Rapid task scheduling method and device under satellite network fault

Abstract

The invention provides a rapid task scheduling method and device under a satellite network fault, and relates to the technical field of satellite communication.The method is applied to a global observation satellite in a satellite computing network, the global observation satellite obtains global information in the network, and when it is determined that a fault computing satellite exists, the global information in the network is obtained. Task information of all uncompleted tasks on the target fault calculation satellite is acquired; generating an initial population by using a genetic algorithm, wherein each individual in the population represents a combined unloading strategy; and performing iterative updating on the initial population according to the global information, the task information, a genetic algorithm and a particle swarm algorithm until a preset number of iterations is reached, so as to take the joint unloading strategy corresponding to the global optimal individual as a task scheduling strategy of the target fault calculation satellite. An execution main body of the method is a global observation satellite applying a genetic algorithm and a particle swarm algorithm, and compared with a reinforcement learning algorithm, the method has the advantage that the requirement for the internal memory of the satellite is lower on the premise of guaranteeing rapid task scheduling under the satellite network fault.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite communications, and in particular to a method and device for rapid task scheduling under satellite network failure. Background Art

[0002] Satellite computing networks are usually composed of satellites distributed in different orbits. These satellites are equipped with computing devices and communication modules, which can process data and exchange information in space. However, satellite networks are affected by environmental conditions, operating systems, limited resources and other factors, and there is a risk of node failure. The impact of satellite network failures is extremely serious, so it is crucial to respond to satellite network failures in an agile manner and ensure network robustness.

[0003] In the existing technology, in order to deal with satellite node failures, intelligent algorithms such as reinforcement learning are deployed on satellite nodes. However, this deployment method requires satellite nodes to store a large amount of experience data on the interaction between the intelligent agent and the environment. As the learning process progresses, the amount of data continues to increase, which is a heavy burden for satellites with limited memory resources. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for rapid scheduling of tasks under satellite network failures, so as to reduce the memory requirements of the satellite while ensuring rapid scheduling of tasks under satellite network failures.

[0005] In a first aspect, the present invention provides a method for fast scheduling tasks under satellite network failure, which is applied to global observation satellites in a satellite computing network, including: obtaining global information in the satellite computing network; wherein the satellite computing network includes: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals; the global information includes: status information of the cloud platform, status information of the computing satellite, status information of the ground terminal, inter-satellite link information and satellite-to-ground link information; when it is determined that there is a faulty computing satellite in the satellite computing network, obtaining task information of all unfinished tasks on the target faulty computing satellite; wherein the target faulty computing satellite represents any satellite among all the faulty computing satellites; based on the task information of all unfinished tasks, using a genetic algorithm to initialize a population, An initial population is obtained; wherein each individual in the initial population represents a joint offloading strategy, and the joint offloading strategy represents a collection of task offloading strategies for all unfinished tasks; the initial population is iteratively updated based on global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint offloading strategy corresponding to the global optimal individual as the task scheduling strategy for the target fault computing satellite; wherein, in the process of updating each generation of population, the particle swarm algorithm is used to optimize the initial offspring population generated by the genetic algorithm, so as to construct the next generation population using the optimized offspring population and individuals with the highest fitness function values ​​in descending order in the initial offspring population; the fitness function value of an individual is negatively correlated with the task processing delay of its corresponding joint offloading strategy.

[0006] Optionally, generating an initial offspring population based on a genetic algorithm includes: step S201, selecting two parent individuals from the current population using a roulette algorithm; step S202, performing a crossover operation on the two parent individuals to obtain two crossover parent individuals; step S203, mutating the two crossover parent individuals according to an adaptive mutation probability to obtain two initial offspring individuals corresponding to the two parent individuals; repeating steps S201 to S203 until the number of initial offspring individuals is greater than or equal to the number of individuals in the initial population, and taking the set of all initial offspring individuals as the initial offspring population.

[0007] Optionally, a crossover operation is performed on two parent individuals, including: randomly generating a starting crossover point and an ending crossover point of the gene segments to be crossed; exchanging the gene segments of the two parent individuals located between the starting crossover point and the ending crossover point to obtain two parent individuals after the crossover; wherein the gene segments represent multiple continuous gene loci, and each gene locus represents a task unloading strategy for an unfinished task in the joint unloading strategy.

[0008] Optionally, the two parent individuals after crossover are mutated according to the adaptive mutation probability to obtain two initial offspring individuals corresponding to the two parent individuals, including: using the formula Calculate the probability of mutation of the parent individuals in the current population and obtain the probability of adaptive mutation; where, represents the preset initial mutation probability, represents the preset final mutation probability, represents the current iteration number of the population, Indicates the preset number of iterations; for the target parent individual, a random number between 0 and 1 is randomly generated; wherein the target parent individual represents any individual of the two parent individuals after crossover; if the random number is less than the adaptive mutation probability, at least one gene locus of the target parent individual is mutated to obtain the initial offspring individual corresponding to the target parent individual; if the random number is greater than or equal to the adaptive mutation probability, the target parent individual is used as its corresponding initial offspring individual.

[0009] Optionally, a particle swarm algorithm is used to optimize the initial offspring population generated based on the genetic algorithm, including: mapping each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm to obtain a first particle population; updating the position and velocity of all particles in the first particle population to obtain a second particle population; mapping each particle in the second particle population to an individual in the genetic algorithm to obtain an optimized offspring population.

