Low earth orbit satellite internet of things ground station deployment method based on regular polygon grid optimization

Abstract

The application relates to the technical field of satellite communication, and discloses a low-orbit satellite Internet of Things ground station deployment method based on regular polygon grid optimization, which comprises the following steps: receiving geographical information of a target deployment area and an effective communication coverage radius of a low-orbit satellite; establishing a dot product ratio optimization model with the minimum deployment quantity per unit area as a target; under the condition of ensuring continuous coverage, performing quantitative analysis and deduction on the dot product ratio of regular triangle, regular quadrilateral and regular hexagon grid configurations; selecting a regular triangle grid with the minimum dot product ratio as the only optimal grid configuration; and calculating the side length and other geometric parameters of the grid according to the effective communication coverage radius, and finally generating and outputting the ground station deployment position in the target deployment area. The application theoretically guarantees the optimality of the deployment scheme through mathematical deduction, can realize continuous coverage of the target deployment area with fewer ground stations, significantly reduces the construction cost, and improves the scientificity and automation level of planning.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, specifically to a method for deploying low-Earth orbit satellite Internet of Things (IoT) ground stations based on regular polygonal grid optimization. Background Technology

[0002] With the rapid development of low-Earth orbit (LEO) satellite IoT constellations, seamless global data coverage has become possible. In the entire satellite communication link, ground stations, as the key hubs connecting satellites and the ground core network, directly determine the service capacity, stability, and construction cost of the entire system through their network scale and layout. To ensure uninterrupted communication services over a vast area, a large number of ground stations need to be deployed to form a collaborative ground network.

[0003] In existing technologies, ground station deployment planning often relies on complex computer simulation analysis or site selection based on engineering experience. Simulation-based methods typically require pre-setting multiple candidate deployment schemes, and then evaluating and selecting these limited schemes by simulating dynamic processes such as satellite transit and link performance. This approach is computationally intensive, time-consuming, and its results largely depend on the initial candidate scheme settings, making it difficult to guarantee obtaining a globally optimal deployment solution. On the other hand, planning methods relying on engineering experience consider more realistic factors such as terrain and infrastructure, but lack unified, quantifiable optimization criteria, which can easily lead to deployment redundancy or coverage blind spots, resulting in unnecessary resource waste.

[0004] Therefore, existing technologies lack a planning method that can provide a deterministic optimal solution for ground station deployment from a theoretical perspective through rigorous mathematical models and quantitative analysis, thereby minimizing the number of ground stations deployed while meeting continuous coverage performance indicators and maximizing the economic benefits of network construction. Summary of the Invention

[0005] The technical problem that this invention aims to solve is that the deployment schemes of low-Earth orbit satellite IoT ground stations in the prior art lack quantitative optimization models and rely more on engineering experience or simulation analysis. This may lead to problems such as redundant deployment of ground stations, uneconomical resource allocation, and low coverage efficiency.

[0006] To address the aforementioned technical problems, this invention provides a method for deploying low-Earth orbit satellite IoT ground stations based on regular polygonal mesh optimization. This method, through establishing a mathematical model and performing geometric derivation, fundamentally ensures the optimality of the deployment scheme.

[0007] The technical solution provided by this invention is as follows:

[0008] A method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization, the method being executed by computer equipment, includes:

[0009] Receive geographic information of the target deployment area and the effective communication coverage radius of low-Earth orbit satellites. ;

[0010] Based on the effective communication coverage radius By quantitatively comparing the dot product ratios of different regular polygon mesh configurations, the regular polygon mesh with the smallest dot product ratio is selected as the optimal mesh configuration.

[0011] Based on the optimal grid configuration and the effective communication coverage radius Calculate the side lengths of the regular polygons that constitute this optimal mesh configuration. ;

[0012] Within the target deployment area, based on the side length Generate the vertex coordinates of the optimal mesh configuration and output the vertex coordinates as the deployment location of the ground station.

[0013] As a further limitation of the technical solution of the present invention, the effective communication coverage radius It is based on the satellite's orbital altitude. The radius of the Earth and the minimum operating elevation angle required by the ground station to ensure communication quality. The calculation is as follows:

[0014] ;

[0015] The core innovation of this invention lies in proposing an optimization criterion based on minimizing the dot product ratio to deterministically select the optimal mesh configuration. The specific implementation is as follows:

[0016] First, the ground station deployment problem is abstracted into a geometric problem, and the dot product ratio is introduced. As an evaluation metric for deployment efficiency, this metric is defined as the number of ground stations required to be deployed per unit area. Its calculation formula is:

[0017] ;

[0018] in, This represents the total number of ground stations. The total deployment area is considered. The optimization objective is to find the dot product ratio. Minimal deployment scheme.

