Task planning variation method and system, task planning method, equipment and medium

Abstract

The invention relates to the technical field of satellite measurement, operation and control, and provides a variation method and system for task planning, a task planning method, equipment and a medium, and the variation method comprises the steps: obtaining a task coding scheme; mapping into a 01 sequence according to a task coding scheme; determining whether variation occurs or not, if variation occurs, randomly selecting a point location from the 01 sequence, and if the point location is 1, setting the point location as 0, and ending variation; if the point location is 0, setting the position of the point location as 1, and obtaining starting and ending moments of a visible arc section corresponding to the point location; traversing all the point locations according to the starting and ending moments, the occupation identifiers and the switching intervals of the visible arc segments corresponding to the point locations, setting the positions of the points with conflicts as 0, and setting the positions of the points without conflicts as 1; and mapping the 01 sequence after variation back to the task coding scheme. According to the scheme, the feasibility failure caused by variation is avoided when the variation operation meets the constraint in the task planning process of the satellite Internet of Things; and the mutation probability does not reduce the search efficiency of the algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite measurement operation control, and particularly relates to a satellite Internet of Things measurement operation control task planning mutation method and system based on a genetic algorithm, a task planning method, equipment and a medium. BACKGROUND

[0002] With the development of space technology, the deployment cost of low-orbit satellites is gradually reduced, and it is possible to provide network access services for ground terminals through low-orbit satellites. Especially in the fields of Internet of Things and Internet of Vehicles, the wide coverage of satellites can effectively solve the coverage blind area problem in areas with insufficient infrastructure and provide more wide-area network coverage.

[0003] In the service process, in order to ensure network connectivity, a backhaul link between the ground station and the satellite needs to be established first. After the backhaul link is successfully established, the satellite can replace the traditional base station to provide data network services for user terminals within its coverage range.

[0004] In the operation process of the low-orbit satellite network, due to the limited coverage range of the satellite, a single satellite is usually not enough to cover the entire target area. At the same time, due to the mobility of the satellite, the coverage range will change over time. Therefore, multiple satellites and multiple stations are usually needed to serve the target area.

[0005] In this process, task planning needs to be performed to determine when to establish a backhaul link, through which station, and for which satellite. In addition, the overall target of the low-orbit satellite constellation needs to be designed, and the coverage of a region needs to be evaluated to guide the direction of task planning.

[0006] The existing satellite task planning usually faces remote sensing satellites and relay communication satellites, and the similarities between them lie in the need to plan the backhaul link between the ground station and the satellite. However, there are great differences in application scenarios, for example, remote sensing satellites only focus on imaging of the target area and pay more attention to the satellite imaging attitude, and there is no strict real-time requirement for the task. Relay communication satellites usually only perform point-to-point relay tasks and do not need to consider large-scale services.

[0007] The satellite Internet of Things coverage optimization scenario not only needs to consider the coverage of the satellite to a range of areas, but also needs to consider the availability of the backhaul link. Only when the satellite and the ground station establish a backhaul link and the terminal is within the coverage range of the satellite, can the terminal be served.

[0008] In the task planning problem, a genetic algorithm is usually used to search for planning results. In the process of using the genetic algorithm, the mutation operation is usually random. Without constraints, the feasible solution may be invalid, especially as the task planning time increases and the number of gene points increases, the random mutation has a large destructive effect on the constraints. The constraint violation caused by mutation is usually handled in the natural selection stage.

[0009] In the task planning process, with the increase of the number of TT&C stations, the number of satellites and the planning market, it is impractical to traverse all schemes. In addition, a large number of 01 variables do not have a clear optimization gradient direction, so a global search method such as genetic algorithm needs to be used. However, the existing satellite task planning problem model is mostly for remote sensing imaging, point-to-point relay communication and other application scenarios, and the imaging related algorithm design does not accurately describe the demand for backhaul and access link of satellite Internet of Things, and at the same time, although the relay communication related algorithm considers the double visibility requirement of relay satellite and two end communication nodes, it does not consider the demand for large area coverage of satellite Internet of Things.

[0010] In the task planning problem, genetic algorithm is usually used to search for planning results. In the process of using genetic algorithm, mutation operation is usually random. Without constraints, the feasible solution may fail, especially with the increase of task planning time and the increase of gene points, random mutation has a large destructive effect on constraints.

