Signal distribution prediction method, device, equipment, storage medium and program product

Abstract

The embodiment of the application provides a signal distribution prediction method, device, equipment, storage medium and program product, the method comprises the following steps: determining a target prediction model from a plurality of prediction models according to the angle of the elevation angle of the signal incidence of a transmission source relative to a preset region and a preset threshold value, the target prediction model is used for predicting the signal distribution of the transmission source in the preset region; determining a plurality of calculation sections in the preset region according to the angle of the azimuth angle of the signal incidence, the boundary information of the preset region and the coordinates of the center point; predicting the predicted signal distribution of the plurality of calculation sections by the target prediction model according to the source parameters of the transmission source and the plurality of calculation sections, and determining the predicted signal distribution of the preset region according to the predicted signal distribution of the plurality of calculation sections. The method is used for accurately predicting the predicted signal distribution of the preset region.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a signal distribution prediction method, apparatus, device, storage medium, and program product. Background Technology

[0002] During the signal propagation process of the transmitter, it is necessary to predict the signal distribution of the transmitter in a preset area so as to deploy various signal receiving devices on the ground.

[0003] In related technologies, a preset area can be divided into multiple computational grids, and the signal of each computational grid can be calculated one by one to obtain the signal distribution of the entire preset area.

[0004] However, the area calculated based on multiple computing grids may exceed the range of the preset area, resulting in lower accuracy of the signal distribution in the preset area. Summary of the Invention

[0005] This application provides a signal distribution prediction method, apparatus, device, storage medium, and program product to solve the technical problem in related technologies where the region calculated based on multiple computing grids may exceed the range of a preset region, resulting in low accuracy of the calculated signal distribution in the preset region.

[0006] In a first aspect, embodiments of this application provide a signal distribution prediction method, the method comprising:

[0007] Based on the elevation angle of the signal incident from the transmitter relative to the preset area and a preset threshold, a target prediction model is determined from multiple prediction models. The target prediction model is used to predict the signal distribution of the transmitter in the preset area.

[0008] Based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point, multiple calculation sections are determined in the preset area;

[0009] Based on the source parameters of the emission source and the multiple calculation cross sections, the predicted signal distribution of the multiple calculation cross sections is predicted by the target prediction model, and the predicted signal distribution of the preset region is determined based on the predicted signal distribution of the multiple calculation cross sections.

[0010] In one possible implementation, based on the azimuth angle of the incident signal, the boundary information of the preset region, and the coordinates of the center point, multiple calculation sections are determined in the preset region, including:

[0011] Based on the azimuth angle and the coordinates of the center point, determine the equation of the first straight line passing through the center point;

[0012] Based on the coordinates of multiple vertices of the preset region, the equation of the first straight line, the preset spacing, the coordinates of the center point, and the azimuth angle, determine the equations of multiple second straight lines;

[0013] The plurality of calculation sections are determined based on the equation of the first straight line, the equations of the plurality of second straight lines, and the boundary information of the preset region.

[0014] In one possible implementation, the equations of multiple second lines are determined based on the coordinates of multiple vertices of the preset region, the equation of the first line, the preset spacing, the coordinates of the center point, and the azimuth angle, including:

[0015] Based on the coordinates of each vertex of the preset region and the equation of the first straight line, the length of the first perpendicular segment from each vertex to the first straight line is determined, thus obtaining the lengths of multiple first perpendicular segments.

[0016] Among the plurality of first perpendicular segments, a target first perpendicular segment is determined, wherein the target first perpendicular segment is the longest first perpendicular segment among the plurality of first perpendicular segments;

[0017] Based on the target first perpendicular line segment and the preset spacing, determine the lengths of multiple second perpendicular line segments passing through the center point;

[0018] The equations of the plurality of second straight lines are determined based on the coordinates of the center point, the lengths of the plurality of second perpendicular line segments, and the angle of the azimuth.

[0019] In one possible implementation, the plurality of computational cross-sections includes an initial computational cross-section and a stepped computational cross-section; based on the source parameters of the emission source and the plurality of computational cross-sections, the predicted signal distribution of the plurality of computational cross-sections is predicted by the target prediction model, including:

[0020] Obtain the geographical and meteorological parameters of the preset area;

[0021] Based on the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal distribution of the initial calculation section is predicted using the target prediction model;

[0022] The predicted signal distribution of the step calculation section is predicted based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters.

[0023] In one possible implementation, predicting the distribution of the predicted signal for the initial calculation section using the target prediction model, based on the source parameters, the geographical parameters, and the meteorological parameters, includes:

[0024] Based on the source parameters, the geographical parameters, and the meteorological parameters, determine the initial signal strength of the transmitting source in the initial calculation section;

[0025] The initial computational section is divided into multiple computational grids according to the preset grid size;

[0026] Based on the source parameters, the initial signal strength, the geographical parameters, and the meteorological parameters, the predicted signal strength of each computation grid in the initial computation cross section is predicted using the target prediction model;

[0027] The predicted signal distribution of the initial computational cross section is determined based on the predicted signal intensity of each computational grid in the initial computational cross section.

[0028] In one possible implementation, predicting the predicted signal distribution of the stepped calculation section based on the predicted signal distribution of the initial calculation section, the source parameters, the geographic parameters, and the meteorological parameters includes:

[0029] According to the preset grid size, the step calculation section is divided into multiple calculation grids;

[0030] A first computational grid and a second computational grid are determined among the plurality of computational grids;

[0031] Based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal intensity of the first calculation grid is predicted using the target prediction model.

[0032] Based on the source parameters, the predicted signal strength of the second computational grid is predicted using a free-space propagation model;

[0033] The predicted signal distribution of the stepping computation section is determined based on the predicted signal strength of the first computation grid and the predicted signal strength of the second computation grid.

[0034] In one possible implementation, determining a first computing grid and a second computing grid among the plurality of computing grids includes:

[0035] The blind zone height is determined based on the pitch angle and the distance between the initial calculation section and the step calculation section;

[0036] The computational grid whose height is less than or equal to the blind zone height among multiple computational grids is determined as the first computational grid;

[0037] The computational grid whose height is greater than the blind zone height among multiple computational grids is identified as the second computational grid.

[0038] In one possible implementation, the plurality of prediction models includes a first prediction model and a second prediction model; based on the elevation angle of the signal incident from the emission source relative to the preset area and a preset threshold, a target prediction model is determined from the plurality of prediction models, including:

[0039] If the pitch angle is less than the preset threshold, the first prediction model is determined as the target prediction model;

[0040] If the pitch angle is greater than or equal to the preset threshold, the second prediction model is determined as the target prediction model.

[0041] Secondly, embodiments of this application provide a signal distribution prediction device, comprising:

[0042] The determining module is used to determine a target prediction model from multiple prediction models based on the elevation angle of the signal incident from the transmitter relative to the preset area and a preset threshold. The target prediction model is used to predict the signal distribution of the transmitter in the preset area.

[0043] The determining module is further configured to determine multiple calculation sections in the preset area based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point;

[0044] The prediction module is used to predict the predicted signal distribution of the multiple calculation cross sections based on the source parameters of the emission source and the multiple calculation cross sections using the target prediction model.

[0045] The determining module is further configured to determine the predicted signal distribution of the preset region based on the predicted signal distribution of the plurality of calculation sections.