[0019] Secondly, to ensure the continuity of satellite communication—that is, to ensure that the circular coverage area, which can be moved arbitrarily within the plane, always contains at least one ground station—this invention sets a geometric constraint: the maximum size of any grid cell is limited to the fact that the geometric shape of that cell can be exactly inscribed within the effective communication coverage radius. The covering circle with radius .

[0020] Based on this constraint, this invention performs a quantitative analysis of the dot product ratio of all regular polygonal configurations (i.e., equilateral triangular meshes, regular quadrilateral meshes, and regular hexagonal meshes) that can be seamlessly spliced ​​in a two-dimensional plane. The derivation shows that, under uniform geometric constraints, the dot product ratios of each configuration are as follows:

[0021] Dot product ratio of equilateral triangular mesh: ;

[0022] Dot product ratio of a regular quadrilateral grid: ;

[0023] Dot product ratio of a regular hexagonal grid: ;

[0024] By comparing the above expressions, we can conclude that... This conclusion is independent of the specific value of the effective communication coverage radius R, thus proving that the equilateral triangular mesh is the only optimal mesh configuration. Therefore, this invention deterministically selects the equilateral triangular mesh.

[0025] After determining the optimal mesh configuration as an equilateral triangular mesh, based on the aforementioned geometric constraints, the side lengths of its element regular polygons are... It is calculated using the following formula:

[0026] ;

[0027] To generate specific vertex coordinates within the target deployment area, the height of the equilateral triangle unit can be further calculated. The generation process can employ an iterative algorithm, generating grid vertices row by row within a minimum bounding rectangle covering the target deployment area, where vertices in odd-numbered rows are horizontally offset from those in even-numbered rows. Subsequently, the coordinates of all generated initial vertices are evaluated, retaining only those located inside or on the geographical boundary of the target deployment area, thus forming the final list of ground station deployment locations.

[0028] Finally, the generated list of deployment locations is output in a preset data format, such as comma-separated values ​​(CSV), Key-Based Markup Language (KML), or Shapefile format.

[0029] This invention provides a method for deploying low-Earth orbit satellite IoT ground stations based on regular polygonal mesh optimization. It offers the following advantages:

[0030] 1. This invention, through the establishment of a mathematical model and geometric derivation, theoretically proves that the equilateral triangular grid is the deployment scheme with the lowest ground station density required to achieve continuous coverage, ensuring the optimality of the deployment scheme. It can complete the coverage of the target deployment area with the fewest number of ground stations, significantly reducing construction costs and subsequent operation and maintenance costs.

[0031] 2. This invention provides a quantitative and repeatable deployment method, whose conclusions are based on deterministic mathematical calculations, overcoming the uncertainty of traditional methods that rely on engineering experience or simulation, and providing a scientific basis for the planning of large-scale ground station networks.

[0032] 3. The entire process of this invention can be automated by a computer program, from receiving parameters to outputting the final deployment plan, without human intervention, which greatly improves the efficiency of ground station deployment planning.

[0033] 4. The optimization model and derivation conclusions of this invention are universal and do not depend on specific satellite parameters. Only the effective communication coverage radius of different low-orbit satellite systems and the geographical information of the target deployment area need to be input to quickly generate the corresponding optimal deployment scheme. It has strong versatility and portability. Attached Figure Description

[0034] Figure 1 This is a flowchart of a ground station deployment method according to an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the specific embodiments of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0036] See attached document Figure 1 , Figure 1 This is a flowchart of a ground station deployment method according to an embodiment of the present invention. The present invention provides a low-Earth orbit satellite IoT ground station deployment method based on regular polygonal grid optimization. This method can be executed by a computer device and may include the following steps:

[0037] S100, Receive deployment parameters. This step obtains the initial input information required to perform the ground station deployment task, specifically the geographical information of the target deployment area and the effective communication coverage radius of the low-Earth orbit satellite.

[0038] S200, Select the optimal grid configuration. This step applies a preset optimization criterion that aims to minimize the number of ground stations deployed per unit area, thereby deterministically selecting a regular polygonal grid as the deployment geometry.

[0039] S300, Calculate the geometric parameters of the deployed mesh. This step calculates the geometric parameters required to generate the mesh based on the mesh configuration selected in step S200 and the input effective communication coverage radius, which mainly include the side lengths of the regular polygons that make up the mesh.