[0010] Optionally, the task offloading strategy for each unfinished task includes one of the following: offloading to a cloud platform, offloading to a healthy computing satellite, offloading to a ground terminal, and offloading to a cloud platform via a healthy computing satellite; mapping each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm, including: matching a corresponding probability distribution interval for each joint offloading strategy based on the total number of unfinished tasks on the target faulty computing satellite and the optional number of task offloading strategies; determining the probability distribution interval corresponding to the joint offloading strategy corresponding to the target initial offspring individual, and obtaining a target probability distribution interval; wherein the target initial offspring individual represents any individual in the initial offspring population; taking the median of the target probability distribution interval as the position of the particle corresponding to the target initial offspring individual; and the particle's velocity is obtained by random initialization.

[0011] Optionally, use the formula Calculate the fitness function value of the individual; where, represents the transmission delay of the i-th unfinished task in the joint offloading strategy, represents the computation delay of the i-th unfinished task in the joint offloading strategy, Indicates the total number of unfinished tasks on the target failure calculation satellite.

[0012] In a second aspect, the present invention provides a task rapid scheduling device under satellite network failure, which is applied to global observation satellites in a satellite computing network, including: a first acquisition module, used to acquire global information in the satellite computing network; wherein the satellite computing network includes: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals; the global information includes: status information of the cloud platform, status information of the computing satellite, status information of the ground terminal, inter-satellite link information and satellite-to-ground link information; a second acquisition module, used to acquire task information of all unfinished tasks on the target faulty computing satellite when it is determined that there is a faulty computing satellite in the satellite computing network; wherein the target faulty computing satellite represents any satellite among all the faulty computing satellites; an initialization module, used to use genetic algorithm to obtain task information of all unfinished tasks based on the task information of all unfinished tasks The algorithm initializes the population to obtain the initial population; wherein each individual in the initial population represents a joint unloading strategy, and the joint unloading strategy represents the set of task unloading strategies for all unfinished tasks; an update and determination module is used to iteratively update the initial population based on global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy for the target fault calculation satellite; wherein, in the process of updating each generation of population, the particle swarm algorithm is used to optimize the initial child population generated by the genetic algorithm, so as to construct the next generation population using the optimized child population and the individuals with the highest fitness function values ​​in descending order in the initial child population; the fitness function value of the individual is negatively correlated with the task processing delay of the corresponding joint unloading strategy.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the method for rapid task scheduling under satellite network failure described in any of the aforementioned embodiments.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for rapid task scheduling under a satellite network failure described in any one of the aforementioned embodiments is implemented.

[0015] The present invention provides a method for rapid task scheduling under satellite network failure. The method proposes a satellite computing network, including: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals. The method is applied to the global observation satellite in the satellite computing network. The global observation satellite obtains global information in the satellite computing network, and when it is determined that there is a faulty computing satellite in the network, it obtains the task information of all unfinished tasks on the target faulty computing satellite, and generates an initial population using a genetic algorithm. Each individual in the population represents a joint unloading strategy. Then, the initial population is iteratively updated according to the global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy of the target faulty computing satellite. The execution subject of the method is the global observation satellite, and the genetic algorithm and particle swarm algorithm are used to solve the task scheduling problem. Compared with the reinforcement learning algorithm, the method has lower memory requirements for the satellite under the premise of ensuring rapid task scheduling under satellite network failure, and is more suitable for the situation where satellite resources are limited. In addition, the combined application of the genetic algorithm and the particle swarm algorithm can effectively improve the stability of algorithm convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a method for rapid task scheduling under satellite network failure provided by an embodiment of the present invention; Figure 2 A satellite-to-ground network task scheduling architecture diagram in a satellite computing network provided by an embodiment of the present invention; Figure 3 A system architecture diagram of a satellite network fault detection and recovery mechanism implemented in an embodiment of the present invention; Figure 4 A performance comparison result diagram of the improved adaptive probability genetic algorithm of the embodiment of the present invention and the traditional genetic algorithm; Figure 5 A functional module diagram of a device for rapid task scheduling under satellite network failure provided by an embodiment of the present invention; Figure 6 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] 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 of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0021] Embodiment 1 Computation offloading is a technology that transfers computing tasks from local devices to other devices or servers with stronger computing power for processing. Computation offloading after satellite network failure is an effective strategy to deal with satellite network failures, aiming to ensure business continuity and service quality by transferring computing tasks from failed satellite network devices to other available computing resources.

[0022] Aiming at the problem of fast task scheduling under satellite network failure, the embodiment of the present invention provides a method for fast task scheduling under satellite network failure, which is applied to global observation satellites in a satellite computing network, such as Figure 1 As shown, the method specifically comprises the following steps: Step S102, obtaining global information in the satellite computing network.

[0023] Figure 2 A satellite-to-ground network task scheduling architecture diagram in a satellite computing network provided by an embodiment of the present invention is as follows: Figure 2 As shown, the satellite computing network includes: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals; the global observation satellite is used to observe the global information in the network, wherein the global information includes: the status information of the cloud platform, the status information of the computing satellite, the status information of the ground terminal, the inter-satellite link information and the satellite-to-ground link information.