[0046] In one possible implementation, the determining module is specifically used for:

[0047] Based on the azimuth angle and the coordinates of the center point, determine the equation of the first straight line passing through the center point;

[0048] Based on the coordinates of multiple vertices of the preset region, the equation of the first straight line, the preset spacing, the coordinates of the center point, and the azimuth angle, determine the equations of multiple second straight lines;

[0049] The plurality of calculation sections are determined based on the equation of the first straight line, the equations of the plurality of second straight lines, and the boundary information of the preset region.

[0050] In one possible implementation, the determining module is further configured to:

[0051] Based on the coordinates of each vertex of the preset region and the equation of the first straight line, the length of the first perpendicular segment from each vertex to the first straight line is determined, thus obtaining the lengths of multiple first perpendicular segments.

[0052] Among the plurality of first perpendicular segments, a target first perpendicular segment is determined, wherein the target first perpendicular segment is the longest first perpendicular segment among the plurality of first perpendicular segments;

[0053] Based on the target first perpendicular line segment and the preset spacing, determine the lengths of multiple second perpendicular line segments passing through the center point;

[0054] The equations of the plurality of second straight lines are determined based on the coordinates of the center point, the lengths of the plurality of second perpendicular line segments, and the angle of the azimuth.

[0055] In one possible implementation, the plurality of computational sections includes an initial computational section and a step computational section; the prediction module is specifically used for:

[0056] Obtain the geographical and meteorological parameters of the preset area;

[0057] Based on the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal distribution of the initial calculation section is predicted using the target prediction model;

[0058] The predicted signal distribution of the step calculation section is predicted based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters.

[0059] In one possible implementation, the prediction module is further configured to:

[0060] Based on the source parameters, the geographical parameters, and the meteorological parameters, determine the initial signal strength of the transmitting source in the initial calculation section;

[0061] The initial computational section is divided into multiple computational grids according to the preset grid size;

[0062] Based on the source parameters, the initial signal strength, the geographical parameters, and the meteorological parameters, the predicted signal strength of each computation grid in the initial computation cross section is predicted using the target prediction model;

[0063] The predicted signal distribution of the initial computational cross section is determined based on the predicted signal intensity of each computational grid in the initial computational cross section.

[0064] In one possible implementation, the prediction module is further configured to:

[0065] According to the preset grid size, the step calculation section is divided into multiple calculation grids;

[0066] A first computational grid and a second computational grid are determined among the plurality of computational grids;

[0067] Based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal intensity of the first calculation grid is predicted using the target prediction model.

[0068] Based on the source parameters, the predicted signal strength of the second computational grid is predicted using a free-space propagation model;

[0069] The predicted signal distribution of the stepping computation section is determined based on the predicted signal strength of the first computation grid and the predicted signal strength of the second computation grid.

[0070] In one possible implementation, the prediction module is further configured to:

[0071] The blind zone height is determined based on the pitch angle and the distance between the initial calculation section and the step calculation section;

[0072] The computational grid whose height is less than or equal to the blind zone height among multiple computational grids is determined as the first computational grid;

[0073] The computational grid whose height is greater than the blind zone height among multiple computational grids is identified as the second computational grid.

[0074] In one possible implementation, the plurality of prediction models includes a first prediction model and a second prediction model; the determining module is further configured to:

[0075] If the pitch angle is less than the preset threshold, the first prediction model is determined as the target prediction model;

[0076] If the pitch angle is greater than or equal to the preset threshold, the second prediction model is determined as the target prediction model.

[0077] Thirdly, embodiments of this application provide a signal distribution prediction device, comprising:

[0078] At least one processor; and

[0079] A memory communicatively connected to the at least one processor; wherein,

[0080] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in any of the first aspects.

[0081] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in any of the first aspects.

[0082] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any one of the first aspects.

[0083] The signal distribution prediction method, apparatus, device, storage medium, and program product provided in this application have the following features: The method determines a target prediction model from multiple prediction models based on the elevation angle of the signal incident source relative to a preset area and a preset threshold; it determines multiple calculation sections within the preset area based on the azimuth angle of the signal incident source and the coordinates of the center point of the preset area; based on the source parameters of the transmitter and the multiple calculation sections, it predicts the predicted signal distribution of the multiple calculation sections using the target prediction model, and determines the predicted signal distribution of the preset area based on the predicted signal distribution of the multiple calculation sections. This method can adaptively determine multiple calculation sections within the preset area based on the azimuth angle of the signal incident source and the coordinates of the center point of the preset area to avoid the problem of the calculation area exceeding the preset area during the calculation process; furthermore, it determines a suitable target prediction model based on the elevation angle of the signal incident source and a preset threshold, so as to accurately predict the predicted signal distribution of the preset area based on the target prediction model and the multiple calculation sections. Attached Figure Description

[0084] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0085] Figure 1 A schematic diagram illustrating an application scenario provided in an embodiment of this application;

[0086] Figure 2 This is one of the flowcharts illustrating the signal distribution prediction method provided in the embodiments of this application;

[0087] Figure 3 A schematic diagram showing the incident azimuth and elevation angles from a transmitter to a ground receiver, provided for an embodiment of this application;

[0088] Figure 4 A schematic diagram of a calculation section in a preset area provided in an embodiment of this application;

[0089] Figure 5 This is a second schematic flowchart of the signal distribution prediction method provided in the embodiments of this application;

[0090] Figure 6 A schematic diagram of a first straight line and a second straight line in a preset region provided in an embodiment of this application;

[0091] Figure 7 The third schematic flowchart of the signal distribution prediction method provided in the embodiments of this application;

[0092] Figure 8 A schematic diagram illustrating the bidirectional propagation process of a signal, provided as an embodiment of this application;

[0093] Figure 9 This is one of the schematic diagrams of the plane wave calculation blind zone of the parabolic equation provided in the embodiments of this application;

[0094] Figure 10 This is the second schematic diagram of the blind zone for plane wave calculation of the parabolic equation provided in the embodiments of this application;

[0095] Figure 11 This is a schematic diagram of the structure of a signal distribution prediction device provided in an embodiment of this application;

[0096] Figure 12 This is a schematic diagram of the structure of the signal distribution prediction device provided in an embodiment of this application.

[0097] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0098] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0099] 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. Unless otherwise specified, 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.

[0100] The collection, storage, use, processing, transmission, provision, and disclosure of data and other information involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0101] It should be noted that the signal distribution prediction method, apparatus, device, storage medium, and program product provided in this application can be used in the field of wireless communication, or in any field other than wireless communication, such as electromagnetics. This application does not limit the application field of the signal distribution prediction method, apparatus, device, storage medium, and program product.

[0102] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0103] To facilitate understanding of the technical solutions of the embodiments of this application, the application scenarios of the embodiments of this application will be introduced below.

[0104] Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. Please refer to [link / reference]. Figure 1 The system includes a receiving source within a predetermined area on the ground, and the transmitting source and the receiving source can communicate via signals. In some examples, the transmitting source can be a satellite, the predetermined area can be a suburban area, and the receiving source can be a terminal, a base station, or other signal receiving equipment.