[0040] S400, Generate Ground Station Deployment Plan. In this step, within the target deployment area, based on the geometric parameters calculated in step S300, a grid covering the area is generated, and the coordinates of all vertices of the grid are extracted to form a list of ground station deployment locations.

[0041] S500, Output Deployment Plan. This step outputs the list of ground station deployment locations generated in step S400 in a preset data format for subsequent engineering implementation.

[0042] In this embodiment, step S100 obtains two basic input pieces of information required to perform the ground station deployment task: one is the geographical information of the target deployment area, and the other is the effective communication coverage radius of the low-Earth orbit satellite. .

[0043] Specifically, the geographic information of the target deployment area is used to define the geographic boundaries of the ground station deployment. This information can be represented by a closed polygon consisting of a sequence of one or more geographic coordinate points, where each coordinate point contains longitude and latitude values. Alternatively, the geographic information can also be in the form of a standard geospatial data file, such as a Shapefile or GeoJSON file.

[0044] Upon receiving this information, the computer device parses it to build a digital boundary model that can be used for subsequent geometric calculations. The methods for parsing geospatial data in different formats are well-known techniques in the field of geographic information systems and will not be elaborated upon here.

[0045] Effective communication coverage radius This is a key physical parameter that represents the radius of the circular area that a single satellite can effectively cover in a simplified two-dimensional planar model. The value is calculated based on the physical characteristics and operational constraints of the satellite communication link. These physical characteristics and constraints specifically include the satellite's orbital altitude. The radius of the Earth and the minimum operating elevation angle required by the ground station to ensure communication quality. .

[0046] In one specific embodiment, parameters The value can be calculated using the following formula:

[0047] ;

[0048] in, Represents the average radius of the Earth; This represents the satellite's orbital altitude above the Earth's surface. This represents the lowest elevation angle at which the ground station antenna can operate effectively.

[0049] During the execution of the method, parameters It can be received directly as a pre-calculated value, or the method can also receive the orbital height. and lowest elevation angle The original physical parameters are obtained, and the above calculations are performed within this step to obtain them. Value. The geographic information and effective communication coverage radius of the target deployment area were obtained. The method then passes these parameters to subsequent steps for processing.

[0050] In this embodiment, step S200, which selects the optimal grid configuration, is based on a mathematical model that provides a quantitative basis for subsequent configuration selection.

[0051] This method first mathematically abstracts the physical deployment problem. The deployment problem of low-Earth orbit satellite IoT ground stations is abstracted as arranging a series of fixed scattered points in a two-dimensional plane to represent the ground stations, and using a dynamically moving circular area to represent the effective communication coverage of the satellite. Under this model, the core problem of deployment optimization is transformed into how to rationally arrange these scattered points to achieve continuous coverage of the moving circle with the fewest scattered points, that is, to ensure that when the moving circle moves arbitrarily within the area, it always contains at least one scattered point.

[0052] To quantify deployment efficiency, this method introduces an evaluation metric, defined as the dot product ratio. This metric represents the number of scattered points required to be deployed per unit area. Dot product ratio The calculation is performed using the following formula:

[0053] ;

[0054] in, This represents the total number of deployed points, which corresponds to the total number of ground stations in the physical model; This represents the total area covered by the deployed points.

[0055] Based on the above definition, the optimization objective is determined to find a scatter plot arrangement that results in a dot product ratio of 1 / 2. The value of is minimized. This is equivalent to minimizing the number of ground stations required per unit area while ensuring continuous coverage. Determining this optimization objective transforms the original engineering deployment problem into a geometric problem.

[0056] To achieve the aforementioned minimization of dot product ratio The optimization objective is to minimize the total area while maintaining continuous coverage. Maximize. This is equivalent to maximizing the area of ​​each grid cell that makes up the total area.

[0057] The area of ​​a grid cell is subject to a core geometric constraint stemming from the requirement to guarantee continuous coverage. In the established mathematical model, this requirement is specifically manifested as: a radius of... The circular coverage area must always contain at least one grid vertex representing a ground station when it moves arbitrarily within the plane.

[0058] If the size of the grid cells is too large, a circular coverage area can be completely contained within a grid cell without touching any of its vertices. This will cause communication interruption, violating the fundamental requirement of continuous coverage.

