Satellite networking mission planning method, device, system, medium and program product

Abstract

The application relates to the technical field of satellites and provides a satellite and ground station networking task planning method and device, an Internet of Things system, a storage medium and a computer program product, the method comprising the following steps: acquiring a task planning period required by a satellite and a ground station to be networked and acquiring ephemeris orbit prediction information of the task planning period; according to the ephemeris orbit prediction information, a task planning algorithm for Internet of Things system coverage optimization oriented to satellite and ground station networking is designed based on a genetic algorithm; the designed task planning algorithm is used to perform coverage optimization on the Internet of Things system of the satellite and the ground station networking, so that the time length during which the Internet of Things system in the target area of the ground station can be connected to the satellite is improved. The scheme can design a task planning algorithm for satellite Internet of Things coverage optimization, the task planning algorithm is used to perform coverage optimization on the satellite Internet of Things, the time length during which the Internet of Things system in the target area can be connected to the satellite is improved, the accuracy of the task planning is improved, and the coverage range is expanded.

Description

Technical Field

[0001] This invention relates to the field of satellite technology, specifically to a satellite-to-ground station networking mission planning method, apparatus, Internet of Things (IoT) system, storage medium, and computer program product, and particularly to a satellite IoT telemetry, tracking, and command (TT&C) mission planning method, apparatus, IoT system, storage medium, and computer program product based on a genetic algorithm. 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 for terrestrial Internet of Things (IoT) systems via LEO satellites. Especially in the fields of IoT and vehicle-to-everything (V2X) communication, the wide-area coverage of satellites can effectively solve the problem of coverage blind spots in areas with poor infrastructure, providing wider network coverage.

[0003] In the process of satellite networking services, in order to ensure network connectivity, it is necessary to first establish a backhaul link between the ground station and the satellite. After the backhaul link is successfully established, the satellite can replace the traditional base station to provide data network services for the Internet of Things system of users 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 for satellite IoT telemetry, tracking, and command (TT&C) to determine when, through which tracking station, and for which satellite a backhaul link will be established. 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] However, the accuracy and coverage of the mission planning for satellite IoT telemetry, tracking, and control in the relevant schemes are insufficient.

[0007] Therefore, there is an urgent need to develop a mission planning method, device, IoT system, storage medium, and computer program product for satellite-to-ground station networking. In particular, there is a satellite IoT telemetry, tracking, and command (TT&C) mission planning method, device, IoT system, storage medium, and computer program product based on a genetic algorithm. This method can design a mission planning algorithm for satellite IoT coverage optimization. Using the designed mission planning algorithm, the coverage of the satellite-to-ground station networked IoT system can be optimized, the coverage rate of relay communication in satellite IoT scenarios can be optimized, the duration for which IoT systems can connect to satellites within the target area can be increased, the accuracy of mission planning for satellite IoT TT&C can be improved, and the coverage range can be expanded.

[0008] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0009] The purpose of this invention is to provide a satellite IoT telemetry, tracking, and command (TT&C) mission planning method, device, IoT system, storage medium, and computer program product based on a genetic algorithm. By designing a mission planning algorithm for satellite IoT coverage optimization in a satellite IoT coverage optimization scenario, and utilizing the designed algorithm, the coverage of the satellite-ground station networked IoT system is optimized. This optimizes the relay communication coverage rate in the satellite IoT scenario, increases the duration for which IoT systems within the target area can connect to the satellite, improves the accuracy of satellite IoT TT&C mission planning, and expands the coverage area.

[0010] To address the aforementioned technical problems, as one aspect of this invention, a mission planning method for satellite-to-ground station networking is provided. This method is applied to mission planning when establishing a backhaul link between a satellite and a ground station in a satellite IoT coverage optimization scenario. The mission planning method for satellite-to-ground station networking includes the following steps: obtaining the mission planning period for which the satellite and ground station need to network, and obtaining the ephemeris orbit prediction information for the mission planning period; designing a mission planning algorithm for coverage optimization of the IoT system for satellite-to-ground station networking based on the ephemeris orbit prediction information and a genetic algorithm; and using the designed mission planning algorithm to optimize the coverage of the IoT system for satellite-to-ground station networking, thereby optimizing the coverage rate of relay communication in the satellite IoT scenario, increasing the duration for which the IoT system within the target area of ​​the ground station can connect to the satellite, improving the accuracy of mission planning for satellite IoT telemetry, tracking, and command, and expanding the coverage area.

[0011] According to an exemplary embodiment of the present invention, obtaining ephemeris orbit prediction information for the mission planning period includes: obtaining the set of satellites in the constellation; obtaining the set of all stations of the ground station used to perform mission planning; obtaining the target area of ​​all stations of the ground station to divide the target area of ​​all stations of the ground station into a grid smaller than the coverage area of ​​the satellites; and obtaining the trajectory prediction information of each satellite in the set of satellites during the mission planning period through orbit prediction.

[0012] According to an exemplary embodiment of the present invention, based on the ephemeris orbit prediction information, a task planning algorithm for optimizing IoT coverage for the satellite is designed using a genetic algorithm. The algorithm includes: preprocessing the ephemeris orbit prediction information to obtain a set of visible arc segments for all regions of all satellites and ground stations; encoding the number of stations in all regions of the ground stations and the number of visible arc segments for each station, using individual genes in the population to represent whether each visible arc segment of each station is used for backhaul service; and initializing and calculating the fitness of each individual in the population using a genetic algorithm based on the fragment representing whether each visible arc segment of each station is used for backhaul service, followed by crossover and mutation processing, and selecting the individual with the highest fitness as the required task planning scheme. Thus, a task planning algorithm for optimizing IoT coverage for the satellite is designed.

