A satellite data transmission method and system

Abstract

The present invention provides a satellite data transmission method and system, which relates to the technical field of data transmission. The method includes: acquiring satellite data; constructing a preliminary scheduling model based on a logistic regression algorithm, inputting the satellite data into the preliminary scheduling model to predict the success probability of task scheduling, and performing a preliminary allocation of tasks; determining whether the tasks are successfully scheduled preliminarily, and directly executing the successfully scheduled tasks; for the tasks that are not successfully scheduled, input them into a fine scheduling model based on a particle swarm optimization algorithm; the fine scheduling model outputs an optimal scheduling scheme that comprehensively considers task priority, transmission time, and resource load balance by optimizing the objective function; according to the optimal satellite data transmission scheduling scheme, execute the tasks that are not successfully scheduled by the preliminary scheduling model, ensure the reasonable allocation of all tasks, thereby improving the resource utilization rate of satellites and ground stations and meeting the requirements of complex tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and particularly to a satellite data transmission method and system. Background Art

[0002] With the development of satellite technology and the continuous expansion of the scale of satellite constellations, satellite data transmission has become an important part of space information technology. Satellites collect data such as the Earth's environment, meteorology, and remote sensing images, providing important support for various scientific research, communication services, and social applications. However, in the face of the increasing number of satellites, the surging data volume, and limited ground station resources, how to efficiently and reliably achieve satellite data transmission tasks has become a key problem to be solved urgently.

[0003] Currently, most satellite data transmission scheduling methods are based on fixed rules or traditional optimization algorithms. For example, some methods statically allocate resources between satellites and ground stations and perform scheduling according to preset time windows and task priorities. These methods usually use simple heuristic algorithms to quickly generate scheduling schemes to meet basic data transmission requirements. At the same time, in recent years, artificial intelligence technology and optimization algorithms have been gradually applied to the field of satellite data scheduling. For example, intelligent optimization algorithms such as genetic algorithms and ant colony algorithms are used to improve scheduling efficiency.

[0004] However, the existing technologies have weak capabilities in adapting to dynamic task requirements and resource constraints in satellite data transmission, resulting in the failure to successfully allocate some high-priority or critical tasks, seriously affecting the task completion rate. At the same time, the problems of uneven satellite resource allocation and task conflicts are prominent, and there is a lack of an efficient cooperation mechanism among multiple satellites, restricting the task processing capabilities and resource utilization efficiency of the constellation network and making it difficult to meet the requirements of efficient cooperation in complex scenarios. Summary of the Invention

[0005] In order to solve the technical problems that the existing technologies have weak capabilities in adapting to dynamic task requirements and resource constraints in satellite data transmission, resulting in the failure to successfully allocate some high-priority or critical tasks, seriously affecting the task completion rate. At the same time, the problems of uneven satellite resource allocation and task conflicts are prominent, and there is a lack of an efficient cooperation mechanism among multiple satellites, restricting the task processing capabilities and resource utilization efficiency of the constellation network and making it difficult to meet the requirements of efficient cooperation in complex scenarios, the present invention provides a satellite data transmission method and system.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect:

[0008] A satellite data transmission method provided by an embodiment of the present invention includes:

[0009] S1: Obtain satellite data;

[0010] S2: Based on the logistic regression algorithm, construct a preliminary scheduling model for satellite data transmission;

[0011] S3: Aiming at improving the task completion rate of satellite data transmission tasks and the utilization rate of satellites and ground stations, construct a fine scheduling model for satellite data transmission;

[0012] S4: Input the satellite data into the preliminary scheduling model for satellite data transmission;

[0013] S5: In the preliminary scheduling model for satellite data transmission, schedule each satellite data transmission task through the logistic regression algorithm;

[0014] S6: Determine whether each satellite data transmission task is successfully scheduled by the preliminary scheduling model for satellite data transmission; if so, execute the satellite data transmission task; otherwise, input the unsuccessfully scheduled satellite data transmission tasks into the fine scheduling model for satellite data transmission;

[0015] S7: In the fine scheduling model for satellite data transmission, output the optimal satellite data transmission scheduling scheme through the particle swarm optimization algorithm;

[0016] S8: According to the optimal satellite data transmission scheduling scheme, execute the satellite data transmission tasks that are not successfully scheduled by the preliminary scheduling model for satellite data transmission.

