1. A multi-satellite collaborative planning method based on a marine moving target is characterized by comprising the following steps:

receiving an offshore monitoring task, and decomposing the offshore monitoring task into one or more observation tasks;

sequencing the plurality of observation tasks to obtain a marine target observation task set; the marine target observation task set comprises a plurality of observation tasks and task characteristics corresponding to each observation task;

converting the marine target observation task set into an observation task sequence set according to the task characteristics; the observation task sequence corresponds to the task characteristics of the observation task;

and inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model to obtain satellite loads corresponding to each observation task sequence in the observation task sequence set, and outputting an optimal planning scheme.

2. The method of claim 1, wherein said converting the set of marine target observation tasks into a set of observation task sequences according to the task features comprises:

quantizing the task characteristics and converting the quantized task characteristics into data in a preset format; the task characteristics comprise id, longitude, latitude, earliest starting time of the task, latest ending time of the task, task time interval, cloud cover and/or task income of the task;

and normalizing the data in the preset format through a Z-Score model to obtain an observation task sequence set.

3. The method of claim 2, wherein the multi-satellite collaborative task allocation model is trained by:

taking historical planning data as a training sample; the historical planning data comprises an observation task sequence and a corresponding calling load result;

and taking the observation task sequence in the training sample set as input, taking a call load result corresponding to the observation sequence as output, and constructing the multi-satellite cooperative task allocation model through a Seq2Seq neural network.

4. The method of claim 3, wherein the Seq2Seq neural network consists of an Encoder + Decoder + Attention structure.

5. The method according to claim 4, wherein the inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model to obtain the satellite load corresponding to each observation task sequence in the observation task sequence set comprises:

inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model, processing the observation task sequence set through the Encoder, and determining the hidden state of the Encoder;

calculating a background variable based on the hidden state of the Encoder and an Attention mechanism;

determining a hidden state of the Decoder based on the background variable;

and determining the satellite load corresponding to each observation task sequence through a Softmax function based on the hidden state of the Decode.

6. The method of claim 5, wherein the computing a context variable based on the Encoder hidden state and the Attention mechanism comprises:

determining the hidden state of the observation task sequence set at each time step based on an Attention mechanism;

based on attention weight, carrying out weighted average on hidden states of all time steps through the Encoder to obtain background variables;

wherein the attention weight comprises:

the attention weight is derived by a softmax function based on the total number of time steps.

7. The method of claim 6, further comprising:

and training the task allocation model by using a Seq2Seq neural network by using the cross entropy as a loss function.

8. A multi-satellite collaborative planning apparatus, comprising:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring and receiving an offshore monitoring task and decomposing the offshore monitoring task into one or more observation tasks;

the sequencing module is used for sequencing the plurality of observation tasks to obtain a marine target observation task set; the marine target observation task set comprises a plurality of observation tasks and task characteristics corresponding to each observation task;

the conversion module is used for converting the marine target observation task set into an observation task sequence set according to the task characteristics; the observation task sequence corresponds to the task characteristics of the observation task;

and the matching module is used for inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model, obtaining satellite loads corresponding to each observation task sequence in the observation task sequence set, and outputting an optimal planning scheme.

9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.

Background

Observation satellites are a type of satellite that uses remote satellite sensors to optically or electronically probe the earth's surface and underlying atmosphere to obtain relevant information. According to the relative size relationship between the field of view of the satellite-borne remote sensor and the area of the observation target, the observation target can be generally divided into a point target and a regional target, the point target has a smaller width relative to the satellite-borne remote sensor, and can be generally a smaller circular or rectangular region, and can be contained in the field of view of a single satellite photograph, for example: airports, ports, etc.; the image of the regional target is usually a polygonal region, is relatively large in width relative to the satellite-borne remote sensor, cannot be completely covered by a single scene or a single strip photo of the satellite-borne remote sensor, and can be completely covered only by multiple observations of a satellite, and the regional target is usually a result of splicing multiple photos together.

In the existing satellite mission planning research, the imaging satellite mission planning problem is mostly modeled as an optimization problem, and then various intelligent optimization algorithms are adopted for solving. The current mainstream intelligent optimization algorithm mainly comprises an ant colony algorithm, a simulated annealing algorithm, a particle swarm algorithm, a genetic algorithm and the like.

The optimization algorithm has the following defects when solving the multi-satellite cooperative task planning problem:

the ant colony algorithm utilizes the positive feedback characteristic of pheromones, can search a better solution in a relatively short time, but is easy to fall into precocity, namely when the pheromone concentration of a certain path is obviously higher than that of other paths, the algorithm can be converged on the path too fast.

