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

Abstract

The present application relates to satellite measurement operation control technical field, provide a kind of task planning variation method and system, task planning method, equipment and medium, variation method includes: obtaining task coding scheme;According to task coding scheme mapping is 01 series;Decide whether to mutate, if mutation, randomly select a point from 01 series, if the point is 1, then it is 0, end mutation;If the point is 0, then the point is 1, obtain the start and end time of the visible arc segment corresponding to the point;According to the start and end time of the visible arc segment corresponding to the point, occupation mark and switching interval, traverse all points, the point of conflict is 0, the point of no conflict is 1;01 series mapping back to task coding scheme is completed in the variation.This scheme satisfies the constraint in the process of guaranteeing satellite internet of things task planning in variation operation, avoid the feasibility failure caused by variation;Variation probability will not reduce the search efficiency of algorithm.

Description

Technical Field

[0001] This invention relates to the field of satellite telemetry, tracking, and command (TT&C) technology, and in particular to a mutation method and system for satellite Internet of Things (IoT) TT&C mission planning based on genetic algorithms, as well as mission planning methods, equipment, and media. Background Technology

[0002] With the development of aerospace technology, the deployment cost of low-Earth orbit (LEO) satellites has gradually decreased, making it possible to provide network access services to ground terminals via LEO satellites. Especially in the fields of the Internet of Things (IoT) and the Internet of Vehicles (IoV), the wide coverage of satellites can effectively solve the problem of coverage blind spots in areas with poor infrastructure, providing wider network coverage.

[0003] During the service process, in order to ensure network connectivity, it is necessary to first establish a backhaul link between the ground station and the satellite. Once the backhaul link is successfully established, the satellite can replace the traditional base station to provide data network services to user terminals within its coverage area.

[0004] During the operation of low-Earth orbit satellite networks, due to the limited coverage area of ​​satellites, a single satellite is usually insufficient to cover the entire target area. Furthermore, due to the mobility of satellites, the coverage area changes over time. Therefore, multiple satellites and multiple stations are typically required to jointly serve the target area.

[0005] This process requires mission planning to determine when, through which tracking stations, and for which satellites to establish backhaul links. Furthermore, it necessitates designing the overall coverage objectives for the low-Earth orbit satellite constellation and assessing coverage conditions in a specific area to guide mission planning.

[0006] Existing satellite mission planning typically targets remote sensing satellites and relay communication satellites, sharing the similarity of needing to plan backhaul links between ground stations and satellites. However, their application scenarios differ significantly. For example, remote sensing satellites focus solely on imaging the target area and are more concerned with the satellite's imaging attitude, without stringent real-time requirements. In contrast, relay communication satellites usually only perform point-to-point relay missions and do not need to consider large-scale service.

[0007] Satellite IoT coverage optimization scenarios not only need to consider satellite coverage of a certain area, but also the availability of backhaul links. Only when the satellite establishes a backhaul link with the ground station and the terminal is within the satellite's coverage area can services be provided to that terminal.

[0008] In task planning problems, genetic algorithms are commonly used to search for planning results. During the use of genetic algorithms, mutation operations are often performed randomly. Without constraints, feasible solutions may become invalid, especially as the duration of task planning increases and the number of gene points increases, the destructive impact of random mutation on constraints becomes significant. Constraint violations caused by mutation are usually addressed during the natural selection phase.

[0009] During mission planning, with the increase in the number of tracking and control stations, satellites, and the planning market, it is impractical to traverse all possible solutions. Furthermore, many 0-1 variables lack clear optimization gradient directions, necessitating the use of global search methods such as genetic algorithms. However, existing satellite mission planning models are mostly geared towards applications such as remote sensing imaging and point-to-point relay communication. Imaging-related algorithm designs do not accurately describe the simultaneous need for backhaul and access links in satellite IoT. Additionally, while relay communication algorithms consider the dual visibility requirement between the relay satellite and the two communication nodes, they do not address the need for large-area coverage in satellite IoT.