[0017] Second aspect:

[0018] A satellite data transmission system provided by an embodiment of the present invention includes:

[0019] A processor;

[0020] A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the satellite data transmission method described in the first aspect is implemented.

[0021] Third aspect:

[0022] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored, and when the program is executed by a processor, the satellite data transmission method described in the first aspect is implemented.

[0023] The beneficial effects brought by the technical solutions provided by the embodiments of the present invention at least include:

[0024] (1) In the present invention, by obtaining satellite data and inputting the satellite data into a logistic regression preliminary scheduling model, rapid allocation and execution transmission of each task are achieved. For tasks that are not successfully allocated, further optimization is performed through a fine scheduling model, and finally an optimal solution is output and the tasks are executed. This two-layer scheduling mechanism realizes real-time adaptation to dynamic task requirements and resource constraints, effectively improves the allocation success rate of high-priority or critical tasks, and thus significantly improves the task completion rate.

[0025] (2) In the present invention, simple tasks are rapidly allocated through preliminary scheduling, and for tasks that are not successfully allocated, a fine scheduling model based on a particle swarm optimization algorithm is used for remedial optimization, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is a schematic flowchart of a satellite data transmission method provided by an embodiment of the present invention;

[0028] Figure 2 It is a schematic structural diagram of a satellite data transmission system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will describe the technical solutions in the present invention in conjunction with the drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" aims to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0031] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0032] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, their intended meanings are the same.

[0033] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0034] Refer to the attached Figure 1 figures, which show a schematic flow chart of a satellite data transmission method provided by an embodiment of the present invention.

[0035] The embodiments of the present invention provide a satellite data transmission method. This method can be implemented by a satellite data transmission device, which can be a terminal or a server. The processing flow of the satellite data transmission method can include the following steps:

[0036] S1: Obtain satellite data.

[0037] In a possible implementation manner, the satellite data includes: transmission task information, ground station information, satellite information, visible time window information, and scheduling time range information.

[0038] Specifically, the transmission task information includes: task ID, affiliated satellite, transmission time window, minimum transmission duration, priority, and frequency band; the ground station information includes: ground station ID, ground station antenna set and its frequency band; the satellite information includes: satellite ID, orbital parameters, satellite antenna set and its frequency band; the visible time window includes: time range, corresponding satellite and ground station; scheduling time range.

[0039] S2: Based on the logistic regression algorithm, construct a preliminary satellite data transmission scheduling model.

[0040] Among them, logistic regression is a simple and efficient classification algorithm. By introducing the Sigmoid function and logarithmic loss function, the linear model is extended to the classification task. It is suitable for solving linearly separable binary classification problems and has the advantage of probability output.

[0041] In the present invention, the advantage of constructing a preliminary scheduling model based on the logistic regression algorithm lies in its high efficiency, interpretability, and probability output ability. It can quickly screen tasks, reduce the computational pressure of complex scheduling, and at the same time preferentially allocate tasks with a high success probability, significantly improving the completion rate and scheduling efficiency of satellite data transmission tasks.

[0042] S3: With the goal of improving the task completion rate of satellite data transmission tasks and the utilization rate of satellites and ground stations, construct a refined satellite data transmission scheduling model.

[0043] Among them, the particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which simulates the collaborative behavior of biological groups such as bird flocks and fish schools, and gradually finds the global optimal solution through information sharing among particles.

[0044] In a possible implementation, the objective function of the satellite data transmission fine scheduling model is specifically:

[0045]

[0046] Among them, F represents the objective function, f1( ) represents the total weighted transmission time function, and f2( ) represents the resource load balancing function.

[0047] In a possible implementation, the total weighted transmission time function is specifically:

[0048]

[0049] Among them, indicates whether the i-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna at the k-th ground station within the z-th time window, indicates that the i-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna at the k-th ground station within the z-th time window, indicates that the i-th task is not transmitted from the m-th antenna on the j-th satellite to the d-th antenna at the k-th ground station within the z-th time window, etw i represents the transmission end time of the i-th task, stw i represents the transmission start time of the i-th task, represents the priority of the i-th task, n t represents the total number of tasks.

