Method and system for multi-point beam flexible coverage planning

Abstract

The application provides a multi-point beam flexible coverage planning method, which comprises the following steps: acquiring satellite latitude and longitude, user quantity, demand matrix, beam width array and upper limit of quantity; converting user latitude and longitude into uv coordinates and generating a coverage area; traversing the beam width, calculating the corresponding uv beam diameter, generating candidate beams by gridizing the coverage area, and selecting the optimal combination after eliminating beams without users through the first round of optimization; screening the optimal beam width and reprocessing; selecting multiple groups of non-dominant combinations through the second round of multi-objective optimization; and selecting the final scheme after visualizing the multiple groups of non-dominant combinations. Thus, the application realizes automatic beam arrangement, saves manpower, shortens the cycle, adapts to fine requirements, improves the capacity utilization rate, can generate multiple schemes for selection, and ensures feasibility.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a multi-beam flexible coverage planning method and system. Background Technology

[0002] Multi-beam technology covers different areas using multiple independent beams, leveraging frequency reuse (the same frequency band being reused in different beams) and spatial isolation to improve system capacity. It is a core technology for achieving high-throughput satellite communication. Coverage planning for high-throughput satellites refers to how to arrange the beams, including the number of beams, beam positions, and beam sizes.

[0003] Due to the constantly diversifying user demands and the dynamic changes in time and space throughout the entire lifecycle of satellites during their on-orbit application phase, high-throughput satellites are evolving towards greater flexibility and even full flexibility. This means not only increasing the total satellite capacity but also allocating that capacity to where users need it most within a specific time and space, thereby effectively improving the utilization rate of satellite capacity resources. Uneven spatial distribution of user demand necessitates flexible beamforming to achieve load transfer and balance between beams, a physical prerequisite for matching satellite capacity with user needs. Uneven temporal distribution of user demand requires dynamic and flexible allocation of resources such as power, frequency, and time slots to achieve capacity transfer and further match user needs in real time. This underscores the importance of flexible on-orbit beamforming.

[0004] To meet this demand, satellite payload and antenna technologies are developing rapidly. Traditional high-throughput satellites mostly use single-feed, single-beam antenna technology, which makes it impossible to reconfigure the satellite's beam arrangement in orbit. However, with the development of technologies such as digital beamforming (DBF), beam rearrangement can be achieved by adjusting the amplitude and phase parameters of each beam in orbit.

[0005] Therefore, traditional multi-beam arrangement methods have two important drawbacks in response to the rapidly evolving on-orbit beamforming requirements of fully flexible high-throughput satellites:

[0006] (1) Beam arrangement relies on manual multiple iterations, which takes a long time and is not suitable for the need for rapid on-orbit response beam rearrangement. Since traditional high-throughput satellites do not involve on-orbit beam rearrangement, but rely on static beam arrangement, that is, beam arrangement in the design phase, they are not very sensitive to the arrangement time. Satellite designers and users often use manual drawing and multiple iterations to complete the beam arrangement.

[0007] (2) It mainly relies on regular beam arrangement, that is, multiple beams arranged in a region are of the same size and the overlapping area between beams is basically the same, such as the common hexagonal honeycomb structure arrangement. This arrangement method is simple and suitable for situations where the capacity demand density is evenly distributed in a region, or where only the overall capacity demand in the region is known but not the specific distribution details. It does not match the application scenarios of fully flexible satellites that are geared towards refined user needs, resulting in the underutilization of satellite capacity resources. Summary of the Invention

[0008] To overcome the aforementioned technical deficiencies, this invention provides a multi-point beam flexible coverage planning method and system for refined scenarios where user needs are unevenly distributed in time and space, and to adapt to the rapid response requirements of fully flexible on-orbit beam reconfiguration of satellites. This method can improve the matching rate of user needs and achieve better utilization of satellite resources.

[0009] To achieve the above-mentioned technical effects, on the one hand, the present invention provides a multi-point beam flexible coverage planning method, including the following steps:

[0010] (1) Obtain satellite latitude and longitude coordinates, number of users, user demand matrix, beamwidth array and upper limit of beam number;

[0011] (2) Convert the user's latitude and longitude coordinates into a UV coordinate system to generate the user coverage area;

[0012] (3) Traverse the beamwidth array and perform the following operations for each beamwidth:

[0013] Calculate the beam diameter corresponding to the uv coordinate system;

[0014] The user coverage area is networked according to the beam diameter to generate a candidate beam set;

[0015] Beams without user coverage are removed through preprocessing;

[0016] The first round of single-objective optimization: an optimization algorithm is used to select beam combinations from the preprocessed candidate beam set that do not exceed the upper limit of the number of beams, with the goal of maximizing the coverage satisfaction rate.