[0013] According to an exemplary embodiment of the present invention, the ephemeris orbit prediction information is preprocessed to obtain a set of visible arc segments for all regions of all satellites and ground stations. This includes: calculating the set of visible arc segments for all satellites and all ground stations based on the ephemeris of all satellites and the coordinates of all ground stations in the trajectory prediction information of each satellite in the satellite set during the mission planning period; classifying the visible arc segments of all satellites and all stations, storing only the visible arc segments of each station and all satellites in the set of each station; and calculating the visible arc segment of each satellite and each grid center point based on the coordinates of the grid center point of all regions of the ground station and the satellite trajectory, thereby obtaining the set of visible arc segments for all regions of all satellites and ground stations.

[0014] According to an exemplary embodiment of the present invention, for the set of visible arc segments in all areas of all satellites and ground stations, the number of stations in all areas of the ground stations and the number of visible arc segments in each station are encoded and designed to use individual genes in the population to represent whether each visible arc segment of each station is used for backhaul service. This includes: for the set of visible arc segments in all areas of all satellites and ground stations, designing the number of gene segments based on the number of stations in all areas of the ground stations, and designing the number of gene points for each segment based on the number of visible arc segments in each station; and representing the content of each gene point using a binary number to indicate whether the visible arc segment is used for backhaul service.

[0015] According to an exemplary embodiment of the present invention, based on the fragments of each individual in the population representing whether each visible arc segment of each station is used for backhaul service, a genetic algorithm is used for initialization and fitness calculation, followed by crossover and mutation processing. The individual with the highest fitness is selected as the required task planning scheme. The process includes: based on the fragments of each individual in the population representing whether each visible arc segment of each station is used for backhaul service, a genetic algorithm is used to randomly initialize a fragment of an individual until all fragments of that individual satisfy the constraints. Then, it is determined whether all individuals in the population satisfy the constraints: if so, the fitness of each individual is calculated; otherwise, the process returns to continue randomly initializing a fragment of an individual. After calculating the fitness of each individual, all regions and all satellites are traversed, and the time set of each region covered by the satellite IoT system is counted. After all regions have been counted and the fitness of each individual has been obtained, natural selection and elite retention are performed, followed by crossover and mutation processing. This process continues until the number of iterations reaches a preset maximum number, and the individual with the highest fitness is selected as the required task planning scheme.

[0016] As a second aspect of the present invention, the present invention provides a satellite-to-ground-station networking mission planning device, applied to mission planning when establishing a backhaul link between a satellite and a ground station in a satellite IoT coverage optimization scenario; the satellite-to-ground-station networking mission planning device includes: an acquisition unit configured to acquire the mission planning period for which the satellite and ground station need to be networked, and to acquire ephemeris orbit prediction information for the mission planning period; a control unit configured to design a mission planning algorithm for coverage optimization of the IoT system for satellite-to-ground-station networking based on the ephemeris orbit prediction information and a genetic algorithm; the control unit is further configured to use the designed mission planning algorithm to optimize the coverage of the IoT system for satellite-to-ground-station networking, optimize the coverage rate of relay communication in the satellite IoT scenario, so as to increase the duration for which the IoT system in the target area of ​​the ground station can connect to the satellite, improve the accuracy of mission planning for satellite IoT telemetry, tracking, and control, and expand the coverage area.

[0017] As a third aspect of the present invention, the present invention provides an Internet of Things system, including: the mission planning device for networking satellites and ground stations as described above.

[0018] As a fourth aspect of the present invention, the present invention provides a storage medium comprising a stored program, wherein, when the program is executed, the device on which the storage medium is located executes the above-described satellite-to-ground station networking mission planning method.

[0019] As a fifth aspect of the present invention, the present invention provides a computer program that, when executed by a processor, implements the steps of the above-described mission planning method for networking satellites and ground stations.

[0020] The beneficial effects of this invention are:

[0021] The method of this invention designs a genetic algorithm that can represent the task planning results in a satellite IoT coverage optimization scenario. It also designs crossover and mutation operations in each iteration of the genetic algorithm, expressing the objective function in the natural selection process. This completes the design of a task planning algorithm for satellite IoT coverage optimization. Furthermore, the designed task planning algorithm is used to optimize the coverage of IoT systems connected to satellites and ground stations, improving the coverage rate of relay communication in satellite IoT scenarios. This increases the duration for which IoT systems within the target area can connect to satellites, improves the accuracy of task planning for satellite IoT telemetry, tracking, and control, and expands the coverage area. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an embodiment of the satellite-to-ground-station networking mission planning method of the present invention;

[0023] Figure 2 This is a flowchart illustrating an embodiment of the method of the present invention for obtaining ephemeris orbit prediction information for the mission planning period;

[0024] Figure 3 This is a flowchart illustrating an embodiment of the task planning algorithm for optimizing IoT coverage for the satellite in the method of the present invention;

[0025] Figure 4 This is a flowchart illustrating an embodiment of the method of the present invention for data preprocessing of the ephemeris orbit prediction information;

[0026] Figure 5 This is a flowchart illustrating an embodiment of the method of the present invention for encoding the number of stations in all areas of the ground station and the number of visible arc segments for each station;

[0027] Figure 6 This is a flowchart illustrating an embodiment of the method of the present invention, which utilizes a genetic algorithm for initialization and fitness calculation followed by crossover and mutation processing.

[0028] Figure 7 This is a schematic diagram of a structure of an embodiment of the mission planning device for satellite-to-ground station networking of the present invention;

[0029] Figure 8 This is a flowchart illustrating a satellite IoT telemetry, tracking, and command (TT&C) mission planning method based on genetic algorithms.

[0030] Figure 9 This is a schematic diagram illustrating the communication status of satellite A in a satellite Internet of Things (IoT) relay communication scenario.

[0031] Figure 10 This diagram illustrates the communication between satellites a, b, and c in a satellite IoT relay communication scenario.