[0059] To circumvent this situation, this method imposes a geometric constraint: the upper limit of the size of any mesh cell is set such that the geometry of that mesh cell can be exactly inscribed in a region of radius [missing value]. The circular coverage area. By limiting the maximum size of the grid cells to this inscribed regular polygon, it is guaranteed that no matter how the circular coverage area is translated, as long as it intersects with the area where the regular polygon grid cell is located, it is impossible to cover a complete area without containing any vertices.

[0060] The setting of these geometric constraints satisfies the technical requirement of continuous coverage on the one hand, and provides a basis for subsequent quantification of the maximum possible area under different grid configurations on the other. Providing a unified computing benchmark is a prerequisite for achieving deployment optimization.

[0061] After establishing the optimization objective and geometric constraints, candidate configurations suitable for constructing a uniform mesh need to be selected from all regular polygons. The feasibility of a configuration depends on its ability to achieve seamless, non-overlapping stitching in a two-dimensional plane.

[0062] This screening process is based on a fundamental principle of plane geometry. If a positive... If polygons can be seamlessly joined to form a grid, then at any common vertex of that grid, there must be... A positive The angles of the polygon converge here, and this The sum of the degrees of the angles is exactly equal to .in, Let be the number of sides of the regular polygon. The number of polygons that converge at the same vertex. and All are positive integers greater than or equal to 3.

[0063] A positive The degree measure of a single interior angle of a polygon It can be calculated using the formula for the sum of interior angles of a polygon:

[0064] ;

[0065] Based on the condition of seamless splicing, we can obtain:

[0066] ;

[0067] Will Substituting the expression, we get:

[0068] ;

[0069] Simplifying the above expression, we can obtain information about integers. and A constraint equation:

[0070] (1)

[0071] To find all positive integer solutions to this equation, we will discuss different cases.

[0072] In the first case, when When the number is odd. Because , can be set ,in It is a positive integer. Substituting it into formula (1), we get Since the right side of the equation is an even number, and Since it is an odd number, It must be an even number. Let... ,in It is a positive integer. Substituting it and simplifying, we get... This formula can be transformed into In order to make It is a positive integer. It must be a positive divisor of 2, that is... Therefore, we can solve this problem. .when hour, ,and ,and then This is the only solution. The physical meaning of this is that equilateral triangles can form a grid, and each common vertex is shared by 6 equilateral triangles.

[0073] In the second scenario, when When the number is even. Since n≥4, we can assume ,in It is a positive integer greater than or equal to 2. Substituting it into formula (1), we get... After simplification, we get:

[0074] (2)

[0075] In this situation, further based on We will discuss the parity of [the property / the property].

[0076] like If it is even, we can assume ,in It is a positive integer. Substituting it into formula (2) yields... Simplify to This formula can be transformed into In order to make It is a positive integer. It must be a positive divisor of 1, that is... Therefore, we can solve this problem. .when hour, ,and ,and then This is a set of solutions. The physical meaning of this is that regular quadrilaterals can form a grid, and each common vertex is shared by 4 regular quadrilaterals.

[0077] like If it is an odd number, we can assume ,in It is a positive integer. Substituting it into formula (2) yields... After sorting, we can get Since the left side of the equation is an even number, therefore It must be an even number, that is It is an odd number. Because... ,so The value can be 3, 5, 7, etc. When When, the equation becomes Solving for .when hour, ,and This is a set of solutions. The physical meaning of this is that regular hexagons can form a grid, and each common vertex is shared by 3 regular hexagons.

[0078] In summary, by solving the constraint equations, it can be concluded that all uniform meshes that can be seamlessly spliced ​​in a two-dimensional plane have only three possible element configurations: equilateral triangular meshes, regular quadrilateral meshes, and regular hexagonal meshes.

[0079] This method further performs a quantitative analysis of the dot product ratio for each configuration to determine the unique configuration that can achieve the optimization objective. This analysis process uniformly adopts the aforementioned geometric constraints, i.e., all mesh elements are taken to have a radius of... The inscribed regular polygon of the covering circle.

[0080] For an equilateral triangular mesh, assume that the mesh is composed of... It consists of equilateral triangle units. Since each equilateral triangle has 3 vertices, and in a fully expanded grid, each vertex is shared by 6 equilateral triangles, the total number of vertices in the grid is... It can be calculated as:

[0081] ;

[0082] According to geometric constraints, the area of ​​each equilateral triangular unit is inscribed in a circle with radius . The area of ​​the equilateral triangle formed by the circle is given by the value of . Therefore, the total area of ​​the grid consisting of n cells is... for:

[0083] ;

[0084] Substituting the total number of vertices and the total area into the dot product ratio formula, we obtain the dot product ratio of the equilateral triangular mesh. for:

