A method, apparatus, device and medium for optimizing station distribution

Abstract

The application discloses a method, device, equipment and medium for optimizing distribution of stations, and relates to the technical field of satellite positioning. The method comprises the following steps: acquiring positions of the stations, and generating a CRDF function to determine multiple distribution attributes of the stations, wherein the stations comprise reference stations and monitoring stations; calling a grid ionosphere model constructed with the reference stations as a benchmark to determine secondary residuals of the stations; and obtaining a distribution attribute table according to the distribution attributes and the secondary residuals, so as to determine the distribution of the stations by using the distribution attribute table. Since the multiple distribution attributes and the secondary residuals of the stations are considered at the same time, the distribution attribute table is obtained, and the optimal distribution of the stations is determined by analyzing the distribution attribute table.

Description

Technical Field

[0001] This application relates to the field of satellite positioning technology, and in particular to a method, apparatus, equipment and medium for optimizing the distribution of satellite stations. Background Technology

[0002] Precise Point Positioning (PPP) technology utilizes precise satellite orbits and satellite clock bias products to achieve centimeter-level high-precision positioning. However, traditional PPP technology suffers from long convergence times due to phase ambiguity parameters absorbing biases such as hardware delays. To further improve positioning accuracy and reduce convergence time, atmospheric delay corrections and hardware delay corrections are calculated using a regional reference network based on the PPP floating-point solution. These corrections enable High-Precision Positioning – Real-Time Kinematic (PPP-RTK) technology. Since PPP-RTK technology can achieve centimeter-level positioning in approximately ten seconds and is not limited by base stations or two-way communication, it has become the preferred solution in the field of high-precision positioning. For PPP-RTK technology, high-precision atmospheric correction products are crucial for achieving rapid convergence. Currently, regional grid atmospheric products are based on high-precision atmospheric information obtained from stations within the region, mapped to regional grid points using mapping relationships. Users can then obtain the atmospheric delay at any location within the region using the atmospheric information from the regional grid points. It is obvious at this point that the number and distribution of monitoring stations within a region will greatly affect the accuracy of regional grid atmospheric products.

[0003] Given the aforementioned problems, finding a suitable distribution of monitoring stations to maximize the accuracy of the calculated regional atmospheric grid correction product is a problem that those skilled in the art are striving to solve. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, equipment, and medium for optimizing the distribution of monitoring stations, so as to select a suitable distribution of monitoring stations to achieve the highest accuracy of the calculated regional atmospheric grid correction product.

[0005] To address the aforementioned technical problems, this application provides a method for optimizing the distribution of monitoring stations, comprising:

[0006] The location of each station is obtained, and a CRDF function is generated to determine various distribution attributes of the stations, including reference stations and monitoring stations.

[0007] The second-order residuals of the stations were determined by calling the grid ionospheric model constructed based on the reference station.

[0008] A distribution attribute table is obtained based on the distribution attributes and the quadratic residuals, so that the distribution of the stations can be determined using the distribution attribute table.

[0009] Preferably, generating the CRDF function includes:

[0010] Map the location of each station onto the grid;

[0011] And establish a rectangular coordinate system based on the grid;

[0012] Obtain the coordinates of the stations after mapping to the grid;

[0013] Generate a CRDF function based on the coordinates of the station.

[0014] Preferably, after generating the CRDF function based on the coordinates of the station, the method further includes:

[0015] Based on the grid, the position of the rectangular coordinate system is established through multiple transformations to determine various distribution attributes of the station.

[0016] Preferably, the second-order residuals of the station are determined by calling a grid ionospheric model constructed based on the reference station, including:

[0017] Obtain the grid point residuals and station residuals corresponding to the stations obtained through the grid ionospheric model;

[0018] The secondary residual is determined based on the grid point residual and the station residual.

[0019] Preferably, the distribution attribute table obtained based on the distribution attributes and the quadratic residual includes:

[0020] A distribution attribute table is obtained based on multiple distribution attributes and quadratic residuals.