[0011] In a large number of genetic algorithm designs related to task planning, the constraints are guaranteed by removing individuals that do not meet the constraints in natural selection truncation. However, this method will reduce search efficiency, and with the complication of constraints, it is easy to cause mutation failure. In order to avoid the negative effects of failed mutation, it is common to reduce the mutation probability to ensure that there are enough feasible solutions in each generation population. However, a lower mutation probability will reduce the global search efficiency and easily fall into local optimum. In addition, it is difficult to reasonably configure the specific value of mutation probability, which requires a lot of trial and error.

[0012] Therefore, it is necessary to provide a satellite Internet of Things measurement operation control task planning mutation method and system based on genetic algorithm, a task planning method, equipment and medium. The task planning algorithm is designed for satellite Internet of Things coverage optimization, and the mutation operation method of genetic algorithm meets the special requirements of satellite Internet of Things task planning. By designing the mutation operation in genetic algorithm, the mutation operation guarantees the satisfaction of constraints in the process of satellite Internet of Things task planning, and avoids the feasibility failure caused by mutation. The mutation probability does not reduce the search efficiency of the algorithm, and can adapt to any configured mutation probability.

[0013] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present application, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0014] The main purpose of the present application is to overcome the problem of large damage to constraints and reduced search efficiency in the mutation of genetic algorithm in satellite measurement control task planning, and provide a mutation method and system of satellite Internet of Things measurement control task planning based on genetic algorithm, a task planning method, equipment and a medium, design a task planning algorithm for satellite Internet of Things coverage optimization, a mutation operation method of genetic algorithm that meets the special needs of satellite Internet of Things task planning; through the design of mutation operation in genetic algorithm, the mutation operation ensures the satisfaction of constraints in the process of satellite Internet of Things task planning, and avoids the feasibility failure caused by mutation; without violating the constraints, the mutation probability will not reduce the search efficiency of the algorithm, and can adapt to any configured mutation probability.

[0015] To achieve the above purpose, the first aspect of the present application provides a mutation method of satellite Internet of Things measurement control task planning based on genetic algorithm, comprising the following steps: S51: obtaining a task coding scheme of a measurement station in a predetermined time period; S52: mapping the 01 sequence of whether a plurality of visible arcs of the measurement station are selected according to the task coding scheme; S53: determining whether to mutate according to the mutation probability, if mutation is performed, randomly selecting a point from the 01 sequence, if the point is 1, setting it to 0, ending the mutation, and executing step S57; if the point is 0, executing step S54; S54: setting the point to 1, obtaining the start and end time of the visible arc corresponding to the point; S55: obtaining an occupation identifier and a switching interval; S56: traversing all points before and after the point according to the start and end time of the visible arc corresponding to the point, the occupation identifier and the switching interval, setting the point to 0 if there is a conflict, setting the point to 1 if there is no conflict, and updating the 01 sequence; S57: mapping the 01 sequence after mutation back to the task coding scheme.

[0016] According to an example embodiment of the present application, step S52 further comprises: sorting the 01 sequence according to the starting tracking time.

[0017] According to an example embodiment of the present application, in step S53, the determination of whether to mutate according to the mutation probability comprises: if the mutation probability is in a specified range, determining to mutate, otherwise not mutating.

[0018] According to an example embodiment of the present application, in step S55, the occupation identifier is ; wherein, the forward auxiliary variable is represented by f, the start time of the visible arc corresponding to the point is represented by t, the backward auxiliary variable is represented by b, an end time of the visible arc segment corresponding to the point position, representing a switching interval; the switching interval is the minimum switching time required for the station to continuously serve two satellites.

[0019] According to an example embodiment of the present application, in step S56, the start and end time of the visible arc segment corresponding to the point position, the occupation identifier and the switching interval are used to traverse all point positions before and after the point position, the point position is set to 0 if there is a conflict, and the point position is set to 1 if there is no conflict, including: Traverse each point position in turn, if the point position has a conflict with the next point position, set the point position to 0, otherwise set the point position to 1; Traverse each point position in turn, if the point position has a conflict with the next point position, set the point position to 0, otherwise set the point position to 1;

[0020] According to an example embodiment of the present application, the start and end time of the visible arc segment corresponding to the point position, the occupation identifier and the switching interval are used to traverse all point positions before and after the point position, the point position is set to 0 if there is a conflict, and the point position is set to 1 if there is no conflict, further including stopping the traversal if the point position is 1 before mutation and still 1 after mutation.