[0024] In the embodiment of the present invention, the status information of the cloud platform includes: the computing resources of the cloud platform (that is, the calculation frequency); the status information of the calculation satellite includes: idle computing resources and node working status. The status information of the calculation satellite j can be expressed as: ,in, represents the idle computing resources of satellite node j, represents the working status of satellite node j, =0 indicates that satellite node j is faulty, that is, satellite node j is a faulty computing satellite; Indicates that satellite node j is working normally, that is, satellite node j is a healthy computing satellite. The status information of the ground terminal includes: the computing resources of the ground terminal The inter-satellite link information indicates the data transmission rate of the link between satellite nodes in the satellite computing network, and the satellite-to-ground link information indicates the data transmission rate of the link between the satellite node and the ground terminal in the satellite computing network.

[0025] Figure 3 FIG. 1 is a system architecture diagram of a satellite network fault detection and recovery mechanism implemented in the method of an embodiment of the present invention, such as Figure 3 As shown, the global observation satellite includes a global observation module, a scheduling decision module, and the computing satellite includes a fault prediction module and a task calculation module. The global observation satellite will periodically and continuously detect the status of satellite nodes, collect and update global network information. At the same time, the computing satellite can perform fault prediction and actively send fault information to the global observation satellite. That is, the embodiment of the present invention actually applies a hybrid mechanism of global observation and active reporting, which provides a guarantee for rapid judgment of satellite faults and rapid scheduling of tasks.

[0026] When it is determined that there is a faulty computing satellite, the global observation satellite will make a task scheduling decision for its unfinished tasks and send the scheduling instructions to the computing satellite. The computing satellite can unload the task to different locations for processing according to the scheduling instructions, thereby ensuring the reliable execution of the task. Compared with the method of making scheduling decisions through the ground control center, the method in the embodiment of the present invention executed by the global observation satellite can reduce the instruction transmission delay and improve the response speed. The specific implementation plan of the scheduling decision module is introduced in detail below.

[0027] Step S104, when it is determined that there is a faulty computing satellite in the satellite computing network, task information of all unfinished tasks on the target faulty computing satellite is obtained.

[0028] The target fault calculation satellite refers to any satellite among all fault calculation satellites.

[0029] Specifically, if the global observation satellite determines that there is a faulty computing satellite in the network, it is necessary to further obtain the task information of all unfinished tasks on each faulty computing satellite, where the i-th unfinished task on the target faulty computing satellite The task information can be expressed as: , express The data size, Indicates execution The number of CPU cycles required.

[0030] Step S106, based on the task information of all unfinished tasks, a population is initialized using a genetic algorithm to obtain an initial population.

[0031] In order to solve the problem of fast scheduling of unfinished computing tasks of faulty computing satellites in satellite network failures, an embodiment of the present invention proposes an adaptive probability genetic algorithm based on particle swarm optimization and improvement to jointly optimize multi-task offloading decisions. First, the global observation satellite generates an initial population using a genetic algorithm, wherein each individual in the initial population represents a joint offloading strategy, and the joint offloading strategy represents a set of task offloading strategies for all unfinished tasks. That is, the individual represents a joint offloading strategy for multiple tasks. In the embodiment of the present invention, the task offloading strategies include the following four: offloading to the cloud platform, offloading to a healthy computing satellite, offloading to a ground terminal, and offloading to the cloud platform via a healthy computing satellite.

[0032] For example, if there are M unfinished tasks on the target fault computing satellite, and each unfinished task has 4 task offloading strategies, then the target fault computing satellite has Therefore, the Y individuals in the initial population are The purpose of the embodiment of the present invention is to randomly select Y joint unloading strategies from feasible joint unloading strategies. The optimal joint offloading strategy is explored from the feasible joint offloading strategies.

[0033] Step S108, iteratively updating the initial population based on global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy for the target fault computing satellite.

[0034] After obtaining the initial population, if only the genetic algorithm is used, then based on its ability to search globally in the solution space, the global optimal individual can be determined by iteratively updating the population. However, the embodiment of the present invention improves the genetic algorithm based on the particle swarm algorithm, which has efficient local exploration capabilities and can improve the overall performance of the algorithm, thereby further enhancing the robustness and adaptability of the method proposed in the embodiment of the present invention and accelerating the convergence speed.

[0035] Specifically, during the updating process of each generation of population, the particle swarm algorithm is used to optimize the initial offspring population generated by the genetic algorithm, so as to construct the next generation population using the optimized offspring population and the individuals with the highest fitness function values ​​in the initial offspring population in descending order. In other words, after the current population is updated by the genetic algorithm to obtain the initial offspring population, it is not directly used as the next generation population, but it is necessary to further optimize the offspring population using the particle swarm algorithm to obtain the optimized offspring population, and then merge the optimized offspring population with the initial offspring population, and calculate the fitness function value of each individual in the merged population, and then select relatively excellent individuals from the merged population according to the fitness function value to construct the next generation population.

[0036] In the embodiment of the present invention, the fitness function value of an individual is negatively correlated with the task processing delay of the corresponding joint unloading strategy. That is, the greater the task processing delay of the joint unloading strategy, the smaller the fitness function value of the corresponding individual; conversely, the smaller the task processing delay of the joint unloading strategy, the larger the fitness function value of the corresponding individual. Therefore, if there are X offspring individuals in the merged population, the fitness function values ​​of these X offspring individuals are sorted in descending order, and the first Y offspring individuals are selected as individuals in the next generation population, where Y represents the number of individuals in the initial population.