[0105] In related technologies, a preset region can be divided into multiple computational grids, and the signal of each computational grid can be calculated one by one to obtain the signal distribution of the entire preset region. However, there may be regions calculated based on multiple computational grids that exceed the range of the preset region, resulting in low accuracy of the calculated signal distribution of the preset region.

[0106] In view of this, embodiments of this application provide a signal distribution prediction method, which includes: determining a target prediction model among multiple prediction models based on the elevation angle of the signal incident source relative to a preset area and a preset threshold; determining multiple calculation sections in the preset area based on the azimuth angle of the signal incident source and the coordinates of the center point of the preset area; predicting the predicted signal distribution of the multiple calculation sections through the target prediction model based on the source parameters of the transmitter and the multiple calculation sections; and determining the predicted signal distribution of the preset area based on the predicted signal distribution of the multiple calculation sections. This method can adaptively determine multiple calculation sections in the preset area based on the azimuth angle of the signal incident source and the coordinates of the center point of the preset area to avoid the problem of the calculation area exceeding the preset area during the calculation process; furthermore, it also determines a suitable target prediction model based on the elevation angle of the signal incident source and a preset threshold, so as to accurately predict the predicted signal distribution of the preset area based on the target prediction model and the multiple calculation sections.

[0107] The method described in this application will now be illustrated through specific embodiments. It should be noted that the following embodiments may exist independently or in combination with each other; identical or similar content will not be repeated in different embodiments.

[0108] Figure 2 This is one of the flowcharts illustrating the signal distribution prediction method provided in this application. The method can be executed by a signal distribution prediction device, or by a signal distribution prediction apparatus within the device. This apparatus can be implemented in software or a combination of software and hardware.

[0109] Please see Figure 2 The method may include the following steps:

[0110] S201. Based on the elevation angle of the signal incident from the source relative to the preset area and the preset threshold, determine the target prediction model from multiple prediction models.

[0111] The launch source can be a launch source located in outer space. For example, the launch source can be a low-Earth orbit satellite, such as a communication satellite, a remote sensing satellite, and an Earth observation satellite.

[0112] The target prediction model is used to predict the signal distribution of the emission source in a preset area.

[0113] This preset area can be determined based on the configuration information entered by the user.

[0114] During signal propagation from the transmitter, the wave equation can be used to predict the signal distribution within a predetermined region. Assuming the signal propagates along the +x-axis, the wave equation satisfies the following formula:

[0115]

[0116] Where k0 is the propagation constant of the signal in vacuum; x represents the propagation distance of the signal along the x-axis; z represents the propagation distance of the signal along the z-axis; u(x,z) represents the signal distribution in the plane formed by x and z; i represents the imaginary unit; if the signal is a horizontally polarized wave, then ψ(x,z) corresponds to E y (x,z), E y (x,z) represents the non-zero electric field components; if the signal is a vertically polarized wave, then ψ(x,z) corresponds to H y (x,z), H y (x,z) represents the non-zero magnetic field components.

[0117] However, geographical and meteorological environments significantly influence signal propagation from a transmitting source. The parabolic equation model, derived from the wave equation, considers not only refraction and diffraction effects but also the influence of geographical and meteorological conditions when predicting signal propagation. Furthermore, its faster computation speed makes it more suitable for determining the field distribution of signal propagation from a transmitting source. Therefore, in some embodiments, the parabolic equation model can be used to simulate the signal distribution of a transmitting source within a predetermined area.

[0118] In some embodiments, the plurality of prediction models and the target prediction model may include a parabolic equation model.

[0119] When using a parabolic equation model to predict signal distribution, the following characteristics exist: When the signal propagates forward along the positive x-axis, if the calculated elevation angle of the parabolic equation is less than 45°, the back reflection effect of the ground on the signal is small, and it is not necessary to consider the back reflection effect. However, if the calculated elevation angle of the parabolic equation is greater than or equal to 45°, the back reflection effect of the ground on the signal is large, and in this case, it is necessary to consider the back reflection effect. Therefore, the signal distribution prediction method provided in this application, when selecting a parabolic equation model as the target prediction model, also considers the relationship between the elevation angle and a preset threshold to further determine a more suitable target prediction model, thereby improving the accuracy of the target prediction model in predicting the signal distribution of a preset area.

[0120] For example, the preset threshold can be set to 45°.

[0121] S202. Based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point, determine multiple calculation sections in the preset area.

[0122] It should be noted that in this method, the difference between the maximum and minimum elevation angles of the transmitting source within the preset area must be less than or equal to a preset difference; similarly, the difference between the maximum and minimum azimuth angles of the transmitting source within the preset area must be less than or equal to a preset difference. In this case, the receiving source can be set up at any point in the preset area beforehand. For example, the receiving source can be located at the center point of the ground within the preset area.

[0123] For example, the receiving source can be a terminal, a base station, or other signal receiving device.

[0124] To facilitate understanding, the following will be combined with... Figure 3 The azimuth and pitch angles will be explained.

[0125] Figure 3 This is a schematic diagram illustrating the incident azimuth and elevation angles from the transmitter to the receiver, provided as an embodiment of this application. Please refer to... Figure 3 Assuming the coordinates of the transmitter are (x0, y0, z0), and a receiver is located within a preset area with coordinates (x1, y1, z1), then the elevation angle of the signal incident from the transmitter to the receiver is... The azimuth angle θ and the azimuth angle satisfy the following formulas respectively:

[0126]

[0127] θ = atan2(y1-y0, x1-x0)

[0128] In some embodiments, the plurality of computational cross-sections may be parallel to each other. By determining a plurality of parallel computational cross-sections, it becomes more convenient for the target prediction model to predict the signal distribution of these plurality of computational interfaces.

[0129] For example, the boundary information of the preset area may include the coordinates of each boundary point of the preset area.

[0130] Figure 4 For a schematic diagram of the calculation section in the preset area provided in the embodiments of this application, please refer to [link / reference]. Figure 4 The preset region includes multiple calculation sections, which are parallel to each other and all perpendicular to the xy plane of the preset region.

[0131] It should be noted that the process of determining multiple calculation sections within the preset area will be... Figure 5 Detailed descriptions are provided in the embodiments.

[0132] S203. Based on the source parameters of the emission source and multiple calculation cross sections, predict the predicted signal distribution of multiple calculation cross sections through the target prediction model, and determine the predicted signal distribution of the preset area based on the predicted signal distribution of multiple calculation cross sections.

[0133] Source parameters may include, but are not limited to: the coordinates of the transmitting source, signal parameters, and direction parameters.

[0134] For example, signal parameters may include the transmission frequency and transmission field strength of the transmitting source.

[0135] In some embodiments, the transmission frequency may also be referred to as the "carrier frequency," which can be the frequency at which the transmitting source sends electromagnetic waves.

[0136] The emission field strength can be defined as the electric field strength of the electromagnetic waves generated by the emission source at the emission point.

[0137] Directional parameters may include, but are not limited to: antenna pattern, azimuth angle, and elevation angle.

[0138] In some embodiments, the signal distribution prediction device can provide a data interface, through which users can input source parameters of the emission source to the signal distribution prediction device.