[0010] In task planning problems, genetic algorithms are often used to search for planning results. During the use of genetic algorithms, mutation operations are usually performed randomly. Without constraints, feasible solutions will become invalid. Especially as the task planning time increases and the number of gene points increases, random mutations can be quite destructive to constraints.

[0011] In the design of genetic algorithms related to a large number of task planning tasks, the constraints are guaranteed by truncating and eliminating individuals that do not meet the constraints through natural selection. However, this method leads to a decrease in search efficiency and is prone to mutation failure as the constraints become more complex. To mitigate the negative impact of failure mutations, a common approach is to reduce the mutation probability to ensure a sufficient number of feasible solutions in each generation. However, a lower mutation probability reduces global search efficiency and makes it prone to getting trapped in local optima. Furthermore, designing the specific numerical value of the mutation probability is difficult and requires extensive experimentation.

[0012] Therefore, there is a need to provide a mutation method and system for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms, as well as mission planning methods, equipment, and media. This includes designing a mission planning algorithm for satellite IoT coverage optimization, a mutation operation method for the genetic algorithm that meets the specific needs of satellite IoT mission planning, and ensuring that the mutation operation in the genetic algorithm satisfies the constraints in the satellite IoT mission planning process, avoiding feasibility failures caused by mutation. Furthermore, the mutation operation does not violate constraints, the mutation probability does not reduce the algorithm's search efficiency, and it can adapt to arbitrarily configured mutation probabilities.

[0013] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0014] The main objective of this invention is to overcome the problems of high constraint destruction and reduced search efficiency caused by mutation in genetic algorithms during satellite telemetry, tracking, and command (TT&C) mission planning. It provides a mutation method and system for satellite IoT TT&C mission planning based on genetic algorithms, along with mission planning methods, equipment, and media. The invention designs a mission planning algorithm for satellite IoT coverage optimization and a mutation operation method for genetic algorithms that meets the specific needs of satellite IoT mission planning. By designing mutation operations in the genetic algorithm, the mutation operations ensure that constraints are met during the satellite IoT mission planning process, avoiding feasibility failures caused by mutation. The mutation probability does not violate constraints and does not reduce the algorithm's search efficiency; it can adapt to arbitrarily configured mutation probabilities.

[0015] To achieve the above objectives, the first aspect of this invention provides a mutation method for satellite Internet of Things (IoT) telemetry, tracking, and command (TT&C) mission planning based on a genetic algorithm, comprising 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.

[0016] According to an exemplary embodiment of the present invention, step S52 further includes: sorting the 01 sequence according to the starting tracking time.

[0017] According to an exemplary embodiment of the present invention, in step S53, deciding whether to mutate based on the mutation probability includes: if the mutation probability is within a specified range, then deciding to mutate; otherwise, not mutating.

[0018] According to an exemplary embodiment of the present invention, 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.

[0019] According to an exemplary embodiment of the present invention, 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, and setting conflicting points to 0 and non-conflicting points to 1, includes: Iterate forward through each point in sequence. If the 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.

[0020] According to an exemplary embodiment of the present invention, the step of traversing all points before and after the point based on the start and end times, occupancy flag and switching interval of the visible arc corresponding to the point, setting conflicting points to 0 and non-conflicting points to 1, further includes stopping the traversal if the point was 1 before the mutation and is still 1 after the mutation.

[0021] As a second aspect of the present invention, the present invention provides a mutation system for satellite Internet of Things telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms, comprising: a scheme acquisition module, a data sequence generation module, a data sequence update module, and a scheme 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.

[0022] As a third aspect of the present invention, the present invention provides a satellite Internet of Things (IoT) telemetry, tracking, and command (TT&C) mission planning method based on a genetic algorithm, comprising 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: 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; S6: Calculate population fitness; S7: If the termination condition is not met, repeat steps S5 to S7 until the termination condition is met.

[0023] As a fourth aspect of the present invention, the present invention provides an electronic device comprising: 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 mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms.

[0024] As a fifth aspect of the present invention, the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the mutation method for satellite Internet of Things telemetry, tracking, and command (TT&C) mission planning based on a genetic algorithm.