The simulated annealing algorithm has strong local search capability and is not easy to fall into a local optimal solution, but has the defects of low convergence speed, long execution time, correlation between the performance of the algorithm and an initial value, sensitive parameters and the like.

Although the particle swarm algorithm has the capability of approaching the optimal solution quickly. However, since all the particles fly toward the optimal solution, all the particles tend to be normalized (the diversity is lost), so that the later convergence rate is obviously slowed down, and meanwhile, when the algorithm converges to a certain precision, the optimization cannot be continued, and the achievable precision is not high.

The genetic algorithm is most widely applied to the multi-satellite collaborative planning, although the global search capability is strong, the genetic algorithm has an obvious defect, mainly, an initial solution of the genetic algorithm is generally generated in a random mode, the search space of the solution is large, the search speed is slow, more time is often spent when the global optimal solution is searched, the calculation efficiency is influenced, and an observation time window is easily missed.

Disclosure of Invention

According to an embodiment of the disclosure, a multi-satellite collaborative planning scheme is provided.

In a first aspect of the disclosure, a multi-satellite collaborative planning method is provided. The method comprises the following steps:

receiving an offshore monitoring task, and decomposing the offshore monitoring task into one or more observation tasks;

sequencing the plurality of observation tasks to obtain a marine target observation task set; the marine target observation task set comprises a plurality of observation tasks and task characteristics corresponding to each observation task;

converting the marine target observation task set into an observation task sequence set according to the task characteristics; the observation task sequence corresponds to the task characteristics of the observation task;

and inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model to obtain satellite loads corresponding to each observation task sequence in the observation task sequence set, and outputting an optimal planning scheme.

Further, the converting the marine target observation task set into an observation task sequence set according to the task features includes:

quantizing the task characteristics and converting the quantized task characteristics into data in a preset format; the task characteristics comprise id, longitude, latitude, earliest starting time of the task, latest ending time of the task, task time interval, cloud cover and/or task income of the task;

and normalizing the data in the preset format through a Z-Score model to obtain an observation task sequence set.

Further, the multi-satellite cooperative task allocation model is trained in the following way:

taking historical planning data as a training sample; the historical planning data comprises an observation task sequence and a corresponding calling load result;

and taking the observation task sequence in the training sample set as input, taking a call load result corresponding to the observation sequence as output, and constructing the multi-satellite cooperative task allocation model through a Seq2Seq neural network.

Further, the Seq2Seq neural network is composed of an Encoder + Decoder + Attention structure.

Further, the inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model to obtain a satellite load corresponding to each observation task sequence in the observation task sequence set includes:

inputting the observation task sequence set into a pre-trained task allocation model, processing the observation task sequence set through the Encoder, and determining the hidden state of the Encoder;

calculating a background variable based on the hidden state of the Encoder and an Attention mechanism;

determining a hidden state of the Decoder based on the background variable;

and determining the satellite load corresponding to each observation task sequence through a Softmax function based on the hidden state of the Decode.

Further, the computing a background variable based on the hidden state of the Encoder and the Attention mechanism includes:

determining the hidden state of the observation task sequence set at each time step based on an Attention mechanism;

based on attention weight, carrying out weighted average on hidden states of all time steps through the Encoder to obtain background variables;

wherein the attention weight comprises:

the attention weight is derived by a softmax function based on the total number of time steps.

Further, still include:

and training the task allocation model by using a Seq2Seq neural network by using the cross entropy as a loss function.

In a second aspect of the present disclosure, a multi-satellite collaborative planning apparatus is provided. The device includes:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring and receiving an offshore monitoring task and decomposing the offshore monitoring task into one or more observation tasks;

the sequencing module is used for sequencing the plurality of observation tasks to obtain a marine target observation task set; the marine target observation task set comprises a plurality of observation tasks and task characteristics corresponding to each observation task;

the conversion module is used for converting the marine target observation task set into an observation task sequence set according to the task characteristics; the observation task sequence corresponds to the task characteristics of the observation task;

and the matching module is used for inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model, obtaining satellite loads corresponding to each observation task sequence in the observation task sequence set, and outputting an optimal planning scheme.

In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.

In a fourth aspect of the present disclosure, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor, implements a method as in accordance with the first aspect of the present disclosure.

The multi-satellite collaborative planning method provided by the embodiment of the application obtains a marine target observation task set; the marine target observation task set comprises a plurality of observation tasks and task characteristics corresponding to each observation task; converting the marine target observation task set into an observation task sequence set according to the task characteristics; the observation task sequence corresponds to the task characteristics of the observation task; and inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model to obtain satellite loads corresponding to each observation task sequence in the observation task sequence set, outputting an optimal planning scheme, finishing the planning of tasks in a short time, realizing the consideration of benefits and calculation efficiency, ensuring the algorithm to have the capability of fast convergence and global search, and improving the calculation efficiency.