[0139] In some embodiments, a step-by-step approach can be used to determine the predicted signal distribution of multiple computational cross-sections. Assuming there are N computational cross-sections, an initial computational cross-section can be determined from these N cross-sections. The initial signal strength of the signal emitted by the source propagating to this initial computational cross-section can be obtained. Based on the source parameters, the initial signal strength, and the preset boundary conditions of the target prediction model, the predicted signal distribution of this initial computational cross-section is predicted using the target prediction model. This initial computational cross-section is designated as the first computational cross-section. From the remaining N-1 computational cross-sections, the i-th computational cross-section to be stepped is determined. Based on the predicted signal distribution of the i-th computational cross-section and the source parameters, the predicted signal distribution of the i-th computational cross-section to be stepped is predicted using the target prediction model. Here, i takes values ​​of 2, 3, ..., N sequentially, until the predicted signal distributions of these N computational cross-sections are obtained. The predicted signal distributions of these N computational cross-sections can be superimposed to obtain the predicted signal distribution of a preset region.

[0140] It should be noted that the process of predicting the distribution of the predicted signal across multiple computational cross-sections will be discussed later. Figure 7 Detailed explanation is provided in the embodiments.

[0141] The signal distribution prediction method provided in this application embodiment can adaptively determine multiple calculation sections in the preset area based on the azimuth angle of the incident signal and the coordinates of the center point of the preset area, so as to avoid the problem of the calculation area exceeding the preset area during the calculation process; and, based on the elevation angle of the incident signal and a preset threshold, a suitable target prediction model is determined so as to accurately predict the predicted signal distribution in the preset area according to the target prediction model and multiple calculation sections.

[0142] The above embodiments involve the process of determining multiple calculation sections in a preset area. Below, in conjunction with... Figure 5 The process of determining multiple calculation sections is explained in detail.

[0143] Figure 5 This is a second schematic flowchart illustrating the signal distribution prediction method provided in this application. Please refer to [link / reference]. Figure 5 The method may include the following steps:

[0144] S501. Determine the equation of the first straight line passing through the center point based on the azimuth angle and the coordinates of the center point.

[0145] In some embodiments, it is assumed that the coordinates of the center point of the preset region are (x... c y c Given an azimuth angle of θ, the equation of the first straight line can be determined using the following formula:

[0146] y = kx + b

[0147] Where k = tanθ, b = kx c -y c x and y are the x and y variables of the equation of the first straight line, respectively, k is the slope of the equation of the first straight line, and b is the constant of the equation of the first straight line.

[0148] In some embodiments, steps S502 to S505 can be performed to determine the equations of multiple second lines based on the coordinates of multiple vertices of a preset region, the equation of a first line, a preset spacing, the coordinates of the center point, and the azimuth angle.

[0149] S502. Based on the coordinates of each vertex of the preset area and the equation of the first straight line, determine the length of the first perpendicular segment from each vertex to the first straight line, and obtain the lengths of multiple first perpendicular segments.

[0150] Assume the preset region has n vertices, and the coordinates of any vertex are denoted as (x, y, y). i y i ), where i takes values ​​of 1, 2, ..., n in sequence. For any vertex among the n vertices, the length d of the first perpendicular segment from that vertex to the first straight line can be calculated using the following formula. i :

[0151]

[0152] S503. Determine the target first perpendicular segment among multiple first perpendicular segments.

[0153] The target first perpendicular segment can be the longest of multiple first perpendicular segments.

[0154] In some embodiments, the length of the first vertical segment of the target can satisfy the following formula:

[0155] d max =max(d1, d2, ..., d n )

[0156] Where d1, d2, and d n Let d represent the lengths of the first perpendicular segments from the 1st, 2nd, and nth vertices to the first straight line. max This indicates the length of the first perpendicular segment of the target.

[0157] S504. Determine the lengths of multiple second perpendicular segments passing through the center point based on the target first perpendicular segment and the preset spacing.

[0158] In some embodiments, the target first perpendicular segment length d can be used as the basis. max The number of second perpendicular segments n is obtained by using the preset spacing Δd:

[0159]

[0160] Figure 6 This is a schematic diagram of a first and second straight lines within a preset region, provided as an embodiment of this application. Please refer to... Figure 6 The preset region may include n straight lines, with a distance of Δd between any two lines. These n lines include one first line and n-1 second lines, where the first line passes through the center point (x0) of the preset region. c y c ).

[0161] In some embodiments, for any second straight line l j The two straight lines l can be calculated using the following formula. j With the center point (x) c y c The length of the second perpendicular segment between () is:

[0162]

[0163] Where j takes the values ​​1, 2, ..., n in sequence.

[0164] S505. Determine the equations of multiple second lines based on the coordinates of the center point, the lengths of multiple second perpendicular line segments, and the azimuth angles.

[0165] The second line can be parallel to the first line.

[0166] For any second line l j It can be based on the second straight line l j The length d of the second perpendicular segment between the center point of the preset area and the center point of the preset areaj The coordinates of the center point (x) c y c The angle between the second perpendicular segment and the azimuth angle θ is calculated using the following formula on the second straight line l. j The coordinates of the perpendicular intersection point (x j y j ):

[0167] x j =x c -d j sinθ;

[0168] y j =d j cosθ

[0169] Based on the coordinates of the perpendicular intersection point (x j y j The angle between the second line l and the azimuth angle θ is calculated using the following formula. j The equation:

[0170] y = kx + b j

[0171] Among them, b j =kx j -y j Let x and y be the equations of the second straight line l. j The x and y variables.

[0172] The signal distribution prediction method provided in this application embodiment can quickly determine the equations of multiple second lines in a preset area based on the coordinates of multiple vertices of a preset area, the equation of a first line, the preset spacing, the coordinates of the center point, and the azimuth angle, so that multiple calculation sections can be accurately determined in the preset area based on the equation of the first line and the equations of the multiple second lines.

[0173] S506. Based on the equation of the first straight line, the equations of multiple second straight lines, and the boundary information of the preset region, determine multiple calculation sections.

[0174] Optionally, the intersection point of the equation of the first straight line with the boundary of the preset region and the intersection point of the equation of each second straight line with the boundary of the preset region can be determined based on the boundary information of the preset region.

[0175] The starting and ending coordinates of the first straight line's equation within the preset region can be determined based on the intersection of the equation with the boundary of the preset region and the signal propagation direction. Furthermore, the maximum step distance of the calculation section corresponding to the first straight line's equation can be determined based on these coordinates. For example, in... Figure 6In the equation of the first straight line, there are two intersection points with the preset area, namely the lower left vertex and the upper right vertex of the preset area. Since the signal propagation direction is from the lower left vertex to the lower right vertex, the coordinates of the lower left vertex can be determined as the starting point coordinates of the equation of the first straight line in the preset area, and the coordinates of the lower right vertex can be determined as the ending point coordinates of the equation of the first straight line in the preset area.

[0176] Similarly, the starting and ending coordinates of the equation of the second line in the preset area can be determined based on the intersection of the equation of the second line with the boundary of the preset area and the direction of signal propagation. The maximum step distance of the calculation section corresponding to the equation of the second line can also be determined based on the starting and ending coordinates of the equation of the second line in the preset area.

[0177] The signal distribution prediction method provided in this application embodiment can adaptively divide the preset area into multiple calculation sections based on the azimuth angle of the signal incident from the emission source relative to the preset area and the coordinates of the center point of the preset area. This avoids the problem of the calculation area exceeding the preset area during the calculation process, which helps to improve the accuracy of predicting the signal distribution in the preset area.

