Satellite data transmission method and system

Abstract

The invention provides a satellite data transmission method and system, and relates to the technical field of data transmission. The method comprises the following steps: 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 preliminary allocation on tasks; whether the task is preliminarily scheduled successfully or not is judged, and the successful task is directly executed; unsuccessful tasks are input into a fine scheduling model based on a particle swarm optimization algorithm; the fine scheduling model outputs an optimal scheduling scheme comprehensively considering task priority, transmission time and resource load balance by optimizing an objective function; according to the optimal satellite data transmission scheduling scheme, tasks which are not successfully scheduled by the preliminary scheduling model are executed, and reasonable allocation of all tasks is ensured, so that the resource utilization rate of satellites and ground stations is improved, and complex task requirements are met.

Description

Technical Field

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

[0002] With the development of satellite technology and the continuous expansion of satellite constellations, satellite data transmission has become an important part of space information technology. Satellites provide important support for various scientific research, communication services and social applications by collecting data such as the earth's environment, meteorology, and remote sensing images. However, faced with the increase in the number of satellites, the surge in data volume, and limited ground station resources, how to efficiently and reliably realize satellite data transmission tasks has become a key issue that needs to be solved urgently.

[0003] At present, 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 schedule according to preset time windows and task priorities. These methods usually use simple heuristic algorithms to quickly generate scheduling solutions to meet basic data transmission needs. 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, existing technologies are weak in adapting to dynamic mission requirements and resource constraints in satellite data transmission, resulting in the inability to successfully allocate some high-priority or critical tasks, which seriously affects the mission completion rate. At the same time, the uneven allocation of satellite resources and mission conflicts are prominent, and there is a lack of efficient coordination mechanisms among multiple satellites, which restricts the task processing capabilities and resource utilization efficiency of the constellation network, making it difficult to meet the needs of efficient collaboration in complex scenarios. Summary of the invention

[0005] In order to solve the problem that the existing technology has a weak ability to adapt to dynamic task requirements and resource constraints in satellite data transmission, resulting in the inability to successfully allocate some high-priority or critical tasks, which seriously affects the task completion rate. At the same time, the uneven distribution of satellite resources and task conflicts are prominent, and there is a lack of efficient coordination mechanism among multiple satellites, which restricts the task processing capability and resource utilization efficiency of the constellation network, and it is difficult to meet the technical problems of efficient collaboration in complex scenarios, the present invention provides a satellite data transmission method and system.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a satellite data transmission method, including:

[0009] S1: Get satellite data;

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

[0011] S3: To improve the task completion rate of satellite data transmission tasks and the utilization rate of satellites and ground stations, a satellite data transmission fine scheduling model is constructed;

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

[0013] S5: In the satellite data transmission preliminary scheduling model, each satellite data transmission task is scheduled by a logistic regression algorithm;

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

[0015] S7: In the satellite data transmission fine scheduling model, outputting an optimal satellite data transmission scheduling solution through a particle swarm optimization algorithm;

[0016] S8: Execute the satellite data transmission tasks that are not successfully scheduled by the satellite data transmission preliminary scheduling model according to the optimal satellite data transmission scheduling solution.

[0017] Second aspect:

[0018] An embodiment of the present invention provides a satellite data transmission system, comprising:

[0019] processor;

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

[0021] The third aspect:

[0022] 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, the satellite data transmission method as described in the first aspect is implemented.

[0023] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0024] (1) In the present invention, by acquiring satellite data and inputting the satellite data into the logistic regression preliminary scheduling model, each task can be quickly allocated and executed. For the tasks that are not successfully allocated, the fine scheduling model is used for further optimization, 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 improving the allocation success rate of high-priority or critical tasks, thereby significantly improving the task completion rate.

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

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic diagram of a flow chart of a satellite data transmission method provided by an embodiment of the present invention;

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

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either 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 between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

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

[0033] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0034] Reference Manual Attached Figure 1 , shows a schematic flow chart of a satellite data transmission method provided by an embodiment of the present invention.

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

[0036] S1: Get satellite data.

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

[0038] Specifically, the transmission task information includes: task ID, satellite, transmission time window, minimum transmission time, 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, a preliminary scheduling model for satellite data transmission is constructed.