[0021] Preferably, a grid ionospheric model is constructed based on a reference station, including:

[0022] Polynomial fitting was performed on the ionospheric atmospheric delay of the station, and multiple polynomial parameters to be fitted were obtained.

[0023] The ionospheric atmospheric delay after polynomial fitting is converted into the first matrix equation;

[0024] Based on the least squares method, the first matrix equation is solved, and the polynomial fitting parameters are determined, where the polynomial fitting parameters are the least squares solution of the first matrix equation obtained by the least squares method.

[0025] The station residuals for each station are determined based on the least squares solution;

[0026] Inverse distance weighted interpolation is performed on the station residuals to determine the grid point residuals at each grid point;

[0027] A grid ionosphere model is constructed based on the polynomial fitting parameters and grid point residuals.

[0028] Preferably, the station residuals are subjected to inverse distance weighted interpolation to determine the grid point residuals at each grid point, including:

[0029] Obtain the distance from the current grid point to the target station;

[0030] The weight coefficients are determined based on the distance, and a weight coefficient matrix is ​​obtained to facilitate inverse distance weighting.

[0031] The station residual matrix, composed of station residuals, is rank-transformed to facilitate interpolation;

[0032] The grid residuals at each grid point are determined based on the weight coefficient matrix and the station residual matrix after rank transformation.

[0033] To address the aforementioned technical problems, this application also provides an apparatus for optimizing the distribution of monitoring stations, comprising:

[0034] The acquisition and generation module is used to acquire the location of each station and generate CRDF functions to determine various distribution attributes of the stations, including reference stations and monitoring stations.

[0035] The module is invoked and determined to invoke the grid ionospheric model built based on the reference station and determine the secondary residual of the station.

[0036] The first module is used to obtain a distribution attribute table based on the distribution attributes and the quadratic residuals, so as to determine the distribution of the stations using the distribution attribute table.

[0037] In addition, the device also includes the following modules:

[0038] Preferably, generating the CRDF function includes:

[0039] The mapping module is used to map the location of each station onto the grid.

[0040] Create a module to establish a rectangular coordinate system based on the grid;

[0041] The first acquisition module is used to acquire the coordinates of the stations after they have been mapped to the grid.

[0042] The generation module is used to generate CRDF functions based on the coordinates of the station.

[0043] Preferably, after generating the CRDF function based on the coordinates of the station, the method further includes:

[0044] The transformation module is used to establish the position of a rectangular coordinate system based on the grid through multiple transformations in order to determine various distribution attributes of the station.

[0045] Preferably, the second-order residuals of the station are determined by calling a grid ionospheric model constructed based on the reference station, including:

[0046] The second acquisition module is used to acquire the grid point residuals and station residuals corresponding to the stations obtained through the grid ionospheric model.

[0047] The first determination module is used to determine the secondary residual based on the grid point residual and the station residual.

[0048] Preferably, the distribution attribute table obtained based on the distribution attributes and the quadratic residual includes:

[0049] The second module is used to obtain a distribution attribute table based on multiple distribution attributes and quadratic residuals.

[0050] Preferably, a grid ionospheric model is constructed based on a reference station, including:

[0051] The polynomial fitting module is used to perform polynomial fitting on the ionospheric atmospheric delay of the station and obtain multiple polynomial parameters to be fitted.

[0052] The conversion module is used to convert the polynomial-fitted ionospheric atmospheric delay into a first matrix equation.

[0053] The solver and determine module is used to solve the first matrix equation based on the least squares method and determine the polynomial fitting parameters, wherein the polynomial fitting parameters are the least squares solution of the first matrix equation obtained by the least squares method.

[0054] The second determination module is used to determine the station residuals of each station based on the least squares solution;

[0055] The inverse distance weighted interpolation module is used to perform inverse distance weighted interpolation on the station residuals to determine the grid point residuals at each grid point;

[0056] The module is used to construct a grid ionosphere model based on polynomial fitting parameters and grid point residuals.

[0057] Preferably, the station residuals are subjected to inverse distance weighted interpolation to determine the grid point residuals at each grid point, including:

[0058] The third acquisition module is used to acquire the distance between the current grid point and the target station;

[0059] The third determining module is used to determine the weight coefficients based on the distance and obtain the weight coefficient matrix to facilitate inverse distance weighting.