[0178] The above embodiments involve the process of predicting the distribution of predicted signals for multiple computational cross-sections using a target prediction model based on source parameters and multiple computational cross-sections. The following will describe this process in conjunction with... Figure 7 This paper provides a detailed explanation of how to predict the distribution of the predicted signal across multiple computational cross-sections using a target prediction model, based on source parameters and multiple computational cross-sections.

[0179] Figure 7 This is the third flowchart illustrating the signal distribution prediction method provided in this application. Please refer to... Figure 7 The method may include the following steps:

[0180] S701. Based on the source parameters of the transmitter, determine the elevation angle and azimuth angle of the signal incident from the transmitter relative to the preset area.

[0181] In some embodiments, the position information of the transmitter can be determined based on the source parameters of the transmitter, and the elevation angle and azimuth angle can be determined based on the position information of the transmitter and the position information of the ground receiving source in the preset area.

[0182] It should be noted that the calculation formulas for the pitch angle and azimuth angle in step S701 can be referred to the calculation formulas for the pitch angle and azimuth angle in step S201, and will not be repeated here.

[0183] S702. Determine whether the pitch angle is less than the preset threshold.

[0184] If yes, it means the pitch angle is less than the preset threshold, and step S703 is executed; if no, it means the pitch angle is greater than or equal to the preset threshold, and step S704 is executed.

[0185] For example, the preset threshold can be set to 45°.

[0186] S703. The first prediction model is determined as the target prediction model.

[0187] The first prediction model can be a forward parabolic equation model, which can be a Feit-Fleck type forward parabolic equation model, also known as a forward "wide-angle parabolic equation (WAPE)" model, and can satisfy the following formula:

[0188]

[0189] Where n is the refractive index of the medium, and n(x,z) represents the refractive index of the medium at the plane formed by x and z.

[0190] Since the forward WAPE model includes square root terms, it is not suitable to solve using the finite difference (FD) method. Instead, the split-step Fourier transform (SSFT) method can be used to solve the forward WAPE model. The SSFT solution of the forward WAPE model is:

[0191]

[0192] Where Δx is the horizontal step size of the computational grid in the horizontal (x-axis) direction of signal propagation; u(x+Δx,z) represents the signal distribution in the plane formed by x+Δx and z; U(x,p) represents the inverse Fourier transform; p is a frequency domain variable, which can also be called the vertical wavenumber or spatial frequency, p = k0 sinθ, where θ is the grazing angle between the signal and the horizontal direction of the ground; U(x,p) represents the p-domain field value during the forward propagation of the signal.

[0193] Understandably, before calculating the SSFT solution of the forward WAPE model, it is necessary to clarify the boundary conditions and initial field of the forward WAPE model.

[0194] The forward WAPE model includes an upper boundary and a surface boundary, where:

[0195] The upper boundary of the forward WAPE model is an absorbing boundary, primarily used to truncate the computational domain of the SSFT. Optionally, a window function can be used to truncate the computational domain of the SSFT, and this window function can satisfy the following formula:

[0196]

[0197] Where W(z) is the window function, Z max This represents the maximum computational height of the WAPE model.

[0198] The surface boundary of the forward WAPE model is an impedance boundary. A finite impedance surface boundary can be described by the following impedance boundary conditions, expressed as:

[0199]

[0200] Wherein, β is used to reflect the impedance characteristics on the boundary surface, and β can satisfy the following formula:

[0201]

[0202] Where, ε r ε' is the relative complex permittivity of the boundary surface. r ′=ε r +i160σλ,ε r Let be the relative permittivity of the surface medium, and σ be the electrical conductivity of the surface medium; a j Let be the angle of the glancing angle on the j-th step surface.

[0203] The initial field of the forward WAPE model can satisfy the following formula:

[0204]

[0205] Where u(x0,z) represents the initial field of the signal in the initial plane formed by x0 and z, α represents the antenna elevation angle, A(α) represents the antenna pattern function, and R ∥or⊥ H represents the horizontal or vertical reflectance of the ground. i The height at which the antenna is installed.

[0206] S704. The second prediction model is determined as the target prediction model.

[0207] The second prediction model can be a two-way parabolic equation model. This two-way parabolic equation model is based on the traditional forward parabolic equation model. It divides the propagation field of the signal in the preset area into two parts: the forward propagation field and the backward propagation field. The backward propagation field calculated by each reflection point is summed into the forward field in steps according to the distance, thereby obtaining the propagation field of the signal in the preset area.

[0208] Assume the wave equation of the signal along the +x direction is: Where, ψ F (x,z) represents the forward propagation field of the signal along the +x direction; the wave equation of the signal along the -x direction is... Where, ψ B (x,z) represents the backward propagation field of the signal along the -x direction. Along the wave propagation path, for multiple calculation sections, the signal distribution at each calculation section during forward propagation is first calculated using the forward parabolic equation. The signal reflection boundary is determined based on the geographical elevation information of the preset area. The reflected signal is then calculated using the backward parabolic equation. Finally, the calculated results of the forward propagation process and the backward propagation process in each calculation section are superimposed to obtain the predicted signal distribution for the preset area. The formula for calculating the superimposed propagation field is: ψ T (x,z)=ψ F (x,z)+ψ B (x,z).

[0209] The second model can include a forward parabolic equation model and a backward parabolic equation model, and u can be solved using the forward parabolic equation model. F (x,z), u is solved using the backward parabolic equation model. B (x,z).

[0210] The backward parabolic equation model can be a Feit-Fleck type backward parabolic equation model, which satisfies the following formula:

[0211]

[0212] The SSFT solution for the Feit-Fleck type backward parabolic equation model is:

[0213]

[0214] Understandably, before calculating the SSFT solution of the Feit-Fleck type backward parabolic equation model, it is necessary to clarify the boundary conditions and initial field of the Feit-Fleck type backward parabolic equation model. It should be noted that the calculation methods for the boundary conditions and initial field of the Feit-Fleck type backward parabolic equation model can refer to the calculation methods for the boundary conditions and initial field of the Feit-Fleck type forward parabolic equation model in step S703.

[0215] To facilitate understanding, the following will be combined with... Figure 8 Explain the forward and backward propagation processes.

[0216] Figure 8 This is a schematic diagram illustrating the bidirectional propagation process of a signal, provided as an embodiment of this application. Please refer to... Figure 8 Assume there are 3 calculation sections, with the 3rd calculation section being the reflecting surface.

[0217] During forward propagation, based on the initial forward signal distribution at the first computational section, the forward signal distribution at the first computational section is calculated using the forward parabolic equation model. Based on the forward signal distribution at the first computational section, the forward signal distribution at the second computational section is calculated step by step using the forward parabolic equation model. Then, based on the signal distribution at the second computational section, the forward signal distribution at the third computational section is calculated step by step using the forward parabolic equation model.

[0218] During the backward propagation process, the forward signal distribution at the third computational section during the forward propagation process is taken as the initial signal distribution. The backward signal distribution when the signal propagates to the third computational section is calculated using the backward parabolic equation model. Based on the backward signal distribution at the third computational section, the backward signal distribution when the signal propagates to the second computational section is calculated step by step using the backward parabolic equation model. Then, based on the backward signal distribution at the second computational section, the backward signal distribution when the signal propagates to the first computational section is calculated step by step using the backward parabolic equation model.

