start module to execute S2.

[0159] In a third aspect, an embodiment of the present invention provides a storage medium storing a computer program for satellite mission planning based on a greedy adaptive annealing contract net algorithm, wherein the computer program enables a computer to execute the satellite mission planning method described above.

[0160] In a fourth aspect, an embodiment of the present invention provides an electronic device, including:

[0161] one or more processors;

[0162] Memory; and

[0163] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for executing the satellite mission planning method as described above.

[0164] It is understandable that the satellite mission planning system, storage medium and electronic device based on the greedy adaptive annealing contract network algorithm provided in the embodiment of the present invention correspond to the satellite mission planning method based on the greedy adaptive annealing contract network algorithm provided in the embodiment of the present invention. The explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the satellite mission planning method, which will not be repeated here.

[0165] In summary, compared with the existing technology, the present invention has the following beneficial effects:

[0166] 1. The embodiment of the present invention solves the problem of inconsistency between main objectives and sub-objectives in distributed satellite task allocation and scheduling problems, which is not considered in traditional contract networks, by establishing a two-level decision-making mathematical model; and obtains higher-quality solutions through a multi-round bidding and tendering process.

[0167] 2. The embodiment of the present invention designs a greedy-based adaptive annealing contract network algorithm. First, a good initial solution can be obtained quickly through the greedy strategy. Second, the diversity of neighborhood solutions is enhanced by designing multiple operators. Finally, the rationality of neighborhood solution generation is further enhanced through the adaptive operator strategy.

[0168] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0169] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. 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. A satellite mission planning method based on a greedy adaptive annealing contract network algorithm, characterized in that: The method includes a plurality of slave satellites and a master satellite for decision-making, and specifically includes: S1. The master satellite obtains the task sequence to be observed and enters the cyclic bidding process, setting q=1 and setting the number of cycle terminations; S2, the master satellite starts the qth bidding and obtains the currently unscheduled task sequence. If the currently unscheduled task sequence is empty, go to S5; otherwise, go to S3; S3. The master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites. Based on a preset slave satellite mathematical model, each of the slave satellites generates a corresponding current bidding solution for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm. If all the bidding solutions are empty, the process proceeds to S5; otherwise, the process proceeds to S4. S4. Based on a preset mathematical model of the master satellite, the master satellite uses a total observation benefit maximization bid evaluation strategy to evaluate bids, selects a winning bid from the bids, updates the planning scheme of the winning slave satellite, and updates the currently unscheduled task sequence; S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding plan, or the total observation benefit reaches the same number of consecutive cycle terminations, then the planning ends and the globally optimal satellite task planning plan is output; otherwise, set q = q + 1 and go to S2; the slave satellite mathematical model in S3 includes an objective function for maximizing the comprehensive benefit: And the first constraint: OTS ij +dur j =OTE ij (4) in ij =e i far j ,i∈{1,…,m} (6) Among them, formula (2) represents the uniqueness constraint, that is, a task can be observed at most once; formula (3) indicates that the task must meet the time window requirement when performing observation; formula (4) represents the relationship between the actual observation end time of the task and the task observation duration; formulas (5-6) represent the energy constraint in satellite planning and the energy consumption calculation method; formula (7) represents the storage constraint; formula (8) represents the calculation method of the disturbance generated by the generated solution; Master satellite S′, slave satellite set S = {S1, S2, ..., S i ,…,S m }, there are m slave satellites in total; T={t1,t2,…,t j ,…,.t n } represents the task sequence to be observed, with a total of n tasks; t j = <P j ,dur j , dt j >, that is, the attributes of the task are represented by triples, where P j Represents task t j The observed return, dur j Represents task t j The observation duration, dt j Represents task t j The task observation deadline; represents the set of visible time windows, representing task t j From the satellite S i The set of visible time windows on NTM ij Indicates the number of time windows; That is, the visible time window is represented by a two-tuple, Represents task t j From the satellite S i The kth visible time window on , and Respectively represent the start time and end time of the visible time window; OTW ij = <OTS ij ,OTE ij >, that is, the observation time window is represented by a two-tuple, OTW ij Represents task t j From the satellite S i The actual observation time window on OTS ij and OTE ij Respectively represent the start time and end time of the observation time window; W p and W r denote the weights of observed returns and disturbances respectively; e i Indicates the satellite S i Unit energy consumption of the observation task; e ij Indicates the satellite S i Observation mission t j Energy consumed; E i Indicates the satellite S i Maximum energy limit; c ij Indicates the satellite S i Observation mission t j Amount of storage consumed; C i Indicates the satellite S i Maximum storage limit; Represents task t j Is it from satellite S i Observe the kth visible time window, if observed, the value is 1, otherwise 0; rd ij Indicates the satellite S i For task t j The disturbance generated during observation; the main satellite mathematical model in S5 includes an objective function to maximize the total observation benefit: And the second constraint: ET iq ≤PTE, i∈{1,…,m},q∈{1,2,…} (11) where formula (9) represents the constraint between task quantities in the bidding process; formula (10) represents the planning time deadline constraint; formula (11) represents the planning time deadline constraint; ET iq Indicates that in the qth bidding, from satellite S i For task sequence T q The observation end time of the generated scenario, and where lst i Indicates the satellite S i The observation end time of the last task; PTE represents the task planning deadline.