[0032] Among them, 102 is the acquisition unit and 104 is the control unit. Detailed Implementation

[0033] The embodiments of the present invention will be described in detail below, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0034] Considering that the accuracy and coverage of mission planning for satellite IoT telemetry, tracking, and command in the relevant schemes are insufficient, specifically, the satellite mission planning in these schemes is usually geared towards remote sensing satellites and relay communication satellites. Their similarity lies in the need to plan the backhaul links between ground stations and satellites. However, the application scenarios differ significantly. For example, remote sensing satellites only focus on imaging the target area and are more concerned with the satellite's imaging attitude, without stringent real-time requirements. Relay communication satellites, on the other hand, typically only perform point-to-point relay missions and do not need to consider large-scale services.

[0035] 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 IoT system is within the satellite's coverage area can services be provided to the IoT system.

[0036] However, 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, a large number of 0-1 variables lack clear optimization gradient directions, thus requiring global search methods such as genetic algorithms. Specifically, 0-1 variables are discrete variables, non-differentiable, and without gradients, making it impossible to find the optimal solution using traditional convex optimization methods. Moreover, most satellite mission planning problem models in related solutions are 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 consider the need for large-area coverage in satellite IoT.

[0037] Therefore, the present invention proposes a mission planning method for satellite-to-ground station networking, specifically a satellite IoT telemetry, tracking, and command (TT&C) mission planning method based on a genetic algorithm. In the scenario of satellite IoT coverage optimization, a mission planning algorithm oriented towards satellite IoT coverage optimization is designed. Using the designed mission planning algorithm, coverage optimization is performed on the satellite-to-ground station networked IoT system, optimizing the relay communication coverage rate in the satellite IoT scenario, increasing the duration for which IoT systems within the target area can connect to satellites, improving the accuracy of mission planning for satellite IoT TT&C, and expanding the coverage area.

[0038] As a first embodiment of the present invention, a mission planning method for networking satellites and ground stations is provided, such as... Figure 1 The diagram illustrates a flowchart of an embodiment of the method of the present invention. This satellite-to-ground-station networking mission planning method is applied to mission planning when establishing a backhaul link between the satellite and the ground station in a satellite IoT coverage optimization scenario. Because the access network provided by the satellite to IoT devices does not require additional design, as long as the satellite has a backhaul network, IoT devices within the coverage area of ​​the satellite's backhaul network are considered to be able to access the backhaul network. Therefore, the solution of the present invention only designs the mission planning between the satellite and the ground station, and the coverage of the satellite IoT is considered in the design process of the mission planning between the satellite and the ground station.

[0039] In the solution of the present invention, such as Figure 1 As shown, the mission planning method for networking the satellite and the ground station includes steps S110 to S130.

[0040] In step S110, the mission planning period for which the satellite and the ground station need to be connected to the network is obtained, and the ephemeris orbit prediction information for the mission planning period is obtained.

[0041] In step S120, based on the ephemeris orbit prediction information, a task planning algorithm for coverage optimization of the Internet of Things system for satellite-to-ground station networking is designed using a genetic algorithm.

[0042] In step S130, the designed task planning algorithm is used to optimize the coverage of the IoT system that connects the satellite and the ground station, thereby improving the coverage of relay communication in the satellite IoT scenario, increasing the duration for which the IoT system in the target area of ​​the ground station can connect to the satellite, improving the accuracy of task planning for satellite IoT telemetry, tracking, and control, and expanding the coverage area.

[0043] In this invention, a mission planning algorithm for satellite IoT coverage optimization is designed to meet the specific needs of satellite IoT mission planning. Through the proposed mission planning algorithm, satellite IoT coverage is optimized, increasing the duration for which IoT systems within the target area can connect to satellites, improving the accuracy of mission planning for satellite IoT telemetry, tracking, and control, and expanding the coverage area.

[0044] In some implementations, the specific process of obtaining the ephemeris orbit prediction information for the mission planning period in step S110 is illustrated in the following exemplary description.

[0045] The following is combined with Figure 2 The flowchart shown is a schematic diagram of an embodiment of the method of the present invention for obtaining ephemeris orbit prediction information for the mission planning period. It further illustrates the specific process of obtaining ephemeris orbit prediction information for the mission planning period in step S110, including steps S210 to S230.

[0046] Step S210: Obtain the set of satellites in the constellation and obtain the set of all ground stations used to perform mission planning.

[0047] Step S220: Obtain the target area of ​​all ground stations, and divide the target area of ​​all ground stations into a grid smaller than the satellite coverage area. And,

[0048] Step S230: Obtain trajectory prediction information for each satellite in the satellite set during the mission planning period through orbit prediction.

[0049] Figure 8 This is a flowchart illustrating a satellite IoT telemetry, tracking, and command (TT&C) mission planning method based on genetic algorithms. Figure 8 As shown, the present invention proposes a satellite IoT telemetry, tracking, and command (TT&C) mission planning method based on a genetic algorithm, which specifically includes:

[0050] Step 1: Data preprocessing.

[0051] Step 11: Obtain ephemeris orbit forecast information based on the input planning time period, as detailed below:

[0052] 1) The time period for the task to be executed is input as the time range;

[0053] This indicates that the current time step needs to be executed. ,arrive Planning of measurement, operation and control tasks in seconds.

[0054] 2) The satellite set in a constellation is marked as , Indicates a satellite.

[0055] 3) The set of ground stations that can be used to perform mission planning is denoted as follows: , This represents the j-th station. Indicates the station The geographical coordinates; ground stations with multi-channel telemetry and control capabilities are regarded as multiple single-channel ground stations with the same coordinates.

[0056] 4) Divide the target area into a grid much smaller than the satellite coverage area. The grid size can be adjusted flexibly, and the center point of the grid is used as a representative point. The target area that needs optimization is marked as follows: , This represents the k-th target region. Indicates the area The geographical coordinates of the center point.

[0057] 5) Obtain the trajectory prediction of each star in the constellation within the planned time period through orbit prediction.

[0058] In the solution of this invention, a method for solving the mission planning problem is designed in the application scenario of low-orbit satellite Internet of Things coverage optimization; the ground is divided into regions, and the cumulative communication time is used as the target for evaluating the overall coverage, which can describe the coverage of a large area.