It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

fig. 1 shows a flow diagram of a multi-star collaborative planning method according to an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of a Seq2Seq structure according to an embodiment of the present disclosure;

FIG. 3 shows a schematic structural diagram of an Encoder and Decode module according to an embodiment of the disclosure;

FIG. 4 shows an Attention mechanism architecture diagram according to an embodiment of the present disclosure

FIG. 5 illustrates a block diagram of a multi-star collaborative planning apparatus, in accordance with an embodiment of the present disclosure;

FIG. 6 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

Fig. 1 shows a flow diagram of a multi-star collaborative planning method 100 according to an embodiment of the present disclosure. The method 100 comprises:

s110, receiving an offshore monitoring task, and decomposing the offshore monitoring task into one or more observation tasks.

In some embodiments, the marine monitoring task generally refers to a task of monitoring a dynamic target, where a monitoring area of a moving target is generally an area that a satellite cannot cover for imaging within a single access time window, and in order to better adapt to a neural network algorithm application subsequently, a ship heading (dynamic target) area may be decomposed into a plurality of static target assemblies, referring to a way that the satellite observes the regional target.

In the present disclosure, a dynamic vessel may be considered as a plurality of intermittent static areas along its voyage direction for cooperative monitoring.

In some embodiments, an offshore monitoring mission is received according to a demand and/or actual application scenario (exploration area, target, etc.), as shown in table 1:

TABLE 1

The data corresponding to the ID1 is monitoring data obtained by monitoring a port, that is, monitoring data obtained by monitoring a static target (the position of multiple observations is not changed); the data corresponding to the ID2 is monitoring data obtained by monitoring the ship, that is, monitoring a dynamic target (the position of multiple observations changes), and obtaining the monitoring data.

In some embodiments, referring to Table 1, each monitoring task may be broken down into multiple (three) observation tasks, depending on the monitoring time.

And S120, sequencing the plurality of observation tasks to obtain a marine target observation task set.

In some embodiments, referring to table 1, the set of marine target observation tasks may include a plurality of observation tasks and task features corresponding to each observation task; the task features may include an id of the monitoring task, a longitude, a latitude, a task earliest start time (visible window start time), a task latest end time (visible window end time), a task time interval, a cloud cover, and/or a task profit, etc.

Wherein, the ID is used for representing task labels, and the IDs of the same task are the same;

the value of the income of the task represents the income value of the user for completing the task, and the larger the value, the more valuable the user for completing the task. For example, when two observation tasks conflict, the task with a larger profit value is selected to be completed, and the task with a smaller profit value is abandoned, so that the comprehensive completion profit of the tasks is maximized, and the purpose of achieving the best tracking and monitoring effect is achieved. The settings may be generally based on historical experience and/or application scenarios.

In some embodiments, the plurality of observation tasks may be ordered according to an observation time order, resulting in a set of marine target observation tasks.

S130, converting the marine target observation task set into an observation task sequence set according to the task characteristics.

Wherein the observation task sequence corresponds to task characteristics of the observation task.

In some embodiments, after selecting the desired task features from the observation tasks, the selected task features are typically quantized. For example, the time-related task characteristics (the task earliest start time, the task latest end time, the task time interval, and the like) are counted in seconds, and the year, month, and day format data is converted into numerical format data for the subsequent normalization processing.

Further, the quantized task features are subjected to normalization processing to avoid differences caused by different dimensions, and the marine target observation task set is converted into an observation task sequence set based on the quantized task features.

Specifically, the quantized task features may be normalized using a Z-Score model:

Z-Score=

wherein, theRepresents a mean value;

the above-mentionedRepresents the standard deviation;

after the Z-Score model is changed, the mean value of all task characteristics is close to 0, and the variance is 1.

In some embodiments, the set of marine target observation tasks is converted to neural network embedded vector inputs (a set of observation task sequences) based on the normalized task features.

Each time step of the feature sequence input aiming at the static target neural network model is arranged according to a visible window in time sequence;

each time step of the characteristic sequence input by the neural network model aiming at the moving target is arranged by combining a plurality of static areas of the area decomposition in the moving direction of the ship.