[0050] In a possible implementation, the resource load balancing function is specifically:

[0051]

[0052] Among them, n g represents the number of ground stations, represents the number of antennas owned by the k-th ground station, represents the total load time of the d-th antenna in the k-th ground station, represents the average antenna load time of the k-th ground station.

[0053] In a possible implementation, the total load time of the d-th antenna in the k-th ground station is specifically:

[0054]

[0055] Among them, n s represents the total number of satellites, and m sj represents the number of antennas of the sj-th satellite.

[0056] The total load time of the d-th antenna in the k-th ground station is specifically:

[0057] .

[0058] In the present invention, a resource load function is set up, considering the resource allocation of satellites and ground stations, optimizing the load distribution, avoiding overloading or idling of some resources, and improving the overall utilization rate of satellites and ground stations.

[0059] In a possible implementation manner, the constraint conditions of the satellite data transmission fine scheduling model specifically include:

[0060] The transmission start time and end time of each task need to be within the transmission time window specified by the task:

[0061]

[0062] Among them, represents the earliest transmission start time of the i-th task, represents the latest transmission start time of the i-th task, represents the actual transmission start time of the i-th task, represents the actual transmission end time of the i-th task.

[0063] In the present invention, through the task time window constraint, it is ensured that the start time and end time of task allocation conform to the transmission time window specified by the task, avoiding time conflicts.

[0064] When the task is allocated to the time window for transmission, the transmission time needs to be within the visible time window of the satellite and the ground station:

[0065]

[0066] Among them, represents the start time of the visible time of the j-th satellite and the k-th ground station in the z-th time window, represents the end time of the visible time of the j-th satellite and the k-th ground station in the z-th time window.

[0067] When the task is allocated to the satellite antenna and the ground station antenna for transmission, the frequency bands of the satellite antenna and the ground station antenna need to match the frequency band of the task:

[0068]

[0069] Among them, represents the operating frequency band of the m-th satellite antenna, represents the operating frequency band of the k-th ground station antenna, represents the required transmission frequency band for the i-th task.

[0070] In the present invention, by constraining the frequency bands of the satellite antenna and the ground station antenna to match the frequency band of the task, it is ensured that the operating frequency bands of the satellite antenna and the ground station antenna are consistent with the task requirement frequency band, avoiding frequency band conflicts and reducing the satellite data transmission efficiency.

[0071] The same ground station antenna and satellite antenna cannot transmit different tasks simultaneously within the same time window:

[0072]

[0073] Among them, represents whether the i1-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna of the k-th ground station within the z-th time window, represents that the i1-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna of the k-th ground station within the z-th time window, represents that the i1-th task is not transmitted from the m-th antenna on the j-th satellite to the d-th antenna of the k-th ground station within the z-th time window, represents whether the i2-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna of the k-th ground station within the z-th time window, represents that the i2-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna of the k-th ground station within the z-th time window, represents that the i2-th task is not transmitted from the m-th antenna on the j-th satellite to the d-th antenna of the k-th ground station within the z-th time window, represents the time window for transmitting the i1-th task, represents the time window for transmitting the i2-th task.

[0074] In the present invention, within the same time window, the same satellite antenna or ground station antenna can only transmit one task, avoiding resource conflicts and resulting in abnormal transmission.

[0075] Within the same time window, the same task can only be assigned to one ground station antenna:

[0076]

[0077] Among them, k1 represents the k1-th ground station, k2 represents the k2-th ground station, d1 represents the d1-th antenna of the k1-th ground station, and d2 represents the d2-th antenna of the k2-th ground station. Indicates whether the i-th task is transmitted from the m-th antenna on the j-th satellite to the d1-th antenna of the k1-th ground station within the z-th time window. Indicates that the i-th task is transmitted from the m-th antenna on the j-th satellite to the d1-th antenna of the k1-th ground station within the z-th time window. Indicates that the i-th task is not transmitted from the m-th antenna on the j-th satellite to the d1-th antenna of the k1-th ground station within the z-th time window. Indicates whether the i-th task is transmitted from the m-th antenna on the j-th satellite to the d2-th antenna of the k2-th ground station within the z-th time window. Indicates that the i-th task is transmitted from the m-th antenna on the j-th satellite to the d2-th antenna of the k2-th ground station within the z-th time window. Indicates that the i-th task is not transmitted from the m-th antenna on the j-th satellite to the d2-th antenna of the k2-th ground station within the z-th time window.