[0040] Among them, logistic regression is a simple and efficient classification algorithm, which extends the linear model to the classification task by introducing the Sigmoid function and the logarithmic loss function. It is suitable for solving linearly separable binary classification problems and has the advantage of probabilistic 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 capability, which can quickly screen tasks and reduce the computational pressure of complex scheduling. At the same time, it gives priority to tasks with high probability of success, significantly improving the completion rate and scheduling efficiency of satellite data transmission tasks.

[0042] S3: A satellite data transmission fine-tuning scheduling model is constructed with the goal of improving the task completion rate of satellite data transmission tasks and increasing the utilization rate of satellites and ground stations.

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

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

[0045]

[0046] Where 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 manner, the total weighted transmission time function is specifically:

[0048]

[0049] in, Indicates whether the i-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 in the z-th time window, It means that in the zth time window, the mth antenna on the jth satellite transmits the i-th task to the d-th antenna of the k-th ground station. indicates that the i-th task was 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, etw i Indicates the transmission end time of the i-th task, stw i Indicates the start time of the i-th task transmission, represents the priority of the i-th task, n t Indicates 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 kth ground station, represents the total load time of the dth antenna in the kth ground station, represents the average antenna loading time of the kth ground station.

[0053] In a possible implementation manner, the total load time of the dth antenna in the kth ground station is specifically:

[0054]

[0055] Among them, n s Indicates the total number of satellites, m sj Indicates the number of antennas for the sjth satellite.

[0056] The total load time of the dth antenna in the kth ground station is specifically:

[0057] .

[0058] In the present invention, a resource load function is set to consider the resource allocation of satellites and ground stations, optimize load distribution, avoid overloading or idleness of some resources, and improve the overall utilization of satellites and ground stations.

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

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

[0061]

[0062] in, 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, Indicates the actual transmission end time of the i-th task.

[0063] In the present invention, task time window constraints are used to ensure that the start time and end time of task allocation conform to the transmission time window specified by the task, thereby avoiding time conflicts.

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

[0065] in, represents the start time of the visible time between the jth satellite and the kth ground station in the zth time window, Indicates the end time of the visibility time between the j-th satellite and the k-th ground station in the z-th time window.

[0066] When a mission is assigned to a satellite antenna and a ground station antenna for transmission, the frequency bands of the satellite antenna and the ground station antenna must match the frequency band of the mission:

[0067] in, represents the operating frequency band of the mth satellite antenna, represents the operating frequency band of the kth ground station antenna, Represents the required transmission frequency band of the i-th task.

[0068] 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 mission, it is ensured that the operating frequency bands of the satellite antenna and the ground station antenna are consistent with the frequency band required by the mission, avoiding frequency band conflicts and reducing satellite data transmission efficiency.

[0069] The same ground station antenna and satellite antenna cannot transmit different tasks at the same time in the same time window:

[0070] in, Indicates whether the i1th task is transmitted from the mth antenna on the jth satellite to the dth antenna of the kth ground station in the zth time window, It means that in the zth time window, the mth antenna on the jth satellite transmits the i1th task to the dth antenna of the kth ground station. indicates that the i1th task was not transmitted from the mth antenna on the jth satellite to the dth antenna of the kth ground station within the zth time window, Indicates whether the i2th task is transmitted from the mth antenna on the jth satellite to the dth antenna of the kth ground station in the zth time window, It means that in the zth time window, the mth antenna on the jth satellite transmits the i2th task to the dth antenna of the kth ground station. indicates that the i2th task was not transmitted from the mth antenna on the jth satellite to the dth antenna of the kth ground station within the zth time window, represents the time window for transmitting the i1th task, Represents the time window for transmitting the i2th task.

[0071] 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 causing transmission anomalies.

[0072] The same task can only be assigned to one ground station antenna in the same time window:

[0073] Wherein, k1 represents the k1th ground station, k2 represents the k2th ground station, d1 represents the d1th antenna of the k1th ground station, and d2 represents the d2th antenna of the k2th 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 in the z-th time window, It means that in the zth time window, the mth antenna on the jth satellite transmits the i-th task to the d1th antenna of the k1th ground station. indicates that the i-th mission was 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 in the z-th time window, It means that in the zth time window, the mth antenna on the jth satellite transmits the i-th task to the d2th antenna of the k2th ground station. It indicates that the i-th task was 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.

[0074] The actual transmission time of the current task must be greater than or equal to the minimum transmission time of the current task:

[0075]

[0076] in, Indicates the minimum transmission duration of the i-th task.

[0077] Each task can only be scheduled once during the entire scheduling time range:

[0078]

[0079] in, represents the total number of antennas on each satellite, Indicates the total number of available time windows.

