Constraint design method, device, system, medium and program product in task planning

Abstract

The application relates to the technical field of satellites and provides a constraint design method and device in satellite networking task planning, 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 for networking of a satellite and a ground station, and acquiring ephemeris orbit prediction information of the task planning period; designing a task planning algorithm for coverage optimization of the Internet of Things system facing the networking of the satellite and the ground station based on a genetic algorithm according to the ephemeris orbit prediction information; performing constraint design on the designed task planning algorithm to obtain a task planning algorithm after constraint design; and performing coverage optimization on the Internet of Things system for the networking of the satellite and the ground station by using the task planning algorithm after constraint design, so that the time length during which the Internet of Things system in a target area of the ground station can connect the satellite is improved. The scheme can perform coverage optimization on the satellite Internet of Things by using the task planning algorithm after constraint design, and improve the communication service stability of the satellite Internet of Things.

Description

Technical Field

[0001] This invention relates to the field of satellite technology, specifically to a constraint design method, apparatus, Internet of Things (IoT) system, storage medium, and computer program product for satellite networking mission planning, and particularly to a constraint design method, apparatus, IoT system, storage medium, and computer program product for satellite IoT coverage optimization mission planning. 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), the wide 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] Furthermore, the solution process also requires ensuring compliance with the constraints related to the satellite IoT. However, the current solutions fail to guarantee these constraints in their mission planning for satellite IoT telemetry, tracking, and command, thus affecting the stability of the satellite IoT's communication services.

[0007] Therefore, there is an urgent need to develop a constraint design method, device, IoT system, storage medium, and computer program product for satellite networking mission planning. In particular, this relates to a constraint design method, device, IoT system, storage medium, and computer program product for satellite IoT coverage optimization mission planning. This method should be able to design a mission planning algorithm for satellite IoT coverage optimization, apply constraints to the designed algorithm to obtain a constrained mission planning algorithm, and utilize this constrained algorithm to optimize satellite IoT coverage, thereby 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, expanding coverage, and enhancing the stability of satellite IoT communication services.

[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 constraint design method, apparatus, Internet of Things (IoT) system, storage medium, and computer program product for satellite networking mission planning. By designing a mission planning algorithm for satellite IoT coverage optimization, constraints are applied to the designed mission planning algorithm to obtain a constrained mission planning algorithm. Using the constrained 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, expanding coverage, and enhancing the stability of satellite IoT communication services.

[0010] To address the aforementioned technical problems, as one aspect of the present invention, a constraint design method for satellite networking mission planning is provided, applied to constrain mission planning when establishing backhaul links between a satellite and a ground station. The constraint design method for satellite networking mission planning includes: obtaining the mission planning period during which the satellite and ground station need to connect to the network, and obtaining ephemeris orbit prediction information for the mission planning period; designing a mission planning algorithm for coverage optimization of the IoT system connecting the satellite and ground station based on the ephemeris orbit prediction information and a genetic algorithm; performing constraint design on the designed mission planning algorithm to obtain a constrained mission planning algorithm; and using the constrained mission planning algorithm to optimize the coverage of the IoT system connecting the satellite and ground station, thereby increasing the duration for which the IoT system within the target area of ​​the ground station can connect to the satellite.

[0011] According to an exemplary embodiment of the present invention, the designed task planning algorithm is constrained to obtain a constrained task planning algorithm, including: setting constraints for IoT communication services between all stations in the ground area and all satellites; the number of constraints is one or more; determining whether the constraints of each station in the ground area are conflict-free based on the set constraints; if the constraints of each station in the ground area are determined to be conflict-free, then calculating the time set of all satellites requiring service based on the visible arc segments of all stations in the ground area and all satellites; filtering out invalid tracking times from the time set of all satellites requiring service to obtain the effective tracking time set of all satellites requiring service; updating the designed task planning algorithm based on the effective tracking time set of all satellites requiring service to obtain the constrained task planning algorithm.

[0012] According to an exemplary embodiment of the present invention, the set constraints include: constraint 1 and constraint 2; determining whether the constraints of each station in all stations in the ground area are conflict-free based on the set constraints includes: traversing each station in all stations in the ground area and filtering out all visible arc segments corresponding to gene points with a planning result of 1; sorting all visible arc segments corresponding to gene points with a planning result of 1 by their start time and comparing them one by one; if the interval between the end time of the previous arc segment and the start time of the next arc segment is greater than the input value of constraint 2, then it is considered that there is no conflict between the previous arc segment and the next arc segment; if for any station in all stations in the ground area, if there is no conflict between any two visible arc segments with a planning result of constraint 1, then it is determined that the planning result is feasible for that station; if the planning result is feasible for all stations in the ground area, then it is considered that constraint 1 and constraint 2 satisfy the constraint design in the satellite networking mission planning, and it is determined that the constraints of each station in all stations in the ground area are conflict-free.

[0013] According to an exemplary embodiment of the present invention, based on the visible arc segments of all stations in the ground area and all satellites, the time set for all satellites requiring service is calculated, including: for each satellite, calculating the set of visible arc segments between that satellite and all stations in the ground area; taking the union of the visible arc segments between that satellite and all stations in the ground area as the time set for that satellite requiring backhaul links; and iterating through all satellites in this way to obtain the time set for all satellites requiring service.

[0014] According to an exemplary embodiment of the present invention, the constraints further include: constraints 3 and 4; for the time set of all satellites requiring service, invalid tracking times are filtered out to obtain the effective tracking time set of all satellites requiring service, including: initializing the time set of each satellite that has been tracked in all satellites; and combining the time of each satellite requiring backhaul links in the time set of all satellites requiring service as the time set of each satellite that needs to be tracked; for all stations in the ground area, the number of visible arc segments corresponding to gene points with a planning result of 1 is counted; according to the number of visible arc segments corresponding to gene points with a planning result of 1 in ascending order, the current planning result is corrected for each station to ensure constraints 3 and 4, so as to filter out invalid tracking times and obtain the effective tracking time set of all satellites requiring service.