[0078] The actual transmission time of the current task needs to be greater than or equal to the minimum transmission duration of the current task:

[0079]

[0080] where, represents the minimum transmission duration of the i-th task.

[0081] Each task can only be scheduled once within the entire scheduling time range:

[0082]

[0083] where, represents the total number of antennas on each satellite, represents the total number of available time windows.

[0084] For two tasks assigned to the same ground station antenna, the interval time between the two tasks needs to be greater than or equal to the antenna switching time:

[0085]

[0086] where C1 represents the antenna switching time, max represents the maximum value, min represents the minimum value, j1 represents the j1-th satellite, j2 represents the j2-th satellite, m1 represents the m1-th antenna, m2 represents the m2-th antenna, z1 represents the z1-th time window, z2 represents the z2-th time window, Indicates whether the i1-th task is transmitted from the m1-th antenna on the j1-th satellite to the d-th antenna of the k-th ground station within the z1-th time window. It means that in the z1 - th time window, the i1 - th task is transmitted from the m1 - th antenna on the j1 - th satellite to the d - th antenna of the k - th ground station. It means that the i1 - th task is not transmitted from the m1 - th antenna on the j1 - th satellite to the d - th antenna of the k - th ground station in the z1 - th time window. It means whether the i2 - th task is transmitted from the m2 - th antenna on the j2 - th satellite to the d - th antenna of the k - th ground station in the z2 - th time window. It means that in the z2 - th time window, the i2 - th task is transmitted from the m2 - th antenna on the j2 - th satellite to the d - th antenna of the k - th ground station. It means that the i2 - th task is not transmitted from the m2 - th antenna on the j2 - th satellite to the d - th antenna of the k - th ground station in the z2 - th time window. It means the i actual transmission start time of the 1 - st task. It means the i actual transmission start time of the 2 - nd task. It means the i actual transmission end time of the 1 - st task. It means the i actual transmission end time of the 2 - nd task.

[0087] In the present invention, by optimizing the objective function (including the total weighted transmission time and resource load balance), an optimal task allocation scheme can be found to ensure that as many tasks as possible are successfully scheduled and executed, significantly improving the task completion rate.

[0088] S4: Input the satellite data into the preliminary satellite data transmission scheduling model.

[0089] S5: In the preliminary satellite data transmission scheduling model, schedule each satellite data transmission task through the logistic regression algorithm.

[0090] In a possible implementation manner, S5 specifically includes:

[0091] S501: Through the logistic regression algorithm, predict the probability of successful transmission of each satellite data transmission task under various scheduling schemes.

[0092] S502: Use the scheduling scheme with the maximum successful transmission probability value to perform preliminary scheduling on the satellite data transmission tasks.

[0093] It should be noted that the scheduling scheme is specifically: from the m - th antenna on the j - th satellite to the d - th antenna of the k - th ground station in the z - th time window.

[0094] S503: When there are resource conflicts among multiple satellite data transmission tasks, the satellite data transmission tasks with higher priorities are preferentially scheduled in descending order of priorities.

[0095] It should be noted that during the preliminary scheduling process of satellite data transmission tasks, the tasks that are not successfully scheduled mainly include the following categories: First, tasks with a predicted successful transmission probability lower than the threshold by the logistic regression model; second, tasks that fail to be allocated available resources due to resource conflicts or overlapping time windows; third, tasks with the actually allocated transmission time insufficient to meet the minimum transmission duration requirement of the tasks; fourth, tasks that cannot be completed due to network bottlenecks or insufficient bandwidth. For these tasks that are not successfully scheduled, they can be marked and put into the fine scheduling model for fine scheduling.

[0096] In the present invention, logistic regression can quickly predict the success probability of each task under different scheduling schemes without the need for complex global optimization, can quickly screen out possible successful scheduling schemes, reduces the scheduling calculation complexity, and improves the scheduling efficiency. At the same time, by predicting and sorting priorities to handle resource conflicts, some tasks that are not successfully scheduled are marked and transferred to the fine scheduling model for further optimization, effectively balancing the contradiction between scheduling speed and accuracy, and ensuring the maximization of task completion rate and system resource utilization.