[0017] (4) Filter the beamwidth with the coverage satisfaction rate closest to 1 point and repeat step (3) but do not include performing the first round of single-target optimization;

[0018] (5) Second round of multi-objective optimization: With the goals of maximizing coverage satisfaction rate, capacity matching rate and minimizing beam number, a multi-objective optimization algorithm is used to select multiple sets of mutually non-dominant optimal beam combinations from the beam combinations;

[0019] (6) Visualize the optimal beam combination and select the final beam arrangement scheme from it.

[0020] Optionally, the uv coordinate transformation in step (2) includes:

[0021] For each user, the intermediate variables x, y, and z are calculated based on the following formula:

[0022] x = 6378000 × cos((user latitude - satellite latitude) × π / 180) × sin((user longitude - satellite longitude))

[0023] degree)×π / 180);

[0024] y = -6378000 × sin((user latitude - satellite latitude) × π / 180);

[0025] z = 42164000 - 6378000 × cos((user latitude - satellite latitude) × π / 180) × cos((user longitude - satellite longitude) × π / 180);

[0026] The uv coordinates are obtained by transforming x, y, and z using trigonometric functions.

[0027] Optionally, the preprocessing in step (3) specifically includes:

[0028] If any beam in the candidate beam set does not contain the UV coordinates of any user within its coverage area, then that beam is removed from the candidate beam set.

[0029] Optionally, the optimization algorithm includes a genetic algorithm or a particle swarm optimization algorithm.

[0030] Optionally, the multi-objective optimization algorithm is the NSGA-II (Non-dominated sorting genetic algorithm) algorithm.

[0031] Optionally, let the user demand matrix be D, with m rows and 3 columns, where the element in the first column of any row represents the longitude of the corresponding user's location; the element in the second column of any row represents the latitude of the corresponding user's location; and the element in the third column of any row represents the capacity demand of the corresponding user.

[0032] Optionally, the beam combination The corresponding capacity matching rate is:

[0033]

[0034] in, Indicates the beam combination The capacity that the i-th beam can provide,

[0035] Indicates the beam combination The i-th beam The sum of the capacity requirements of the users serving the service, D j3 This represents the element in the j-th row and 3-th column of the input user demand matrix D.

[0036] Optionally, the optimal beam combination that does not dominate each other is defined as: there is no beam combination that is superior to another beam combination in all three indicators: coverage satisfaction rate, capacity matching rate, and number of beams.

[0037] Optionally, the beam diameter is calculated based on the following formula:

[0038]

[0039] w = 35786000 × tan(πB) x / 180);

[0040] Among them, B x This refers to the corresponding element in the beamwidth array.

[0041] On the other hand, the present invention also provides a multi-beam flexible coverage planning system, the system being configured to implement the multi-beam flexible coverage planning method as described above.

[0042] The multi-beam flexible coverage planning method and system provided by this invention are designed for refined user needs. Through a multi-stage, multi-objective beam arrangement optimization method, it achieves automated beam arrangement and can generate multiple optimization schemes for selection, saving labor costs, shortening the arrangement cycle, and ensuring that the arrangement scheme is feasible. Attached Figure Description

[0043] Figure 1 A flowchart illustrating the steps of a multi-beam flexible coverage planning method according to an embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the user point distribution formed by the transformed uv coordinate system using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0045] Figure 3 This is a schematic diagram of a beam set formed after preprocessing using the multi-point beam flexible coverage planning method provided in an embodiment of the present invention;

[0046] Figure 4 This is a diagram illustrating the first set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0047] Figure 5This is a diagram illustrating the second set of mutually non-dominant optimal beam combinations obtained using the multi-point beam flexible coverage planning method provided in an embodiment of the present invention.

[0048] Figure 6 This is a diagram illustrating the third set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0049] Figure 7 This is a diagram illustrating the fourth set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0050] Figure 8 This is a diagram illustrating the fifth set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0051] Figure 9 This is a diagram illustrating the sixth set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0052] Figure 10 This is a diagram illustrating the seventh set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0053] Figure 11 This is a diagram illustrating the eighth set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0054] Figure 12 This is a diagram illustrating the ninth set of mutually non-dominant optimal beam combinations obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention.