[0015] According to an exemplary embodiment of the present invention, for all stations in the ground area, the number of visible arc segments corresponding to gene points with a planning result of 1 is counted; based on the number of visible arc segments corresponding to gene points with a planning result of 1 in ascending order, the current planning result is corrected for each station to ensure constraints 3 and 4, including: based on the number of visible arc segments corresponding to gene points with a planning result of 1 in ascending order, starting from the first station, traversing the visible arc segments corresponding to each gene point with a planning result of 1 at that station; calculating the intersection of the arc segment and the time set of the backhaul link required by the satellite corresponding to the arc segment, as the contribution set of the arc segment to the actual service; calculating the... The portion of the contribution set that does not intersect with the tracked time set is taken as the effective contribution set. The sum of the lengths of the time segments in the effective contribution set is calculated as the effective contribution value for tracking using that arc segment. It is then determined whether the ratio between the effective contribution value and the preset tracking arc segment duration is greater than the preset input value. If so, the arc segment is retained, and the tracked time set is updated to the union of the tracked time set and the effective contribution set. Otherwise, the task corresponding to the arc segment is canceled. This is used to correct the current planning result of the station, ensuring constraints 3 and 4. In this way, the current planning results of all stations are corrected, ensuring constraints 3 and 4.

[0016] As a second aspect of the present invention, the present invention provides a constraint design device for satellite networking mission planning, applied to constrain mission planning when establishing a backhaul link between a satellite and a ground station; the constraint design device for satellite networking mission planning includes: an acquisition unit configured to acquire the mission planning period during which the satellite and the ground station need to connect to the network, and 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 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 further configured to perform constraint design on the designed mission planning algorithm to obtain a constrained mission planning algorithm; the control unit is further configured to use the constrained mission planning algorithm to perform coverage optimization on the IoT system for satellite-to-ground station networking, so as to increase the duration for which the IoT system in the target area of ​​the ground station can connect to the satellite.

[0017] As a third aspect of the present invention, the present invention provides an Internet of Things system, including: the constraint design device for satellite networking mission planning 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 constraint design method in the satellite networking mission planning described above.

[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 constraint design method in the satellite networking mission planning described above.

[0020] The beneficial effects of this invention are:

[0021] The method of this invention designs a task planning algorithm for satellite Internet of Things (IoT) coverage optimization, applies constraints to the designed task planning algorithm to obtain a constrained task planning algorithm, and uses the constrained task planning algorithm to optimize satellite IoT coverage, thereby increasing the duration for which IoT systems in the target area can connect to satellites, improving the accuracy of task planning for satellite IoT telemetry, tracking, and control, expanding the coverage area, and improving the stability of satellite IoT communication services. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating an embodiment of the constraint design method in satellite networking mission planning of the present invention.

[0023] Figure 2 This is a flowchart illustrating an embodiment of the constraint design for the designed task planning algorithm in the method of the present invention.

[0024] Figure 3 This is a flowchart illustrating an embodiment of the method of the present invention for determining whether the constraints of each station in all stations in a ground area are conflict-free;

[0025] Figure 4 This is a flowchart illustrating an embodiment of the method of the present invention for calculating the time set of all satellites that require service;

[0026] Figure 5 This is a flowchart illustrating an embodiment of the method for filtering out invalid tracking times in the present invention;

[0027] Figure 6 This is a flowchart illustrating an embodiment of the method of the present invention that corrects the current planning result station by station.

[0028] Figure 7 This is a schematic diagram of an embodiment of the constraint design device in satellite networking mission planning of the present invention;

[0029] Figure 8 This is a schematic diagram of the overall process of constraint design method in a satellite Internet of Things coverage optimization task planning method;

[0030] Figure 9 A flowchart illustrating the process of determining constraint 1 and constraint 2;

[0031] Figure 10 A flowchart illustrating the process of compiling the set of visible arc segments between each satellite and all ground regions; Step 7 corresponds to the flowchart.

[0032] Figure 11 A flowchart illustrating the process of revising the current planning results;

[0033] Figure 12 This is a flowchart illustrating a task planning method for optimizing satellite IoT coverage.

[0034] Among them, 102 is the acquisition unit and 104 is the control unit. Detailed Implementation

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] In addition, the solution process must also ensure compliance with the relevant constraints of the satellite IoT, namely: 1. Any single-channel station can only serve one satellite at a time; because a single-channel station is limited by its equipment capacity and can only process one signal, it cannot serve two satellites simultaneously. 2. After any station finishes serving, a switching interval is required before it can serve the next satellite; because it takes time for the station to adjust its angle to align with the satellite, the lack of a switching interval may lead to the loss of some signals. 3. Any single satellite can only be served by a single ground station at a time; because if two stations send signals to the same satellite simultaneously, the inability to accurately synchronize the signals will cause strong interference, resulting in the inability to receive both signals correctly.

[0040] In other words, in order to improve the utilization rate of ground stations, the visible arcs will be used to track the satellite in their entirety during the 01 planning process. At the same time, in order to ensure that a single satellite can only be served by a single station, the relevant constraint (3) will be to abandon a certain visible arc when the visible arcs of two stations and the same satellite overlap (for example, station 2-satellite a[100s,400s] is visible, station 3-satellite a[300s,600s] is visible, and the final planning shows that station 2 serves satellite a[100,400s] arc indication is 1, and station 3 serves satellite a[300s,600s] arc indication is 0).

[0041] However, there might be a situation where no other suitable tracking station can take over after 400 seconds, causing satellite a to lose service to the ground area after that time. Furthermore, considering that two tracking stations can simultaneously align with the same satellite without affecting satellite reception, redundant tracking stations can be arranged to provide backup service for the satellite, immediately filling in when the current station's service ends. That is, it is permissible for tracking stations to not fully utilize the visible arc of the satellite, sacrificing ground station utilization efficiency, to ensure the stability of satellite IoT communication services.