[0060] The rank transformation module is used to transform the rank of the station residual matrix composed of station residuals to facilitate interpolation;

[0061] The fourth determination module is used to determine the grid residuals at each grid point based on the weight coefficient matrix and the station residual matrix after rank transformation.

[0062] To address the aforementioned technical problems, this application also provides a device for optimizing the distribution of monitoring stations, comprising:

[0063] Memory, used to store computer programs;

[0064] A processor is used to direct computer programs to implement methods for optimizing the distribution of monitoring stations.

[0065] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for optimizing the distribution of all the aforementioned monitoring stations.

[0066] This application provides a method for optimizing the distribution of monitoring stations, comprising: acquiring the locations of each monitoring station and generating a CRDF function to determine multiple distribution attributes of the stations, wherein the monitoring stations include reference stations and monitoring stations; calling a grid ionospheric model constructed based on the reference stations to determine the quadratic residuals of the stations; and obtaining a distribution attribute table based on the distribution attributes and the quadratic residuals to facilitate the determination of the station distribution. Since multiple distribution attributes and quadratic residuals of the stations are considered simultaneously, a distribution attribute table is obtained, and the optimal station distribution is determined by analyzing the distribution attribute table.

[0067] This application also provides a device, equipment, and medium for optimizing the distribution of monitoring stations, with the same effect as above. Attached Figure Description

[0068] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is an overall schematic diagram of a PPP-RTK server obtaining a distributed attribute table, provided in an embodiment of this application.

[0070] Figure 2 This is a flowchart of a method for optimizing the distribution of monitoring stations provided in an embodiment of this application;

[0071] Figure 3 This is a schematic diagram illustrating the transformation of a rectangular coordinate system according to an embodiment of this application.

[0072] Figure 4 A structural diagram of a device for optimizing the distribution of monitoring stations provided in an embodiment of this application;

[0073] Figure 5 This is a diagram of a device structure for optimizing the distribution of monitoring stations, provided in an embodiment of this application. Detailed Implementation

[0074] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0075] The core of this application is to provide a method, apparatus, equipment, and medium for optimizing the distribution of monitoring stations, which can select a suitable distribution of monitoring stations to achieve the highest accuracy of the calculated regional atmospheric grid correction product.

[0076] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0077] Current research on PPP-RTK server-side atmospheric products mainly focuses on accuracy assessment under specific station distribution conditions. Research considering station distribution primarily concentrates on studies related to the unsampled ionospheric threat model of Satellite Based Augmentation System (SBAS). To address the significant impact of the number and distribution of stations on the accuracy of regional grid atmospheric products, this application proposes a method, apparatus, equipment, and medium for optimizing station distribution, detailed below:

[0078] Figure 1 This is an overall schematic diagram of a PPP-RTK server obtaining a distributed attribute table, as provided in an embodiment of this application. Figure 1 As shown, under a specific simulated station distribution pattern, the attribute values ​​of that distribution can be obtained. Figure 1 The distribution attributes are reflected in the cumulative relative distance function (CRDF). Each station can accurately calculate the oblique atmospheric result at its location based on its own observations and related precise orbital clock errors and hardware deviations. Based on the atmospheric results of each station, the station residuals and grid point residuals can be calculated, and then the station secondary residuals can be calculated. According to various simulated distribution scenarios, the distribution attribute table can be obtained, and then the optimal station distribution method under specific constraints can be determined. Figure 2 This is a flowchart illustrating a method for optimizing the distribution of monitoring stations provided in an embodiment of this application; combined with... Figure 1 ,like Figure 2 As shown, the method for optimizing the distribution of this monitoring station includes:

[0079] S20: Obtain the location of each station and generate a CRDF function.

[0080] To determine the various distribution attributes of the stations, which include reference stations and monitoring stations.

[0081] Specifically, the functions for generating CRDF include:

[0082] Map the location of each station onto the grid;

[0083] And establish a rectangular coordinate system based on the grid;

[0084] Obtain the coordinates of the stations after mapping to the grid;

[0085] Generate a CRDF function based on the coordinates of the station.