[0021] As a second aspect of the present application, the present application provides a mutation system for satellite Internet of Things measurement operation control task planning based on a genetic algorithm, comprising a scheme acquisition module, a number sequence generation module, a number sequence update module and a scheme generation module; The scheme acquisition module is used to acquire a task coding scheme of a station within a predetermined time period; The number sequence generation module is used to map the task coding scheme to a 01 number sequence indicating whether a plurality of visible arc segments of the station are selected; The number sequence update module is used to determine whether to mutate according to a mutation probability, if mutation is performed, randomly select a point position from the 01 number sequence, if the point position is 1, set it to 0, end the mutation and turn to the scheme generation module; if the point position is 0, set it to 1, acquire the start and end time of the visible arc segment corresponding to the point position, acquire the occupation identifier and the switching interval, traverse all point positions before and after the point position according to the start and end time of the visible arc segment corresponding to the point position, the occupation identifier and the switching interval, set the point position to 0 if there is a conflict, and set the point position to 1 if there is no conflict, and update the 01 number sequence; The scheme generation module is used to map the mutated 01 number sequence back to the task coding scheme.

[0022] As a third aspect of the present application, the present application provides a task planning method for satellite Internet of Things measurement operation control based on a genetic algorithm, comprising the following steps: S1: acquire an initial weight and an initial threshold value; S2: establishing a fitness function; S3: selecting operation; S4: crossover operation, obtaining a task coding scheme of multiple stations in a predetermined time period; S5: according to the mutation method of the satellite Internet of Things measurement and operation control task planning based on the genetic algorithm, mutating the task coding scheme of each station to combine into a population; S6: calculating population fitness; S7: if the termination condition is not met, steps S5 to S7 are performed again until the termination condition is met.

[0023] As a fourth aspect of the present application, the present application provides an electronic device comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the mutation method of the satellite Internet of Things measurement and operation control task planning based on the genetic algorithm.

[0024] As a fifth aspect of the present application, the present application provides a computer readable medium having a computer program stored thereon, the program being executed by a processor to implement the mutation method of the satellite Internet of Things measurement and operation control task planning based on the genetic algorithm.

[0025] The advantage of the present application is: In the design of the genetic algorithm mutation process, all constraints required by the ground station are guaranteed (switching interval constraints, ground station utilization constraints, and ground station serving only a single satellite at the same time); the greedy utilization of the ground station is still guaranteed in the 0-1 mutation process; the 1-0 mutation only reduces the utilization of the visible arc segment once, establishes a conflict-free isolation region, improves the reuse rate in subsequent traversal, and improves the execution speed of subsequent mutation operations.

[0026] As can be seen, the present scheme designs a task planning algorithm for satellite Internet of Things coverage optimization, and a mutation operation method of the genetic algorithm that meets the special needs of satellite Internet of Things task planning; by designing the mutation operation in the genetic algorithm, the mutation operation guarantees the satisfaction of the constraints in the satellite Internet of Things task planning process, and avoids the feasibility failure caused by mutation; without violating the constraints, the mutation probability does not reduce the search efficiency of the algorithm, and can adapt to any configuration of the mutation probability. BRIEF DESCRIPTION OF DRAWINGS

[0027] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters indicate like elements throughout the figures. The following detailed description includes specific details for the purpose of providing a thorough understanding of the present application. However, it will be apparent to those skilled in the art that the application can be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the present application.

[0028] Figure 1 A step diagram of a task planning method for satellite IoT measurement operation control based on a genetic algorithm is schematically shown.

[0029] Figure 2 A step diagram of a mutation method for satellite IoT measurement operation control task planning based on a genetic algorithm is schematically shown.

[0030] Figure 3 A block diagram of an electronic device is schematically shown.

[0031] Figure 4 A block diagram of a computer medium is schematically shown. DETAILED DESCRIPTION

[0032] Example embodiments now will be described more fully hereinafter with reference to the accompanying drawings. Example embodiments, may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the example embodiments to those skilled in the art. Like reference numerals refer to like elements throughout the figures.

[0033] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application can be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, devices, and operations have not been shown or described in detail to avoid obscuring aspects of the application.

[0034] The block diagrams in the drawings show only the functionality of the embodiments and do not imply that the functions must be implemented in a particular manner. For example, the functions of one or more of the illustrated components can be implemented in hardware, software, firmware, or any combination thereof. Further, the illustrated components can be implemented in one or more physical devices, which can each have one or more of the components illustrated in the drawings. The diagrams are intended to be as clear and concise as possible to facilitate understanding of the application.