[0042] Therefore, the present invention proposes a constraint design method in satellite networking mission planning, specifically a constraint design method in a satellite IoT coverage optimization mission planning method. This method can design a mission planning algorithm for satellite IoT coverage optimization, apply constraints to the designed algorithm to obtain a constrained mission planning algorithm, and then use this constrained algorithm to optimize satellite IoT coverage, thereby 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, expanding coverage, and enhancing the stability of satellite IoT communication services.

[0043] As a first embodiment of the present invention, a constraint design method for satellite networking mission planning is provided, such as... Figure 1 The diagram shows a flowchart of an embodiment of the method of the present invention. This constraint design method in satellite networking mission planning is applied to constrain mission planning when establishing a backhaul link between a satellite and a ground station; in the solution of the present invention, as... Figure 1 As shown, the constraint design method in the satellite networking mission planning includes steps S110 to S140.

[0044] 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.

[0045] 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.

[0046] In step S130, the designed task planning algorithm is constrained to obtain the task planning algorithm after the constraint design.

[0047] In step S140, the task planning algorithm after the constraint design is used to optimize the coverage of the IoT system connecting the satellite and the ground station, so as to improve the duration for which the IoT system in the target area of ​​the ground station can connect to the satellite.

[0048] The primary objective of this invention is to design a mission planning algorithm for satellite IoT coverage optimization, such as proposing a constraint design method within the mission planning approach to meet the specific needs of satellite IoT mission planning. Specifically, when designing the mission planning algorithm for satellite IoT coverage optimization, the backhaul links between the station and the satellite are planned based on the visible arc segments between the satellite and the station, as well as the visible arc segments between the satellite and the target area, thereby increasing the sum of coverage times for all target areas. The second objective is to allow partial use of the visible arc segments through reasonable constraint design, avoiding ineffective waste of station resources while simultaneously improving the service performance of the satellite IoT.

[0049] In some implementations, the specific process of obtaining the ephemeris orbit prediction information for the mission planning period in step S110 includes: obtaining the satellite set in the constellation; obtaining the set of all ground stations used for mission planning; obtaining the target areas of all ground stations, and dividing the target areas of all ground stations into grids smaller than the satellite coverage area; and obtaining the trajectory prediction information of each satellite in the satellite set during the mission planning period through orbit prediction.

[0050] Figure 12 This is a flowchart illustrating a task planning method for optimizing satellite IoT coverage. Figure 12 As shown, the constraint design method in the satellite IoT coverage optimization task planning method proposed in this invention specifically includes:

[0051] Step S1: Data preprocessing.

[0052] Step S11: Obtain ephemeris orbit prediction information based on the input planning time period, as detailed below:

[0053] 1) The time period for the task to be executed is input as the time range;

[0054] This indicates that the current time step needs to be executed. ,arrive Planning of measurement, operation and control tasks in seconds.

[0055] 2) The satellite set in a constellation is marked as , Indicates a satellite.

[0056] 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.

[0057] 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 taken as the representative point; the target area that needs to be optimized is marked as follows; , This represents the k-th target region. Indicates the area The geographical coordinates of the center point.

[0058] 5) Obtain the trajectory prediction of each star in the constellation within the planned time period through orbit prediction.

[0059] 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.

[0060] 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 includes: preprocessing the ephemeris orbit prediction information to obtain a set of visible arc segments for all regions of all satellites and ground stations. For the set of visible arc segments for all regions of all satellites and ground stations, the number of stations in all regions of the ground stations and the number of visible arc segments for each station are encoded to represent whether each visible arc segment of each station is used for backhaul service using individual genes in the population. 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, after initialization and fitness calculation using a genetic algorithm, crossover and mutation are performed, and the individual with the highest fitness is selected as the required task planning scheme. Thus, a task planning algorithm for optimizing IoT coverage for the satellite is designed.

[0061] The present invention proposes a constraint design method for satellite IoT coverage optimization task planning, comprising: firstly, designing an encoding method in a genetic algorithm to represent the task planning results, which greatly satisfies the constraints; secondly, designing the 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 task planning algorithm; and then, using the task planning algorithm after the constraint design, optimizing the coverage of the satellite IoT, increasing the duration for which the IoT system can connect to the satellite within the target area, improving the accuracy of the task planning for satellite IoT telemetry, tracking, and control, expanding the coverage area, and improving the stability of the satellite IoT communication service.

[0062] In some implementations, the specific process of preprocessing the ephemeris orbit prediction information to obtain the set of visible arc segments for all regions of all satellites and ground stations includes: for all satellites and all ground stations in the trajectory prediction information of each satellite in the satellite set during the mission planning period, 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; categorizing 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 for 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 stations and the satellite trajectories, thereby obtaining the set of visible arc segments for all regions of all satellites and ground stations.

[0063] like Figure 12 As shown, the constraint design method in the satellite IoT coverage optimization task planning method proposed in this invention further includes:

[0064] In step S1, step S12 involves calculating the set of visible arc segments for all satellites and all stations based on the ephemeris and station coordinates.

[0065] 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.

[0066] Step S13: Classify the visible arc segments of satellites and stations according to the station. The set corresponding to each station only stores the visible arc segments of that station and all satellites.

[0067] 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.

[0068] 8) Set of visible arcs for each satellite and each ground station Xinput Represented as:

[0069] (1).

[0070] 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 .

[0071] This set contains the start and end times of the visible arcs for all satellites and all ground stations.

[0072] 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:

[0073] (2).

[0074] 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 .

[0075] Step S14: 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.

[0076] 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.

[0077] 11) The set of visible arcs corresponding to the coordinates of each star and each region point. Y input Represented as:

[0078] (3).

[0079] 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 .

[0080] 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.

[0081] In some implementations, 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 the specific process of whether each visible arc segment at each station is used for backhaul service. This includes: for the set of visible arc segments across all regions of all satellites and ground stations, designing the number of gene segments based on the number of stations in all regions of the ground stations, and designing the number of gene points for each segment based on the number of visible arc segments at each station. The content of each gene point is then represented using a binary number to indicate whether the visible arc segment is used for backhaul service.

[0082] like Figure 12 As shown, the constraint design method in the satellite IoT coverage optimization task planning method proposed in this invention further includes:

[0083] Step S15, 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.