[0025] The advantages of this invention are: In designing the genetic algorithm mutation process, this scheme ensures all the constraints required by the ground station (constraints on switching intervals, ground station utilization, and ground station serving only a single satellite at any given time). The 0-1 mutation process still ensures the greedy utilization of the ground station. The 1-0 mutation only reduces the utilization of one visible arc segment, establishes a conflict-free isolation area, improves the reuse rate in subsequent traversals, and increases the execution speed of subsequent mutation operations.

[0026] As can be seen, this solution designs a task planning algorithm for satellite IoT coverage optimization, and a genetic algorithm mutation operation method to meet the special needs of satellite IoT task planning. By designing the mutation operation in the genetic algorithm, the mutation operation ensures that the constraints in the satellite IoT task planning process are met and avoids feasibility failure caused by mutation. It does not violate the constraints, the mutation probability does not reduce the search efficiency of the algorithm, and it can adapt to any configured mutation probability. Attached Figure Description

[0027] The above and other objects, features, and advantages of this application will become more apparent from the detailed description of exemplary embodiments with reference to the accompanying drawings. The drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0028] Figure 1 The diagram illustrates the steps of a mission planning method for satellite IoT telemetry, tracking, and command based on a genetic algorithm.

[0029] Figure 2 The diagram illustrates the steps of a mutation method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on a genetic algorithm.

[0030] Figure 3 A schematic diagram of the electronic device is shown.

[0031] Figure 4 A schematic diagram of the structure of a computer medium is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0033] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0035] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0036] It should be understood that although the terms first, second, third, etc., may 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. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this application. As used herein, the term "and / or" includes all combinations of any one and more of the associated listed items.

[0037] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing this application, and therefore cannot be used to limit the scope of protection of this application.

[0038] According to a first specific embodiment of the present invention, the present invention provides a satellite IoT telemetry, tracking, and command (TT&C) mission planning system based on a genetic algorithm, comprising: a weight threshold acquisition module, a fitness function establishment module, a selection operation module, a crossover operation module, a mutation system for satellite IoT TT&C mission planning based on a genetic algorithm, a population fitness calculation module, and a termination module.

[0039] The weight and threshold acquisition module is used to obtain the initial weight and initial threshold.

[0040] The fitness function creation module is used to create fitness functions.

[0041] The operation selection module is used to select operations.

[0042] The cross-operation module is used to perform cross-operations to obtain task coding schemes for multiple stations within a predetermined time period.

[0043] The mutation system for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms is used to mutate the mission coding scheme of each station according to the mutation method of satellite IoT TT&C mission planning based on genetic algorithms, and combine them into a population.

[0044] A mutation system for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms includes: a scheme acquisition module, a data generation module, a data update module, and a scheme generation module.

[0045] The acquisition module is used to acquire the task coding scheme of a station within a predetermined time period; The generation module is used to map the selection of multiple visible arc segments at the station to a 01 sequence based on the task coding scheme.

[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 of 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. Indicates the end time of the first visible arc segment. Indicates the start time of the second visible arc segment. This indicates the end time of the second visible arc segment.

[0057] S52: A sequence of 0s and 1s mapped to whether multiple visible arc segments of the station are selected, based on the task coding scheme.

[0058] For any encoding scheme of genetic algorithm The selection result can be mapped to a sequence of 0s and 1s. ,in These represent the start and end times of the i-th visible arc segment of the ground station, respectively. 0 indicates that the visible arc segment is not selected, and 1 indicates that the visible arc segment is selected.

[0059] The 01 sequence is also sorted by the start tracking time. It is then sorted from smallest to largest by the start tracking time. .

[0060] 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 execute step S57; if the point is 0, execute step S54.

[0061] The decision to mutate based on the mutation probability includes: if the mutation probability is within a specified range, then mutation is performed; otherwise, no mutation is performed. The specified range for the mutation probability is 0.1 to 0.2.