[0085] (3)

[0086] For a regular quadrilateral grid, assume that the grid is composed of... The grid is composed of square cells. Each square has 4 vertices, and each vertex is shared by the 4 squares, therefore the total number of vertices in the grid is... It can be calculated as:

[0087] ;

[0088] The area of ​​each square unit is inscribed in a circle with a radius of . The area of ​​the square within the circle is given by the value of . Therefore, the total area of ​​the grid consisting of n cells is... for:

[0089] ;

[0090] Its dot product ratio for:

[0091] (4)

[0092] For a regular hexagonal mesh, assume that the mesh is composed of The grid is composed of regular hexagonal cells. Each regular hexagon has 6 vertices, and each vertex is shared by 3 regular hexagons. Therefore, the total number of vertices in the grid is... It can be calculated as:

[0093] ;

[0094] The area of ​​each regular hexagonal unit is the area of ​​the regular hexagon inscribed in a circle of radius RR, and its value is... Therefore, by Total area of ​​the grid composed of individual cells for:

[0095] ;

[0096] Its dot product ratio for:

[0097] (5)

[0098] Through the above calculations, expressions for the dot product ratio of the three feasible grid configurations under unified constraints were obtained.

[0099] By comparing the results of formulas (3), (4), and (5), we can determine which formula can achieve the desired dot product ratio. Minimize the unique configuration.

[0100] For comparison purposes, common factors in the expressions can be ignored. Only the numerical coefficients are compared. Through numerical calculation or mathematical derivation, the following inequality relationships can be determined to hold:

[0101] ;

[0102] This inequality indicates that, for the same effective communication coverage radius... Below, dot product ratio The value is the smallest, and the dot product ratio is... Secondly, the dot product ratio The value is the largest.

[0103] This quantitative comparison result provides a correlation with the effective communication coverage radius. Regardless of the specific values, the conclusion is that deploying using an equilateral triangular grid is the only optimal solution that minimizes the number of ground stations required per unit area while satisfying the geometric constraints of continuous coverage.

[0104] Therefore, in this step S200, the method selects an equilateral triangular mesh as the geometry to be used in subsequent deployment steps based on this unique optimal conclusion.

[0105] In this embodiment, step S300 transforms the abstract mesh configuration into precise numerical values ​​that can be used for coordinate generation. This step is based on the effective communication coverage radius received in step S100. Given the selected equilateral triangle configuration, calculate the core geometric parameters required to generate the mesh. The first parameter calculated is the side length of the equilateral triangle that makes up the mesh cell. .

[0106] The side length The calculation follows the geometric constraints set in step S200, that is, the equilateral triangular element is inscribed in a region with a radius of... The maximum size is achieved by covering the circle. Based on the geometric relationship between an equilateral triangle and its circumcircle, its side length... With the radius of the circumcircle There exists a fixed mathematical relationship between them, which can be expressed by the following formula:

[0107] ;

[0108] in, Represents the side length of the equilateral triangle that makes up the grid cell; Represents the effective communication coverage radius.

[0109] To facilitate the iterative generation of mesh vertices in subsequent step S400, this step can further calculate other relevant geometric parameters. In a specific embodiment, the height of the equilateral triangular unit is calculated. The calculation formula is as follows:

[0110] ;

[0111] in, This represents the height of the equilateral triangle unit, which is the vertical distance from a vertex to its opposite side.

[0112] After the calculation is completed, the output of step S300 includes the side length. and height The geometric parameters are passed to step S400 as the basis for generating specific ground station deployment coordinates.

[0113] In this embodiment, step S400 involves generating a complete equilateral triangular mesh within the target deployment area defined in step S100, based on the geometric parameters calculated in step S300, and extracting the coordinates of all vertices of the mesh.

[0114] This step first establishes an initial reference point for mesh generation within the target deployment area. In a specific embodiment, this reference point can be a corner point of the smallest bounding rectangle covering the target deployment area, such as the lower left corner vertex, with coordinates denoted as . .

[0115] Starting from this reference point, an iterative algorithm is used to generate a mesh of vertices covering the entire target deployment area. This algorithm is based on the side lengths of equilateral triangles. and height This determines the relative positions of the vertices. For example, mesh vertices can be generated row by row, where the horizontal spacing between vertices in even-numbered rows (rows 0, 2, 4...) is... Its coordinates can be represented as The vertices of odd-numbered rows (rows 1, 3, 5...) are offset horizontally compared to even-numbered rows. Its coordinates can be represented as ,in and The index is an integer. This iterative process continues until the generated grid completely covers the smallest bounding rectangle of the target deployment area, thus ensuring that all deployment points that may be located within the target deployment area are generated.