[0084] 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:

[0085] (4).

[0086] 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.

[0087] 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.

[0088] In some implementations, step S330, which involves initializing and calculating 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 using a genetic algorithm, followed by crossover and mutation processing, and selecting the individual with the highest fitness as the required task planning scheme, includes: Based on the fragment representing whether each visible arc segment of each station is used for backhaul service, using a genetic algorithm, randomly initializing 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 continue randomly initializing a fragment for each individual. After calculating the fitness of each individual, traversing all regions and all satellites, and statistically analyzing the time set of each region covered by the satellite IoT system. 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 a preset maximum. Finally, the individual with the highest fitness is selected as the required task planning scheme.

[0089] like Figure 12 As shown, the constraint design method in the satellite IoT coverage optimization task planning method proposed in this invention further includes:

[0090] Step S2, Genetic Algorithm Initialization:

[0091] Step S21: Randomly initialize a fragment of an individual.

[0092] 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.

[0093] Step S22: Determine whether all segments of the individual satisfy the constraints: if yes, proceed to step S23; otherwise, return to step S21.

[0094] 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.

[0095] 15) Generate all fragments one by one to form an individual.

[0096] Step S23: Determine whether all individuals in the population satisfy the constraints: if yes, proceed to step S3; otherwise, return to step S21.

[0097] 16) Generate all initial individuals that meet the population size requirements.

[0098] 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.

[0099] Step S3: Fitness calculation.

[0100] Step S31: Calculate fitness for each individual: Traverse all arcs with points of 1 and count the time set of each satellite being served.

[0101] 17) Calculate the time set of each satellite being served based on individual gene loci.

[0102] Step S32: Traverse all regions and accumulate the service duration for each region.

[0103] Step S33: Traverse all satellites and use intersection operations to count the time set of the area covered.

[0104] 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.

[0105] Step S34: Determine whether the statistics of a certain area and all satellites are completed: if yes, proceed to step S35; otherwise, return to step S33.

[0106] Step S35: Determine whether statistics for all regions have been completed and the fitness of each individual has been obtained: if yes, proceed to step S4; otherwise, return to step S32.

[0107] 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.

[0108] 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.

[0109] Step S4: Execute the genetic algorithm process.

[0110] Step S41, Natural Selection + Elite Preservation: Elite preservation + tournament generation of new populations.

[0111] 20) Calculate the fitness of each individual. The process for calculating the fitness of each individual specifically includes:

[0112] 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].

[0113] 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].

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 22) Select the remaining individuals in a tournament format to serve as the parents of the next generation of offspring.

[0118] In the solution of this invention, an elite retention strategy is adopted to ensure that the optimal solution is not lost.

[0119] Step S42, Crossover operation: Cross the entire fragment with probability to generate a new individual.

[0120] 23) Set the crossover probability to <0.5.

[0121] 24) Randomly select two parent individuals as two child individuals.

[0122] 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.

[0123] 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.

[0124] Step S43, Mutation Operation: Mutate each point of each segment of all individuals according to probability.

[0125] 26) Set the mutation probability.

[0126] 27) For each gene segment in each offspring.

[0127] 28) Determine whether to perform mutation on the fragment based on the mutation probability.

[0128] 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.

[0129] Step S44: Determine if the maximum number of rounds has been reached: If yes, proceed to step S5; otherwise, return to step S3.

[0130] In the solution of this invention, a constraint check is performed on each mutation operation, and the search result is a guaranteed feasible solution.

[0131] Step S5, Algorithm Iteration: The algorithm output is decoded into the task planning result.

[0132] Step S51: The individual with the highest fitness is used as the output planning scheme, where the visible arc segments corresponding to all points coded as 1 are selected arc segments, and the corresponding station performs backhaul service for the corresponding satellite.

[0133] 30) Execute the algorithm iteration according to the genetic algorithm framework, and retain the optimal solution during the process.

[0134] In the solution of this invention, specific algorithm details suitable for satellite Internet of Things coverage optimization are designed.

[0135] 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 command (TT&C), expanding coverage, and enhancing the stability of satellite IoT communication services.

[0136] 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 S14 (checking the feasibility of constraints for each gene segment) and step S29 (checking feasibility after mutation of any gene segment). Other operations will not violate the constraints.

[0137] 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.

[0138] 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.

[0139] In some implementations, the specific process of constraining the designed task planning algorithm in step S130 to obtain the constrained task planning algorithm is described in the following exemplary description.

[0140] The following is combined Figure 2 The flowchart shown is a schematic diagram of an embodiment of the method of the present invention for constraining the designed task planning algorithm. It further illustrates the specific process of constraining the designed task planning algorithm in step S130, including steps S210 to S250.

[0141] Step S210: Set constraints for IoT communication services between all stations and all satellites in the ground area; the number of constraints is one or more; the more than one constraint includes constraints 1, 2, 3 and 4.

[0142] Step S220: Based on the set constraints, determine whether the constraints of each station in all stations in the ground area are conflict-free.

[0143] Step S230: If it is determined that there is no conflict in the constraints of each station among all stations in the ground area, then based on the visible arc segments of all stations in the ground area and all satellites, the time set of all satellites that need to be served is calculated.

[0144] Step S240: For the set of times when all satellites need service, filter out invalid tracking times to obtain the set of valid tracking times when all satellites need service.

[0145] Step S250: Based on the set of effective tracking times required for all satellites, update the designed mission planning algorithm to obtain the constrained mission planning algorithm.

[0146] Figure 8 This is a schematic diagram illustrating the overall process of constraint design methods in a satellite IoT coverage optimization task planning approach. Figure 8 As shown, the constraint design method in the satellite IoT coverage optimization task planning method includes:

[0147] Step 1, Constraint 1: Any station can only serve a single satellite at a time.

[0148] Step 2, Constraint 2: For any station, the switching time between serving two different satellites must be greater than a certain input value.

[0149] Step 3, Constraint 3: Any satellite, at any given time, can only be served by a single tracking station.