[0055] Figure 13 This is a diagram illustrating the tenth set of optimal beam combinations that do not dominate each other, obtained using the multi-beam flexible coverage planning method provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] It should be noted that references to "an embodiment," "embodiment," "example embodiment," etc., in this specification refer to the described embodiment including specific features, structures, or characteristics, but not every embodiment must include these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0058] Furthermore, certain terms are used in the specification and subsequent claims to refer to specific components or parts. Those skilled in the art will understand that manufacturers may use different names or terms to refer to the same component or part. This specification and subsequent claims do not distinguish components or parts by differences in name, but rather by differences in function. The terms "comprising" and "including" used throughout the specification and subsequent claims are open-ended and should be interpreted as "including but not limited to." Additionally, the term "connection" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connections made through other means.

[0059] This invention addresses the uneven spatiotemporal distribution of user needs and the requirement for fully flexible on-orbit satellite beam reconfiguration. It achieves flexible coverage planning through the following concept: First, input parameters such as satellite position and user needs, convert user latitude and longitude to UV coordinates, and determine the coverage area; traverse beamwidths, generate candidate beams and preprocess them, and select combinations with high coverage satisfaction rates through the first round of optimization; after selecting the optimal beamwidth, generate multiple schemes through a second round of multi-objective optimization, and finally determine the beam arrangement by combining visual manual decision-making.

[0060] The specific principles of the multi-beam flexible coverage planning method of this application will be described below with reference to specific embodiments.

[0061] Figure 1 This invention illustrates a multi-beam flexible coverage planning method according to an embodiment of the present invention, comprising the following steps:

[0062] S101: Obtain satellite latitude and longitude coordinates, number of users, user demand matrix, beamwidth array, and upper limit of beam count. Let the user demand matrix be D, with m rows and 3 columns, where the element in the first column of any row represents the longitude of the corresponding user's location; the element in the second column of any row represents the latitude of the corresponding user's location; and the element in the third column of any row represents the capacity demand of the corresponding user.

[0063] Specifically, this is achieved by obtaining the following input data:

[0064] Input the satellite's longitude (Lon), with positive numbers for east longitude and negative numbers for west longitude, in degrees; and the satellite's latitude (Lat), with positive numbers for north latitude and negative numbers for south latitude, in degrees.

[0065] Input the number of users m, where m is a positive integer.

[0066] Input a user requirement matrix D, with m rows and 3 columns. The element D in the i-th row and 1-th column is... i1 This represents the longitude of the location of the i-th user, with positive numbers for east longitude and negative numbers for west longitude, in degrees; the element D in the i-th row and 2nd column. i1 This represents the latitude of the i-th user's location, with positive numbers for north latitude and negative numbers for south latitude, in degrees; the element D in the i-th row and 3rd column... i1 A positive number represents the capacity requirement of the i-th user, in Mbps.

[0067] Input an optional beamwidth array B, representing the set of all possible beamwidth values ​​in degrees.

[0068] Input the upper limit value N for the number of beams, where N is a positive integer.

[0069] For example, in a specific application example, enter the satellite's longitude of 50° and its latitude of 0°. Enter the number of users as 6707.

[0070] Input the user demand matrix, randomly generate the values ​​of the first two columns of the matrix, with a value range of -180 to 180, and randomly select positive values ​​for the third column, ensuring that the sum of the third column is 100000.

[0071] Input an optional beamwidth array [0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0].

[0072] The maximum number of input beams is 90.

[0073] S102: Convert the user's latitude and longitude coordinates into the uv coordinate system to generate the user coverage area. The uv coordinate system is a planar coordinate system converted from the user's latitude and longitude (geographic coordinates), used to simplify the calculation of satellite beam coverage. In this step S102, specifically, the longitude in the first column and the latitude in the second column of matrix D are converted into u coordinates and v coordinates in the uv coordinate system, respectively, to obtain matrix D′.

[0074] Furthermore, the UV coordinate transformation includes: for each user, calculating the intermediate variables x, y, and z based on the following formula:

[0075] x = 6378000 × cos((user latitude - satellite latitude) × π / 180) × sin((user longitude - satellite longitude))

[0076] degree)×π / 180);

[0077] y = -6378000 × sin((user latitude - satellite latitude) × π / 180);

[0078] z = 42164000 - 6378000 × cos((user latitude - satellite latitude) × π / 180) × cos((user longitude - satellite longitude) × π / 180);

[0079] The uv coordinates are obtained by transforming x, y, and z using trigonometric functions.