[0062] In the 1-0 mutation, the 01 sequence A random point is selected in the algorithm. If the value is 1, it is set to zero, and the mutation ends. This zeroing operation seems to reduce one utilization, but it sets up a conflict-free region. The subsequent 0-1 mutation greedy reversal process will stop in this conflict-free region, which can reuse some results and reduce the modification of the original solution by the 0-1 mutation.

[0063] 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.

[0064] Generate a new 01 sequence. Set the position i of this point to 1. At this point, the start and end times of the visible arc segment are respectively... .

[0065] S55: Obtain the occupancy indicator and switching interval.

[0066] Occupied by: ;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. Indicates the switching interval.

[0067] Forward auxiliary variables represent auxiliary timestamp variables related to the mutation point and the retrieval progress during the forward conflict detection process.

[0068] The backward auxiliary variable represents an auxiliary timestamp variable representing the retrieval progress during the backward conflict detection process, which is related to the mutation point.

[0069] The initial values ​​of the two auxiliary variables are: .

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

[0071] 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.

[0072] Based on the start and end times, occupancy flags, and switching intervals of the visible arcs corresponding to the given point, traverse all points before and after the given point. Set points with conflicts to 0 and points without conflicts to 1. Also, if a point was 1 before the mutation and is still 1 after the mutation, then stop traversing.

[0073] Based on the start and end times, occupancy flag, and switching interval of the visible arc segment corresponding to the given point, traverse all points before and after the given point, setting conflicting points to 0 and non-conflicting points to 1. This includes: S561: Iterate forward through each point in sequence. If the point conflicts with the next point, set the point to 0; otherwise, set the point to 1.

[0074] This step ensures the feasibility of the preceding sequence in 0-1 mutation: From the previous position of this point Begin by iterating backwards through each point. ; like If the visible arc segment conflicts with i, then the point is moved to that position. Set to 0; like Then there is no conflict, configuration ;renew ; Since the original solution before compilation was conflict-free, until... ==1 and (That is, when both the new and old codes are 1 at this point, it can be guaranteed that there will be no difference in subsequent traversals), stop the search, and reuse the results from points 0 to b for the mutated result. Or stop searching until b==0.

[0075] S562: Iterate through each point in sequence. If the point conflicts with the previous point, set the point to 0; otherwise, set the point to 1.

[0076] This step ensures the feasibility of subsequent steps in 0-1 mutations: From the next position of that point Begin by iterating backwards through each point. ; like If the visible arc segment conflicts with i, then the point is moved to that position. Set to 0; like Then there is no conflict, configuration ;renew ; Since the original solution before compilation was conflict-free, until... ==1 and (That is, when both the new and old codes are 1 at this point, it can be guaranteed that there will be no difference in subsequent traversals), stop the search, and reuse the results from points a to end for the mutated result. Or stop searching until a==end.

[0077] Final result It is the result of the variation that ensures the feasible solution constraint.

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

[0079] Encode according to the original algorithm method, When mapped to genetic algorithm encoding, it can be adapted to any encoding method.

[0080] The original algorithm can be encoded in any way, as long as it follows the original rules. The information contained therein is written into the original encoding. The purpose of encoding conversion is to adapt to genetic algorithms with any encoding method and ensure that the original algorithm continues to advance.

[0081] The mutation operation of a single station is considered a mutation operation on a segment of a fixed location within an individual. Mutation operations are performed on multiple stations, and the set of multiple stations is considered a single individual. The set of multiple stations corresponds to a measurement and control task planning scheme, and multiple measurement and control task planning schemes correspond to multiple individuals. These individuals are then combined into a population. All the obtained individuals (complete encoding schemes) are grouped into a population, and the next round of search is performed.

[0082] S6: Calculate the population fitness.

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

[0084] The termination condition is one of the following three methods: 1. Set an indicator f, and if any individual's fitness value f' > f, that means the indicator requirement is met, and the process is stopped; 2. Set the maximum number of iterations m; 3. Arbitrarily set a window w < m, and terminate if the optimal fitness has not improved for consecutive w rounds.