[0150] Step 4, Constraint 4: The utilization rate of each tracking by the ground station must be greater than a certain arbitrary input value η;

[0151] Constraint protection methods in search algorithms. Specifically, during the genetic algorithm search process, search results that do not satisfy constraints 1 and 2 are eliminated, and results that do not satisfy constraints 3 and 4 are modified to satisfy constraints 3 and 4. See [link to relevant documentation]. Figure 11 The example shown.

[0152] Step 5: Check the feasibility constraints related to no conflicts at the stations.

[0153] Step 6: Determine if there are no conflicts: If yes, proceed to Step 7; otherwise, end the current determination. Specifically, while ensuring constraints 1 and 2, constraints 3 and 4 are checked and adjusted to improve the utilization rate of ground stations.

[0154] Step 7: Based on the visible arc of the ground area and the satellite, calculate the set of time periods that the satellite needs to provide service.

[0155] Step 8: Filter out invalid tracking and improve station utilization by reducing the period during which satellites are repeatedly tracked, and then end the current constraint judgment.

[0156] Step 9: The updated task planning results ensure that constraints 3 and 4 are used as the results of this search.

[0157] The primary objective of this invention is to design a mission planning algorithm for satellite IoT coverage optimization, meeting the specific needs of satellite IoT mission planning. A secondary objective is to allow partial use of visible arc segments through reasonable constraint design, such as providing a constraint design in a satellite IoT coverage optimization mission planning method and an example of constraint checking in an arbitrary search algorithm, thereby avoiding ineffective waste of station resources and improving the service performance of satellite IoT. A specific method for feasibility checking is designed to ensure the constraint design described in this invention and adapt it to arbitrary search algorithms. 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, expanding coverage, and enhancing the stability of satellite IoT communication services.

[0158] In some implementations, the constraints set include: constraint 1 and constraint 2.

[0159] The specific process of determining whether the constraints of each station in all stations in the ground area are conflict-free according to the set constraints in step S220 is illustrated in the following exemplary description.

[0160] The following is combined Figure 3 The diagram illustrates a flowchart of an embodiment of the method of the present invention for determining whether the constraint conditions of each station among all stations in the ground area are conflict-free. It further explains the specific process of determining whether the constraint conditions of each station among all stations in the ground area are conflict-free in step S220, including steps S310 to S340.

[0161] Step S310: Traverse all stations in the ground area and filter out the visible arc segments corresponding to all gene points with a planning result of 1.

[0162] Step S320: For all visible arc segments corresponding to gene points with a planning result of 1 obtained from the screening, sort them by the start time and compare them one by one. If the interval between the end time of the previous arc segment and the start time of the next arc segment is greater than the input value of constraint 2, then it is considered that there is no conflict between the previous arc segment and the next arc segment.

[0163] Step S330: If for any station among all stations in the ground area, there are no conflicts in any pair of visible arcs corresponding to gene points with a planning result of 1, then the planning result is determined to be feasible for that station.

[0164] Step S340: If the planning result is feasible for all stations in the ground area, then it is considered that constraint 1 and constraint 2 satisfy the constraint design in the satellite networking mission planning, and it is determined that the constraint conditions of each station in all stations in the ground area are not conflicting.

[0165] Each visible arc segment of the station-satellite mapping corresponds to a gene locus on an individual; 0 indicates that the arc segment is not applicable, and 1 indicates that the arc segment is used. For example... Figure 8 As shown, the constraint design method in the satellite IoT coverage optimization task planning method further includes: In step 5, to ensure constraint 1 and constraint 2, in the search algorithm, for each station, all visible arc segments corresponding to gene points with a planning result of 1 are sorted in ascending order according to their start time. They are compared one by one from the first visible arc segment onwards. If the interval between the end time of the previous arc segment and the start time of the next time segment is greater than the input value in constraint 2, then these two arc segments do not conflict. For any station, if all visible arc segments with a planning result of 1 are not conflicted pairwise, then the planning result is feasible for that station. If the planning result is feasible for all stations, then constraint 1 and constraint 2 are considered satisfied.

[0166] Figure 9 The flowchart for determining constraints 1 and 2 corresponds to the specific process in step 5. For example... Figure 9 As shown, the process for determining constraint 1 and constraint 2 includes:

[0167] Step 21: Iterate through each station, then proceed to step 22.

[0168] Step 22: Read the minimum switching interval T_H0 required in constraint 2, and then execute step 23.

[0169] Step 23: Filter out the visible arc segments with a planning result of 1 for this station, and then proceed to step 24.

[0170] Step 24: Sort the data in ascending order of start time. Starting from i=1, read the end time Tend of the i-th arc segment and the start time Tstart of the (i+1)-th time segment, and then execute step 25.

[0171] Step 25: Determine whether the start time of the (i+1)th time period Tstart - the end time of the i-th arc Tend > 0: If yes, proceed to step 26; otherwise, consider that constraint 1 of the station is not satisfied, the planning result of this group does not meet the requirements, and then end the process of judging constraint 1 and constraint 2.

[0172] Step 26: Determine whether the start time of the (i+1)th time period Tstart - the end time of the i-th arc Tend > the minimum switching interval T_H0 required in constraint 2. If yes, the station is considered to be conflict-free, and then step 27 is executed; otherwise, the station is considered to be not satisfied with constraint 2, the planning result of this group does not meet the requirements, and then the process of judging constraint 1 and constraint 2 ends.

[0173] Step 27: Determine whether all stations have been traversed: If yes, the planning result of this group is considered to meet the requirements, and the process of determining constraint 1 and constraint 2 ends; otherwise, return to step 21 to continue traversing the remaining stations.

[0174] In the solution of this invention, by allowing reserved station resources, the constraint that a single satellite can only be served by a single station at a time is relaxed, while avoiding the phenomenon that effective visible arc segments are not utilized.

[0175] In some implementations, the specific process of calculating the set of times that all satellites need to serve based on the visible arcs of all stations and all satellites in step S230 is described in the following exemplary description.

[0176] The following is combined Figure 4 The flowchart shown is a schematic diagram of an embodiment of the method of the present invention for calculating the time set of all satellites that need to be served. The specific process of calculating the time set of all satellites that need to be served in step S230 is further explained, including steps S410 to S430.