[0080] For two tasks assigned to the same ground station antenna, the interval between the two tasks must be greater than or equal to the antenna switching time:

[0081] Wherein, C1 represents the antenna switching time, max represents the maximum value, min represents the minimum value, j1 represents the j1th satellite, j2 represents the j2th satellite, m1 represents the m1th antenna, m2 represents the m2th antenna, z1 represents the z1th time window, z2 represents the z2th time window, Indicates whether the i1th task is transmitted from the m1th antenna on the j1th satellite to the dth antenna of the kth ground station in the z1th time window, It means that in the z1th time window, the m1th antenna on the j1th satellite transmits the i1th task to the dth antenna of the kth ground station. indicates that the i1th task was not transmitted from the m1th antenna on the j1th satellite to the dth antenna of the kth ground station within the z1th time window, Indicates whether the i2th task is transmitted from the m2th antenna on the j2th satellite to the dth antenna of the kth ground station in the z2th time window, It means that in the z2th time window, the m2th antenna on the j2th satellite transmits the i2th task to the dth antenna of the kth ground station. indicates that the i2th task was not transmitted from the m2th antenna on the j2th satellite to the dth antenna of the kth ground station within the z2th time window, Indicates i The actual transmission start time of a task, Indicates i The actual transmission start time of the 2 tasks, Indicates i The actual transmission end time of a task, Indicates i The actual transfer end time of the 2 tasks.

[0082] In the present invention, by optimizing the objective function (including total weighted transmission time and resource load balancing), the optimal task allocation scheme can be found to ensure that as many tasks as possible are successfully scheduled and executed, thereby significantly improving the task completion rate.

[0083] S4: Inputting the satellite data into a preliminary satellite data transmission scheduling model.

[0084] S5: In the preliminary satellite data transmission scheduling model, each satellite data transmission task is scheduled through the logistic regression algorithm.

[0085] In a possible implementation, S5 specifically includes:

[0086] S501: Using a logistic regression algorithm, predict the probability of successful transmission of each satellite data transmission task when various scheduling schemes are adopted.

[0087] S502: Perform preliminary scheduling of satellite data transmission tasks using a scheduling scheme with the highest probability of successful transmission.

[0088] It should be noted that the specific scheduling plan is: in the zth time window, the mth antenna on the jth satellite sends signals to the dth antenna on the kth ground station.

[0089] S503: When there is a resource conflict among multiple satellite data transmission tasks, the satellite data transmission tasks with higher priorities are scheduled first in descending order of priority.

[0090] It should be noted that in the initial scheduling of satellite data transmission tasks, the tasks that were not successfully scheduled mainly include the following categories: first, tasks whose probability of successful transmission is lower than the threshold predicted by the logistic regression model; second, tasks that failed to be allocated available resources due to resource conflicts or time window overlaps; third, tasks whose actual allocated transmission time is insufficient to meet the minimum transmission time requirement of the task; and fourth, tasks that cannot be completed due to network bottlenecks or insufficient bandwidth. For these tasks that were not successfully scheduled, they can be marked and put into the fine scheduling model for fine scheduling.

[0091] 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, and can quickly screen out possible successful scheduling schemes, reducing the complexity of scheduling calculations and improving scheduling efficiency. At the same time, resource conflicts are handled through prediction and priority sorting, and some unsuccessfully scheduled tasks 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.

[0092] S6: Determine whether each satellite data transmission task is successfully scheduled by the satellite data transmission preliminary scheduling model; if so, execute the satellite data transmission task; otherwise, input the unsuccessfully scheduled satellite data transmission task into the satellite data transmission fine scheduling model.

[0093] Specifically, in the scheduling process, it is necessary to determine whether each satellite data transmission task has been successfully assigned to available resources for transmission through the satellite data transmission preliminary scheduling model. If the task is successfully scheduled by the preliminary scheduling model, it means that the scheduling plan of the task meets all constraints (such as time window, frequency band matching, resource availability, etc.), then the satellite data transmission task is directly executed without further optimization; if the preliminary scheduling model fails to successfully assign the task (for example, due to resource conflicts, insufficient visible time windows or other limiting factors), the task is marked as unassigned and input into the satellite data transmission fine scheduling model. The fine scheduling model will further analyze the constraints of the unassigned tasks through the particle swarm optimization algorithm, re-evaluate and optimize the scheduling plan to maximize the task completion rate and resource utilization, and ensure that the unassigned tasks can also obtain a reasonable scheduling plan and be successfully executed.