[0097] S6: Determine whether each satellite data transmission task is successfully scheduled by the preliminary satellite data transmission scheduling model; if so, execute the satellite data transmission task; otherwise, input the unsuccessfully scheduled satellite data transmission tasks into the fine satellite data transmission scheduling model.

[0098] Specifically, in the scheduling process, it is necessary to determine whether each satellite data transmission task has been successfully allocated available resources for transmission through the preliminary satellite data transmission scheduling model. If a task is successfully scheduled by the preliminary scheduling model, it means that the scheduling scheme of this task meets all constraint conditions (such as time window, frequency band matching, resource availability, etc.), then directly execute this satellite data transmission task without further optimization; if the preliminary scheduling model fails to successfully allocate the task (for example, due to resource conflicts, insufficient visible time window or other limiting factors), then mark this task as unallocated and input it into the fine satellite data transmission scheduling model. The fine scheduling model will further analyze the constraint conditions of the unallocated tasks through the particle swarm optimization algorithm, re-evaluate and optimize the scheduling scheme to maximize the task completion rate and resource utilization, and ensure that the unallocated tasks can also obtain a reasonable scheduling scheme and be successfully executed.

[0099] In the present invention, by ensuring that the preliminary scheduling model quickly processes simple and highly probable successful tasks and directly executes these tasks, delays are avoided, and the real-time performance of the system is improved. At the same time, for tasks that are not successfully allocated, by inputting them into the fine scheduling model, the allocation scheme is further optimized, resources are utilized to the maximum extent, the possibility of task failure is reduced, and it is ensured that all tasks have the opportunity to be reasonably scheduled, thereby improving the task completion rate and resource utilization rate.

[0100] S7: In the satellite data transmission fine scheduling model, the optimal satellite data transmission scheduling scheme is output through the particle swarm optimization algorithm.

[0101] In a possible implementation manner, S7 is specifically:

[0102] In the satellite data transmission fine scheduling model, using the objective function of the satellite data transmission fine scheduling model as the fitness function of the particle swarm optimization algorithm, under the constraints of the constraint conditions, the optimal satellite data transmission scheduling scheme is output through the particle swarm optimization algorithm.

[0103] Optionally, S7 specifically includes:

[0104] S701: Using the objective function of the satellite data transmission fine scheduling model as the fitness function of the particle swarm optimization algorithm.

[0105] S702: Set the parameters of the particle swarm optimization algorithm, where the parameters of the particle swarm optimization algorithm include population size, inertia weight, acceleration factor, mutation probability, neighborhood range, mutation factor, and maximum number of iterations.

[0106] S703: Initialize the positions and velocities of each particle in the population, where each particle represents a feasible scheduling scheme.

[0107] S704: Calculate the fitness of each particle in the neighborhood range of each particle, and select the particle with the highest fitness value as the local optimal position within the neighborhood range.

[0108] S705: Update the velocities and positions of each particle according to the local optimal position:

[0109]

[0110]

[0111] Among them, V i (t + 1) represents the velocity of the i-th particle at the (t + 1)-th iteration, w represents the inertia weight, c1, c2 represent constants, r1, r2 represent constants, Pbest i represents the position with the optimal fitness value of the i-th particle, GbestDenotes the global optimal position, X i (t) represents the position of the i-th particle at the t-th iteration, w max Denotes the maximum value of the inertia weight, w min Denotes the minimum value of the inertia weight, t represents the t-th iteration, T max Denotes the maximum number of iterations.

[0112]

[0113] Among them, X i (t + 1) represents the position of the i-th particle at the (t + 1)-th iteration.

[0114] Optionally, after the position update, a boundary check is required to ensure that the velocity or position of the particle does not exceed the predefined search space.

[0115] S706: Generate a random number rand, rand ∈ (0, 1), and determine whether the random number rand is less than the preset mutation probability. If so, perform a first mutation operation on the current individual optimal position, that is, the local optimal position, to obtain a first trial position, and enter S707; otherwise, perform a second mutation operation on the current individual optimal position to obtain a second trial position, and enter S708.

[0116] In a possible implementation manner, the formula of the first mutation operation is specifically:

[0117]

[0118] Among them, T1 represents the first trial position, X i Represents the current position of the i-th particle, F represents the mutation factor, F ∈ (0, 1), Pbest r Represents the individual optimal position of the first particle randomly selected from the population.