[0177] Step S410: For each of all satellites, count the set of visible arc segments between that satellite and all stations in the ground area.

[0178] Step S420: Calculate the union of the visible arc segments of the satellite and all stations in the ground area, and use this as the time set of the satellite's backhaul links.

[0179] Step S430: Repeat this process to iterate through all satellites and obtain the set of service times required by all satellites.

[0180] like Figure 8 As shown, the constraint design method in the satellite IoT coverage optimization task planning method further includes: In step 7, firstly, for each satellite, the set of visible arc segments between the satellite and all ground areas is counted, and the union of these sets is taken as the set of times when the satellite needs a backhaul link. For example, if satellite a-area 1 [100-400] is visible, satellite a-area 2 [600-800] is visible, and satellite a-area 3 [700-900] is visible, then satellite a [100,400;600,900;] needs a backhaul link.

[0181] Figure 10This is a flowchart illustrating the process of compiling the set of visible arc segments between each satellite and all ground regions. Step 7 corresponds to this flowchart, detailing the specific steps involved. For example... Figure 10 As shown, the process of calculating the set of visible arc segments for each satellite and all ground regions includes:

[0182] Step 31: Iterate through each star i, initialize the required return time set B_i to be empty, and then execute step 32.

[0183] Step 32: Traverse all ground regions j, read the set of visible arc segments S_ij of satellite i and all ground regions j, and then execute step 33.

[0184] Step 33: Let the required return time set B_i = the set of visible arc segments of satellite i and all ground regions j S_ij, and then execute step 34.

[0185] Step 34: Determine if the traversal of all regions has ended: if yes, proceed to step 35; otherwise, return to step 32.

[0186] Step 35: Determine if the traversal of all satellites has ended: If yes, end the process of counting the set of visible arc segments of each satellite and all ground areas; otherwise, return to step 31 to continue traversing the remaining satellites.

[0187] In the solution of this invention, by reserving station resources, the stability of the low-orbit satellite backhaul link in the satellite Internet of Things is improved, the number of times the backhaul to the Internet of Things system is reduced, and the coverage performance is improved.

[0188] In some implementations, the constraints also include constraint 3 and constraint 4.

[0189] For the specific process of filtering out invalid tracking times from the set of time times for all satellites that require service in step S240, see the following exemplary description.

[0190] The following is combined Figure 5 The flowchart of an embodiment of the method of the present invention for filtering out invalid tracking time is shown, and the specific process of filtering out invalid tracking time in step S240 is further explained, including steps S510 to S520.

[0191] Step S510: Initialize the time set of each satellite that has been tracked in all satellites; and combine the time set of each satellite that needs to transmit backlinks in the time set of all satellites that need to be served, as the time set of each satellite that needs to be tracked.

[0192] Step S520: For all stations in the ground area, count the number of visible arc segments corresponding to gene points with a planning result of 1; sort the visible arc segments corresponding to gene points with a planning result of 1 from smallest to largest, and correct the current planning result for each station to ensure constraints 3 and 4, so as to filter out invalid tracking time and obtain the set of effective tracking time required by all satellites.

[0193] like Figure 8 As shown, the constraint design method in the satellite IoT coverage optimization task planning method further includes: in step 8, the current planning result is modified to ensure constraints 3 and 4. The specific steps are as follows:

[0194] a) Initialize the time set A for each satellite that has been tracked to be empty, and the time set B for each satellite that needs to be tracked to be the time set for the satellite that needs to transmit back the link obtained in step 7.

[0195] b) For all stations, count the number of visible arc segments corresponding to gene points with a planning result of 1, sort them from smallest to largest, and execute the procedure for each station to correct the current planning result, including constraints 3 and 4.

[0196] In the solution of this invention, the utilization rate of ground stations is used as an indicator to limit the amount of reserved resources, thereby improving the overall utilization efficiency of ground stations and avoiding excessive waste of station resources.

[0197] In some implementations, in step S520, the number of visible arc segments corresponding to gene points with a planning result of 1 is counted for all stations in the ground area; the current planning result is corrected for each station in ascending order according to the number of visible arc segments corresponding to gene points with a planning result of 1, and the specific process of ensuring constraints 3 and 4 is described in the following exemplary description.

[0198] The following is combined Figure 6 The flowchart of an embodiment of the method of the present invention, which modifies the current planning result station by station, is shown below. The specific process of modifying the current planning result station by station in step S520 is further explained, including steps S610 to S660.

[0199] Step S610: Sort the visible arcs corresponding to gene points with a planning result of 1 in ascending order, starting from the first station, and traverse the visible arcs corresponding to each gene point with a planning result of 1 at that station.

[0200] Step S620: Calculate the intersection of the time set of the backhaul link required by the arc segment and the satellite corresponding to the arc segment, and use it as the contribution set of the arc segment to the actual service.

[0201] Step S630: Calculate the non-intersecting portion of the contribution set with the already tracked time set, and use it as the valid contribution set.

[0202] Step S640: Calculate the sum of the lengths of the time segments in the effective contribution set, which is used as the effective contribution value for tracking using the arc segment.

[0203] Step S650: Determine whether the ratio between the effective contribution value and the preset tracking arc duration is greater than the preset input value. If yes, retain the arc and update the tracked time set to the union of the tracked time set and the effective contribution set. Otherwise, cancel the task corresponding to the arc to correct the current planning result of the station and ensure constraints 3 and 4.

[0204] Step S660: In this way, the current planning results of all stations are corrected to ensure constraints 3 and 4.

[0205] like Figure 8 As shown, the constraint design method in the satellite IoT coverage optimization task planning method further includes: in step 8b), for all stations, counting the number of visible arc segments corresponding to gene points with a planning result of 1, sorting them from smallest to largest, and executing the method for each station one by one, as follows:

[0206] i. Starting from the first station, iterate through the visible arcs corresponding to each gene point with a planning result of 1, and execute:

[0207] 41. Query the time set B required by the backhaul link for the satellite corresponding to this arc segment.