[0219] For any given computational cross section, the forward and backward signal distributions of each cross section are superimposed to obtain the predicted signal distribution of that cross section. Based on the predicted signal distribution of each computational interface, the predicted signal distribution of the preset region is obtained.

[0220] It should be noted that the embodiments of this application are illustrated using three calculation sections as an example. In some embodiments, the number of calculation sections can be any value greater than 1. For example, the number of calculation sections can be 2, 4, ...

[0221] In this embodiment, the pitch angle of the transmitter relative to the preset area can be divided into large angle (≥45°) and small angle (<45°) based on a preset threshold. When the transmitter is incident at a large angle, the influence of ground reflection on the signal is taken into account, and a two-way parabolic equation model is selected to predict the distribution of the transmitter signal in the preset area, so that the predicted signal distribution is more accurate.

[0222] S705. Based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point, determine multiple calculation sections in the preset area.

[0223] Multiple calculation sections can include initial calculation sections and step calculation sections.

[0224] It should be noted that the specific execution process of step S705 can be referred to Figure 5The specific execution process of the embodiments will not be described in detail here.

[0225] S706. Obtain the geographic and meteorological parameters of the preset area.

[0226] The shape of the preset area can be a regular polygon, for example, the preset area can be a rectangular area.

[0227] Understandably, during signal propagation, the degree of influence of reflection and diffraction varies depending on the elevation of the terrain within the preset area. Furthermore, the types of media on the Earth's surface are complex and diverse; for example, they can include forest types, river types, and building types. The dielectric constants of different media types vary significantly, making the media type have a substantial impact on the calculation of the surface reflection coefficient and on signal propagation.

[0228] Therefore, considering the influence of elevation data and medium type on the signal propagation process of the preset area, in order to more accurately predict the signal propagation process, the geographical parameters selected in this application embodiment may include elevation data and electromagnetic parameters of the medium.

[0229] Elevation data can include information such as geographic coordinates and elevation variations of complex terrain, which can be used to indicate terrain with large variations and irregular shapes.

[0230] The medium can include forests, rivers, ground, buildings, etc. The electromagnetic parameters of the medium can be used to indicate the propagation characteristics of signals in the medium. The electromagnetic parameters of the medium can include the dielectric constant, conductivity, and complex dielectric constant of the medium.

[0231] In some embodiments, the elevation data of a preset area can be determined as follows: acquire a digital elevation map in GeoTIFF format; determine the complex terrain of the preset area based on the GeoTIFF digital elevation map according to the positioning information of the preset area, and extract the geographic coordinates and undulation height information of the complex terrain.

[0232] In some embodiments, the medium type of the surface environment in a preset area can be determined based on the geographic image map and the GlobeLand30 dataset, and the electromagnetic parameters of the medium corresponding to the preset area can be determined based on the medium type.

[0233] Meteorological parameters may include, but are not limited to, the refractive index of air.

[0234] In some embodiments, meteorological information of a preset area can be obtained, which may include, but is not limited to, air temperature, humidity, and air pressure of the preset area. After obtaining the meteorological information of the preset area, the meteorological information can be substituted into the empirical formula for the dielectric constant of air to calculate the dielectric constant of air corresponding to the preset area, and the refractive index of the air in the preset area can be determined based on the dielectric constant of air.

[0235] S707. Based on the source parameters, geographical parameters, and meteorological parameters, predict the distribution of the predicted signal for the initial calculation section using the target prediction model.

[0236] In some embodiments, the predicted signal distribution of the initial computing interface can be determined as follows: the initial signal strength of the transmitting source in the initial computing section is determined based on source parameters, geographical parameters, and meteorological parameters; the initial computing section is divided into multiple computing grids according to a preset grid size; the predicted signal strength of each computing grid in the initial computing section is predicted by a target prediction model based on source parameters, initial signal strength, geographical parameters, and meteorological parameters; and the predicted signal distribution of the initial computing section is determined based on the predicted signal strength of each computing grid in the initial computing section.

[0237] In the region between the transmitter and the near-ground surface, since there are fewer factors affecting signal propagation, the signal propagation process can be approximated as free space propagation. The following formula for calculating free space propagation loss can be used to calculate the propagation loss of the signal from the transmitter to the initial calculation section:

[0238] PL = 32.4 + 20lg(f) + 20lg(d)

[0239] Where PL is the free space propagation loss of the signal, f is the transmission frequency of the signal from the source, in MHz; and d is the signal propagation distance, in km.

[0240] Based on the transmitted signal strength and propagation loss of the transmitter, the propagation signal strength of the transmitter to the initial calculation section is determined.

[0241] Based on the propagating signal strength, boundary conditions, source parameters, meteorological parameters, and geographical parameters, the initial signal strength of the transmitting source at the initial calculation cross section can be determined using the following formula:

[0242]

[0243] Where u(x0,z) represents the initial signal strength at the initial calculation cross section formed by x0 and z, α represents the antenna elevation angle, A(α) represents the antenna pattern function, and R ∥or⊥ H represents the horizontal or vertical reflectance of the ground. i The height at which the antenna is installed.

[0244] S708. Based on the predicted signal distribution, source parameters, geographical parameters, and meteorological parameters of the initial calculation section, predict the predicted signal distribution of the step calculation section.

[0245] In some embodiments, the predicted signal distribution of the stepping computational cross section can be determined as follows: the stepping computational cross section is divided into multiple computational grids according to a preset grid size; a first computational grid and a second computational grid are determined among the multiple computational grids; the predicted signal intensity of the first computational grid is predicted using a target prediction model based on the predicted signal distribution of the initial computational cross section, source parameters, geographical parameters, and the meteorological parameters; the predicted signal intensity of the second computational grid is predicted using a free space propagation model based on the emission source parameters; and the predicted signal distribution of the stepping computational cross section is determined based on the predicted signal intensity of the first computational grid and the predicted signal intensity of the second computational grid.

[0246] The propagation process of the signal from the source near the ground can be approximated as the propagation process of a plane wave. During this propagation, when the parabolic equation is used to step from the initial calculation section to the next step calculation section, a blind zone appears in the plane wave field propagation, such as... Figure 9 As shown.

[0247] Figure 9 This is one of the schematic diagrams illustrating the blind zone for plane wave calculation of the parabolic equation provided in this application embodiment. Please refer to... Figure 9 When the parabolic equation is iteratively calculated from the initial computational section to the step computational section, the elevation angle of the signal propagation causes a height difference between the initial and step computational sections. This results in three types of regions within the step computational section: a computational blind zone, a parabolic equation computational region, and a redundant region. Specifically: the computational grids in the computational blind zone need to be filled in; the computational grids in the parabolic equation computational region can use a target prediction model to calculate the signal distribution of each grid; and the computational grids in the redundant region can be discarded.

[0248] In some embodiments, the blind zone height can be determined based on the pitch angle and the distance between the initial computational section and the step computational section; the computational grids whose height is less than or equal to the blind zone height among the multiple computational grids are determined as the first computational grids; and the computational grids whose height is greater than the blind zone height among the multiple computational grids are determined as the second computational grids.