[0080] Taking the i-th row as an example, calculate the intermediate variables x, y, and z:

[0081] x=6378000×cos((D i2 -Lat)π / 180)×sin((D i1 -Lon)π / 180);

[0082] y=-6378000×sin((D i2 -Lat)π / 180);

[0083] z = 42164000 - 6378000 × cos((D i2 -Lat)π / 180)×cos((D i1 -Lon)π / 180);

[0084] Then, based on the aforementioned intermediate variables, the matrix D′ in the i-th row is calculated. i1 、D′ i2 :

[0085]

[0086] Similarly, the coordinates of each row of matrix D are converted into u-u coordinates.

[0087] Calculate the user coverage area: represented by the uv coordinate range, where the u coordinate range is min(D′). 11 ,D′ 21 ,…D′ m1 )~max(D′ 11 ,D′ 21 ,…D′ m1 The range of the v coordinates is min(D′). 12 ,D′ 22 ,…D′ m2 )~max(D′ 12,D′ 22 ,…D′ m2 ).

[0088] To illustrate with the above application example, by converting the longitude in the first column and the latitude in the second column of the matrix user demand matrix into u and v coordinates in the uv coordinate system, respectively, the points formed by the converted u and v coordinates are as follows: Figure 2 As shown.

[0089] The generated user coverage area has the following coordinate ranges: u-coordinates -0.0422 to 0.0790 and v-coordinates -0.0605 to 0.1179.

[0090] S103: Traverse the beamwidth array and perform the following operation for each beamwidth (specifically, poll array B and perform the operation for each element B). x B x Perform the following operations (a to d) respectively on ∈B:

[0091] a. Calculate the beam diameter corresponding to the uv coordinate system; that is, calculate B. x The corresponding uv coordinate system beam diameter d x Specifically, the beam diameter d x Calculated based on the following formula:

[0092]

[0093] w = 35786000 × tan(πB) x / 180);

[0094] Among them, B x This refers to the corresponding element in the beamwidth array.

[0095] b. Network the user coverage area according to the beam diameter to generate a candidate beam set;

[0096] In practice, the user coverage area will be divided into 0.1d... x ×0.1d x The grid is discretized, and any grid that is less than one grid is treated as one, forming a grid set Λ, with the number of grids in the set being ξ.

[0097] Then, taking the center of each grid in the grid set Λ as the center, and d... x Given a diameter, a candidate beam set Φ is generated, transforming the multi-beam arrangement problem into an optimal combination problem of selecting no more than N beams from the candidate beam set Φ.

[0098] c. Remove beams without user coverage through preprocessing; the preprocessing specifically includes: for any beam in the candidate beam set, if its coverage does not include the uv coordinates of any user, then remove the beam from the candidate beam set.

[0099] Specifically, for the i-th beam Φ in the candidate beam set Φ i If i∈[1,2,…,ξ], then all points (D′) j1 ,D′ j2 ), j∈[1,2,…,m] are not in Φ i Within the range, then Φ i Remove from Φ to form a new beam set Φ′;

[0100] d. First round of single-objective optimization: An optimization algorithm is used to maximize coverage satisfaction rate, selecting beam combinations from the preprocessed candidate beam set that do not exceed the upper limit of the number of beams. This is the first round of optimization, where the optimal beam combination is selected from Φ′ using the optimization algorithm. and The number of elements is no greater than N, so that the coverage satisfaction rate η is maximized, and the value of η is recorded.

[0101] The optimization algorithm used in the first round of optimization is not limited to a specific algorithm; it can be a genetic algorithm or a particle swarm optimization algorithm, etc.

[0102] Coverage satisfaction rate η refers to the coverage satisfaction rate of all points (D′). j1 ,D′ j2 ),j∈[1,2,…,m] in The percentage of each beam range.

[0103] Using the above application example as an example, by polling the beamwidth array [0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0], the steps are performed for each element value. Taking 0.2 as an example:

[0104] The calculated beam diameter in the uv coordinate system corresponding to a beamwidth of 0.2° is 0.0034.

[0105] The user coverage area is divided into 0.1d x ×0.1d x The grid is discretized, and any grid that is less than one grid is treated as one, forming a grid set with a total of 205713 grids.

[0106] A beam set is generated with the center of each grid in the grid set as the center and a diameter of 0.0034.

[0107] Preprocessing forms a new beam set, such as Figure 3 As shown.