[0059] In some implementations, the specific process of designing a task planning algorithm for optimizing IoT coverage for the satellite based on the ephemeris orbit prediction information and a genetic algorithm in step S120 is illustrated in the following exemplary description.

[0060] The following is combined with Figure 3 The flowchart shown is an embodiment of the method of the present invention for designing a task planning algorithm for optimizing IoT coverage for the satellite. It further illustrates the specific process of designing the task planning algorithm for optimizing IoT coverage for the satellite in step S120, including steps S310 to S330.

[0061] Step S310: Perform data preprocessing on the ephemeris orbit prediction information to obtain the set of visible arc segments for all regions of all satellites and ground stations.

[0062] Step S320: For the set of visible arc segments in all areas of all satellites and ground stations, design an encoding for the number of stations in all areas of the ground stations and the number of visible arc segments in each station, so as to use individual genes in the population to represent whether each visible arc segment of each station is used for backhaul service.

[0063] Step S330: Based on the fragments of each individual in the population used to represent whether each visible arc segment of each station is used for backhaul service, the genetic algorithm is used for initialization and fitness calculation, followed by crossover and mutation processing. The individual with the highest fitness is selected as the required task planning scheme. In this way, a task planning algorithm for optimizing IoT coverage for the satellite is designed.

[0064] The present invention proposes a satellite IoT telemetry, tracking, and command (TT&C) mission planning method based on a genetic algorithm, comprising: firstly, designing an encoding method in the genetic algorithm that can represent the mission planning results, which greatly satisfies the constraints; secondly, designing a crossover operation in each iteration of the genetic algorithm to ensure that the constraints are not violated (i.e., ensuring that the crossover operation does not break the constraints); subsequently, designing a mutation operation to allow random expansion of the search direction and avoid getting trapped in local optima; finally, expressing the objective function in the natural selection process to complete the design of the mission planning algorithm; and then, using the designed mission planning algorithm, optimizing the coverage of the satellite-ground station networked IoT system, optimizing the relay communication coverage in the satellite IoT scenario, increasing the duration for which the IoT system can connect to the satellite in the target area, improving the accuracy of satellite IoT TT&C mission planning, and expanding the coverage area.

[0065] In some implementations, the specific process of preprocessing the ephemeris orbit prediction information in step S310 to obtain the set of visible arc segments for all regions of all satellites and ground stations is described in the following exemplary description.

[0066] The following is combined with Figure 4 The flowchart shown is a schematic diagram of an embodiment of the method of the present invention for data preprocessing of the ephemeris orbit prediction information. It further illustrates the specific process of data preprocessing of the ephemeris orbit prediction information in step S310, including steps S410 to S430.

[0067] Step S410: For all satellites and all ground stations in the trajectory prediction information of each satellite in the satellite set during the mission planning period, calculate the set of visible arc segments of all satellites and all ground stations based on the ephemeris of all satellites and the coordinates of all ground stations.

[0068] Step S420: Classify the visible arcs of all satellites and all stations, and save only the visible arcs of that station and all satellites in the set of each station.

[0069] Step S430: Based on the coordinates of the grid center points of all areas of the ground station and the satellite trajectory, calculate the visible arc segments between each satellite and each grid center point, thereby obtaining the set of visible arc segments between all satellites and all areas of the ground station.

[0070] like Figure 8 As shown, the proposed method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms further includes:

[0071] In step 1, step 12, based on the ephemeris and station coordinates, calculate the set of visible arc segments for all satellites and all stations.

[0072] 6) Based on the ground station coordinates and satellite trajectory, calculate the start and end times of all visible arcs for each satellite and each ground station within the planned time.

[0073] Step 13: Categorize the visible arcs of satellites and stations according to the stations. The set corresponding to each station only stores the visible arcs of that station and all satellites.

[0074] 7) Considering limitations such as station resource utilization, visible arc segments that do not meet the conditions, such as those with too short a time, are removed.

[0075] 8) Set of visible arcs for each satellite and each ground station X input Represented as:

[0076] (1).

[0077] in, Indicates star With ground station A visible arc segment begins at up to ,For example Indicates star With the station A visible arc segment begins at up to .

[0078] This set contains the start and end times of the visible arcs for all satellites and all ground stations.

[0079] 9) The data is grouped according to the stations to which the visible arc segments belong, and sorted by start time, forming a set of observable arc segments for each ground station: For the set of observable arc segments for each ground station, sorted by start time:

[0080] (2).

[0081] in, Indicates ground station The associated set, where express arrive Within the time frame, ground station Responsible for tracking stars Services. For example: In Indicates star With the station A visible arc segment begins at up to ; In Indicates star With the station A visible arc segment begins at up to .

[0082] Step 14: Calculate the set of visible arc segments for all satellites and all regions based on the ephemeris and the coordinates of the region's center point.

[0083] 10) Calculate the visible arc segment between each star and each grid center point based on the coordinates of the grid center point and the satellite trajectory.

[0084] 11) The set of visible arcs corresponding to the coordinates of each star and each region point. Y input Represented as:

[0085] (3).

[0086] in, Indicates star With the region A visible arc segment begins at up to This set contains the start and end times of visible arcs for all satellites and all region coordinates, for example... Indicates star With the region A visible arc segment begins at up to .

[0087] In the present invention, the ephemeris orbit prediction information is preprocessed to obtain a set of visible arc segments for all regions of all satellites and ground stations. This is beneficial to improving the accuracy of subsequent coding design for the number of stations in all regions of the ground station and the number of visible arc segments for each station, thereby improving the accuracy of mission planning.

[0088] In some implementations, step S320 involves encoding the number of stations in all areas of the ground station and the number of visible arc segments for each station, for the set of visible arc segments in all areas of all satellites and ground stations. This is to utilize individual genes in the population to represent the specific process of whether each visible arc segment of each station is used for backhaul service. See the following exemplary description.