[0086] Furthermore, after generating the CRDF function based on the coordinates of the station, the following specific steps are also included:

[0087] Based on the grid, the position of the rectangular coordinate system is established through multiple transformations to determine various distribution attributes of the station.

[0088] For a given measuring station, its "effect" on the surrounding area gradually decreases with increasing distance. The "effect" on a point within the area can be considered as the sum of the "effects" of all measuring stations on that point. Assuming the center point of the area is chosen as the origin of the coordinate system, the area coordinates are limited to a range of ±1. It should be noted that the maximum value of the "effect" is 1, and the minimum value is 0.

[0089] Different station distributions mean different effects on a certain point in the region. This characteristic can be used to describe the different distribution attributes of the stations and can be represented by generating CRDF functions. Figure 3 This is a schematic diagram illustrating the position of a transformed rectangular coordinate system provided in an embodiment of this application, as shown below. Figure 3 As shown, to facilitate the description of this characteristic, the bottom left corner grid point of the region is selected as the origin, the east direction is the x-axis, and the north direction is the y-axis. The effect of all stations in the region relative to the coordinate origin is calculated. This avoids the situation where attribute values ​​are the same but the actual station distribution is symmetrical due to the sign of the coordinates. The effect of each station relative to the coordinate origin can be expressed as:

[0090]

[0091] Where n is the number of stations, x i and y iLet Lx and Ly be the coordinates of each station in the regional grid coordinate system, and Lx and Ly be the length and height of the grid region, respectively.

[0092] After obtaining the CRDF function, in order to calculate the performance indicators of atmospheric products under different station distributions—that is, to determine the optimal distribution by comparing the quadratic residuals of regional grid atmospheric products under various station distribution states—it is difficult to meet this requirement using only existing station distribution scenarios. Therefore, it is necessary to simulate a sufficient number of station distribution scenarios. It should be noted that the core of simulating a sufficient number of station distribution scenarios is to utilize different data exclusion and coordinate system transformation methods, select different station distribution methods, and thus simulate multiple station distribution scenarios. Figure 3 In the diagram, point 1 is a grid point, point 2 is a station, point 3 is the grid center point, point 4 is a station to be removed, and Y' and X' are the x-axis and y-axis of the new coordinate system after transformation.

[0093] At this point, assuming there are n stations in the region, the total number of station distributions that can be simulated are:

[0094]

[0095] By simulating the distribution of various monitoring stations, the atmospheric product performance of regional grids with different numbers of monitoring stations, different station distribution attributes, and different grid sizes can be obtained.

[0096] S21: Call the grid ionospheric model built based on the reference station to determine the secondary residual of the station.

[0097] S22: Obtain the distribution attribute table based on the distribution attributes and the quadratic residual.

[0098] This is to facilitate the determination of the distribution of stations using the distribution attribute table.

[0099] The specific steps for determining the secondary residuals of a station by calling upon a grid ionospheric model constructed based on a reference station include:

[0100] Obtain the grid point residuals and station residuals corresponding to the stations obtained through the grid ionospheric model;

[0101] The secondary residual is determined based on the grid point residual and the station residual.

[0102] Similarly, the steps to obtain the distribution attribute table based on the distribution attributes and quadratic residuals specifically include:

[0103] A distribution attribute table is obtained based on multiple distribution attributes and quadratic residuals.

[0104] The method for optimizing station distribution provided in this application includes: obtaining the location of each station and generating a CRDF function to determine multiple distribution attributes of the stations, wherein the stations include reference stations and monitoring stations; calling a grid ionospheric model constructed based on the reference station to determine the quadratic residuals of the stations; and obtaining a distribution attribute table based on the distribution attributes and the quadratic residuals to facilitate the determination of the station distribution. Since multiple distribution attributes and quadratic residuals of the stations are considered simultaneously, the optimal station distribution is determined through further analysis of the distribution attribute table.