[0208] 42. Calculate the intersection of the arc segment and the required return time set, which is regarded as the contribution set C of the arc segment to the actual business. If there is no contribution, set it to zero to save station resources and avoid invalid tracking.

[0209] 43. Calculate the portion of the contribution set C that has no intersection with the tracked set A, and consider it as the effective contribution set D (D=CA∩C). Calculate the sum of the lengths of time segments in D, and use it as the effective contribution value for tracking using that arc segment. If the contribution value / tracking arc segment duration is greater than any input value η in step 4, then retain the arc segment and update A=A∪C. If the contribution value / tracking arc segment duration is less than or equal to any input value η in step 4, then set the planning result of the arc segment to 0 and cancel the task corresponding to the arc segment.

[0210] The tracking arc duration is defined as follows: for example, if the visible arc is [100, 600], then the arc duration is 600s - 100s = 500s. Since the satellite is visible to the station but not necessarily to the target area, the contribution value represents the effective tracking time that provides relay. The arc duration indicates the actual tracking time between the station and the satellite, and also the time spent occupying station resources. η represents the expected station resource utilization rate.

[0211] 44. End, jump to the next arc segment.

[0212] ii. After completing all arcs at a certain station, jump to the next station.

[0213] Figure 11 This is a flowchart illustrating the process of revising the current planning results, corresponding to the specific steps in step 8. For example... Figure 11 As shown, the process for revising the current planning results includes:

[0214] Step 51: Initialize the time set A_i of each satellite that has been tracked to be empty, and then execute step 52.

[0215] Step 52: For all stations, count the number of visible arc segments corresponding to gene sites with a planning result of 1, sort them from smallest to largest, traverse all stations, and then execute step 53.

[0216] Step 53: Traverse the visible arc segments corresponding to each gene location with a planning result of 1 at the station; then execute step 54.

[0217] Step 54: Query the time set Bi of the backhaul link required for the satellite i corresponding to the arc segment, and then execute step 55.

[0218] Step 55: Calculate the intersection of the arc segment and the time set Bi of the required backhaul link, which is regarded as the contribution set C of the arc segment to the actual service. Determine whether the contribution set C of the arc segment to the actual service is empty: if yes, proceed to step 56; otherwise, set the arc segment planning result to 0 and cancel the arc segment tracking to save station resources.

[0219] Step 56: Calculate the portion of the arc segment whose contribution set C to the actual business has no intersection with the tracked set A_i, and consider it as the effective contribution set D of the arc segment (D=CA∩C); count the sum of the lengths of the time segments in the effective contribution set D of the arc segment as the effective contribution value of using the arc segment for tracking; determine whether the effective contribution value of using the arc segment for tracking is greater than any input value η in step 4: if yes, execute step 57; otherwise, set the arc segment planning result to 0 and cancel the arc segment tracking to save station resources.

[0220] Step 57: Retain the arc segment, the actual service is the time period corresponding to set D, update A_i=A_i∪D; the remaining time sets of the arc segment are redundant reservations, that is, the station only aligns with the satellite but does not actually transmit services, then proceed to step 58.

[0221] Step 58: Determine if the traversal of all arc segments has ended: if yes, proceed to step 59; otherwise, return to step 53.

[0222] Step 59: Determine if the traversal of all stations has ended: If yes, end the process of correcting the current planning result; otherwise, return to step 52.

[0223] In the solution of this invention, during the process of adjusting the planning results, the planning is implemented starting from the stations with sparser tasks. When duplicate and redundant tracking occurs, non-hotspot stations can be prioritized to perform tracking, thereby reducing the task pressure on hotspot stations.

[0224] The technical solution of this embodiment involves designing a task planning algorithm for satellite IoT coverage optimization, and then constraining the designed task planning algorithm to obtain a constrained task planning algorithm. This constrained task planning algorithm is then used to optimize satellite IoT coverage, increasing the duration for which IoT systems within the target area can connect to satellites, improving the accuracy of task planning for satellite IoT telemetry, tracking, and control, expanding the coverage area, and enhancing the stability of satellite IoT communication services.

[0225] According to a second embodiment of the present invention, a constraint design apparatus for satellite networking mission planning, corresponding to a constraint design method in satellite networking mission planning, is also provided. See also Figure 7 The diagram shows a structural schematic of an embodiment of the device of the present invention. This constraint design device for satellite networking mission planning is used to constrain mission planning when establishing a backhaul link between a satellite and a ground station; in the solution of the present invention, such as... Figure 7 As shown, the constraint design device in the satellite networking mission planning includes: an acquisition unit 102 and a control unit 104.

[0226] 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.

[0227] 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.

[0228] The control unit 104 is also configured to perform constraint design on the designed task planning algorithm to obtain a task planning algorithm after constraint design. The specific functions and processing of the control unit 104 are described in step S130.

[0229] The control unit 104 is also configured to utilize the constrained task planning algorithm to optimize the coverage of the IoT system connecting the satellite and the ground station, thereby increasing the duration for which the IoT system within the target area of ​​the ground station can connect to the satellite. The specific functions and processing of this control unit 104 are described in step S140.

[0230] The primary objective of this invention is to design a mission planning algorithm for satellite IoT coverage optimization, such as proposing a constraint design method within the satellite IoT coverage optimization mission planning approach to meet the specific needs of satellite IoT mission planning. The second objective is to allow partial use of visible arc segments through reasonable constraint design, avoiding ineffective waste of station resources while simultaneously improving the service performance of the satellite IoT.

[0231] 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.

[0232] According to a third embodiment of the present invention, an Internet of Things system corresponding to a constraint design device in satellite networking mission planning is provided, comprising: the constraint design device in satellite networking mission planning described above.

[0233] 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.

[0234] According to a fourth embodiment of the present invention, a storage medium corresponding to a constraint design method in satellite networking mission planning is provided. The storage medium includes a stored program, wherein, when the program is executed, the device where the storage medium is located executes the constraint design method in satellite networking mission planning described above.

[0235] 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.