[0037] The embodiment of the present invention provides a method for rapid task scheduling under satellite network failure. The method proposes a satellite computing network, including: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals. The method is applied to the global observation satellite in the satellite computing network. The global observation satellite obtains global information in the satellite computing network, and when it is determined that there is a faulty computing satellite in the network, it obtains the task information of all unfinished tasks on the target faulty computing satellite, and generates an initial population using a genetic algorithm. Each individual in the population represents a joint unloading strategy. Then, the initial population is iteratively updated according to the global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy of the target faulty computing satellite. The execution subject of the method is the global observation satellite, and the genetic algorithm and particle swarm algorithm are used to solve the task scheduling problem. Compared with the reinforcement learning algorithm, the method has lower memory requirements for the satellite under the premise of ensuring rapid task scheduling under satellite network failure, and is more suitable for the situation where satellite resources are limited. In addition, the combined application of the genetic algorithm and the particle swarm algorithm can effectively improve the stability of algorithm convergence.

[0038] In an optional implementation, generating an initial offspring population based on a genetic algorithm specifically includes the following steps: Step S201, using the roulette algorithm to select two parent individuals from the current population.

[0039] Specifically, first calculate the total fitness of the current population , set the fitness function value of the y-th parent individual in the current population to be expressed as , then the total fitness of the current population is: , where Y represents the population size, that is, the total number of individuals in the population.

[0040] Next in Generate a random number pick within the value range of Represents the cumulative fitness of the first k parent individuals in the current population, if the random number pick satisfies the following formula: , then select the kth parent individual. Referring to the above method, generate two random numbers and determine their cumulative fitness range to select two parent individuals from the current population.

[0041] Step S202, performing a crossover operation on two parent individuals to obtain two crossover parent individuals.

[0042] In order to increase the diversity of the population and promote global search capabilities, after selecting two parent individuals, a crossover operation is performed on the two parent individuals to obtain two crossover parent individuals. The embodiment of the present invention does not specifically limit the method of the crossover operation, and the user can select according to actual needs, for example, single-point crossover, multi-point crossover, uniform crossover, etc.

[0043] Step S203, mutating the two parent individuals after the crossover according to the adaptive mutation probability, to obtain two initial child individuals corresponding to the two parent individuals.

[0044] In order to improve species diversity and further enhance the global retrieval capability of the algorithm, after obtaining two crossover parent individuals, the embodiment of the present invention mutates the two crossover parent individuals based on the adaptive mutation probability, thereby obtaining two initial offspring individuals. The adaptive mutation probability changes with the number of population iterations. The more population iterations, the smaller the adaptive mutation probability, thereby accelerating the convergence speed of the algorithm.

[0045] Steps S201 to S203 are repeatedly executed until the number of initial offspring individuals is greater than or equal to the number of individuals in the initial population, and the set of all initial offspring individuals is taken as the initial offspring population.

[0046] Two initial offspring individuals can be generated by executing one round of steps S201 to S203. In order to obtain all initial offspring individuals, the above steps need to be repeated. If the number Y of individuals in the initial population is an even number, then the number of repetitions is Y / 2; if the number Y of individuals in the initial population is an odd number, then the number of repetitions is (Y+1) / 2. Accordingly, the number of individuals in all offspring populations can be maintained by randomly deleting an initial offspring individual or by other means. The population size can also be changed to Y+1. This change in population size does not affect the performance of the algorithm.

[0047] In an optional implementation manner, the above step S202, performing a crossover operation on two parent individuals, specifically includes the following steps: Step S2021, randomly generate the starting intersection point and the ending intersection point of the gene segment to be crossed.

[0048] Step S2022, exchanging the gene segments between the starting intersection and the ending intersection of the two parent individuals to obtain two parent individuals after crossing; wherein the gene segments represent a plurality of continuous gene loci, and each gene locus represents a task offloading strategy for an unfinished task in the joint offloading strategy.

[0049] Based on the above description, it can be seen that the embodiment of the present invention adopts a multi-point crossover method to perform a crossover operation on two parent individuals. By randomly selecting two crossover points and exchanging the gene fragments of the parent individuals in segments, it can better maintain gene diversity and avoid falling into a local optimum too early.

[0050] For ease of understanding, the following example illustrates that if the parent individuals include a total of M gene loci, and the randomly generated starting and ending crossover points are 3 and 7 respectively, then performing a multi-point crossover on the two parent individuals is equivalent to exchanging multiple consecutive gene loci of the two parent individuals: 3, 4, 5, 6, and 7 correspond to gene fragments.

[0051] In an optional implementation manner, the above step S203, mutating the two parent individuals after crossover according to the adaptive mutation probability to obtain two initial offspring individuals corresponding to the two parent individuals, specifically includes the following steps: Step S2031, using the formula Calculate the probability of mutation of the parent individuals in the current population and obtain the probability of adaptive mutation; where, represents the preset initial mutation probability, represents the preset final mutation probability, represents the current iteration number of the population, Indicates the preset number of iterations.

[0052] Step S2032, randomly generate a random number between 0 and 1 for the target parent individual; wherein the target parent individual represents any individual of the two parent individuals after crossover.