[0035] The flowcharts shown in the drawings are only illustrative, and are not necessarily required to include all the contents and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to actual conditions.

[0036] It should be understood that although the terms first, second, third, etc. can be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the present application concepts. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more of the items.

[0037] Those skilled in the art can understand that the modules or flows in the drawings are not necessarily required to implement the present application, and therefore cannot be used to limit the protection scope of the present application.

[0038] According to the first specific embodiment of the present application, the present application provides a satellite Internet of Things measurement operation control task planning system based on a genetic algorithm, comprising: a weight threshold acquisition module, an adaptability function establishment module, a selection operation module, a crossover operation module, a satellite Internet of Things measurement operation control task planning mutation system based on a genetic algorithm, a population adaptability calculation module, and a termination module.

[0039] The weight threshold acquisition module is used to acquire an initial weight and an initial threshold.

[0040] The adaptability function establishment module is used to establish an adaptability function.

[0041] The selection operation module is used for selection operation.

[0042] The crossover operation module is used for crossover operation to obtain a task coding scheme of multiple stations within a predetermined time period.

[0043] The satellite Internet of Things measurement operation control task planning mutation system based on a genetic algorithm is used to mutate the task coding scheme of each station according to a satellite Internet of Things measurement operation control task planning mutation method based on a genetic algorithm, and combine them into a population.

[0044] The satellite Internet of Things measurement operation control task planning mutation system based on a genetic algorithm comprises a scheme acquisition module, a number sequence generation module, a number sequence update module, and a scheme generation module.

[0045] The acquisition module is used to acquire a task coding scheme of a station within a predetermined time period; The generation module is used to map the task coding scheme to a 01 number sequence of whether a plurality of visible arc segments of the station are selected.

[0046] The update module determines whether to mutate based on the mutation probability. If mutation is performed, a point is randomly selected from the 01 sequence. If the point is 1, it is set to 0, the mutation ends, and the module proceeds to the scheme generation module. If the point is 0, it is set to 1, and the start and end times of the visible arc corresponding to the point are obtained. The occupancy flag and switching interval are obtained. Based on the start and end times of the visible arc corresponding to the point, the occupancy flag, and the switching interval, all points before and after the point are traversed. Points with conflicts are set to 0, and points without conflicts are set to 1, and the 01 sequence is updated.

[0047] The generation module is used to map the mutated 01 sequence back to the task encoding scheme.

[0048] The population fitness calculation module is used to calculate population fitness.

[0049] The termination module is used to determine the termination condition. If the termination condition is not met, the mutation and population fitness are recalculated until the termination condition is met.

[0050] According to a second specific embodiment of the present invention, the present invention provides a satellite IoT telemetry, tracking, and command (TT&C) mission planning method based on a genetic algorithm, employing the mission planning system of the first specific embodiment. Figure 1 As shown, it includes the following steps: S1: Obtain the initial weights and initial thresholds.

[0051] S2: Establish the fitness function.

[0052] S3: Selection operation.

[0053] S4: Cross operation to obtain the task coding scheme for multiple stations within a predetermined time period.

[0054] S5: According to the mutation method of satellite IoT telemetry, tracking, and control mission planning based on genetic algorithm, the mission coding scheme of each station is mutated and combined into a population.

[0055] A mutation method for satellite IoT telemetry, tracking, and control mission planning based on genetic algorithms, such as... Figure 2 As shown, it includes the following steps: S51: Obtain the task coding scheme for a station within a predetermined time period.

[0056] The task coding scheme includes the start and end times of multiple visible arc segments within a predetermined time period for a single station. Among them, A all Indicates the task coding scheme. Indicates the start time of the first visible arc segment. denotes the end time of the first visible arc segment, denotes the start time of the second visible arc segment, denotes the end time of the second visible arc segment.

[0057] S52: mapping the 01 sequence of whether the multiple visible arc segments of the station are selected according to the task coding scheme.

[0058] The selection result of the coding scheme of any genetic algorithm can be mapped into a string of 01 sequences , wherein respectively represent the start time and the end time of the i-th visible arc segment of the ground station. 0 represents that the visible arc segment is not selected, and 1 represents that the visible arc segment is selected.

[0059] The 01 sequence is also sorted according to the start tracking time. The start tracking time is sorted from small to large .