[0105] Based on the above embodiments, as a more preferred embodiment, a grid ionospheric model is constructed using a reference station as a benchmark, including:

[0106] Polynomial fitting was performed on the ionospheric atmospheric delay of the stations, and multiple polynomial parameters to be fitted were obtained; the specific ionospheric atmospheric delay of each station can be expressed as:

[0107]

[0108] Among them, C 00 C 01 C 10 C 11 Let Δφ be the parameters of the polynomial to be fitted. j ,Δλ j j = 1, 2, ..., n represent the differences in latitude and longitude of each station relative to the center of the region, n is the number of stations in the region, and L j j = 1, 2, ..., n represents the atmospheric delay obtained from each station.

[0109] The ionospheric atmospheric delay after polynomial fitting is converted into the first matrix equation;

[0110] Transforming the above equation into the first matrix equation, the specific form of which is: Ax=L.

[0111] Based on the least squares method, the first matrix equation is solved, and the polynomial fitting parameters are determined, where the polynomial fitting parameters are the least squares solution of the first matrix equation obtained by the least squares method.

[0112] Among them, the least squares solution x * The form of is expressed as the following formula:

[0113] x * =[C * 00 C * 01 C * 10 C * 11 ]T =(A T A) -1 A T b

[0114] The station residuals for each station are determined based on the least squares solution;

[0115] Therefore, the station residuals of each station can be expressed in the following formula:

[0116] [ΔL1 ΔL2…ΔL n ] T n×1 =Ax * -b

[0117] Inverse distance weighted interpolation is performed on the station residuals to determine the grid point residuals at each grid point;

[0118] A grid ionosphere model is constructed based on the polynomial fitting parameters and grid point residuals.

[0119] The formula for determining the grid point residuals for each grid point is as follows: Inverse distance weighted interpolation is performed on the station residuals to determine the grid point residuals for each grid point.

[0120]

[0121] At this point, the weighting coefficient is needed to describe the distance to the grid points; the greater the distance, the smaller the weighting coefficient. In the above formula, m represents the total number of grid points in the area, and w... ij Let represent the weight coefficient between the i-th grid point and the j-th station. The formula for the weight coefficient is as follows:

[0122]

[0123] Among them, S ij The distance between the i-th grid point and the j-th station. Then, the weight coefficient matrix can be expressed as: The station residual matrix after rank transformation can be expressed as:

[0124] Determine the polynomial component I based on the polynomial fitting parameters. P ;

[0125] Determine residual component I based on grid residual. G ;

[0126] A grid ionosphere model is constructed based on the polynomial components and residual components.

[0127] The formula for the residual components is as follows:

[0128]

[0129] Then, w k The weight values ​​of the grid points participating in the interpolation calculation are obtained by weighting the inverse distance. N represents the residual of the grid points involved in the interpolation; N is the number of grid points involved in the interpolation, typically the 3 or 4 closest grid points are selected.

[0130] The grid ionosphere model constructed at this point can be represented as:

[0131] I = I P -I G .

[0132] The essence of the above algorithm is to map the atmospheric residuals of the stations to regional grid points. Users use the residual information of the grid points to calculate the atmospheric residuals at their own location, and then compensate for these atmospheric residuals in the polynomial modeling part to obtain accurate atmospheric delay information. The station-level quadratic residuals represent the difference between the station residuals and the grid point residuals, and this difference reflects the error introduced by the entire regional grid. Therefore, the regional station-level quadratic residuals can be used as a performance indicator to describe the atmospheric products of the regional grid, i.e.:

[0133]

[0134] in, Let denoted as the root mean square of the second-order residuals of all stations within the region, and ΔL as the station residuals.

[0135] It should also be noted that this application describes the distribution attributes of PPP-RTK regional stations by constructing a CRDF function, using the station's quadratic residual as a performance index, and simulating various different station distributions using existing historical data. It presents an optimal station distribution scheme considering the performance of atmospheric products from the PPP-RTK server-side regional grid. Existing methods for describing station distribution attributes mainly include the Relative Centroid Metric (RCM), Maximum Separate Angle (MSA), and Empty Pie Space (EPS). However, all of these methods have limitations; for example, RCM is not sensitive to changes in angle. Therefore, it is necessary to use a CRDF function to describe the distribution attributes.