2. The satellite mission planning method according to claim 1, wherein: The rd ii There are three types of disturbances: The task is arranged into the idle time slice of the original task sequence. ij =0; Task scheduling causes the task in the original task sequence to be replaced, at this time 0 <rd ij ≤1; the task arrangement causes the tasks in the original task sequence to be unobservable, at this time rd ij >1.

3. The satellite mission planning method according to claim 1 or 2, wherein: In S3, a greedy adaptive annealing contract network algorithm is used to generate the corresponding current bidding scheme, which specifically includes: S10. Each slave satellite attempts to sequentially insert tasks in a currently unscheduled task sequence into the observation plan of the slave satellite using a greedy rule of maximizing benefit, and obtains an initial solution of each slave satellite for the currently unscheduled task sequence; S20, setting the initial temperature, isothermal step length, and cooling rate, and taking the initial solution as the current optimal solution; S30, entering a loop, the number of loops being equal to the size of the isothermal step; each time a preset operator is selected to generate a neighborhood solution, the Metropolis criterion is used to determine whether to accept the neighborhood solution, and the selection probability of each operator is updated based on the adaptive operator rule to update the current optimal solution; S40, the iteration is completed at this temperature, and the temperature is lowered according to the cooling rate; S50, loop S30 to S40 to iteratively update the solution, and stop the loop when the termination condition is met; S60, each of the slave satellites obtains the current optimal solution for the currently unscheduled task sequence, and obtains the corresponding current bidding solution based on the intersection of the current optimal solution and the currently unscheduled task sequence.

4. The satellite mission planning method according to claim 3, wherein: The operators preset in S30 include insert, delete, and shift operators; updating the selection probability of each operator based on the adaptive operator rule specifically includes: S100: First, the three operators are assigned the same probability of being selected and enter the annealing process; S200, select neighborhood operator Q i , i = {1, 2, 3}, generating a neighborhood solution of the initial solution; S300, compare the comprehensive benefits of the initial solution and the neighborhood solution. If the comprehensive benefit value of the neighborhood solution is less than the comprehensive benefit value of the initial solution, reduce the operator Q i The probability of being selected; if the comprehensive benefit values ​​of the two are equal, the probability will not be changed; if the comprehensive benefit value of the neighborhood solution is greater than the comprehensive benefit value of the initial solution, the operator Q will be increased. i The probability of being selected.

5. The satellite mission planning method according to claim 1 or 2, wherein: After the main satellite in S1 obtains the sequence of tasks to be observed, it re-sorts the sequence of tasks to be observed from large to small according to the benefits; if there are multiple tasks with the same benefits, they are sorted again from small to large according to the task observation duration, and then enter the circular bidding process.

6. A satellite mission planning system based on a greedy adaptive annealing contract network algorithm, characterized in that: A system for executing the satellite mission planning method according to claim 1, comprising a plurality of slave satellites and a master satellite for decision-making, the system specifically comprising: The task acquisition module is used to execute S1, wherein the master satellite acquires the task sequence to be observed and enters the cyclic bidding process, setting q=1 and setting the number of cycle terminations; The bidding start module is used to execute S2, the master satellite starts the qth bidding, obtains the task sequence that is not currently scheduled, and if the task sequence that is not currently scheduled is empty, the program confirmation module executes S5; otherwise, the program generation module executes S3; The scheme generation module is configured to execute S3, wherein the master satellite broadcasts the currently unscheduled task sequence to each of the slave satellites; and each of the slave satellites generates a corresponding current bidding scheme for the currently unscheduled task sequence using a greedy adaptive annealing contract network algorithm according to a preset slave satellite mathematical model. If all the bidding schemes are empty, the scheme confirmation module executes S5; otherwise, the scheme evaluation module executes S4. a proposal evaluation module, configured to execute S4, evaluate bids using a total observation benefit maximization evaluation strategy based on a preset master satellite mathematical model, select a winning proposal from the bidding proposals, update the planning proposal of the winning slave satellite, and update the currently unscheduled task sequence; The scheme confirmation module is used to execute S5. If all tasks in the task sequence to be observed are arranged, or there is no bidding scheme, or the total observation benefit reaches the same number of consecutive cycle terminations, the planning ends and the globally optimal satellite mission planning scheme is output; otherwise, set q = q + 1 and switch to the bidding start module to execute S2.

7. A storage medium, characterized in that: The computer program for satellite mission planning based on the greedy adaptive annealing contract network algorithm is stored, wherein the computer program enables a computer to execute the satellite mission planning method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: include: one or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for executing the satellite mission planning method according to any one of claims 1 to 5.

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