[0060] S53: determining whether to mutate according to the mutation probability. If mutation is performed, a point is randomly selected from the 01 sequence. If the point is 1, it is set to 0, the mutation is ended, and step S57 is executed. If the point is 0, step S54 is executed.

[0061] Determining whether to mutate according to the mutation probability includes: if the mutation probability is in a specified range, it is determined to mutate, otherwise it is not mutated. The specified range of the mutation probability is 0.1-0.2.

[0062] In the 1-0 mutation, a point is randomly selected from the 01 sequence . If it is 1, it is set to 0, and the mutation is ended. The 0 operation seems to reduce the utilization once, but sets a conflict-free area, and the subsequent 0-1 mutation greedy reversal process will be truncated in the conflict-free area. Part of the results can be reused to reduce the changes of the original solution by the 0-1 mutation.

[0063] S54: setting the point to 1, and obtaining the start and end times of the visible arc segment corresponding to the point.

[0064] generating a new 01 sequence , setting the point i to 1, ; at this time, the start and end times of the visible arc segment corresponding to the point are .

[0065] S55: obtaining the occupation identifier and the switching interval.

[0066] The occupation identifier is ; wherein denotes a forward auxiliary variable, denotes the start time of the visible arc segment corresponding to the point, represents the backward auxiliary variable, represents the end time of the visible arc segment corresponding to the point position, represents the switching interval.

[0067] The forward auxiliary variable represents the auxiliary time stamp variable of the search progress in the forward conflict detection process related to the variant point position.

[0068] The backward auxiliary variable represents the auxiliary time stamp variable of the search progress in the backward conflict detection process related to the variant point position.

[0069] The initial value of the two auxiliary variables is: .

[0070] The switching interval is the minimum switching time required for the station to continuously serve two satellites.

[0071] S56: According to the start and end time of the visible arc segment corresponding to the point position, the occupation identifier and the switching interval, traverse all the point positions forward and backward of the point position, set the point position to 0 if there is a conflict, and set the point position to 1 if there is no conflict, and update the 01 sequence.

[0072] According to the start and end time of the visible arc segment corresponding to the point position, the occupation identifier and the switching interval, traverse all the point positions forward and backward of the point position, set the point position to 0 if there is a conflict, and set the point position to 1 if there is no conflict, and also include if the point position is 1 before the point position is changed and still 1 after the point position is changed, stop traversing.

[0073] According to the start and end time of the visible arc segment corresponding to the point position, the occupation identifier and the switching interval, traverse all the point positions forward and backward of the point position, set the point position to 0 if there is a conflict, and set the point position to 1 if there is no conflict, including: S561: Traverse each point position in turn, if the point position and the next point position exist conflict, set the point position to 0, otherwise set the point position to 1.

[0074] This step is the prefeasibility guarantee in 0-1 mutation: Start from the previous point position of the point position , traverse each point position forward . If , it is judged that the visible arc segment exists conflict with i, and the point position is set to 0; If , there is no conflict, and is configured ; update ; Since the original solution before compilation is conflict-free, until ==1 and (i.e. if the new encoding and the old encoding are both 1 at this point, it can be guaranteed that the subsequent traversal is also different), stop searching, and the results from 0~b points are reused to the mutated results, ; or stop searching until b==0.

[0075] S562: Traverse each point in turn, if the point conflicts with the previous point, set the point to 0, otherwise set the point to 1.

[0076] This step is the post-order feasibility guarantee in 0-1 mutation: From the next bit of the point , traverse each point in turn; If , it is judged that the visible arc segment conflicts with i, and the point is set to 0; If , there is no conflict, and is configured; ; update ; Since the original solution before compilation is conflict-free, stop searching until ==1 and (i.e. if the new encoding and the old encoding are both 1 at this point, it can be guaranteed that the subsequent traversal is also different), stop searching, and the results from a~end points are reused to the mutated results, ; or stop searching until a==end.

[0077] The final is the mutated result that guarantees the feasibility of the solution.

[0078] S57: Map the mutated 01 sequence back to the task encoding scheme.

[0079] Map to the genetic algorithm encoding according to the original algorithm encoding method, which can adapt to any encoding method.

[0080] The original algorithm encoding method can be any method, as long as the information contained in is written into the original encoding according to the original rules. The purpose of encoding conversion is to adapt to any encoding method of genetic algorithm, and to guarantee the continuation of the original algorithm.