[0094] In the present invention, by ensuring that the preliminary scheduling model quickly processes simple tasks with a high probability of success, and directly executing 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 assigned, they are input into the fine scheduling model to further optimize the allocation plan, maximize the use of resources, reduce the possibility of task failure, and ensure that all tasks have the opportunity to be reasonably scheduled, thereby improving the task completion rate and resource utilization.

[0095] S7: In the satellite data transmission fine scheduling model, the optimal satellite data transmission scheduling solution is output through the particle swarm optimization algorithm.

[0096] In a possible implementation manner, S7 specifically includes:

[0097] 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 constraints, the particle swarm optimization algorithm is used to output the optimal satellite data transmission scheduling solution.

[0098] Optionally, S7 specifically includes:

[0099] S701: Using the objective function of the satellite data transmission fine scheduling model as the fitness function of the particle swarm optimization algorithm.

[0100] S702: Setting the particle swarm optimization algorithm parameters, wherein the particle swarm optimization algorithm parameters include population size, inertia weight, acceleration factor, mutation probability, neighborhood range, mutation factor, and maximum number of iterations.

[0101] S703: Initialize the position and velocity of each particle in the population, where each particle represents a feasible scheduling solution.

[0102] S704: Calculate the fitness of each particle in the neighborhood of each particle, and select the particle with the highest fitness value as the local optimal position in the neighborhood.

[0103] S705: Update the speed and position of each particle according to the local optimal position:

[0104]

[0105] Among them, V i (t+1) represents the velocity of the ith particle at the t+1 iteration, w represents the inertia weight, c1, c2 represent constants, r1, r2 represent constants, Pbest i represents the position where the fitness value of the i-th particle is optimal, Gbest represents the global optimal position, X i (t) represents the position of the i-th particle at the t-th iteration, w max Indicates the maximum value of the inertia weight, w min represents the minimum value of the inertia weight, t represents the tth iteration, T max Indicates the maximum number of iterations.

[0106]

[0107] Among them, X i (t+1) represents the position of the i-th particle at the t+1-th iteration.

[0108] Optionally, a bounds check is performed after the position update to ensure that the particle's velocity or position does not exceed the predefined search space.

[0109] 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, i.e., the local optimal position, to obtain a first test position, and enter S707; otherwise, perform a second mutation operation on the current individual optimal position, to obtain a second test position, and enter S708.

[0110] In a possible implementation, the formula of the first mutation operation is specifically:

[0111]

[0112] Where T1 represents the first test 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.

[0113] In a possible implementation, the formula of the second mutation operation is specifically:

[0114]

[0115] Among them, T2 represents the second test position, Lbest i Pbest represents the position with the highest fitness value of all particles in the neighborhood of the i-th particle, F represents the mutation factor, F∈(0,1), 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.

[0116] In the present invention, by introducing the first mutation and the second mutation operation when updating the particle position, the diversity of the search space is increased, and the particle swarm can jump out of the local optimal solution, thereby improving the global optimization ability. At the same time, the mutation operation enables particles to try more potential solutions, thereby increasing the probability of searching for the global optimal solution. The first mutation operation expands the search range by randomly selecting the perturbation of the individual optimal position; the second mutation operation combines the optimal position of fitness in the neighborhood to refine the search and improve the accuracy of the solution.

[0117] S707: According to the first test position, update the local optimal position of the particle and the global optimal position of the particle, and proceed to S709.

[0118] In a possible implementation manner, S707 specifically includes:

[0119] S7071: Calculate the fitness value of the particle corresponding to the first test position.

[0120] S7072: According to the fitness value of the particle corresponding to the first test position, the first target position for the next iteration is selected by the following formula:

[0121]

[0122] Where T1 represents the first test position, f(T1) represents the fitness value of the first test position, and f(X i (t)) represents the fitness value of the position of the i-th particle at the t-th iteration.

[0123] S7073: Based on the first target position, update the individual optimal position using the following formula:

[0124]

[0125] Among them, 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+1th iteration, f(Pbest i ) represents the fitness value of the historical individual optimal position of the i-th particle.

[0126] S7074: Based on the updated individual optimal position, update the global optimal position using the following formula:

[0127]

[0128] Among them, Gbest represents the global optimal position, and f(Gbest) represents the fitness value of the global optimal position.