[0119] In a possible implementation manner, the formula of the second mutation operation is specifically:

[0120]

[0121] Among them, T2 represents the second trial position, Lbest i Represents the position with the highest fitness value among all particles in the neighborhood of the i-th particle, F represents the mutation factor, F ∈ (0, 1), Pbest r Represents the individual optimal position of the first particle randomly selected from the population, Pbest s Represents the individual optimal position of the second particle randomly selected from the population.

[0122] In the present invention, by introducing the first mutation and the second mutation operations when updating the particle positions, the diversity of the search space is increased, and the particle swarm can jump out of the local optimal solution, thereby enhancing the global optimization ability. At the same time, the mutation operations enable the particles to try more potential solutions, thus increasing the probability of finding the global optimal solution. The first mutation operation expands the search range by randomly selecting perturbations of the individual optimal positions; the second mutation operation combines the position with the optimal fitness in the neighborhood to refine the search and improve the accuracy of the solution.

[0123] S707: Update the local optimal position and the global optimal position of the particle according to the first trial position, and proceed to S709.

[0124] In a possible implementation manner, S707 specifically includes:

[0125] S7071: Calculate the fitness value of the particle corresponding to the first trial position.

[0126] S7072: According to the fitness value of the particle corresponding to the first trial position, select the first target position for the next iteration through the following formula:

[0127]

[0128] where, T1 represents the first trial position, f(T1) represents the fitness value of the first trial position, and f(X i (t)) represents the fitness value of the position of the i-th particle at the t-th iteration.

[0129] S7073: Based on the first target position, update the individual optimal position through the following formula:

[0130]

[0131] where, Pbest i represents the optimal position of the individual optimal position of the i-th particle in all iterations, f(X i (t + 1)) represents the fitness value of the position of the i-th particle at the (t + 1)-th iteration, and f(Pbest i ) represents the fitness value of the historical individual optimal position of the i-th particle.

[0132] S7074: Based on the updated individual optimal position, update the global optimal position through the following formula:

[0133]

[0134] where, Gbest represents the global optimal position, and f(Gbest) represents the fitness value of the global optimal position.

[0135] S708: Update the local optimal position and the global optimal position of the particle according to the second test position, and proceed to S709.

[0136] In a possible implementation, S708 specifically includes:

[0137] S7081: Calculate the fitness value of the particle corresponding to the second test position.

[0138] S7082: According to the fitness value of the particle corresponding to the first test position, select the second target position for the next iteration through the following formula:

[0139]

[0140] where T2 represents the second test position, f(T2) represents the fitness value of the second test position, and f(X i (t)) represents the fitness value of the position of the i-th particle at the t-th iteration.

[0141] S7083: Based on the second target position, update the individual optimal position through the following formula:

[0142]

[0143] where Pbest i represents the optimal position of the individual optimal position of the i-th particle in all iterations, f(X i (t + 1)) represents the fitness value of the position of the i-th particle at the (t + 1)-th iteration, and f(Pbest i ) represents the fitness value of the historical individual optimal position of the i-th particle.

[0144] S7084: Based on the updated individual optimal position, update the global optimal position through the following formula:

[0145]

[0146] where Gbest represents the global optimal position, and f(Gbest) represents the fitness value of the global optimal position.

[0147] S709: Increment the iteration count by 1, and determine whether the iteration count has reached the maximum iteration count. If so, end the training and output the scheduling scheme corresponding to the particle at the global optimal position as the optimal satellite data transmission scheduling scheme. Otherwise, return to S704.

[0148] In the present invention, through the collaborative search mechanism of the particle swarm optimization algorithm, combined with the inertia weight, acceleration factor, and mutation operation, it is possible to rapidly approximate the optimal scheduling scheme under complex constraint conditions and improve the scheduling efficiency. At the same time, particle swarm optimization controls the speed change of particles through the inertia weight, and combines the local optimal position and the global optimal position to guide the particles to find the best balance between global search and local development, ensuring that the optimization process can fully explore the solution space and quickly converge to the optimal solution.

[0149] S8: According to the optimal satellite data transmission scheduling scheme, execute the satellite data transmission tasks that have not been successfully scheduled by the preliminary satellite data transmission scheduling model.