[0249] The free space propagation model can be used to predict the signal distribution of a signal propagating in free space. The free space propagation model can be any model used to predict the signal distribution of a signal (or electromagnetic wave) propagating in free space. This application does not limit the type of free space propagation model.

[0250] Below, in conjunction with Figure 10 The process of determining the height of the blind spot is explained.

[0251] Figure 10 This is the second schematic diagram illustrating the blind zone for plane wave calculation of the parabolic equation provided in this embodiment. Please refer to... Figure 10 When the parabolic equation is iteratively calculated from the initial calculation section to the step calculation section, there is a coverage blind zone with a height of h. The height h of the coverage blind zone can satisfy the following formula:

[0252]

[0253] Where h is the height of the coverage blind zone, and Δx is the distance between the initial calculation section and the step calculation section.

[0254] For the second computational grid within the coverage blind zone, ignoring the influence of geographical and meteorological parameters on signal propagation in this preset area, the signal distribution of each second computational grid is approximated using a free-space propagation model. The height range for blind spot filling is: N z ·Δz-h~N z ·Δz, where N z Δz represents the number of grids along the z-direction of the parabolic equation, and Δz represents the grid step size along the z-direction of the parabolic equation.

[0255] It is understandable that when stepping from the initial calculation section to the current stepping calculation section, the angle of the signal elevation is determined based on the distance Δx between the two calculation sections and the angle of the signal elevation. The height h of the coverage blind zone is determined, and blind spot compensation is performed on this area. By performing blind spot compensation, the signal strength of the coverage blind zone can be avoided when calculating the field of the first step calculation section, which helps to improve the accuracy of calculating the signal strength of the step calculation section.

[0256] It is understandable that there can be multiple step calculation sections. The predicted signal distribution, source parameters, geographical parameters and meteorological parameters of the current step calculation section can be used to predict the predicted signal distribution of the next step calculation section to be stepped, and so on, until the predicted signal distribution of all step calculation sections is obtained.

[0257] The signal distribution prediction method provided in this application can divide the elevation angle of the transmitter relative to a preset area into large angles (≥45°) and small angles (<45°) based on a preset threshold. When the transmitter is incident at a large angle, considering the influence of ground reflection on the signal, a two-way parabolic equation model is selected to predict the predicted signal distribution of the transmitter in the preset area, making the predicted signal distribution more accurate. It can also adaptively divide the preset area into multiple computational sections and adaptively divide the mesh of each computational section; and use a segmented calculation method (free space propagation and near-ground propagation) to determine the predicted signal distribution of each computational section, making the predicted signal distribution of the preset area more accurate.

[0258] Figure 11 This is a schematic diagram of a signal distribution prediction device provided in an embodiment of this application. The data processing device can be a chip or a chip module. Please refer to... Figure 11 The signal distribution prediction device 10 includes:

[0259] The determining module 11 is used to determine a target prediction model from multiple prediction models based on the elevation angle of the signal incident from the transmitter relative to the preset area and a preset threshold. The target prediction model is used to predict the signal distribution of the transmitter in the preset area.

[0260] The determining module 11 is further configured to determine multiple calculation sections in the preset area based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point;

[0261] Prediction module 12 is used to predict the predicted signal distribution of the multiple calculation cross sections based on the source parameters of the emission source and the multiple calculation cross sections using the target prediction model;

[0262] The determining module 11 is further configured to determine the predicted signal distribution of the preset region based on the predicted signal distribution of the plurality of calculation sections.

[0263] In one possible implementation, the determining module 11 is specifically used for:

[0264] Based on the azimuth angle and the coordinates of the center point, determine the equation of the first straight line passing through the center point;

[0265] Based on the coordinates of multiple vertices of the preset region, the equation of the first straight line, the preset spacing, the coordinates of the center point, and the azimuth angle, determine the equations of multiple second straight lines;

[0266] The plurality of calculation sections are determined based on the equation of the first straight line, the equations of the plurality of second straight lines, and the boundary information of the preset region.

[0267] In one possible implementation, the determining module 11 is further configured to:

[0268] Based on the coordinates of each vertex of the preset region and the equation of the first straight line, the length of the first perpendicular segment from each vertex to the first straight line is determined, thus obtaining the lengths of multiple first perpendicular segments.

[0269] Among the plurality of first perpendicular segments, a target first perpendicular segment is determined, wherein the target first perpendicular segment is the longest first perpendicular segment among the plurality of first perpendicular segments;

[0270] Based on the target first perpendicular line segment and the preset spacing, determine the lengths of multiple second perpendicular line segments passing through the center point;

[0271] The equations of the plurality of second straight lines are determined based on the coordinates of the center point, the lengths of the plurality of second perpendicular line segments, and the angle of the azimuth.

[0272] In one possible implementation, the plurality of computational sections includes an initial computational section and a step computational section; the prediction module 12 is specifically used for:

[0273] Obtain the geographical and meteorological parameters of the preset area;

[0274] Based on the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal distribution of the initial calculation section is predicted using the target prediction model;

[0275] The predicted signal distribution of the step calculation section is predicted based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters.

[0276] In one possible implementation, the prediction module 12 is further configured to:

[0277] Based on the source parameters, the geographical parameters, and the meteorological parameters, determine the initial signal strength of the transmitting source in the initial calculation section;

[0278] The initial computational section is divided into multiple computational grids according to the preset grid size;

[0279] Based on the source parameters, the initial signal strength, the geographical parameters, and the meteorological parameters, the predicted signal strength of each computation grid in the initial computation cross section is predicted using the target prediction model;

[0280] The predicted signal distribution of the initial computational cross section is determined based on the predicted signal intensity of each computational grid in the initial computational cross section.

[0281] In one possible implementation, the prediction module 12 is further configured to:

[0282] According to the preset grid size, the step calculation section is divided into multiple calculation grids;

[0283] A first computational grid and a second computational grid are determined among the plurality of computational grids;

[0284] Based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal intensity of the first calculation grid is predicted using the target prediction model.

[0285] Based on the source parameters, the predicted signal strength of the second computational grid is predicted using a free-space propagation model;

[0286] The predicted signal distribution of the stepping computation section is determined based on the predicted signal strength of the first computation grid and the predicted signal strength of the second computation grid.

[0287] In one possible implementation, the prediction module 12 is further configured to:

[0288] The blind zone height is determined based on the pitch angle and the distance between the initial calculation section and the step calculation section;

[0289] The computational grid whose height is less than or equal to the blind zone height among multiple computational grids is determined as the first computational grid;

[0290] The computational grid whose height is greater than the blind zone height among multiple computational grids is identified as the second computational grid.

[0291] In one possible implementation, the plurality of prediction models includes a first prediction model and a second prediction model; the determining module 11 is further configured to:

[0292] If the pitch angle is less than the preset threshold, the first prediction model is determined as the target prediction model;

[0293] If the pitch angle is greater than or equal to the preset threshold, the second prediction model is determined as the target prediction model.

[0294] The signal distribution prediction device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0295] Figure 12 This is a schematic diagram of the signal distribution prediction device provided in an embodiment of this application. Please refer to... Figure 12The signal distribution prediction device 20 may include a memory 21 and a processor 22. Exemplarily, the memory 21 and the processor 22 are interconnected via a bus 23.