[0053] Step S2033: if the random number is less than the adaptive mutation probability, mutate at least one gene locus of the target parent individual to obtain an initial offspring individual corresponding to the target parent individual.

[0054] Step S2034: If the random number is greater than or equal to the adaptive mutation probability, the target parent individual is used as its corresponding initial child individual.

[0055] Specifically, the embodiment of the present invention adopts an adaptive mutation probability function. It can be seen from the expression of the adaptive mutation probability that the mutation probability can be dynamically adjusted according to the current number of iterations of the population. This adaptive mutation probability mechanism can mutate individuals based on a larger probability in the early stage of the algorithm to improve species diversity, and reduce the individual mutation probability in the later stage, thereby accelerating the convergence speed.

[0056] In an optional implementation, the initial offspring population generated based on the genetic algorithm is optimized using a particle swarm algorithm, specifically comprising the following steps: Step S301, mapping each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm to obtain a first particle swarm.

[0057] Step S302, updating the positions and velocities of all particles in the first particle group to obtain a second particle group.

[0058] Step S303, mapping each particle in the second particle group to an individual in the genetic algorithm to obtain an optimized offspring population.

[0059] Specifically, the embodiment of the present invention introduces a particle swarm algorithm to conduct detailed exploration of the initial offspring population, and through the movement of particles in the solution space and information sharing, it can quickly converge to the vicinity of the local optimal solution. Specifically, for each initial offspring population, a particle swarm method is used to conduct local exploration. First, each initial offspring individual in the population needs to be mapped to a particle in the particle swarm algorithm. The embodiment of the present invention does not specifically limit the mapping method, and users can choose according to actual needs.

[0060] After obtaining the first particle swarm, the velocity update formula and position update formula of the particles in the prior art particle swarm algorithm are used to update the position and velocity of each particle to obtain the second particle swarm. Finally, the method of mapping the initial offspring population to the "opposite" of the first particle population is used to map all particles in the second particle population to the genetic algorithm to obtain the optimized offspring population.

[0061] In an optional implementation, the task offloading strategy for each unfinished task includes one of the following: offloading to a cloud platform, offloading to a healthy computing satellite, offloading to a ground terminal, or offloading to a cloud platform via a healthy computing satellite; the above step S301, mapping each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm, specifically includes the following steps: Step S3011, based on the target failure, the total number of unfinished tasks on the satellite and the optional number of task offloading strategies are calculated, and a corresponding probability distribution interval is matched for each joint offloading strategy.

[0062] Step S3012, determining the probability distribution interval corresponding to the joint unloading strategy corresponding to the target initial offspring individual, and obtaining the target probability distribution interval; wherein the target initial offspring individual represents any individual in the initial offspring population.

[0063] Step S3013, taking the median of the target probability distribution interval as the position of the particle corresponding to the target initial offspring individual; the speed of the particle is obtained by random initialization.

[0064] For ease of understanding, refer to the example above. Assume that there are M unfinished tasks on the target fault calculation satellite, and each unfinished task has 4 task offloading strategies. Then the target fault calculation satellite has Based on this, we can determine Probability distribution intervals: , ,… Then, each joint unloading strategy is matched and bound with each probability distribution interval.

[0065] If the probability distribution interval of the joint unloading strategy corresponding to the target initial offspring individual is , then the median of the interval That is, the position of the particle corresponding to the target initial offspring individual. The velocity of the particle can be expressed as a vector, and the components of the vector represent the adjustment direction and amplitude of the corresponding task offloading strategy.

[0066] Correspondingly, after obtaining the second particle group, the position values ​​of the particles in the second particle group can be matched to the corresponding probability distribution interval, and then according to the matching relationship between the joint unloading strategy determined above and the probability distribution interval, it can be mapped to the genetic algorithm to obtain the corresponding optimized offspring individuals.

[0067] In an optional implementation, using the formula Calculate the fitness function value of the individual; where, represents the transmission delay of the i-th unfinished task in the joint offloading strategy, represents the computation delay of the i-th unfinished task in the joint offloading strategy, Indicates the total number of unfinished tasks on the target failure calculation satellite.

[0068] In the embodiment of the present invention, the optimization goal of the joint offloading strategy is to minimize the processing delay (transmission delay + calculation delay) of unfinished tasks. Therefore, the fitness function value is negatively correlated with the task processing delay of the joint offloading strategy. In view of the complex state of the satellite network, the processing time of the task may vary greatly. In order to suppress the influence of large values ​​on the fitness function value, the embodiment of the present invention uses logarithmic transformation for the processing delay of all unfinished tasks. The logarithmic transformation can compress the value, making the fitness function value more balanced, thereby more fairly evaluating various task allocation schemes.

[0069] The following is a detailed introduction to the transmission delay and computational delay of each task offloading strategy for the i-th unfinished task.

[0070] 1. Unload to the healthy computing satellite: , ,in, represents satellite j and satellite The data transmission rate of the intersatellite link is Indicates satellite The computation frequency assigned to task i.

[0071] 2. Unloading to the ground terminal: , ,in, represents the data transmission rate of the satellite-to-ground link between satellite j and ground terminal i, represents the computing frequency assigned by the ground terminal to task i.

[0072] 3. Uninstall to the cloud platform: , ,in, represents the data transmission rate of the link between satellite j and the cloud platform, Represents the computing frequency assigned by the cloud platform to task i.