[0150] Specifically, according to the optimal scheduling scheme, arrange the task execution, configure the antenna resources and working frequency bands of the satellite to ensure communication with the corresponding ground station of the task, configure the working frequency bands of the ground station antennas, dock with the satellite antennas, and execute the tasks in the scheduling scheme to ensure that all tasks can be completed.

[0151] In the present invention, through clear task arrangements and resource configurations, resource conflicts and waste can be minimized to the greatest extent, and the communication efficiency between the satellite and the ground station can be improved. Strictly following the optimization scheme for the actual execution of tasks not only ensures the smooth completion of tasks, but also enhances the system's adaptability to complex task scenarios, thereby improving the task completion rate and resource utilization rate and meeting the requirements of multi-task simultaneous scheduling.

[0152] The beneficial effects brought by the technical solution provided in the embodiment of the present invention at least include:

[0153] (1) In the present invention, by acquiring satellite data and inputting the satellite data into the logistic regression preliminary scheduling model, rapid allocation and execution transmission of each task are realized. For the tasks that have not been successfully allocated, further optimization is carried out through the refined scheduling model, and finally the optimal scheme is output and the tasks are executed. This double-layer scheduling mechanism realizes real-time adaptation to dynamic task requirements and resource constraints, effectively improves the allocation success rate of high-priority or critical tasks, and thus significantly improves the task completion rate.

[0154] (2) In the present invention, simple tasks are quickly allocated through preliminary scheduling, and for the tasks that have not been successfully allocated, remedial optimization is carried out using the refined scheduling model based on the particle swarm optimization algorithm, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation.

[0155] Refer to the attached Figure 2 illustration, which shows the structural schematic diagram of a satellite data transmission system provided by the present invention.

[0156] The present invention also provides a satellite data transmission system 20, which is applied to the above satellite data transmission method and includes:

[0157] Processor 201.

[0158] Memory 202, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor 201, the satellite data transmission method as in the method embodiment is implemented.

[0159] The satellite data transmission system 20 provided by the present invention can execute the above satellite data transmission method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0160] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0161] (1) In the present invention, by acquiring satellite data and inputting the satellite data into the logistic regression preliminary scheduling model, rapid allocation and execution transmission of each task are realized. For the tasks that are not successfully allocated, further optimization is carried out through the fine scheduling model, and finally the optimal solution is output and the task is executed. This two-layer scheduling mechanism realizes real-time adaptation to dynamic task requirements and resource constraints, effectively improves the allocation success rate of high-priority or critical tasks, and thus significantly improves the task completion rate.

[0162] (2) In the present invention, simple tasks are quickly allocated through preliminary scheduling, and for the tasks that are not successfully allocated, a fine scheduling model based on the particle swarm optimization algorithm is used for remedial optimization, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation.

[0163] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0164] It should also be understood that the memory in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0165] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0166] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context before and after.

[0167] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0168] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0169] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0170] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0171] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0174] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0175] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the satellite data transmission method as described in the method embodiment.

[0176] The computer-readable storage medium provided by the present invention can implement the steps and effects of the satellite data transmission method in the above method embodiment. To avoid repetition, the present invention will not elaborate further.

[0177] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0178] (1) In the present invention, by obtaining satellite data and inputting the satellite data into the logical regression preliminary scheduling model, rapid allocation and execution transmission of each task are realized. For tasks that are not successfully allocated, further optimization is carried out through the fine scheduling model, and finally the optimal solution is output and the task is executed. This double-layer scheduling mechanism realizes real-time adaptation to dynamic task requirements and resource constraints, effectively improves the allocation success rate of high-priority or critical tasks, and thus significantly improves the task completion rate.

[0179] (2) In the present invention, simple tasks are quickly allocated through preliminary scheduling, and for tasks that are not successfully allocated, a fine scheduling model based on the particle swarm optimization algorithm is used for remedial optimization, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation.

[0180] As described above, only the specific embodiments of the present invention are given, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0181] The following points need to be explained:

[0182] (1) The attached drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general designs.

[0183] (2) For clarity, in the attached drawings used to describe the embodiments of the present invention, the thickness of layers or regions is enlarged or reduced, that is, these drawings are not drawn to actual scale. It can be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be intermediate elements.

