Multi-satellite parallel data transmission resource scheduling method based on genetic algorithm

Abstract

The invention provides a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, and belongs to the technical field of ground task management and control of spaceflight earth observation satellites. The method comprises the steps that historical data transmission resource use rules of all satellites are mined, and data transmission resource use rule models of all the satellites are established; in a data transmission resource scheduling process, each satellite generates a corresponding resource demand set according to a data transmission timeliness requirement, a priority degree and a data transmission resource use rule model of an important task, conflict resolution is carried out on a conflicting resource use demand set by utilizing a genetic algorithm, and a data transmission resource scheduling result is obtained through a multi-round parallel resource matching and conflict resolution process. The multi-satellite data transmission resource planning scheme is obtained, large-scale satellite data transmission resource scheduling is achieved, the large-scale remote sensing satellite group resource use efficiency is effectively improved, and the implementation rate of reconnaissance tasks is improved. According to the method, the data transmission resource distribution income among multiple satellites can be comprehensively planned, and the intelligent scheduling capability level of large-scale remote sensing satellite group resources can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ground mission management and control of space earth observation satellites, and in particular to a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm. Background Art

[0002] Currently, the management and division of labor of observation tasks and data transmission resources by the space satellite mission management department are as follows: the resource management unit plans the data transmission resources and releases the available data transmission resource windows to the task management unit; the task management unit internally allocates the data transmission resources based on the observation tasks to meet the data transmission requirements of the tasks. With the development of space technology, the number of satellites has increased sharply, and it has become increasingly difficult to coordinate data transmission resources manually. There is an urgent need for a method that can automatically allocate data transmission resources according to tasks.

[0003] The existing data transmission resource planning technologies mainly focus on one type or a small number of satellites. The task management unit still needs to coordinate resource conflicts between different models, and the process is relatively cumbersome and time-consuming. With the gradual construction and application of large-scale constellations and satellite groups, manual resource coordination can no longer meet the current task requirements. Summary of the Invention

[0004] In view of this, the present invention provides a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, which can uniformly plan the data transmission resources of all satellites and can be used in the data transmission resource planning application scenarios of large-scale remote sensing satellite groups.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, comprising the following steps:

[0007] Step 1, screening out the multi-satellite task set ImportTaskSet according to the task priority, and obtaining all matchable data transmission resource sets AllResourceSet according to the task planning intervals of different satellites to form the data input for data transmission resource scheduling;

[0008] Step 2, based on the usage of historical data transmission resources, obtaining the data transmission resource usage preferences, resource usage duration requirements of satellites, and the satellite unavailable resource sets to form the data transmission resource usage rules for each satellite model; each satellite model performs data transmission resource matching in parallel to form an initial solution Resource for multi-satellite data transmission resource planning;

[0009] Step 3: Model the multi-satellite resource conflict resolution problem as a classic genetic algorithm optimization problem. Group the initial solution Resource of the multi-satellite data transmission resource planning by resource devices to obtain the device grouping set ResourceGroup. Each device is further divided into multiple conflicting resource device time interval segment groups, and each conflicting resource device time interval segment group is a most basic gene unit Gene. Combine all gene units to obtain a chromosome. At the beginning of the genetic algorithm, initialize the population by generating multiple chromosomes. During the genetic algorithm process, evaluate the chromosomes, select a better set of chromosomes, perform genetic evolution and mutation, further select better chromosomes, and finally select a better set of data transmission resources through different iterations and evolutions to complete one data transmission resource conflict resolution. Finally, lock the matched data transmission resources, and continue to iterate on the important tasks that do not match the data transmission resources due to conflict resolution, and continue to match the data transmission resources in the remaining data transmission resource pool.

[0010] Further, the specific method of Step 2 is as follows:

[0011] Step 2.1: Group the multi-satellite task set ImportTaskSet and the data transmission resource set AllResourceSet by satellite, and perform resource matching in parallel among satellites.