[0089] The following is combined with Figure 5 The flowchart shown is an embodiment of the method of the present invention that encodes the number of stations in all areas of the ground station and the number of visible arc segments of each station. It further illustrates the specific process of encoding the number of stations in all areas of the ground station and the number of visible arc segments of each station in step S320, including steps S510 to S520.

[0090] Step S510: For the set of visible arc segments in all regions of all satellites and ground stations, design the number of gene fragments based on the number of stations in all regions of the ground stations, and design the number of gene sites for each fragment based on the number of visible arc segments in each station.

[0091] Step S520: Use binary numbers to represent whether the visible arc segment is used for backhaul service.

[0092] like Figure 8 As shown, the proposed method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms further includes:

[0093] Step 15, Encoding Design: Design the number of gene fragments based on the number of stations; design the number of gene points for each fragment based on the number of visible arcs at each station; each gene point contains a {0,1} binary number, indicating whether the arc is used for backhaul service. Specifically, this is achieved using a genetic algorithm module for individual gene encoding design within the population.

[0094] 12) Design for each individual in the population There are a series of consecutive gene segments, and the j-th segment is set... One 0, 1 gene locus:

[0095] (4).

[0096] in, This indicates the number of elements (visible arcs) in the set. Each point corresponds to one visible arc. 0 indicates that arc tracking is not performed, and 1 indicates that arc tracking is performed. Indicates star With ground station A visible arc segment begins at up to This collection contains all satellites. With ground station The start and end times of the visible arc segment. The p-th point in the j-th segment is 1, representing the ground station. For The satellite corresponding to the p-th arc segment in the set provides telemetry, tracking, and command (TT&C) services, and the mission establishes a backhaul link. Formula (4) is a part of Formula (2), which lists the set of visible arc segments corresponding to all stations, and Formula (4) represents the set of visible arc segments corresponding to the j-th station.

[0097] In the solution of this invention, the communicable arc segment is represented by the intersection operation of visible arc segments, which describes the requirement for the simultaneous existence of backhaul and access links in satellite relay, reflecting the characteristics of the Internet of Things (IoT) scenario. A visible arc segment is a closed interval set represented by a start time and an end time, such as [100, 500]. The intersection operation of two visible arc segments is consistent with the intersection operation of standard mathematical concepts, for example: [100, 500] ∩ [400, 600] = [100, 600]; [100, 500] ∩ [550, 600] = empty set.

[0098] In some implementations, step S330 involves initializing and calculating the fitness of each individual in the population based on the fragment used to represent whether each visible arc segment of each station is used for backhaul service, performing crossover and mutation processing, and selecting the individual with the highest fitness as the required task planning scheme. See the following exemplary description for details.

[0099] The following is combined with Figure 6 The diagram shows a flowchart of an embodiment of the method of the present invention, in which a genetic algorithm is used for initialization and fitness calculation followed by crossover and mutation processing. The specific process of using a genetic algorithm for initialization and fitness calculation followed by crossover and mutation processing in step S330 is further explained, including steps S610 to S630.

[0100] Step S610: Based on the fragments used to represent whether each visible arc segment of each station is used for backhaul service for each individual in the population, a fragment of an individual is randomly initialized using a genetic algorithm until all fragments of that individual satisfy the constraints. Then, it is determined whether all individuals in the population satisfy the constraints: if so, the fitness of each individual is calculated; otherwise, it is returned to continue randomly initializing a fragment of an individual.

[0101] Step S620: After calculating the fitness of each individual, traverse all regions and all satellites to count the time set of each region covered by the satellite Internet of Things system.

[0102] Step S630: After all regions have been statistically analyzed and the fitness of each individual has been obtained, natural selection and elite retention are performed, followed by crossover and mutation processing until the number of iterations reaches the preset maximum number. Then, the individual with the highest fitness is selected as the required task planning scheme.

[0103] like Figure 8 As shown, the proposed method for satellite IoT telemetry, tracking, and command (TT&C) mission planning based on genetic algorithms further includes:

[0104] Step 2, Genetic Algorithm Initialization:

[0105] Step 21: Randomly initialize a fragment of an individual.

[0106] 13) During individual initialization, it is necessary to ensure the constraint of the number of service satellites for the ground station. Taking a single service satellite as an example, 0 and 1 gene fragments of the ground station are randomly generated.

[0107] Step 22: Determine whether all fragments of the individual satisfy the constraints: if yes, proceed to step 23; otherwise, return to step 21.

[0108] 14) For each gene fragment, check the constraint feasibility. If all visible arc segments represented by the points do not overlap in time, it is considered feasible. If it is not feasible, randomly select a point with a value of 1 and set it to zero until it is feasible.

[0109] 15) Generate all fragments one by one to form an individual.

[0110] Step 23: Determine whether all individuals in the population satisfy the constraints: if yes, proceed to step 3; otherwise, return to step 21.

[0111] 16) Generate all initial individuals that meet the population size requirements.

[0112] In the solution of this invention, a suitable genetic algorithm encoding scheme is designed for the satellite Internet of Things coverage optimization target and constraints.

[0113] Step 3: Fitness calculation.

[0114] Step 31: Calculate fitness for each individual: Traverse all arcs with points of 1 and count the time set of each satellite being served.

[0115] 17) Calculate the time set of each satellite being served based on individual gene loci.

[0116] Step 32: Traverse all regions and accumulate the service duration for each region.

[0117] Step 33: Traverse all satellites and use intersection operations to count the time set of the area covered.

[0118] 18) For each region, iterate through all satellites, calculate the intersection of the service time of each satellite and the visible arc of the region and the satellite, and remove duplicates to be counted as the set of communication time for that region.

[0119] Step 34: Determine whether the statistics for a certain area and all satellites are complete: if yes, proceed to step 35; otherwise, return to step 33.

[0120] Step 35: Determine whether statistics for all regions have been completed and the fitness of each individual has been obtained. If yes, proceed to step 4; otherwise, return to step 32.

[0121] 19) Calculate the cumulative communication time of all target regions as the fitness of an individual. If there are weights, the weighted sum can be calculated.