[0136] This application mainly describes the distribution attributes of PPP-RTK server area stations by constructing a CRDF function, uses the root mean square error of the residuals (secondary residuals) of all stations as a performance index to describe the atmospheric product performance index under different station distribution states, and simulates various different station distribution patterns by combining historical station data.

[0137] In the above embodiments, the method for optimizing the distribution of monitoring stations has been described in detail. This application also provides embodiments corresponding to the apparatus for optimizing the distribution of monitoring stations. It should be noted that this application describes the embodiments of the apparatus from two perspectives: one is based on functional modules, and the other is based on hardware.

[0138] Figure 4 This is a structural diagram of a device for optimizing the distribution of monitoring stations provided in an embodiment of this application, as shown below. Figure 4 As shown, this application also provides an apparatus for optimizing the distribution of monitoring stations, comprising:

[0139] The acquisition and generation module 40 is used to acquire the location of each station and generate CRDF functions to determine various distribution attributes of the stations, including reference stations and monitoring stations.

[0140] Module 41 is invoked and determined to invoke the grid ionospheric model constructed based on the reference station and determine the secondary residual of the station.

[0141] The first module 42 is used to obtain a distribution attribute table based on the distribution attributes and the secondary residuals, so as to determine the distribution of the stations using the distribution attribute table.

[0142] In addition, the device also includes the following modules:

[0143] Preferably, generating the CRDF function includes:

[0144] The mapping module is used to map the location of each station onto the grid.

[0145] Create a module to establish a rectangular coordinate system based on the grid;

[0146] The first acquisition module is used to acquire the coordinates of the stations after they have been mapped to the grid.

[0147] The generation module is used to generate CRDF functions based on the coordinates of the station.

[0148] Preferably, after generating the CRDF function based on the coordinates of the station, the method further includes:

[0149] The transformation module is used to establish the position of a rectangular coordinate system based on the grid through multiple transformations in order to determine various distribution attributes of the station.

[0150] Preferably, the second-order residuals of the station are determined by calling a grid ionospheric model constructed based on the reference station, including:

[0151] The second acquisition module is used to acquire the grid point residuals and station residuals corresponding to the stations obtained through the grid ionospheric model.

[0152] The first determination module is used to determine the secondary residual based on the grid point residual and the station residual.

[0153] Preferably, the distribution attribute table obtained based on the distribution attributes and the quadratic residual includes:

[0154] The second module is used to obtain a distribution attribute table based on multiple distribution attributes and quadratic residuals.

[0155] Preferably, a grid ionospheric model is constructed based on a reference station, including:

[0156] The polynomial fitting module is used to perform polynomial fitting on the ionospheric atmospheric delay of the station and obtain multiple polynomial parameters to be fitted.

[0157] The conversion module is used to convert the polynomial-fitted ionospheric atmospheric delay into a first matrix equation.

[0158] The solver and determine module is used to solve the first matrix equation based on the least squares method and determine the polynomial fitting parameters, wherein the polynomial fitting parameters are the least squares solution of the first matrix equation obtained by the least squares method.

[0159] The second determination module is used to determine the station residuals of each station based on the least squares solution;

[0160] The inverse distance weighted interpolation module is used to perform inverse distance weighted interpolation on the station residuals to determine the grid point residuals at each grid point;

[0161] The module is used to construct a grid ionosphere model based on polynomial fitting parameters and grid point residuals.

[0162] Preferably, the station residuals are subjected to inverse distance weighted interpolation to determine the grid point residuals at each grid point, including:

[0163] The third acquisition module is used to acquire the distance between the current grid point and the target station;

[0164] The third determining module is used to determine the weight coefficients based on the distance and obtain the weight coefficient matrix to facilitate inverse distance weighting.

[0165] The rank transformation module is used to transform the rank of the station residual matrix composed of station residuals to facilitate interpolation;

[0166] The fourth determination module is used to determine the grid residuals at each grid point based on the weight coefficient matrix and the station residual matrix after rank transformation.