[0108] The first optimization phase involves using an optimization algorithm to maximize the coverage satisfaction rate η, with the maximized η value being 7.32%.

[0109] S104: Select the beamwidth with the coverage satisfaction rate closest to 1, and re-execute step S103, but excluding the first round of single-objective optimization. That is, after polling array B, select the beamwidth B corresponding to the one with an η value less than 1 and closest to 1. y B y ∈B; and then targeting B y Repeat the above operations a to c.

[0110] For example, after polling array B, select the beamwidth corresponding to the value of η that is less than 1 and closest to 1 as 1°; then, for a beamwidth of 1°, repeat the above operations a to c.

[0111] S105: Second Round of Multi-Objective Optimization: With the objectives of maximizing coverage satisfaction rate, capacity matching rate, and minimizing the number of beams, a multi-objective optimization algorithm is used to select multiple sets of mutually exclusive optimal beam combinations from the given beam combinations. This step is the second round of optimization, specifically aiming to maximize coverage satisfaction rate, maximize capacity matching rate, and minimize the number of elements in the beam combination. A multi-objective optimization algorithm is used to select 10 mutually exclusive beam combinations from Φ′. and The number of elements is no greater than N.

[0112] This embodiment does not limit the specific multi-objective optimization algorithm; it can be the NSGA-II algorithm or other algorithms.

[0113] The optimal beam combination that does not dominate each other is defined as follows: there is no beam combination that is superior to another beam combination in all three indicators: coverage satisfaction rate, capacity matching rate, and number of beams.

[0114] Furthermore, the beam combination The corresponding capacity matching rate is:

[0115]

[0116] in, Indicates the beam combination The capacity that the i-th beam can provide,

[0117] Indicates the beam combination The i-th beam The sum of the capacity requirements of the users serving the service, Dj3 This represents the element in the j-th row and 3-th column of the input user demand matrix D.

[0118] Beamforming The i-th beam The users of the service refer to those located in the beam Within the coverage area, and with the beam The center distance is less than that of the beam combination All users at the distance of any other beam center.

[0119] To illustrate with the above application example, in the second round of optimization, with the objectives of maximizing coverage satisfaction rate, maximizing capacity matching rate, and minimizing the number of elements in the beam combination, a multi-objective optimization algorithm is adopted to select 10 non-dominant beam combinations, with each combination having no more than 90 elements.

[0120] S106: Visualize the optimal beam combination and select the final beam arrangement scheme from it.

[0121] Step S106 may include a post-processing stage and a decision-making stage, and in specific implementation:

[0122] In the post-processing stage, the beam combination is drawn on a single image. Includes all beams and all points (D′) j1 ,D′ j2 ),j∈[1,2,…,m]; and so on, draw the beam combinations respectively. There are a total of 10 corresponding images.

[0123] During the decision-making phase, the decision-maker selects one image from the 10 images presented, and uses the beam combination corresponding to the selected image as the final beam arrangement result.

[0124] For example, based on the above application example, 10 images corresponding to 10 non-dominant beam combinations are selected as follows: Figures 4-13 As shown. Decision-makers based on Figures 4-13 The results shown can be used to select one image, for example, Figure 4 ,but Figure 4 The corresponding beam combination serves as the final beam arrangement result. This embodiment can be based on individual decision-making or decision-making through a pre-trained agent.

[0125] The present invention also provides a multi-beam flexible coverage planning system, which is configured to implement the multi-beam flexible coverage planning method as described in the above embodiments. Specifically, this system is configured to execute the steps of the method described in the above embodiments, the specific steps of which are as described above and will not be repeated here.

[0126] In summary, the multi-beam flexible coverage planning method and system provided by this invention obtains satellite latitude and longitude, number of users, demand matrix, beamwidth array, and upper limit of the number; converts user latitude and longitude into UV coordinates and generates coverage areas; iterates through beamwidths, calculates the corresponding UV beam diameters, grids the coverage areas to generate candidate beams, eliminates beams without users, and selects the optimal combination after a first round of optimization; filters the optimal beamwidth and reprocesses it; a second round of multi-objective optimization selects multiple sets of non-dominant combinations; and the final scheme is selected after visualizing the multiple sets of non-dominant combinations. Therefore, this invention achieves automated beam arrangement, saves manpower, shortens the cycle, adapts to refined requirements, improves capacity utilization, generates multiple schemes for selection, and ensures feasibility.

[0127] The advantages of this invention compared to the prior art are:

[0128] (1) This invention saves manpower costs, shortens the arrangement cycle, and can quickly respond to the needs of on-orbit beam rearrangement.