[0073] 4. Offload to the cloud platform via the healthy computing satellite: , ,in, Indicates satellite The data transmission rate of the link between the satellite j and the cloud platform, that is, this strategy considers two transmission delays, namely, from satellite j to satellite Transmission delay and satellite Transmission latency to the cloud platform.

[0074] In summary, the embodiment of the present invention discloses a rapid task scheduling mechanism for satellite network failures, which provides an effective solution for reliable task processing in satellite node failure scenarios. In the present invention, global observation satellites are used to obtain the status information of each satellite node and the multi-dimensional information of the network. The computing satellite will also actively send the fault information it detects, which helps the global observation satellite to grasp the fault situation in time and avoid scheduling delays due to untimely information acquisition. This hybrid mechanism of global observation and active reporting can speed up the fault response speed, reduce the impact of faults on task execution, and provide guarantee for rapid scheduling of network tasks. A high-speed and reliable communication link is established between the global observation satellite and each satellite node, which can transmit network fault information and unloading instructions to nodes in the network. Based on rich data experience, the global satellite can quickly make task scheduling decisions, reasonably allocate network resources, and ensure that tasks are quickly and effectively transmitted to other nodes for execution.

[0075] The embodiment of the present invention adopts an improved adaptive genetic algorithm, which can effectively jointly optimize multi-task offloading decisions. The genetic algorithm can perform a global search in the search space. It starts the search from a population, which enables it to explore multiple areas at the same time, increasing the chance of finding the global optimal solution and speeding up the search. It is very suitable for the scenario of rapid switching of satellite fault network tasks. The improved adaptive genetic algorithm introduces a multi-point crossover mechanism, which helps to maintain the diversity of the population. Through the adaptive mutation probability mechanism, a high mutation probability is maintained in the early stage of algorithm exploration to avoid reaching the local optimum too early. The mutation probability is reduced in the later stage of exploration, and the algorithm convergence speed is accelerated, thereby effectively improving the performance of the algorithm. At the same time, the particle swarm optimization improvement mechanism is introduced to increase the local exploration capability.

[0076] Figure 4 This is a performance comparison result diagram of the improved adaptive probability genetic algorithm of the embodiment of the present invention and the traditional genetic algorithm. Figure 4 It can be seen that the algorithm adopted by the present invention has more stable convergence and higher fitness than the traditional genetic algorithm, and thus has better performance.

[0077] Embodiment 2 An embodiment of the present invention also provides a device for rapid task scheduling under satellite network failures. The device is applied to a global observation satellite in a satellite computing network and is mainly used to execute the method for rapid task scheduling under satellite network failures provided in the above-mentioned embodiment 1. The following is a specific introduction to the device for rapid task scheduling under satellite network failures provided in an embodiment of the present invention.

[0078] Figure 5 is a functional module diagram of a task rapid scheduling device under satellite network failure provided by an embodiment of the present invention, such as Figure 5As shown, the device mainly includes: a first acquisition module 10, a second acquisition module 20, an initialization module 30, and an update and determination module 40, wherein: The first acquisition module 10 is used to acquire global information in the satellite computing network; wherein the satellite computing network includes: global observation satellites, a cloud platform, multiple computing satellites and multiple ground terminals; the global information includes: status information of the cloud platform, status information of the computing satellite, status information of the ground terminal, inter-satellite link information and satellite-to-ground link information.

[0079] The second acquisition module 20 is used to acquire task information of all unfinished tasks on a target faulty computing satellite when it is determined that there is a faulty computing satellite in the satellite computing network; wherein the target faulty computing satellite represents any satellite among all the faulty computing satellites.

[0080] The initialization module 30 is used to initialize the population using a genetic algorithm based on the task information of all unfinished tasks to obtain an initial population; wherein each individual in the initial population represents a joint unloading strategy, and the joint unloading strategy represents a collection of task unloading strategies for all unfinished tasks.

[0081] The updating and determining module 40 is used to iteratively update the initial population based on global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy of the target fault calculation satellite; wherein, during each generation of population update, the particle swarm algorithm is used to optimize the initial offspring population generated based on the genetic algorithm, so as to construct the next generation population using the optimized offspring population and the individuals with the highest fitness function values ​​in descending order in the initial offspring population; the fitness function value of the individual is negatively correlated with the task processing delay of the corresponding joint unloading strategy.

[0082] The embodiment of the present invention provides a device for rapid task scheduling under satellite network failure. The device proposes a satellite computing network, including: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals. The device is applied to the global observation satellite in the satellite computing network. The global observation satellite obtains global information in the satellite computing network, and when it is determined that there is a faulty computing satellite in the network, it obtains the task information of all unfinished tasks on the target faulty computing satellite, and uses a genetic algorithm to generate an initial population. Each individual in the population represents a joint unloading strategy. Then, the initial population is iteratively updated according to the global information, task information, genetic algorithm and particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy of the target faulty computing satellite. The execution subject of the device is the global observation satellite, and the genetic algorithm and particle swarm algorithm are used to solve the task scheduling problem. Compared with the reinforcement learning algorithm, the device has lower memory requirements for the satellite under the premise of ensuring rapid task scheduling under satellite network failure, and is more suitable for the situation where satellite resources are limited. In addition, the combined application of the genetic algorithm and the particle swarm algorithm can effectively improve the stability of algorithm convergence.