[0184] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0185] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A satellite data transmission method, characterized in that, Including: S1: Obtain satellite data; S2: Based on the logistic regression algorithm, construct a preliminary scheduling model for satellite data transmission; S3: With the goal of improving the task completion rate of satellite data transmission tasks and the utilization rate of satellites and ground stations, construct a fine scheduling model for satellite data transmission; S4: Input the satellite data into the preliminary scheduling model for satellite data transmission; S5: In the preliminary scheduling model for satellite data transmission, schedule each satellite data transmission task through the logistic regression algorithm; S6: Determine whether each satellite data transmission task is successfully scheduled by the preliminary scheduling model for satellite data transmission; if so, execute the satellite data transmission task; otherwise, input the unsuccessfully scheduled satellite data transmission tasks into the fine scheduling model for satellite data transmission; S7: In the fine scheduling model for satellite data transmission, output the optimal satellite data transmission scheduling plan through the particle swarm optimization algorithm; S8: According to the optimal satellite data transmission scheduling plan, execute the satellite data transmission tasks that are not successfully scheduled by the preliminary scheduling model for satellite data transmission; Among them, the objective function of the fine scheduling model for satellite data transmission is specifically: ； Among them, F represents the objective function, f1( ) represents the total weighted transmission time function, and f2( ) represents the resource load balancing function; Among them, the total weighted transmission time function is specifically: ； Among them, Indicates whether the i-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna on the k-th ground station within the z-th time window. Indicates that the i-th task is transmitted from the m-th antenna on the j-th satellite to the d-th antenna on the k-th ground station within the z-th time window. Indicates that the i-th task is not transmitted from the m-th antenna on the j-th satellite to the d-th antenna on the k-th ground station within the z-th time window, etc. i Indicates the transmission end time of the i-th task, stw i Indicates the transmission start time of the i-th task. Indicates the priority of the i-th task, n t Indicates the total number of tasks; Among them, the resource load balancing function is specifically: ； where n g represents the number of ground stations, represents the number of antennas owned by the k-th ground station, represents the total load time of the d-th antenna in the k-th ground station, represents the average antenna load time of the k-th ground station.

2. The satellite data transmission method according to claim 1, wherein The satellite data includes: transmission task information, ground station information, satellite information, visible time window information, and scheduling time range information.

3. The satellite data transmission method according to claim 1, wherein The constraint conditions of the fine scheduling model for satellite data transmission specifically include: The transmission start time and end time of each task need to be within the specified transmission time window of the task; when the task is assigned to a time window for transmission, the transmission time needs to be within the visible time window of the satellite and the ground station; When the task is assigned to the satellite antenna and the ground station antenna for transmission, the frequency bands of the satellite antenna and the ground station antenna need to match the frequency band of the task; On the same ground station antenna and satellite antenna within the same time window, different tasks cannot be transmitted simultaneously; For the same task within the same time window, it can only be assigned to one ground station antenna; The actual transmission time of the current task needs to be greater than or equal to the minimum transmission duration of the current task; Each task can only be scheduled once within the entire scheduling time range; For two tasks assigned to the same ground station antenna, the interval time between the two tasks needs to be greater than or equal to the antenna switching time.

4. The satellite data transmission method according to claim 1, characterized in that The specific content of S5 includes: S501: Through the logistic regression algorithm, predict the probability of successful transmission of each satellite data transmission task under various scheduling schemes; S502: Use the scheduling scheme with the maximum successful transmission probability value to perform preliminary scheduling on the satellite data transmission tasks; S503: When there are resource conflicts among multiple satellite data transmission tasks, prioritize scheduling the satellite data transmission tasks with higher priorities in descending order of priority.

5. The satellite data transmission method according to claim 3, wherein The specific content of S7 is: In the satellite data transmission fine scheduling model, the objective function of the satellite data transmission fine scheduling model is used as the fitness function of the particle swarm optimization algorithm. Under the constraints of the constraint conditions, an optimal satellite data transmission scheduling scheme is output through the particle swarm optimization algorithm.

6. A satellite data transmission system, characterized in that, It includes: A processor; A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the satellite data transmission method according to any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, the satellite data transmission method according to any one of claims 1 to 5 is implemented.

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