[0129] S708: According to the second test position, update the local optimal position of the particle and the global optimal position of the particle, and proceed to S709.

[0130] In a possible implementation manner, S708 specifically includes:

[0131] S7081: Calculate the fitness value of the particle corresponding to the second test position.

[0132] S7082: According to the fitness value of the particle corresponding to the first test position, the second target position for the next iteration is selected by the following formula:

[0133]

[0134] 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.

[0135] S7083: Based on the second target position, update the individual optimal position using the following formula:

[0136]

[0137] Among them, 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+1th iteration, f(Pbest i ) represents the fitness value of the historical individual optimal position of the i-th particle.

[0138] S7084: Based on the updated individual optimal position, update the global optimal position using the following formula:

[0139]

[0140] Among them, Gbest represents the global optimal position, and f(Gbest) represents the fitness value of the global optimal position.

[0141] S709: The number of iterations is increased by 1 to determine whether the number of iterations reaches the maximum number of iterations. If so, the training is terminated and the scheduling scheme corresponding to the particle at the global optimal position is output as the optimal satellite data transmission scheduling scheme. Otherwise, return to S704.

[0142] In the present invention, through the collaborative search mechanism of the particle swarm optimization algorithm, combined with inertia weight, acceleration factor and mutation operation, it is possible to quickly approach the optimal scheduling solution under complex constraints and improve scheduling efficiency. At the same time, particle swarm optimization controls the speed change of particles through inertia weight, and combines the optimal position of the neighborhood and the global optimal position to guide 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.

[0143] S8: Execute the satellite data transmission tasks that are not successfully scheduled by the satellite data transmission preliminary scheduling model according to the optimal satellite data transmission scheduling plan.

[0144] Specifically, according to the optimal scheduling plan, arrange the task execution, configure the satellite's antenna resources and working frequency band, ensure communication with the ground station corresponding to the task, configure the working frequency band of the ground station antenna, dock with the satellite antenna, execute the tasks in the scheduling plan, and ensure that all tasks can be completed.

[0145] In the present invention, through clear task arrangement and resource allocation, resource conflicts and waste can be minimized, and the communication efficiency between satellites and ground stations can be improved. The actual execution of tasks is strictly carried out according to the optimization plan, which not only ensures the smooth completion of tasks, but also enhances the adaptability of the system to complex task scenarios, thereby improving the task completion rate and resource utilization, and meeting the needs of simultaneous scheduling of multiple tasks.

[0146] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0147] (1) In the present invention, by acquiring satellite data and inputting the satellite data into the logistic regression preliminary scheduling model, each task can be quickly allocated and executed. For the tasks that are not successfully allocated, the fine scheduling model is used for further optimization, 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 improving the allocation success rate of high-priority or critical tasks, thereby significantly improving the task completion rate.

[0148] (2) In the present invention, simple tasks are quickly assigned through preliminary scheduling, and for tasks that are not successfully assigned, a fine scheduling model based on the particle swarm optimization algorithm is used to perform remedial optimization, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation.

[0149] Reference Manual Attached Figure 2 , showing a schematic structural diagram of a satellite data transmission system provided by the present invention.

[0150] The present invention further provides a satellite data transmission system 20, which is applied to the above-mentioned satellite data transmission method, comprising:

[0151] Processor 201.

[0152] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the satellite data transmission method of the method embodiment is implemented.

[0153] The satellite data transmission system 20 provided by the present invention can execute the above-mentioned satellite data transmission method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0154] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0155] (1) In the present invention, by acquiring satellite data and inputting the satellite data into the logistic regression preliminary scheduling model, each task can be quickly allocated and executed. For the tasks that are not successfully allocated, the fine scheduling model is used for further optimization, 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 improving the allocation success rate of high-priority or critical tasks, thereby significantly improving the task completion rate.

[0156] (2) In the present invention, simple tasks are quickly assigned through preliminary scheduling, and for tasks that are not successfully assigned, a fine scheduling model based on the particle swarm optimization algorithm is used to perform remedial optimization, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation.

[0157] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0158] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0159] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. 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.). 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 available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0160] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0161] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers 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 mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0162] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order 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.

[0163] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0164] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0165] In the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] 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.

[0168] 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 computer-readable storage medium. 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 methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0169] 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, the satellite data transmission method described in the method embodiment is implemented.

[0170] A 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, and to avoid repetition, the present invention will not go into details.