[0236] According to a fifth embodiment of the present invention, a computer program product corresponding to the constraint design method in satellite networking mission planning is provided, comprising a computer program that, when executed by a processor, implements the steps of the constraint design method in satellite networking mission planning described above.

[0237] 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.

[0238] 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.

[0239] 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 constraint design method for satellite networking mission planning, characterized in that, It is used to constrain mission planning when establishing backhaul links between satellites and ground stations; The constraint design method in the satellite networking mission planning 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, a task planning algorithm for coverage optimization of an Internet of Things (IoT) system for satellite-to-ground station networking is designed using a genetic algorithm. The designed task planning algorithm is constrained to obtain the constrained task planning algorithm. Using the task planning algorithm designed with the aforementioned constraints, coverage optimization is performed on the IoT system connecting satellites and ground stations to improve the duration for which the IoT system within the target area of ​​the ground station can connect to the satellite. Constraint 1: Any station can only serve a single satellite at a time. Constraint 2: The switching time between serving two different satellites by any station must be greater than a certain input value. Constraint 3: Any satellite can only be served by a single station at any given time. Constraint 4: The utilization rate of each tracking by the ground station must be greater than a certain arbitrary input value η. The process includes constraining the designed task planning algorithm to obtain a constrained task planning algorithm, including: Set constraints for IoT communication services between all ground stations and all satellites; the number of such constraints is one or more. Based on the set constraints, determine whether the constraints of each station in all stations in the ground area are conflict-free; If it is determined that there are no conflicts in the constraints of each station among all stations in the ground area, then the set of time periods required for service by all satellites is calculated based on the visible arc segments of all stations in the ground area and all satellites. For the set of times when all satellites need service, invalid tracking times are filtered out to obtain the set of valid tracking times when all satellites need service. Based on the set of effective tracking times required for all satellites to be served, the designed mission planning algorithm is updated to obtain the mission planning algorithm after the constraint design. Constraints 3 and 4, for the set of times when all satellites require service, filter out invalid tracking times to obtain the set of valid tracking times for all satellites requiring service, including: Initialize the time set of each satellite that has been tracked in all satellites; and combine the time set of each satellite that needs to transmit backlinks in the time set of all satellites that need to be serviced, as the time set of each satellite that needs to be tracked; For all stations in the ground area, count the number of visible arc segments corresponding to gene points with a planning result of 1; sort the visible arc segments corresponding to gene points with a planning result of 1 from smallest to largest, and correct the current planning result for each station to ensure constraints 3 and 4, so as to filter out invalid tracking time and obtain the set of effective tracking time required by all satellites. For all stations in the ground area, count the number of visible arc segments corresponding to gene points with a planning result of 1; sort the gene points with a planning result of 1 from smallest to largest according to the number of visible arc segments, and revise the current planning result for each station to ensure constraints 3 and 4, including: Sort the visible arcs corresponding to gene points with a planning result of 1 in ascending order, starting from the first station, and iterate through the visible arcs corresponding to each gene point with a planning result of 1 at that station. Calculate the intersection of the time set of the backhaul link required by the arc segment and the corresponding satellite of the arc segment, and use it as the contribution set of the arc segment to the actual service. The portion of the contribution set that does not intersect with the already tracked time set is taken as the valid contribution set; Calculate the sum of the lengths of the time segments in the effective contribution set, and use it as the effective contribution value for tracking using that arc segment; Determine whether the ratio between the effective contribution value and the preset tracking arc duration is greater than the preset input value: if so, retain the arc and update the tracked time set to the union of the tracked time set and the effective contribution set; otherwise, cancel the task corresponding to the arc to correct the current planning result of the station and ensure constraints 3 and 4. Therefore, the current planning results for all stations are revised to ensure constraints 3 and 4.

2. The constraint design method in satellite networking mission planning according to claim 1, characterized in that, Constraint 1, Constraint 2, Based on the set constraints, determine whether the constraints of each station in all stations in the ground area are conflict-free, including: For each station in all stations in the ground area, filter out the visible arc segments corresponding to all gene points with a planning result of 1. For all visible arc segments corresponding to gene points with a planning result of 1 obtained from the screening, sort them by the start time and compare them one by one. If the interval between the end time of the previous arc segment and the start time of the next arc segment is greater than the input value of constraint 2, then it is considered that there is no conflict between the previous arc segment and the next arc segment. If, for any station among all stations in the ground area, there are no conflicts between any two visible arc segments for which all planning results are constrained to 1, then the planning result is determined to be feasible for that station. If the planning results are feasible for all stations in the ground area, then constraints 1 and 2 are considered to satisfy the constraint design in the satellite networking mission planning, and it is determined that the constraint conditions of each station in all stations in the ground area are not conflicting.

3. The constraint design method in satellite networking mission planning according to claim 1, characterized in that, Based on the visible arcs of all ground stations and all satellites, the set of service times required by all satellites is calculated, including: For each of all satellites, count the set of visible arc segments between that satellite and all stations in the ground region; The union of the visible arc segments of the satellite and all stations in the ground region is used as the time set of the satellite's backhaul links. By iterating through all satellites in this way, we can obtain the set of service times required by all satellites.

4. A constraint design apparatus for satellite networking mission planning, employing the constraint design method for satellite networking mission planning as described in any one of claims 1 to 3, to realize constraint design in satellite networking mission planning, characterized in that, It is used to constrain mission planning when establishing backhaul links between satellites and ground stations; The constraint design device in the satellite networking mission planning 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 further configured to perform constraint design on the designed task planning algorithm to obtain a task planning algorithm after constraint design. The control unit is also configured to use the constrained task planning algorithm to optimize the coverage of the IoT system connecting the satellite and the ground station, so as to increase the duration for which the IoT system in the target area of ​​the ground station can connect to the satellite.

5. An Internet of Things (IoT) system, characterized in that, include: The constraint design device for satellite networking mission planning as described in claim 4.

6. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to execute the constraint design method in the satellite networking mission planning of any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the constraint design method in satellite networking mission planning as described in any one of claims 1 to 3.

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