[0012] Step 2.2: In each satellite, divide the tasks according to the time distribution of the tasks. Each task-intensive distribution area is a pile, and adopt the resource insertion allocation principle to perform resource allocation.

[0013] Step 2.3: When performing single-satellite resource insertion allocation, for each task pile, obtain the receiving resources between the start time of this task pile and the start time of the next task pile, obtain the data transmission resource usage rules of this type of satellite, and generate the data transmission resources matched by this task pile.

[0014] Step 2.4: All satellites perform resource matching in parallel to complete the data transmission resource matching of all task piles and generate the initial solution Resource of the multi-satellite data transmission resource planning.

[0015] Further, the specific method of Step 3 is as follows:

[0016] Step 3.1: Group the initial solution Resource of the multi-satellite data transmission resource planning by device to obtain the device grouping set ResourceGroup.

[0017] Step 3.2, obtain the data transmission device switching time interval requirement TRequire of different devices, and pile up each resource group ResourceGroupi according to the data transmission device switching time interval requirement TRequire to obtain a piling set GeneSet. Each piling is encoded into a most basic chromosome gene unit Gene, and different resource combinations within the piling are different genes;

[0018] Step 3.3, initialize the population, the population size is n, and the length of each chromosome is where size represents the total number of elements in the corresponding set; when generating chromosomes, randomly combine the resources in each resource pile of each resource device, select the resource encoding as 1 and do not select the encoding as 0. Each resource pile generates a gene unit Gene with a random combination of 0 and 1, and all the gene units Gene in the resource pile set are combined into a chromosome to obtain a chromosome population;

[0019] Step 3.4, construct an evaluation function. Among them, the data transmission timeliness evaluation function is defined as follows:

[0020]

[0021] where, rec i .sxx is the sum of the data transmission timeliness of the task set corresponding to the i-th data transmission resource, obsList is the receiving task set corresponding to the i-th data transmission resource, rec.kssj is the receiving start time of the i-th data transmission resource, and obsList i kssj is the observation start time of the i-th task in the receiving task set corresponding to the i-th data transmission resource; score sxx is the data transmission timeliness score, recList is the resource set, and weight is the weight of each resource;

[0022] The task arrangement rate evaluation function is defined as follows:

[0023]

[0024] where, score is the task arrangement rate score, recList is the resource set, and recList i .obsNum is the number of tasks received by the i-th data transmission resource in the resource set;

[0025] According to a certain scoring index, calculate the scores of all chromosomes in the population, and select the top m chromosomes with the highest scores as the basic chromosome group for genetic evolution and mutation;

[0026] Step 3.5: Perform evolution, crossover, and mutation operations on the selected basic chromosome sets to generate m new chromosomes and place them in the population;

[0027] Repeat Step 3.4 and Step 3.5 until the preset timeout T is exceeded or the score of the chromosome with the highest score exceeds the preset threshold S, and obtain the chromosome with the highest score;

[0028] Step 3.6: Decode the chromosome with the highest score to obtain a conflict-free resource set, and complete the resource conflict resolution for the initial solution Resource of the data transmission resource planning;

[0029] Step 3.7: Determine whether the preset number of iterations is reached or there is no data transmission resource conflict. If so, output the resource set after resource conflict resolution; otherwise, lock the data transmission resources after data transmission resource conflict resolution, set the corresponding data transmission resource labels for the tasks, and repeat Step 2 and Step 3 to continue the data transmission resource matching for the tasks that have not been matched with data transmission resources.

[0030] The present invention has the following beneficial effects compared with the prior art:

[0031] 1. The present invention adopts a multi-satellite parallel data transmission resource planning algorithm, which has high timeliness in the large-scale satellite constellation data transmission resource coordination application scenario and can realize large-scale satellite constellation data transmission resource planning;

[0032] 2. The present invention uses a genetic algorithm to solve the resource conflict problem among multiple satellites, can comprehensively consider the overall benefits of multi-satellite data transmission resource allocation, and can improve the overall data transmission efficiency of large-scale satellite constellations in large-scale satellite constellation application scenarios. Brief Description of the Drawings

[0033] Figure 1 is a flowchart of a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm.