The time steps can be set according to the overall monitoring time of the task, for example, if a port target monitoring task period is 5 days, a satellite needs to shoot a target (port) for many times within 5 days, each time step input characteristic of the port target source monitoring task is determined according to each monitoring window, the port target is static, visible time windows of multiple satellite loads can appear at different time points, each revisit time window can be used as the input of one time step, if a source monitoring task overall period is 5 days, the recurrent neural network needs to define at least 5 time steps, and the revisit capability of the source monitoring target within 24 hours can be realized.

In some embodiments, to facilitate describing the multi-star to sea observation task schedulability problem, the following definitions are made:

=<，……>

wherein, theRepresenting an observation task;

the above-mentionedRepresenting task characteristics;

the n, represents the total number of features (e.g., 8);

the i represents a number (id) of an observation task;

the observation task sequence is denoted by X, i.e.:

X={︱i∈[1，N]，N∈}

wherein, the N represents the length of the task sequence;

definition (，……，) Is composed ofThe predecessor task of (1); (，……，) Is composed ofThe back drive task of (2); it should be noted that, if the current task is the first task, the current task is the first task=If the current task is the last task, then the predecessor task does not exist, and similarly,=then there is no back-drive task.

And S140, inputting the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model to obtain satellite loads corresponding to each observation task sequence in the observation task sequence set, and outputting an optimal planning scheme.

In some embodiments, the multi-star collaborative task allocation model may be obtained by:

taking historical planning data (Train) as a training sample; the historical planning data comprises an observation task sequence and a corresponding calling load result;

taking an observation task sequence in the training sample set as input, taking a call load result corresponding to the observation sequence as output, and constructing the multi-satellite cooperative task allocation model through a Seq2Seq neural network;

wherein the content of the first and second substances,

as shown in fig. 2, the Seq2Seq neural network includes an Encoder and a Decoder, the left part of fig. 2 is the Encoder and the right part is the Decoder, i.e., a coding-decoding model.

Wherein the Encoder is used for converting an input sequence (Train) into a vector with a fixed length;

the Decoder is used for converting the fixed length vectors into output sequences;

based on the vector and the output sequence with fixed length, the potential relation between the current task and the front drive task set and/or the rear drive task set is modeled through Attention, namely, the potential relation between the moving tracks of the moving observed object is determined, so that the moving object can be accurately detected (moving ships on the sea and the like) in the follow-up process;

furthermore, the encoder and the decoder can be selected according to actual application scenes, including CNN, RNN, BiRNN, GRU, LSTM and the like, and can be freely combined according to actual conditions; for example, BiRNN is used in encoding, RNN is used in decoding, or RNN is used in encoding, LSTM is used in decoding, and the like.

In the present disclosure, the encoder and decoder are preferably LSTMs with long-term memory capability to facilitate sequence modeling.

In some embodiments, cross entropy may be used as a loss function, and optimization of data and parameters (obtaining an optimal solution that minimizes the loss function) is performed through an Adam optimization algorithm, which has the advantages of both AdaGrad and RMSProp algorithms, is suitable for solving the optimization problem including large-scale data and parameters, and has efficient calculation and less required memory.

In some embodiments, Y is defined as the observation to which X corresponds, i.e.:

Y={︱i∈[1，N]，N∈}

wherein, theTo represent an observation taskThe corresponding satellite calls the load result;

further, the air conditioner is provided with a fan,

definition ofAnd (3) a system planning result (a historical planning result) corresponding to the observation task sequence X:

={︱i∈[1，N]，N∈}

wherein, theRepresenting observation tasksCorresponding real calculation results;

further, the air conditioner is provided with a fan,

train is defined as historical planning data:

Train={<，>︱i∈[1，M]，M∈}；

in summary, the set of observation task sequences generated based on the mapping relationship of the historical planning data, which is expressed by Test, can be expressed as:

Test={︱i∈[1，m]，m∈}

that is, the Test is a neural network embedded vector input (observation task sequence set).

Specifically, the Test is input into the Encoder as a neural network embedding vector. As shown in FIG. 3, let the current time step be t, and the hidden state based on the last time stepInput through LstmFeature vector of=<，……>Transition to hidden state at current time stepI.e. hidden state based on last time stepGo through forgetting doorInput gateOutput gateThe feature vector is combinedTransitioning to a hidden state at a current time stepThe transformation relationship of the hidden layer can be represented by the following formula:

=Lstm（，）

as shown in fig. 4, potential relationships between the current task and its predecessor task set and/or successor task set are modeled based on the Attention, and the moving object can be effectively and accurately monitored based on the potential relationships, that is, the moving object can be decomposed into a plurality of static targets for monitoring.