[0122] In the solution of this invention, the visible arc segment is fully utilized, reducing the complexity of mission planning and improving the utilization rate of ground stations.

[0123] Step 4: Execute the genetic algorithm process.

[0124] Step 41, Natural Selection + Elite Preservation: Elite preservation + tournament generation of new populations.

[0125] 20) Calculate the fitness of each individual. The process for calculating the fitness of each individual specifically includes:

[0126] The first step is to calculate the time set of each satellite being served by the ground station based on the planning results corresponding to the gene points on the individual. For example, satellite a[100,300] is served by station 1, [200,500] is served by station 2, and [600,700] is served by station 3. Then, the union operation is performed, and the time set of satellite a being served is [100,300]∪[200,500]∪[600,700]=[100,500;600,700].

[0127] The second step is to obtain the set of times each region is covered by each satellite by the satellite through intersection operation, based on the visible arc of each region and the service time set of the corresponding satellite. For example, if region 1 [0,300] is visible to satellite a, and the service time set of satellite a is [100,500;600,700], then the time set of region 1 covered by satellite a is [0,300]∩[100,500;600,700]=[100,300].

[0128] The third step is to statistically process the time set of all stars covering each region and the cumulative coverage time. For example, if the time set of region 1 covered by star a is [100, 300] and the time set of the time covered by star b is [200, 400; 600, 800], then the time set of all stars covering region 1 is obtained by union operation: [100, 300] ∪ [200, 400; 600, 800] = [100, 400; 600, 800]. The cumulative coverage time of region 1 is the sum of the lengths of each segment in the time set (400-100) + (800-600) = 500.

[0129] The fourth step is to calculate the fitness of the entire individual as the sum of the cumulative coverage time of each region. For example, if region 1 has a cumulative coverage of 500s and region 2 has a cumulative coverage of 300s, then the overall fitness is 500s + 300s = 800s.

[0130] 21) In each round, the N elite individuals with the best fitness (N is an even number) are retained and directly copied as offspring for the next round.

[0131] 22) Select the remaining individuals in a tournament format to serve as the parents of the next generation of offspring.

[0132] In the solution of this invention, an elite retention strategy is adopted to ensure that the optimal solution is not lost.

[0133] Step 42, Crossover operation: Cross over the entire fragment with probability to generate a new individual.

[0134] 23) Set the crossover probability to <0.5.

[0135] 24) Randomly select two parent individuals as two child individuals.

[0136] 25) Starting from the first gene segment, based on the crossover probability, select whether to exchange the entire gene segment between the two offspring individuals.

[0137] In the solution of this invention, the method of full-segment crossing is used to ensure that the crossing operation will not break the constraints of any ground station.

[0138] Step 43, Mutation Operation: Mutate each point of each segment of all individuals according to probability.

[0139] 26) Set the mutation probability.

[0140] 27) For each gene segment in each offspring.

[0141] 28) Determine whether to perform mutation on the fragment based on the mutation probability.

[0142] 29) If mutation is performed on any gene segment, a gene site is randomly selected, and 01 inversion is performed to check the feasibility. If it is not feasible, the inversion position is reselected until it is feasible.

[0143] Step 44: Determine if the maximum number of rounds has been reached: If yes, proceed to step 5; otherwise, return to step 3.

[0144] In the solution of this invention, a constraint check is performed on each mutation operation, and the search result is a guaranteed feasible solution.

[0145] Step 5, Algorithm Iteration: The algorithm output is decoded into the task planning result.

[0146] Step 51: The individual with the highest fitness is taken as the output planning scheme, where the visible arc segments corresponding to all points coded as 1 are taken as the selected arc segments, and the corresponding station performs backhaul service for the corresponding satellite.

[0147] 30) Execute the algorithm iteration according to the genetic algorithm framework, and retain the optimal solution during the process.

[0148] In the solution of this invention, specific algorithm details suitable for satellite Internet of Things coverage optimization are designed.

[0149] The primary objective of this invention is to design a mission planning algorithm for satellite IoT coverage optimization, meeting the specific requirements of satellite IoT mission planning. The second objective is to solve the satellite IoT mission planning problem by designing encoding methods, crossover, mutation, and other operations within a genetic algorithm. Furthermore, the proposed mission planning algorithm optimizes satellite IoT coverage, increasing the duration of satellite connectivity for IoT systems within the target area, improving the accuracy of mission planning for satellite IoT telemetry, tracking, and control, and expanding the coverage area.

[0150] The special requirement refers to the following: equipment in a ground area can only enjoy the data services provided by the satellite if and only if the satellite, ground station, and ground area are simultaneously visible, and the ground station provides backhaul services to the satellite. Visibility is determined by the satellite's trajectory and the coordinates of the station and area, and is considered an input quantity, represented as a visible arc segment. Whether the ground station provides backhaul services when both the satellite and ground station are visible is an optimization variable of the task planning algorithm, determined by the algorithm's output. Unsatisfactory solutions will be eliminated during the execution of the genetic algorithm, such as in step 14 (checking the feasibility of constraints for each gene segment) and step 29 (checking feasibility after mutation of any gene segment). Other operations will not violate the constraints.

[0151] Encoding, crossover, and mutation are the standard operating procedures of a genetic algorithm. The role of encoding is to design a set of rules to describe the task planning scheme in a form that the genetic algorithm can execute, that is, to mutate the task planning scheme onto the chromosome. The crossover operation is used to exchange parts of two existing different schemes to generate new schemes to expand the search space. The mutation operation is used to change a part of the existing scheme to avoid getting trapped in local optima.

[0152] Coverage optimization for satellite IoT can be achieved by using algorithms to determine whether each visible arc segment between the station and the satellite is used for tracking (i.e., establishing a backhaul service), thus extending the cumulative coverage time of the satellite over the selected area. Coverage of an area at a given moment is defined as follows: if a satellite establishes a backhaul link with any ground station, and the ground area is visible to the satellite, then that area is covered at that moment. Genetic algorithms continuously improve the fitness of individual satellites, thereby increasing the cumulative coverage time of the planned solution.