[0034] Figure 2 is a schematic diagram of data transmission resource conflict and coding. Detailed Embodiments

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further elaborates on the present invention in detail with reference to specific embodiments and the accompanying drawings.

[0036] A multi-satellite parallel data transmission resource scheduling method based on genetic algorithm. This method first mines the historical data transmission resource usage rules of each satellite and establishes a data transmission resource usage rule model for each satellite. During the process of data transmission resource scheduling, each satellite generates a corresponding resource demand set according to the data transmission timeliness requirements, priority levels, and data transmission resource usage rule models of important tasks, and uses the genetic algorithm to resolve conflicts in the conflicting resource usage demand sets. Through multiple rounds of parallel resource matching and conflict resolution processes, a multi-satellite data transmission resource planning scheme is obtained, realizing the data transmission resource scheduling of large-scale satellites, effectively improving the resource usage efficiency of large-scale remote sensing satellite constellations, and enhancing the implementation rate of reconnaissance tasks.

[0037] As Figure 1 shown, the method specifically includes the following steps:

[0038] Step 1, before task planning, screen out the multi-satellite key task set ImportTaskSet according to task priorities, and obtain the global data transmission resources AllResourceSet according to the task planning intervals of different satellites, forming the data input for the resource planning process.

[0039] Step 2, based on the usage of historical data transmission resources, summarize the data transmission resource usage rules of each model. Each model combines the data transmission resource usage rules and conducts parallel data transmission resource matching to form an initial solution Resource for multi-satellite data transmission resource planning. There may be conflicts between resources in the initial solution. The specific steps are as follows:

[0040] Step 2.1, first group the important task set ImportTaskSet and the data transmission resource set AllResourceSet that need to perform data transmission resource matching by satellite, and perform resource matching in parallel among satellites;

[0041] Step 2.2, in each satellite, divide the tasks according to the time distribution of the tasks. Each task-intensive distribution area is a pile, and the resource allocation principle of inserting spaces is adopted for resource allocation;

[0042] Step 2.3, when performing single-satellite resource insertion and allocation, for each task pile, obtain the receiving resources between the start time of this task pile and the start time of the next task pile, obtain the data transmission resource usage rule model dedicated to this type of satellite, and generate the data transmission resources matching this task pile;

[0043] Step 2.4, all satellites perform resource matching in parallel. After completing the data transmission resource matching of all task piles, generate an initial solution Resource for multi-satellite data transmission resource planning;

[0044] Step 3: Model the multi-star resource conflict resolution problem as a classic genetic algorithm optimization problem. Group the initial solution Resource of the data transmission resource planning by device to obtain a device grouping set. Each device can be further divided into multiple conflicting resource device time interval segments, and each such segment is a basic gene unit n. The conflict situation and coding rules of the resources are as Figure 2 shown. Combine all gene units to form a chromosome. At the beginning of the genetic algorithm, initialize the population by generating multiple chromosomes. Evaluate the chromosomes, select a set of better chromosomes, perform genetic evolution and mutation, further select even better chromosomes, and finally select a better data transmission resource set through different iterations and evolutions to complete one round of data transmission resource conflict resolution. Lock the matched data transmission resources, continue to iterate on the important tasks that did not match data transmission resources due to conflict resolution, continue to match data transmission resources in the remaining data transmission resource pool, and repeat Steps 2 and 3 until the termination condition of the algorithm is reached. The specific steps are as follows:

[0045] Step 3.1: Group the resource set Resource by device to obtain a grouping set ResourceGroup. Resources within each group may conflict.