[0085] In the process of designing mutation of the genetic algorithm, this solution guarantees all the constraints required by the ground station (the constraint on handover interval, the constraint on ground station utilization, and the constraint that a ground station only serves one satellite at the same time); the 0-1 mutation process still guarantees the greedy utilization of the ground station; 1-0 mutation only reduces the utilization of a visible arc segment once, and will establish a conflict-free isolation area, improve the reusability in subsequent traversal, and increase the execution speed of subsequent mutation operations.

[0086] It can be seen that this solution is a mutation operation method of genetic algorithm that meets the special requirements of satellite IoT task planning for a task planning algorithm oriented to satellite IoT coverage optimization; by designing the mutation operation in genetic algorithm, the mutation operation ensures the satisfaction of constraints in the process of satellite IoT task planning and avoids the feasibility failure caused by mutation; without violating constraints, the mutation probability will not reduce the search efficiency of the algorithm, and can adapt to mutation probabilities of any configuration.

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

[0088] The following describes with reference to Figure 3 the electronic device 300 according to this embodiment of the present application. Figure 3 The illustrated electronic device 300 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present application.

[0089] As Figure 3 shown, the electronic device 300 is in the form of a general-purpose computing device. Components of the electronic device 300 may 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 code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in this specification according to various exemplary embodiments of this application. For example, the processing unit 310 can perform the steps shown in the second specific embodiment.

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

[0092] The storage unit 320 may also include a program / utility 3204 having a set (at least one) program module 3205, such program module 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0093] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0094] Electronic device 300 can also communicate with one or more external devices 300' (e.g., keyboard, pointing device, Bluetooth device, etc.), enabling users to communicate with devices that interact with electronic device 300, and / or any device (e.g., router, modem, etc.) that allows electronic device 300 to communicate with one or more other computing devices. This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. Network adapter 360 can communicate with other modules of electronic device 300 via bus 330. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0095] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware.

[0096] Therefore, according to a fourth specific embodiment of the present invention, the present invention provides a computer-readable medium. For example... Figure 4 As shown, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the above-described method according to the embodiments of the present invention.

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

[0098] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0099] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0100] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the functions of the second specific embodiment.

[0101] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0102] Through the description of the above embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions of the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of the present invention.

[0103] Exemplary embodiments of the present invention have been specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, arrangements, or implementations described herein; rather, the present invention is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

[0104] Exemplary embodiments of the present invention have been specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, arrangements, or implementations described herein; rather, the present invention is intended to cover various modifications and equivalent arrangements contained within 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; 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. This indicates the switching interval; the switching interval is the minimum switching time required for the station to continuously serve two satellites. 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. 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. If the value of a point is 1 before the mutation and remains 1 after the mutation, then stop traversing. 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. 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 determines whether to mutate based on the mutation probability. If mutation is to occur, a point is randomly selected from the 0-1 sequence. If the point is 1, it is set to 0, the mutation ends, and the process 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 that point are obtained. The occupancy flag and switching interval are also obtained. Based on the start and end times of the visible arc corresponding to that point, the occupancy flag, and the switching interval, all points before and after that point are traversed. Points with conflicts are set to 0, and points without conflicts are set to 1, updating the 0-1 sequence. The occupancy flag 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. The process of traversing all points before and after the point based on the start and end times of the visible arc corresponding to that point, the occupancy flag, and the switching interval, setting conflicting points to 0 and non-conflicting points to 1, includes: traversing each point forward sequentially; if the point conflicts with the next point, setting that point to 0, otherwise setting it to 1; traversing each point backward sequentially; if the point conflicts with the previous point, setting that point to 0, otherwise setting it to 1; if the point was 1 before the mutation and remains 1 after the mutation, stopping the traversal; the scheme generation module is used to map the mutated 01 sequence back to the task coding scheme.

5. 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 1-3 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.

6. 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 mutation method for satellite IoT telemetry, tracking, and command mission planning based on genetic algorithms as described in any one of claims 1-3.

7. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the mutation method for satellite IoT telemetry, tracking, and command mission planning based on genetic algorithms as described in any one of claims 1-3.

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