[0153] In the solution of this invention, in the satellite Internet of Things (IoT) scenario, in the satellite IoT relay communication scenario, due to the need for real-time communication and the characteristics of satellite relay, the coverage is defined as the duration of communication / total duration.

[0154] Due to the characteristics of two-hop communication scenarios in real-time relay, a communication period is defined as the overlapping time when the IoT device and the satellite can communicate, and the satellite is assigned a backhaul task by a certain station. For example... Figure 9 As shown, star a is assigned a data transmission task in [t0,t2], and star a is visible to region 1 in [t1,t3]. Therefore, [t0,t2]∩[t1,t3]=[t1,t2] is the set of times when region 1 can be relayed by star a.

[0155] In the solution of this invention, during the calculation of long-term coverage, it is first necessary to statistically analyze the fragmented communicable time segments of each region, and then use a union set operation to determine the non-repeating time segments, for example... Figure 10 As shown, [t0,t1], [t2,t4], and [t3,t5] are the communication time segments between this region and different satellites, and the non-repeating communication duration is [t0,t1]∪[t2,t4]∪[t3,t5]=[t0,t1;t2,t5].

[0156] In the relay communication mission planning process, due to the special definition of coverage, it is impossible to calculate the independent benefits of using a certain arc segment. An evaluation needs to be performed based on the overall planning results, which is significantly different from data transmission mission planning and imaging mission planning.

[0157] In the solution of this invention, the ultimate goal of coverage optimization in real-time relay communication scenarios is to improve the availability of two-hop communication. This requires considering not only the availability of the station-satellite link but also the availability of the satellite-area link during the evaluation process. Furthermore, real-time communication scenarios require the simultaneous availability of both-hop links. However, data transmission mission planning only focuses on the availability of the station-satellite link, and imaging mission planning only focuses on the visibility of the satellite-ground area. Moreover, the joint data transmission and imaging mission planning does not have a strong real-time requirement. Therefore, the mission planning for real-time relay communication coverage optimization for satellite IoT differs significantly from traditional data transmission and imaging mission planning in terms of scenario modeling, quantification of evaluation indicators, and genetic algorithm fitness calculation.

[0158] The technical solution of this embodiment, in the context of satellite IoT coverage optimization, designs an encoding method in a genetic algorithm that can represent the task planning results, designs crossover operations and mutation operations in each iteration of the genetic algorithm, and expresses the objective function in the natural selection process to complete the design of a task planning algorithm for satellite IoT coverage optimization. Then, using the designed task planning algorithm, coverage optimization is performed on the IoT system connecting satellites and ground stations, optimizing the coverage rate of relay communication in the satellite IoT scenario. This increases the duration for which IoT systems within the target area can connect to satellites, improves the accuracy of task planning for satellite IoT telemetry, tracking, and control, and expands the coverage area.

[0159] According to a second embodiment of the present invention, a satellite-to-ground-station networking mission planning apparatus corresponding to a satellite-to-ground-station networking mission planning method is also provided. See also Figure 7 The diagram shows a structural schematic of an embodiment of the device of the present invention. This satellite-to-ground-station networking mission planning device is applied to mission planning when establishing a backhaul link between the satellite and the ground station in a satellite IoT coverage optimization scenario. In the solution of the present invention, as... Figure 7 The satellite-to-ground station networking mission planning method includes: an acquisition unit 102 and a control unit 104.

[0160] The acquisition unit 102 is configured to acquire the mission planning period during which the satellite and ground station need to connect to the network, and to acquire the ephemeris orbit prediction information for the mission planning period. The specific functions and processing of the acquisition unit 102 are described in step S110.

[0161] The control unit 104 is configured to design a mission planning algorithm for coverage optimization of an Internet of Things (IoT) system connecting satellites and ground stations, based on the ephemeris orbit prediction information and a genetic algorithm. The specific functions and processing of this control unit 104 are described in step S120.

[0162] The control unit 104 is also configured to utilize the designed mission planning algorithm to optimize the coverage of the IoT system connecting the satellite and the ground station, thereby optimizing the coverage rate of relay communication in the satellite IoT scenario. This increases the duration for which the IoT system within the target area of ​​the ground station can connect to the satellite, improves the accuracy of mission planning for satellite IoT telemetry, tracking, and control, and expands the coverage area. The specific functions and processing of this control unit 104 are further described in step S130.

[0163] In this invention, a mission planning algorithm for satellite IoT coverage optimization is designed to meet the specific needs of satellite IoT mission planning. Through the proposed mission planning algorithm, satellite IoT coverage is optimized, increasing the duration for which IoT systems within the target area can connect to satellites, improving the accuracy of mission planning for satellite IoT telemetry, tracking, and control, and expanding the coverage area.

[0164] Since the processing and functions implemented by the device in this embodiment are basically the same as the embodiments, principles and examples of the aforementioned methods, any details not covered in the description of this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0165] According to a third embodiment of the present invention, an Internet of Things system corresponding to a satellite-to-ground-station networked mission planning device is provided, comprising: the satellite-to-ground-station networked mission planning device described above.

[0166] Since the processing and functions implemented by the IoT system in this embodiment are basically the same as those of the aforementioned device embodiments, principles and examples, any details not covered in this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0167] According to a fourth embodiment of the present invention, a storage medium corresponding to a mission planning method for satellite-to-ground station networking is provided. The storage medium includes a stored program, wherein the program controls the device where the storage medium is located to execute the mission planning method for satellite-to-ground station networking described above during runtime.

[0168] Since the processing and functions implemented by the storage medium in this embodiment are basically the same as the embodiments, principles and examples of the aforementioned methods, any details not covered in this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0169] According to a fifth embodiment of the present invention, a computer program product corresponding to a mission planning method for satellite-to-ground station networking is provided, comprising a computer program that, when executed by a processor, implements the steps of the mission planning method for satellite-to-ground station networking described above.