[0046] Step 3.2: Obtain the data transmission device switching time interval requirement TRequire for different devices. Divide each resource group ResourceGroupi into piles according to the data transmission device switching time interval requirement TRequire to obtain a pile set GeneSet. Each pile is a basic chromosome unit Gene, and different resource combinations within a pile are different genes. Evolve for Gene, and the generation of new Genes from different resource combinations is evolution.

[0047] Step 3.3: Initialize the population. The population size is n, and the length of each chromosome is When generating chromosomes, randomly combine the resources in each resource pile of each resource device to generate gene segments Gene, and various different Gene combinations form a chromosome population.

[0048] Step 3.4: Construct an evaluation function. Commonly used evaluation functions mainly include a task arrangement rate evaluation function and a data transmission timeliness evaluation function. The data transmission timeliness evaluation function is defined as follows, where rec i .sxx is the sum of the data transmission timeliness of the task set corresponding to the i-th data transmission resource, and obsList is the task set corresponding to the i-th data transmission resource. score sxx is the data transmission timeliness score, recList is the resource set, and weight is the weight of each resource.

[0049]

[0050] The task arrangement rate evaluation function is defined as follows, where score is the task arrangement rate score, recList is the resource set, and recList i .obsNum is the number of tasks received by the i-th data transmission resource in the resource set, and weight is the weight of each resource.

[0051]

[0052] According to a certain scoring index, calculate the scores of all chromosomes in the population, and select the top m chromosomes with the highest scores as the basic chromosomes for genetic evolution and mutation.

[0053] Step 3.5, perform evolution and crossover mutation processing on the selected basic chromosomes to generate m new chromosomes, and repeat steps 3.4 and 3.5 until the algorithm termination condition is reached to obtain the chromosome with the highest score.

[0054] Step 3.6, according to the chromosome coding rule, decode the chromosome with the highest score to obtain a conflict-free resource set, and complete the resource conflict resolution of the initial solution Resource of the data transmission resource planning.

[0055] Step 3.7, determine whether the termination condition of the parallel resource scheduling algorithm is reached. If it is reached, output the resource set after resource conflict resolution; if the termination condition is not reached, lock the data transmission resources after data transmission resource conflict resolution, set the corresponding data transmission resource labels for the tasks, and repeat step 2 and step 3 to continue the data transmission resource matching for the tasks that have not been matched with data transmission resources due to data transmission resource conflicts.

[0056] In summary, the present invention uses a genetic algorithm to model the resource coordination problem of remote sensing satellite constellations, can comprehensively plan the data transmission resource allocation benefits among multiple satellites, and can obtain a multi-satellite resource allocation result with better overall benefits in large-scale satellite constellation applications. Moreover, the entire algorithm process takes a short time, and can effectively improve the intelligent scheduling ability level of large-scale remote sensing satellite constellation resources.

[0057] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-satellite parallel data transmission resource scheduling method based on genetic algorithm, characterized in that: The following steps are involved: Step 1: Filter out the multi-satellite task set ImportTaskSet according to the task priority, obtain all matching data transmission resource sets AllResourceSet according to the task planning intervals of different satellites, and form data input for data transmission resource scheduling; Step 2: Based on the historical usage of data transmission resources, obtain the satellite's data transmission resource usage preference, resource usage time requirements, and satellite unusable resource set to form data transmission resource usage rules for each type of satellite; each type of satellite conducts data transmission resource matching in parallel to form an initial solution for multi-satellite data transmission resource planning; Step 3, model the multi-satellite resource conflict resolution problem as a classic genetic algorithm optimization problem, group the initial solution Resource of multi-satellite data transmission resource planning according to resource equipment, and obtain the equipment group set ResourceGroup, where each equipment is divided into multiple conflicting resource equipment time interval fragment groups, and each conflicting resource equipment time interval fragment group is a most basic gene unit Gene; combine all gene units to obtain a chromosome, and at the beginning of the genetic algorithm, initialize the population by generating multiple chromosomes; in the genetic algorithm process, evaluate the chromosomes, select a better chromosome set, perform genetic evolution and mutation, and further select a better chromosome, and finally select a better data transmission resource set through different iterations and evolutions to complete a data transmission resource conflict resolution; finally, lock the matched data transmission resources, continue to iterate for important tasks that have not matched data transmission resources due to conflict resolution, and continue to match data transmission resources in the remaining data transmission resource pool.