[0171] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0172] (1) In the present invention, by acquiring satellite data and inputting the satellite data into the logistic regression preliminary scheduling model, each task can be quickly allocated and executed. For the tasks that are not successfully allocated, the fine scheduling model is used for further optimization, 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 improving the allocation success rate of high-priority or critical tasks, thereby significantly improving the task completion rate.

[0173] (2) In the present invention, simple tasks are quickly assigned through preliminary scheduling, and for tasks that are not successfully assigned, a fine scheduling model based on the particle swarm optimization algorithm is used to perform remedial optimization, effectively reducing resource conflicts and improving the rationality and utilization efficiency of resource allocation.

[0174] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0175] There are a few points to note:

[0176] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0177] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is 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 may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0178] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0179] The above are only specific embodiments 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 based on the protection scope of the claims.

Claims

1. A satellite data transmission method, characterized in that: include: S1: Get satellite data; S2: Based on the logistic regression algorithm, a preliminary scheduling model for satellite data transmission is constructed; S3: To improve the task completion rate of satellite data transmission tasks and the utilization rate of satellites and ground stations, a satellite data transmission fine scheduling model is constructed; S4: inputting the satellite data into the satellite data transmission preliminary scheduling model; S5: In the satellite data transmission preliminary scheduling model, each satellite data transmission task is scheduled by a logistic regression algorithm; S6: Determine whether each satellite data transmission task is successfully scheduled by the satellite data transmission preliminary scheduling model; if so, execute the satellite data transmission task; otherwise, input the unsuccessfully scheduled satellite data transmission task into the satellite data transmission fine scheduling model; S7: In the satellite data transmission fine scheduling model, outputting an optimal satellite data transmission scheduling solution through a particle swarm optimization algorithm; S8: Execute the satellite data transmission tasks that are not successfully scheduled by the satellite data transmission preliminary scheduling model according to the optimal satellite data transmission scheduling solution.

2. The satellite data transmission method according to claim 1, characterized in that: The satellite data includes: transmission mission 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, characterized in that: The objective function of the satellite data transmission fine scheduling model is specifically: ； Where F represents the objective function, f1( ) represents the total weighted transmission time function, and f2( ) represents the resource load balancing function.

4. The satellite data transmission method according to claim 3, characterized in that: The total weighted transmission time function is specifically: ； in, Indicates whether the i-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 in the z-th time window, It means that in the zth time window, the mth antenna on the jth satellite transmits the i-th task to the d-th antenna of the k-th ground station. indicates that the i-th task was 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, etw i Indicates the transmission end time of the i-th task, stw i Indicates the start time of the i-th task transmission, represents the priority of the i-th task, n t Indicates the total number of tasks.

5. The satellite data transmission method according to claim 3, characterized in that: The resource load balancing function is specifically: ； Among them, n g represents the number of ground stations, represents the number of antennas owned by the kth ground station, represents the total load time of the dth antenna in the kth ground station, represents the average antenna loading time of the kth ground station.

6. The satellite data transmission method according to claim 1, characterized in that: The constraints of the satellite data transmission fine scheduling model specifically include: The transmission start and end time of each task must be within the transmission time window specified by the task; when a task is assigned to a time window for transmission, the transmission time must be within the visible time window of the satellite and ground station; When a task is assigned to a satellite antenna and a ground station antenna for transmission, the frequency bands of the satellite antenna and the ground station antenna must match the frequency band of the task; The same ground station antenna and satellite antenna cannot transmit different tasks simultaneously in the same time window; The same task can only be assigned to one ground station antenna in the same time window; The actual transmission time of the current task must be greater than or equal to the minimum transmission time 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 between the two tasks must be greater than or equal to the antenna switching time.

7. The satellite data transmission method according to claim 1, characterized in that: The S5 specifically includes: S501: predicting the probability of successful transmission of each satellite data transmission task when various scheduling schemes are adopted by using a logistic regression algorithm; S502: Performing preliminary scheduling of satellite data transmission tasks using a scheduling scheme with the maximum success transmission probability value; S503: When there is a resource conflict among multiple satellite data transmission tasks, the satellite data transmission tasks with higher priorities are scheduled first in descending order of priority.

8. The satellite data transmission method according to claim 6, characterized in that: S7 is specifically: 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 constraints, the particle swarm optimization algorithm is used to output the optimal satellite data transmission scheduling solution.

9. A satellite data transmission system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the satellite data transmission method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the satellite data transmission method according to any one of claims 1 to 8 is implemented.

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