[0116] After generating a vertex set that covers the entire bounding rectangle, a boundary processing step is performed on each vertex in the set. This process aims to filter out all vertices located inside or on the geographic boundary of the target deployment area.

[0117] Specifically, this boundary processing procedure compares the coordinates of each generated vertex with the digital boundary model of the target deployment area obtained in step S100. This comparison is achieved by executing a point-in-polygon determination algorithm. If the determination result shows that a vertex is located inside or on the boundary of the polygonal region, the coordinates of the vertex are retained and added to a list, which is the initial ground station deployment location list. Conversely, if the vertex is located outside the polygonal region, the coordinates of the vertex are discarded. The point-in-polygon determination algorithm is a well-known technique in the field of computational geometry and will not be described in detail here.

[0118] After performing boundary processing on all generated vertices, step S400 finally generates a list of ground station deployment locations and passes it to subsequent steps.

[0119] In this embodiment, step S500 outputs the deployment location list containing multiple geographic coordinate points generated in the aforementioned steps in one or more preset data formats for subsequent engineering implementation or integration with other systems.

[0120] In one specific embodiment, the output deployment location list has a defined data structure. For example, the list could be a collection of multiple records, each representing a ground station deployment location and containing a unique site identifier (ID), a longitude value, and a latitude value.

[0121] To adapt to different downstream applications, the preset data format can be one of several standard formats. As one implementation, the data format can be comma-separated values ​​(CSV) format, facilitating tabular viewing and data processing. Alternatively, the data format can be a GIS-specific file format, such as a Key-Based Markup Language (KML) file or a Shapefile file, to facilitate visualization and spatial analysis on a GIS platform. By outputting these standard format files, the deployment plan generated by this method can be directly used by engineers or relevant planning software.

[0122] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization, characterized in that, Includes the following steps: Receive geographic information of the target deployment area and the effective communication coverage radius of low-Earth orbit satellites; Based on the effective communication coverage radius, the dot product ratio of different regular polygon mesh configurations is quantitatively compared, and the regular polygon mesh with the smallest dot product ratio is selected as the optimal mesh configuration. The quantitative comparison is based on a geometric constraint: the unit regular polygons of the different regular polygon mesh configurations are inscribed in a coverage circle with the effective communication coverage radius as the radius. The dot product ratio is the number of ground stations required per unit area, calculated as the ratio of the total number of ground stations to the total deployment area. The optimal mesh configuration is an equilateral triangular mesh. Based on the optimal grid configuration and the effective communication coverage radius, calculate the side length of the regular polygons that constitute the optimal grid configuration. Within the target deployment area, the vertex coordinates of the optimal mesh configuration are generated based on the side length, and the vertex coordinates are output as the deployment location of the ground station.

2. The method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization according to claim 1, characterized in that, The different regular polygonal mesh configurations include: equilateral triangle mesh, regular quadrilateral mesh, or regular hexagonal mesh.

3. The method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization according to claim 2, characterized in that, The equilateral triangular mesh was selected as the optimal mesh configuration based on the dot product ratio of the equilateral triangular mesh under the given geometric constraints. The dot product ratio is smaller than that of the regular quadrilateral grid. The dot product ratio of the regular hexagonal mesh ,in The effective communication coverage radius is defined as follows.

4. The method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization according to claim 1, characterized in that, The side length of the unit regular polygon Through formula The calculation shows that, among which The effective communication coverage radius is defined as follows.

5. The method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization according to claim 1, characterized in that, The effective communication coverage radius is calculated based on the satellite's orbital altitude, the Earth's radius, and the ground station's minimum operating elevation angle.

6. The method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization according to any one of claims 1-5, characterized in that, The step of generating the vertex coordinates of the optimal mesh configuration includes: Generate an initial set of vertex coordinates within the smallest bounding rectangle covering the target deployment area; For each coordinate in the initial set of vertex coordinates, the coordinates located inside or on the boundary of the target deployment area are retained.

7. The method for deploying low-Earth orbit satellite IoT ground stations based on regular polygon mesh optimization according to claim 6, characterized in that, It also includes calculating the height h of the unit regular polygon, and the step of generating the vertex coordinates of the optimal mesh configuration adopts an iterative algorithm, wherein the vertices of the odd-numbered rows are offset in the horizontal direction compared with the vertices of the even-numbered rows.

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