[0083] Optionally, the device further comprises: a generation module, configured to generate an initial offspring population based on a genetic algorithm, the generation module comprising: The selection unit is used to select two parent individuals from the current population using a roulette algorithm.

[0084] The crossover unit is used to perform a crossover operation on two parent individuals to obtain two parent individuals after crossover.

[0085] The mutation unit is used to mutate the two parent individuals after the crossover according to the adaptive mutation probability, so as to obtain two initial offspring individuals corresponding to the two parent individuals.

[0086] The repeated calling unit is used to repeatedly call the selection unit, the crossover unit and the mutation unit until the number of initial offspring individuals is greater than or equal to the number of individuals in the initial population, and the set of all initial offspring individuals is used as the initial offspring population.

[0087] Optionally, the crossover unit is specifically used for: The starting intersection point and the ending intersection point of the gene fragment to be crossed are randomly generated.

[0088] The gene segments of two parent individuals located between the starting intersection and the ending intersection are exchanged to obtain two parent individuals after crossover; wherein the gene segments represent multiple continuous gene loci, and each gene locus represents a task unloading strategy for an unfinished task in the joint unloading strategy.

[0089] Optionally, the mutation unit is specifically used for: Using the formula Calculate the probability of mutation of the parent individuals in the current population and obtain the probability of adaptive mutation; where, represents the preset initial mutation probability, represents the preset final mutation probability, represents the current iteration number of the population, Indicates the preset number of iterations.

[0090] For the target parent individual, a random number between 0 and 1 is randomly generated; wherein the target parent individual represents any individual of the two parent individuals after crossover.

[0091] If the random number is less than the adaptive mutation probability, at least one gene locus of the target parent individual is mutated to obtain the initial offspring individual corresponding to the target parent individual.

[0092] If the random number is greater than or equal to the adaptive mutation probability, the target parent individual is used as its corresponding initial offspring individual.

[0093] Optionally, the device further includes: an optimization module, which is used to optimize the initial offspring population generated based on the genetic algorithm using a particle swarm algorithm, and the optimization module includes: The first mapping unit is used to map each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm to obtain a first particle swarm.

[0094] The updating unit is used to update the positions and velocities of all particles in the first particle group to obtain a second particle group.

[0095] The second mapping unit is used to map each particle in the second particle group to an individual in the genetic algorithm to obtain an optimized offspring population.

[0096] Optionally, the task offloading strategy for each unfinished task includes one of the following: offloading to a cloud platform, offloading to a healthy computing satellite, offloading to a ground terminal, and offloading to a cloud platform via a healthy computing satellite; the first mapping unit is specifically used to: The total number of unfinished tasks on the satellite and the optional number of task offloading strategies are calculated based on the target failure, and the corresponding probability distribution interval is matched for each joint offloading strategy.

[0097] Determine the probability distribution interval corresponding to the joint unloading strategy corresponding to the target initial offspring individual, and obtain the target probability distribution interval; wherein the target initial offspring individual represents any individual in the initial offspring population.

[0098] The median of the target probability distribution interval is used as the position of the particle corresponding to the target initial offspring individual; the speed of the particle is obtained by random initialization.

[0099] Optionally, using the formula Calculate the fitness function value of the individual; where, represents the transmission delay of the i-th unfinished task in the joint offloading strategy, represents the computation delay of the i-th unfinished task in the joint offloading strategy, Indicates the total number of unfinished tasks on the target failure calculation satellite.

[0100] Embodiment 3 See also Figure 6 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0101] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0102] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0103] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0104] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0105] A computer program product of a method and apparatus for rapid scheduling of tasks under satellite network failures provided in an embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor, wherein the instructions included in the program code can be used to execute the method described in the previous method embodiment. For specific implementation, please refer to the method embodiment, which will not be described in detail here.

[0106] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0107] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0108] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0109] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0110] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0111] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0112] 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 replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapid task scheduling under satellite network failure, characterized in that: Global observation satellites used in satellite computing networks include: Acquire global information in the satellite computing network; wherein the satellite computing network includes: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals; the global information includes: status information of the cloud platform, status information of the computing satellite, status information of the ground terminal, inter-satellite link information and satellite-to-ground link information; When it is determined that there is a faulty computing satellite in the satellite computing network, task information of all unfinished tasks on the target faulty computing satellite is obtained; wherein the target faulty computing satellite represents any satellite among all the faulty computing satellites; Based on the task information of all the unfinished tasks, a population is initialized using a genetic algorithm to obtain an initial population; wherein each individual in the initial population represents a joint unloading strategy, and the joint unloading strategy represents a set of task unloading strategies of all the unfinished tasks; The initial population is iteratively updated based on the global information, the task information, the genetic algorithm and the particle swarm algorithm until a preset number of iterations is reached, so that the joint unloading strategy corresponding to the global optimal individual is used as the task scheduling strategy of the target fault computing satellite; wherein, during each generation of population update, the particle swarm algorithm is used to optimize the initial offspring population generated based on the genetic algorithm, so as to construct the next generation population using the optimized offspring population and individuals with the highest fitness function values ​​in descending order in the initial offspring population; the fitness function value of an individual is negatively correlated with the task processing delay of its corresponding joint unloading strategy.