[0296] Memory 21 is used to store program instructions;

[0297] The processor 22 is used to execute the program instructions stored in the memory, so that the signal distribution prediction device 20 performs the method shown in the above method embodiment.

[0298] It is understood that the signal distribution prediction device 20 may include at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described in the above embodiments.

[0299] The signal distribution prediction device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0300] This application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described signal distribution prediction method.

[0301] This application embodiment may also provide a computer program product, including a computer program that, when executed by a processor, can implement the above-described signal distribution prediction method.

[0302] All or part of the steps in the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above-described method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), random access memory (RAM), flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof.

[0303] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable signal distribution prediction device to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable signal distribution prediction device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0304] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable signal distribution prediction device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0305] These computer program instructions can also be loaded onto a computer or other programmable signal distribution prediction device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0306] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

[0307] In this application, the term "comprising" and its variations can refer to non-limiting inclusion; the term "or" and its variations can refer to "and / or". The terms "first", "second", etc., in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

Claims

1. A signal distribution prediction method, characterized in that, The method includes: Based on the elevation angle of the signal incident from the transmitter relative to the preset area and a preset threshold, a target prediction model is determined from multiple prediction models. These multiple prediction models include a first prediction model and a second prediction model. The first prediction model is a forward parabolic equation model, and the second prediction model is a bidirectional parabolic equation model. The target prediction model is used to predict the signal distribution of the transmitter in the preset area. Specifically, if the elevation angle is less than the preset threshold, the first prediction model is determined as the target prediction model; if the elevation angle is greater than or equal to the preset threshold, the second prediction model is determined as the target prediction model. Based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point, multiple calculation sections are determined in the preset area; Based on the source parameters of the emission source and the multiple calculation cross sections, the predicted signal distribution of the multiple calculation cross sections is predicted by the target prediction model, and the predicted signal distribution of the preset region is determined based on the predicted signal distribution of the multiple calculation cross sections. Based on the azimuth angle of the incident signal, the boundary information of the preset region, and the coordinates of the center point, multiple calculation sections are determined within the preset region, including: Based on the azimuth angle and the coordinates of the center point, determine the equation of the first straight line passing through the center point; Based on the coordinates of each vertex of the preset region and the equation of the first straight line, the length of the first perpendicular segment from each vertex to the first straight line is determined, thus obtaining the lengths of multiple first perpendicular segments. Among the plurality of first perpendicular segments, a target first perpendicular segment is determined, wherein the target first perpendicular segment is the longest first perpendicular segment among the plurality of first perpendicular segments; Based on the target first perpendicular line segment and the preset spacing, determine the lengths of multiple second perpendicular line segments passing through the center point; wherein, the second perpendicular line segment is the perpendicular line segment between the second straight line and the center point; the second straight line is parallel to the first straight line and is spaced apart by the preset spacing; The equations of the plurality of second straight lines are determined based on the coordinates of the center point, the lengths of the plurality of second perpendicular line segments, and the angle of the azimuth. The plurality of calculation sections are determined based on the equation of the first straight line, the equations of the plurality of second straight lines, and the boundary information of the preset region.

2. The method according to claim 1, characterized in that, The plurality of calculation sections include an initial calculation section and a step calculation section; Based on the source parameters of the emission source and the multiple computational cross-sections, the target prediction model predicts the predicted signal distribution of the multiple computational cross-sections, including: Obtain the geographical and meteorological parameters of the preset area; Based on the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal distribution of the initial calculation section is predicted using the target prediction model; The predicted signal distribution of the step calculation section is predicted based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters.

3. The method according to claim 2, characterized in that, Based on the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal distribution of the initial calculation section is predicted using the target prediction model, including: Based on the source parameters, the geographical parameters, and the meteorological parameters, determine the initial signal strength of the transmitting source in the initial calculation section; The initial computational section is divided into multiple computational grids according to the preset grid size; Based on the source parameters, the initial signal strength, the geographical parameters, and the meteorological parameters, the predicted signal strength of each computation grid in the initial computation cross section is predicted using the target prediction model; The predicted signal distribution of the initial computational cross section is determined based on the predicted signal intensity of each computational grid in the initial computational cross section.

4. The method according to claim 2 or 3, characterized in that, Based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal distribution of the step calculation section is predicted, including: The step calculation section is divided into multiple calculation grids according to the preset grid size; A first computational grid and a second computational grid are determined among the plurality of computational grids; Based on the predicted signal distribution of the initial calculation section, the source parameters, the geographical parameters, and the meteorological parameters, the predicted signal intensity of the first calculation grid is predicted using the target prediction model. Based on the source parameters, the predicted signal strength of the second computational grid is predicted using a free-space propagation model; The predicted signal distribution of the stepping computation section is determined based on the predicted signal strength of the first computation grid and the predicted signal strength of the second computation grid.

5. The method according to claim 4, characterized in that, Determining a first computing grid and a second computing grid among the plurality of computing grids includes: The blind zone height is determined based on the pitch angle and the distance between the initial calculation section and the step calculation section; The computational grid whose height is less than or equal to the blind zone height among multiple computational grids is determined as the first computational grid; The computational grid whose height is greater than the blind zone height among multiple computational grids is identified as the second computational grid.

6. A signal distribution prediction device, characterized in that, include: A determination module is used to determine a target prediction model from multiple prediction models based on the elevation angle of the signal incident from the transmitter relative to a preset area and a preset threshold. The multiple prediction models include a first prediction model and a second prediction model, wherein the first prediction model is a forward parabolic equation model and the second prediction model is a bidirectional parabolic equation model. The target prediction model is used to predict the signal distribution of the transmitter in the preset area. Specifically, if the elevation angle is less than the preset threshold, the first prediction model is determined as the target prediction model; if the elevation angle is greater than or equal to the preset threshold, the second prediction model is determined as the target prediction model. The determining module is further configured to determine multiple calculation sections in the preset area based on the azimuth angle of the incident signal, the boundary information of the preset area, and the coordinates of the center point; The prediction module is used to predict the predicted signal distribution of the multiple calculation cross sections based on the source parameters of the emission source and the multiple calculation cross sections using the target prediction model. The determining module is further configured to determine the predicted signal distribution of the preset region based on the predicted signal distribution of the plurality of calculation sections; The determining module is specifically used to: determine the equation of a first straight line passing through the center point based on the azimuth angle and the coordinates of the center point; determine the length of a first perpendicular segment from each vertex of the preset region to the first straight line based on the coordinates of each vertex of the preset region and the equation of the first straight line, thereby obtaining the lengths of multiple first perpendicular segments; determine a target first perpendicular segment among the multiple first perpendicular segments, wherein the target first perpendicular segment is the longest first perpendicular segment among the multiple first perpendicular segments; determine the lengths of multiple second perpendicular segments passing through the center point based on the target first perpendicular segment and a preset distance; wherein the second perpendicular segment is the perpendicular segment between the second straight line and the center point; the second straight line is parallel to the first straight line and spaced apart by a preset distance; determine the equations of the multiple second straight lines based on the coordinates of the center point, the lengths of the multiple second perpendicular segments, and the azimuth angle; and determine the multiple calculated sections based on the equations of the first straight line, the equations of the multiple second straight lines, and the boundary information of the preset region.

7. A signal distribution prediction device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, are used to implement the method of any one of claims 1 to 5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 5.

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