2. The method for scheduling multi-satellite parallel data transmission resources based on genetic algorithm according to claim 1, characterized in that: The specific method of step 2 is: Step 2.1, group the multi-satellite task set ImportTaskSet and the data transmission resource set AllResourceSet by satellite, and perform resource matching between satellites in parallel; Step 2.2: In each satellite, tasks are divided into piles according to their time distribution. Each task-intensive distribution area is a pile, and resource allocation is performed using the principle of resource interspersed allocation. Step 2.3: When allocating single-satellite resources in a gap, for each task stack, obtain the receiving resources between the start time of the task stack and the start time of the next task stack, obtain the data transmission resource usage rules of the satellite of this model, and generate the data transmission resources matching the task stack; Step 2.4: All satellites perform resource matching in parallel, complete the data transmission resource matching of all task stacks, and generate the initial solution Resource for multi-satellite data transmission resource planning.

3. The method for resolving data transmission resource conflicts based on genetic algorithm according to claim 1, characterized in that: The specific method of step 3 is: Step 3.1, the initial solution Resource of multi-satellite data transmission resource planning is grouped according to the equipment to obtain the equipment group set ResourceGroup; Step 3.2, obtain the data transmission device switching time interval requirement TRequire of different devices, divide each resource group ResourceGroupi into piles according to the data transmission device switching time interval requirement TRequire, and obtain the pile set GeneSet, where each pile is encoded into a most basic chromosome gene unit Gene, and different resources in the pile are combined into different genes; Step 3.3, initialize the population, the population size is n, and the length of each chromosome is Where size represents the total number of elements in the corresponding set. When generating chromosomes, the resources in each resource stack of each resource device are randomly combined, and the selected resources are coded as 1, and the unselected resources are coded as 0. Each resource stack generates a gene unit Gene that is a random combination of 0 and 1. All the gene units Gene in the resource stack set are combined into a chromosome to obtain a chromosome population. Step 3.4, construct an evaluation function, where the data transmission timeliness evaluation function is defined as follows: Among them, rec i .sxx is the sum of the data transmission timeliness of the task set corresponding to the i-th data transmission resource, obsList is the receiving task set corresponding to the i-th data transmission resource, rec.kssj is the receiving start time of the i-th data transmission resource, obsL i stikssj is the observation start time of the i-th task in the receiving task set corresponding to the i-th data transmission resource; score sxx It is the score of data transmission timeliness, recList is the resource collection, and weight is the weight of each resource; The task scheduling rate evaluation function is defined as follows: Among them, score is the task scheduling rate score, recList is the resource collection, recList i .obsNum is the number of tasks received by the i-th data transmission resource in the resource set; According to a certain scoring index, the scores of all chromosomes in the population are calculated, and the top m chromosomes are selected as the basic chromosome group for genetic evolution and variation; Step 3.5, perform evolution and crossover mutation on the selected basic chromosome set to generate m new chromosomes and put them into the population; Repeat steps 3.4 and 3.5 until the preset timeout T is exceeded or the score of the chromosome with the highest score exceeds the preset threshold S, and the chromosome with the highest score is obtained; Step 3.6, decode the chromosome with the highest score to obtain a conflict-free resource set, and complete the resource conflict resolution of the initial solution Resource for data transmission resource planning; Step 3.7, determine whether the preset number of iterations has been reached or no data transmission resource conflicts have been achieved. If so, output the resource set after the resource conflicts have been resolved; otherwise, lock the data transmission resources after the data transmission resource conflicts have been resolved, set the corresponding data transmission resource label for the task, and repeat steps 2 and 3 to continue matching data transmission resources for tasks that have not matched data transmission resources.

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