2. The method for rapid task scheduling under satellite network failure according to claim 1, characterized in that: Generate the initial offspring population based on the genetic algorithm, including: Step S201, using a roulette algorithm to select two parent individuals from the current population; Step S202, performing a crossover operation on the two parent individuals to obtain two crossover parent individuals; Step S203, mutating the two parent individuals after the crossover according to the adaptive mutation probability, to obtain two initial offspring individuals corresponding to the two parent individuals; Repeat steps S201 to S203 until the number of the initial offspring individuals is greater than or equal to the number of individuals in the initial population, and take the set of all the initial offspring individuals as the initial offspring population.

3. The method for rapid task scheduling under satellite network failure according to claim 2, characterized in that: Performing a crossover operation on the two parent individuals includes: Randomly generate the starting intersection point and the ending intersection point of the gene fragment to be crossed; The gene fragments of the two parent individuals located between the starting intersection and the ending intersection are exchanged to obtain two parent individuals after crossing; wherein the gene fragments represent a plurality of continuous gene loci, and each gene locus represents a task unloading strategy for an unfinished task in the joint unloading strategy.

4. The method for rapid task scheduling under satellite network failure according to claim 2, characterized in that: Mutating the two parent individuals after the crossover according to the adaptive mutation probability to obtain two initial offspring individuals corresponding to the two parent individuals, including: Using the formula The probability of mutation of the parent individuals in the current population is calculated to obtain the adaptive mutation probability; wherein, represents the preset initial mutation probability, represents the preset final mutation probability, represents the current iteration number of the population, represents the preset number of iterations; For the target parent individual, a random number between 0 and 1 is randomly generated; wherein the target parent individual represents any one of the two parent individuals after the crossover; If the random number is less than the adaptive mutation probability, mutating at least one gene locus of the target parent individual to obtain an initial offspring individual corresponding to the target parent individual; If the random number is greater than or equal to the adaptive mutation probability, the target parent individual is used as its corresponding initial offspring individual.

5. The method for rapid task scheduling under satellite network failure according to claim 1, characterized in that: The particle swarm algorithm is used to optimize the initial offspring population generated by the genetic algorithm, including: Mapping each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm to obtain a first particle swarm; Updating the positions and velocities of all particles in the first particle group to obtain a second particle group; Each particle in the second particle group is mapped to an individual in the genetic algorithm to obtain the optimized offspring population.

6. The method for rapid task scheduling under satellite network failure according to claim 5, characterized in that: The task offloading strategy of each of the unfinished tasks includes one of the following: offloading to the cloud platform, offloading to a healthy computing satellite, offloading to a ground terminal, and offloading to the cloud platform via a healthy computing satellite; Mapping each initial offspring individual in the initial offspring population to a particle in the particle swarm algorithm comprises: Calculate the total number of unfinished tasks on the satellite and the optional number of task offloading strategies based on the target fault, and match a corresponding probability distribution interval for each joint offloading strategy; Determine the probability distribution interval corresponding to the joint unloading strategy corresponding to the target initial offspring individual, and obtain the target probability distribution interval; wherein the target initial offspring individual represents any individual in the initial offspring population; The median of the target probability distribution interval is used as the position of the particle corresponding to the target initial offspring individual; the speed of the particle is obtained by random initialization.

7. The method for rapid task scheduling under satellite network failure according to claim 1, characterized in that: Using the formula Calculate the fitness function value of the individual; wherein, represents the transmission delay of the i-th unfinished task in the joint offloading strategy, represents the computation delay of the i-th unfinished task in the joint offloading strategy, Indicates the total number of unfinished tasks on the target fault computing satellite.

8. A task rapid scheduling device under satellite network failure, characterized in that: Global observation satellites used in satellite computing networks include: A first acquisition module is used to acquire global information in the satellite computing network; wherein the satellite computing network includes: a global observation satellite, a cloud platform, multiple computing satellites and multiple ground terminals; the global information includes: status information of the cloud platform, status information of the computing satellite, status information of the ground terminal, inter-satellite link information and satellite-to-ground link information; A second acquisition module is used to acquire task information of all unfinished tasks on a target faulty computing satellite when it is determined that there is a faulty computing satellite in the satellite computing network; wherein the target faulty computing satellite represents any satellite among all the faulty computing satellites; An initialization module, configured to initialize a population using a genetic algorithm based on the task information of all the unfinished tasks to obtain an initial population; wherein each individual in the initial population represents a joint unloading strategy, and the joint unloading strategy represents a collection of task unloading strategies of all the unfinished tasks; The updating and determining module is used to iteratively update the initial population based on the global information, the task information, the genetic algorithm and the particle swarm algorithm until a preset number of iterations is reached, so as to use the joint unloading strategy corresponding to the global optimal individual as the task scheduling strategy of the target fault calculation satellite; wherein, during each generation of population update, the particle swarm algorithm is used to optimize the initial offspring population generated based on the genetic algorithm, so as to construct the next generation population using the optimized offspring population and the individuals with the highest fitness function values ​​in descending order in the initial offspring population; the fitness function value of the individual is negatively correlated with the task processing delay of the corresponding joint unloading strategy.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method for rapid task scheduling under satellite network failure described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for rapid task scheduling under satellite network failure according to any one of claims 1 to 7 is implemented.

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