[0170] Since the processing and functions implemented by the computer program product in this embodiment are basically corresponding to the embodiments, principles and examples of the aforementioned methods, any details not covered in the description of this embodiment can be found in the relevant descriptions in the aforementioned embodiments, and will not be repeated here.

[0171] In summary, it is readily understood by those skilled in the art that, without conflict, the aforementioned advantageous methods can be freely combined and superimposed.

[0172] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A mission planning method for networking satellites and ground stations, characterized in that, Applications include task planning for establishing backhaul links between satellites and ground stations in satellite IoT coverage optimization scenarios; The mission planning method for networking satellites and ground stations includes: Obtain the mission planning period during which the satellite and ground station need to be connected to the network, and obtain the ephemeris orbit prediction information for the mission planning period; Based on the ephemeris orbit prediction information, and using a genetic algorithm, a task planning algorithm for optimizing the coverage of an IoT system connecting satellites and ground stations is designed. This includes: preprocessing the ephemeris orbit prediction information to obtain a set of visible arc segments for all regions of all satellites and ground stations; encoding the number of stations in all regions of the ground stations and the number of visible arc segments for each station, using individual genes in the population to represent whether each visible arc segment of each station is used for backhaul service; and using the fragments of each individual in the population representing whether each visible arc segment of each station is used for backhaul service, initializing and calculating fitness using a genetic algorithm, followed by crossover and mutation processing, selecting the individual with the highest fitness as the required task planning scheme. Thus, a task planning algorithm for optimizing IoT coverage for the satellites is designed. The process involves using a genetic algorithm to initialize and calculate the fitness of each individual in the population, based on the fragment representing whether each visible arc segment of each station is used for backhaul service. Crossover and mutation are then performed, and the individual with the highest fitness is selected as the desired task planning scheme. This includes: using a genetic algorithm to randomly initialize a fragment for each individual until all fragments of that individual satisfy the constraints; then determining whether all individuals in the population satisfy the constraints: if so, calculating the fitness of each individual; otherwise, returning to the previous step to randomly initialize a fragment for another individual; after calculating the fitness of each individual, traversing all regions and all satellites, and counting the time sets of each region covered by the satellite IoT system; until all regions have been counted and the fitness of each individual is obtained, natural selection and elite retention are performed, followed by crossover and mutation, until the number of iterations reaches a preset maximum. The individual with the highest fitness is then selected as the desired task planning scheme. The designed mission planning algorithm is used to optimize the coverage of the IoT system that connects satellites and ground stations, thereby improving the coverage of relay communication in satellite IoT scenarios. This increases the duration for which IoT systems within the target area of ​​the ground station can connect to the satellite, improves the accuracy of mission planning for satellite IoT telemetry, tracking, and control, and expands the coverage area.

2. The mission planning method for satellite-ground station networking according to claim 1, characterized in that, Obtaining ephemeris orbital prediction information for the mission planning period includes: Obtain the set of satellites in the constellation, and obtain the set of all ground stations used to perform mission planning; Obtain the target area of ​​all ground stations, and then divide the target area of ​​all ground stations into a grid smaller than the satellite coverage area; and, The trajectory prediction information of each satellite in the satellite set during the mission planning period is obtained through orbit prediction.

3. The mission planning method for satellite-ground station networking according to claim 1, characterized in that, The ephemeris orbit prediction information is preprocessed to obtain the set of visible arc segments for all regions of all satellites and ground stations, including: For all satellites and all ground stations in the trajectory prediction information of each satellite in the satellite set during the mission planning period, calculate the set of visible arc segments of all satellites and all ground stations based on the ephemeris of all satellites and the coordinates of all ground stations. The visible arcs of all satellites and all stations are categorized, and the set of each station only stores the visible arcs of that station and all satellites; Based on the coordinates of the grid center points of all areas of the ground station and the satellite trajectories, the visible arc segments between each satellite and each grid center point are calculated, thereby obtaining the set of visible arc segments between all satellites and all areas of the ground station.

4. The mission planning method for satellite-ground station networking according to claim 1, characterized in that, For the set of visible arc segments across all regions of all satellites and ground stations, an encoding design is implemented for the number of stations in all regions of the ground stations and the number of visible arc segments at each station. This design utilizes individual genes in the population to represent whether each visible arc segment at each station is used for backhaul services, including: For the set of visible arc segments in all regions of all satellites and ground stations, the number of gene fragments is designed based on the number of stations in all regions of the ground stations, and the number of gene sites for each fragment is designed based on the number of visible arc segments in each station. The content of each gene point is represented by a binary number to indicate whether the visible arc segment is used for backhaul service.

5. A satellite-to-ground station networking mission planning device that uses the mission planning method for satellite-to-ground station networking as described in claim 1 to realize satellite-to-ground station networking mission planning, characterized in that, Applications include task planning for establishing backhaul links between satellites and ground stations in satellite IoT coverage optimization scenarios; The mission planning device that connects the satellite and the ground station includes: The acquisition unit is configured to acquire the mission planning period during which the satellite and the ground station need to connect to the network, and to acquire the ephemeris orbit prediction information for the mission planning period. The control unit is configured to design a task planning algorithm for coverage optimization of an Internet of Things (IoT) system for satellite-to-ground station networking, based on the ephemeris orbit prediction information and a genetic algorithm. The control unit is also configured to use the designed mission planning algorithm to optimize the coverage of the IoT system connecting the satellite and the ground station, optimize the coverage of relay communication in the satellite IoT scenario, so as to increase the duration for which the IoT system in the target area of ​​the ground station can connect to the satellite, improve the accuracy of mission planning for satellite IoT telemetry, tracking and control, and expand the coverage area.

6. An Internet of Things (IoT) system, characterized in that, include: The mission planning apparatus for networking satellites and ground stations as described in claim 5.

7. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, the device containing the storage medium is controlled to perform the mission planning method for satellite-to-ground station networking as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the mission planning method for networking satellites and ground stations as described in any one of claims 1 to 4.

Images