[0081] The mutation operation of a station is a part of the mutation operation of the fixed position in an individual. Multiple stations are mutated, and the set of multiple stations is regarded as an individual. The set of multiple stations corresponds to a measurement control task planning scheme, and multiple measurement control task planning schemes correspond to multiple individuals. Multiple individuals are combined into a population. All obtained individuals (complete encoding scheme) are combined into a population for the next round of search.

[0082] S6: Calculate the fitness of the population.

[0083] S7: If the termination condition is not met, repeat steps S5 to S7 until the termination condition is met.

[0084] The termination condition is one of the following three ways: 1. Set the index f, and any individual fitness value f'>f, that is, reach the index requirement, then stop; 2. Set the maximum limit round m; 3. Set any window w<m, and terminate if the best fitness does not improve for w consecutive rounds.

[0085] In the design of the mutation process of the genetic algorithm, all the constraints required by the ground station (constraints on switching intervals, constraints on ground station utilization, and constraints on the ground station serving only a single satellite at the same time) are guaranteed; the greedy use of the ground station is still guaranteed in the 0-1 mutation process; the 1-0 mutation only reduces the utilization of the visible arc segment once, establishes a conflict-free isolation region, improves the reuse rate in subsequent traversal, and improves the execution speed of subsequent mutation operations.

[0086] As can be seen, the present scheme designs a task planning algorithm for satellite Internet of Things coverage optimization, and satisfies the mutation operation method of the genetic algorithm that meets the special requirements of satellite Internet of Things task planning; through the design of the mutation operation in the genetic algorithm, the mutation operation guarantees the satisfaction of the constraints in the satellite Internet of Things task planning process, and avoids the feasibility failure caused by mutation; without violating the constraints, the mutation probability does not reduce the search efficiency of the algorithm, and can adapt to any configured mutation probability.

[0087] According to a third specific embodiment of the present application, an electronic device is provided, such as Figure 3 as shown in Figure 3 is a block diagram of an electronic device according to an exemplary embodiment.

[0088] The electronic device 300 according to this embodiment of the present application will be described below with reference to Figure 3 FIG. 1. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present application.

[0089] As shown in Figure 3 FIG. 1, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 can include, but are not limited to, at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), a display unit 340, etc.

[0090] The storage unit stores program codes which can be executed by the processing unit 310, so that the processing unit 310 performs the steps described in the present specification according to various exemplary embodiments of the present application. For example, the processing unit 310 can perform the steps shown in the second embodiment.

[0091] The storage unit 320 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 3201 and / or a cache memory 3202, and can further include a read-only memory (ROM) 3203.

[0092] The storage unit 320 can further include program / utility 3204 having a set of programs / modules 3205, including an operating system, one or more application programs, other programs, and programmatic data, each or any combination thereof, which can include implementation of a network environment.

[0093] The bus 330 can be representative of one or more of several types of bus structures, including a storage unit bus or bus controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0094] The electronic device 300 can also communicate with one or more external devices 300' such as a keyboard or pointing device, a Bluetooth device, etc. so that a user can interact with the electronic device 300 and / or any devices (e.g., a router, a modem, etc.) the electronic device 300 can communicate with such as a router, a modem, etc. The communication can be facilitated through input / output (I / O) interface 350. Further, the electronic device 300 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the public network, such as the Internet, through network adapter 360. The network adapter 360 can communicate with the other modules of the electronic device 300 through the bus 330. It should be appreciated that although not shown, other hardware and / or software modules can be used in conjunction with the electronic device 300, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0095] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by software in combination with the necessary hardware.

[0096] Therefore, according to the fourth embodiment of the present application, the present application provides a computer readable medium. As shown inFigure 4 As shown, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or on a network, and includes a plurality of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above-mentioned method according to the embodiments of the present application.

[0097] The software product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0098] The computer readable storage medium can include a data signal carried in a baseband or as part of a carrier wave propagating through a transmission medium, in which readable program codes are borne. Such a propagating data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in conjunction with an instruction execution system, device or component. The program code contained on the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0099] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, etc., and a conventional procedural programming language such as the "C" language or a similar programming language. The program code can be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, through an Internet service provider to connect through the Internet).

[0100] The computer readable medium described above carries one or more programs, which when executed by the device, cause the computer readable medium to implement the functions of the second embodiment.

[0101] Those skilled in the art can understand that the above modules can be distributed in the device according to the description of the embodiments, and can also be changed in one or more devices different from the embodiments. The modules of the above embodiments can be combined into one module, or further split into multiple sub-modules.