[0129] (2) This invention addresses refined user needs, allowing for flexible selection of beamwidths within a practical range, with the overlap ratio between beams corresponding to the density of user demand. In areas with low user demand density, beam overlap is minimal or nonexistent; in areas with high user demand density, beam overlap is greater, improving satellite capacity utilization. Furthermore, the uniform beamwidth across all beams facilitates onboard digital beamforming and operator management.

[0130] (3) The present invention can quickly converge the layout scheme through the first stage of optimization, reduce the search space, and create conditions for the second stage of fine optimization. The second stage adopts multi-objective optimization, which can take into account more scheme determination factors and generate multiple optimization schemes for selection, ensuring that the layout scheme is feasible.

[0131] This invention can be implemented on a computer as a computer-based method, or in dedicated hardware, or a combination of both. Executable code or portions thereof for the method according to the invention can be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Optionally, the computer program product includes non-transitory program code components stored on a computer-readable medium so as to execute the method according to the invention when the program product is executed on a computer.

[0132] In an optional embodiment, the computer program includes computer program code components adapted to perform all the steps of the method according to the invention when the computer program is run on a computer. Optionally, the computer program is embodied on a computer-readable medium.

[0133] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of the present invention is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0134] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A multi-beam flexible coverage planning method, characterized in that, Including the following steps: (1) Obtain satellite latitude and longitude coordinates, number of users, user demand matrix, beamwidth array and upper limit of beam number; Let the user demand matrix be D, with m rows and 3 columns, where the element in the first column of any row represents the longitude of the corresponding user's location; the element in the second column of any row represents the latitude of the corresponding user's location; and the element in the third column of any row represents the capacity demand of the corresponding user. (2) Convert the user's latitude and longitude coordinates into a UV coordinate system to generate the user coverage area; (3) Traverse the beamwidth array and perform the following operations for each beamwidth: Calculate the beam diameter corresponding to the uv coordinate system. ; The user coverage area is divided into Perform gridding, with the center of each grid as the center of a circle, and... Given the diameter, generate a set of candidate beams; Beams without user coverage are removed through preprocessing; The first round of single-objective optimization: an optimization algorithm is used to select beam combinations from the preprocessed candidate beam set that do not exceed the upper limit of the number of beams, with the goal of maximizing the coverage satisfaction rate. (4) Filter the beamwidth with the coverage satisfaction rate closest to 1 and repeat step (3) but do not include performing the first round of single-objective optimization; (5) Second round of multi-objective optimization: With the goals of maximizing coverage satisfaction rate, capacity matching rate and minimizing beam number, a multi-objective optimization algorithm is used to select multiple sets of optimal beam combinations that do not dominate each other from the beam combinations; (6) Visualize the optimal beam combination and select the final beam arrangement scheme from it.

2. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The conversion of user latitude and longitude coordinates to the UV coordinate system in step (2) includes: For each user, the intermediate variables x, y, and z are calculated based on the following formula: x = 6378000 × cos ((user latitude - satellite latitude) × π / 180) × sin ((user longitude - satellite longitude) × π / 180); y = -6378000 × sin ((user latitude - satellite latitude) × π / 180); z = 42164000 - 6378000 × cos ((user latitude - satellite latitude) × π / 180) × cos ((user longitude - satellite longitude) × π / 180); The uv coordinates are obtained by transforming x, y, and z using trigonometric functions.

3. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The preprocessing in step (3) specifically includes: If any beam in the candidate beam set does not contain the UV coordinates of any user within its coverage area, then that beam is removed from the candidate beam set.

4. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The optimization algorithm includes genetic algorithm or particle swarm optimization algorithm.

5. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The multi-objective optimization algorithm is the NSGA-II algorithm.

6. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The beam combination The corresponding capacity matching rate is: ； in, Indicates the beam combination The Middle i The capacity that a single beam can provide Indicates the beam combination The Middle i beam The sum of the capacity requirements of the users serving the service. Represents the input user demand matrix D No. j The element in the third column of the row.

7. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The optimal beam combination that does not dominate each other is defined as follows: there is no beam combination that is superior to another beam combination in all three indicators: coverage satisfaction rate, capacity matching rate, and number of beams.

8. The multi-beam flexible coverage planning method according to claim 1, characterized in that, The beam diameter is calculated based on the following formula: ； ； in, This refers to the corresponding element in the beamwidth array.

Images