Specifically, the attention weight of each time step is adjusted through the encoder, so that the hidden state of each time step is calculated and obtained, and the hidden states of all the time steps are obtainedWeighted average to obtain background variableThe calculation formula is as follows:

=

wherein T is the total number of time steps;

the above-mentionedT =1, … …, T;

the hidden state of the encoder at time step t isAnd the total time step is T. Then the decoder is at time stepBackground variable of (A), (B)) Weighted average of hidden states for all encoders, saidIs a probability distribution;

generally theThe value of (2) can be directly calculated by a softmax function, but in the present disclosure, the consideration is given from the practical situation of finding to tracking for multiple times according to the moving target on the sea, after the moving target on the sea is found, the next tracking monitoring area can be usually judged by the current speed and heading, that is, the probability of directly (first step) tracking after finding the target is high, and the probability of tracking finding decreases due to interference of multiple factors such as heading and speed during subsequent tracking, so in the present disclosure, the attention weight is calculated by the following formula:

=，t=1，……，T

in the decoder, the output of the last time step is outputAnd background variablesAs input, hidden state based on last time stepConvert it to a hidden state at the current time stepThe transform representing the hidden layer of the decoder is represented by a function g:

=g（，，）

referring to fig. 2, after obtaining the hidden state of the decoder, the feature vector is transmitted to a full connection layer (context), after performing nonlinear processing, the probability of the corresponding category is output through a Softmax function, and the current output at each time step is calculatedI.e. output feature labels 0, 1, … …, m]The feature tag represents each satellite payload invocation selection.

In some embodiments, the observation task sequence set Test is used as a neural network embedded vector and input to the task allocation model to obtain a satellite load corresponding to each observation task sequence in the observation task sequence set, and an optimal planning scheme is output, that is, the corresponding satellite loads are respectively called to complete the observation tasks.

Further, the effectiveness of the method is verified through the following experiments, 5 satellites and 500 targets are generated through STK (satellite toolkit) software, the 5 satellites and 500 targets are respectively processed through the multi-satellite collaborative planning method and the conventional genetic algorithm of the present disclosure, the optimal planning scheme based on the multi-satellite collaborative planning method and the conventional genetic algorithm is respectively obtained, the obtained optimal planning scheme is compared and analyzed, and in order to reduce errors and better verify the data effectiveness, all data are obtained by averaging after the algorithm is run for 10 times, as shown in Table 2. Therefore, the multi-satellite collaborative planning method provided by the disclosure can shorten the calculation time, improve the planning benefit, and the execution efficiency is obviously superior to that of a general genetic algorithm.

Problem size	Using an algorithm	Imaging gain	Scheduling imaging tasks	Calculating time consumption s
					5 satellites	The method of the disclosure	2242.2	334.5	11
500 targets	Genetic algorithm	2034.9	312.1	67.3

TABLE 2

According to the embodiment of the disclosure, the following technical effects are achieved:

the task allocation model is constructed based on the seq2seq neural network, the task set is processed to allocate satellite loads, the satellite loads which can be called in the marine target observation task set can be accurately predicted at low calculation cost, the search calculation amount of a genetic algorithm is effectively avoided, a feasible solution with high benefit can be obtained in a short time, and compared with the traditional evolutionary algorithm, the scheme can complete the task planning in a short time and realize the consideration of the benefit and the calculation efficiency. Meanwhile, the potential relation between the current task and the front-driving task set and/or the rear-driving task set is modeled through the Attention mechanism, so that the moving object can be effectively and accurately monitored, namely, the moving object can be decomposed into a plurality of static targets for monitoring.

It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.

The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.

Fig. 5 shows a block diagram of a multi-star collaborative planning apparatus 500 according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus 500 includes:

an obtaining module 510, configured to receive an offshore monitoring task and decompose the offshore monitoring task into a plurality of observation tasks;

a sorting module 520, configured to sort the multiple observation tasks to obtain an offshore target observation task set; the marine target observation task set comprises a plurality of observation tasks and task characteristics corresponding to each observation task;

a conversion module 530, configured to convert the marine target observation task set into an observation task sequence set according to the task characteristics; the observation task sequence corresponds to the task characteristics of the observation task;

and the matching module 540 is configured to input the observation task sequence set into a pre-trained multi-satellite cooperative task allocation model, obtain a satellite load corresponding to each observation task sequence in the observation task sequence set, and output an optimal planning scheme.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.

FIG. 6 illustrates a schematic block diagram of an electronic device 600 that may be used to implement embodiments of the present disclosure. As shown, device 600 includes a Central Processing Unit (CPU) 601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the device 600 can also be stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processing unit 601 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU 601, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, CPU 601 may be configured to perform method 100 by any other suitable means (e.g., by way of firmware).

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.