[0102] Through the above description of the embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a plurality of instructions to make a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) execute the method according to the embodiments of the present application.

[0103] The example embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structure, arrangement or implementation method described herein; on the contrary, the present application is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the appended claims.

[0104] The example embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the detailed structure, arrangement or implementation method described herein; on the contrary, the present application is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the appended claims.

Claims

1. A mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms, characterized in that, Includes the following steps: S51: Obtain the task coding scheme for a station within a predetermined time period; S52: A sequence of 0s and 1s mapped to whether multiple visible arc segments of the station are selected according to the task coding scheme; S53: Determine whether to mutate based on the mutation probability. If mutation is to be performed, randomly select a point from the 01 sequence. If the point is 1, set it to 0, end the mutation, and proceed to step S57; if the point is 0, proceed to step S54. S54: Set the position of this point to 1, and obtain the start and end times of the visible arc segment corresponding to this point; S55: Obtain the occupancy indicator and switching interval; S56: Based on the start and end times, occupancy flags and switching intervals of the visible arcs corresponding to the point, traverse all points before and after the point, set the points with conflicts to 0, and the points without conflicts to 1, and update the 01 sequence. S57: Map the mutated 01 sequence back to the task encoding scheme.

2. The mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms according to claim 1, characterized in that, Step S52 also includes: sorting the 01 sequence according to the starting tracking time.

3. The mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms according to claim 1, characterized in that, In step S53, deciding whether to mutate based on the mutation probability includes deciding to mutate if the mutation probability is within a specified range, and not mutating otherwise.

4. The mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms according to claim 3, characterized in that, In step S55, the occupancy identifier is ;in, Indicates a forward auxiliary variable. This indicates the start time of the visible arc segment corresponding to that point. Represents a backward auxiliary variable. This indicates the end time of the visible arc segment corresponding to that point. The switching interval is the minimum switching time required for the station to continuously serve two satellites.

5. The mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms according to claim 1, characterized in that, In step S56, the step of traversing all points before and after the point based on the start and end times, occupancy flags, and switching intervals of the visible arc segment corresponding to the point, setting conflicting points to 0 and non-conflicting points to 1, includes: Iterate forward through each point in sequence. If a point conflicts with the next point, set the point to 0; otherwise, set the point to 1. Iterate through each point in turn. If a point conflicts with the previous point, set the point to 0; otherwise, set the point to 1.

6. The mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms according to claim 5, characterized in that, The step of traversing all points before and after the point based on the start and end times, occupancy flags, and switching intervals of the visible arc corresponding to the point, setting conflicting points to 0 and non-conflicting points to 1, also includes stopping the traversal if the point was 1 before the mutation and is still 1 after the mutation.

7. A mutation system for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms, characterized in that, include: The module includes a solution acquisition module, a sequence generation module, a sequence update module, and a solution generation module. The scheme acquisition module is used to acquire the task coding scheme of a station within a predetermined time period. The sequence generation module is used to map the task coding scheme to a 01 sequence indicating whether multiple visible arc segments of the station are selected. The sequence update module is used to determine whether to mutate based on the mutation probability. If mutation is to be performed, a point is randomly selected from the 01 sequence. If the point is 1, it is set to 0, the mutation ends, and the module proceeds to the scheme generation module. If the point is 0, it is set to 1, and the start and end times of the visible arc corresponding to the point are obtained. The occupancy flag and switching interval are obtained. Based on the start and end times of the visible arc corresponding to the point, the occupancy flag, and the switching interval, all points before and after the point are traversed. Points with conflicts are set to 0, and points without conflicts are set to 1, and the 01 sequence is updated. The scheme generation module is used to map the mutated 01 sequence back to the task coding scheme.

8. A mission planning method for satellite IoT telemetry, tracking, and command based on genetic algorithms, characterized in that, Includes the following steps: S1: Obtain the initial weights and initial thresholds; S2: Establish the fitness function; S3: Selection operation; S4: Cross operation to obtain the task coding scheme for multiple stations within a predetermined time period; S5: The mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms according to any one of claims 1-6 mutates the mission coding scheme of each station and combines them into a population; S6: Calculate population fitness; S7: If the termination condition is not met, repeat steps S5 to S7 until the termination condition is met.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the rocket launch data information processing method as described in any one of claims 1-6.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the rocket launch data information processing method as described in any one of claims 1-6.

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