[0167] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0168] Figure 5 A device structure diagram for optimizing station distribution is provided in an embodiment of this application, as shown below. Figure 5 As shown, a device for optimizing the distribution of monitoring stations includes:

[0169] Memory 50 is used to store computer programs;

[0170] The processor 51 is used to execute a computer program to implement the steps of the station distribution optimization method mentioned in the above embodiments.

[0171] The equipment for optimizing the distribution of monitoring stations provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0172] The processor 51 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 51 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 51 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 51 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 51 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0173] The memory 50 may include one or more computer-readable storage media, which may be non-transitory. The memory 50 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 50 is used to store at least the following computer program, which, after being loaded and executed by the processor 51, is capable of implementing the relevant steps of the station distribution optimization method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 50 may also include an operating system and data, and the storage method may be temporary or permanent storage. The operating system may include Windows, Unix, Linux, etc. The data may include, but is not limited to, the station distribution optimization method.

[0174] In some embodiments, the equipment for optimizing the distribution of monitoring stations may further include a display screen, input / output interfaces, communication interfaces, a power supply, and a communication bus.

[0175] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the equipment for optimizing station distribution and may include more or fewer components than shown.

[0176] The device for optimizing the distribution of measuring stations provided in this application includes a memory 50 and a processor 51. When the processor 51 executes the program stored in the memory 50, it can implement the method for optimizing the distribution of measuring stations.

[0177] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0178] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] The foregoing has provided a detailed description of a method, apparatus, device, and medium for optimizing station distribution according to this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0180] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for optimizing the distribution of monitoring stations, characterized in that, include: The location of each station is obtained, and a CRDF function is generated to determine various distribution attributes of the stations, wherein the stations include reference stations and monitoring stations; The second-order residual of the station is determined by calling the grid ionospheric model constructed based on the reference station. A distribution attribute table is obtained based on the distribution attributes and the quadratic residuals, so as to determine the distribution of the stations using the distribution attribute table; The CRDF generation function includes: Map the location of each of the aforementioned stations onto the grid; A rectangular coordinate system is established based on the grid. Obtain the coordinates of the station after mapping to the grid; The CRDF function is generated based on the coordinates of the station; ； Where n is the number of monitoring stations. and These are the coordinates of each station in the regional grid coordinate system. and These represent the length and height of the grid area, respectively. After generating the CRDF function based on the coordinates of the station, the method further includes: Based on the grid, the position of the rectangular coordinate system is established through multiple transformations to determine various distribution attributes of the station.

2. The method for optimizing station distribution according to claim 1, characterized in that, The process of calling upon the grid ionospheric model constructed based on the reference station to determine the second-order residual of the station includes: Obtain the grid point residuals and station residuals corresponding to the station obtained through the grid ionospheric model; The secondary residual is determined based on the grid point residual and the station residual.

3. The method for optimizing station distribution according to claim 1, characterized in that, The step of obtaining the distribution attribute table based on the distribution attribute and the quadratic residual includes: The distribution attribute table is obtained based on the various distribution attributes and the quadratic residual.

4. The method for optimizing station distribution according to claim 2, characterized in that, The grid ionosphere model is constructed based on the reference station, including: Polynomial fitting was performed on the ionospheric atmospheric delay of the station to obtain multiple polynomial parameters to be fitted. The ionospheric atmospheric delay after polynomial fitting is converted into the first matrix equation; Based on the least squares method, the first matrix equation is solved, and polynomial fitting parameters are determined, wherein the polynomial fitting parameters are the least squares solution of the first matrix equation obtained by the least squares method. The station residuals of each station are determined based on the least squares solution. Inverse distance weighted interpolation is performed on the station residuals to determine the grid point residuals at each grid point; The grid ionosphere model is constructed based on the polynomial fitting parameters and the grid point residuals.

5. The method for optimizing station distribution according to claim 4, characterized in that, The step of performing inverse distance-weighted interpolation on the station residuals to determine the grid point residuals at each grid point includes: Obtain the distance from the current grid point to the target station; The weight coefficients are determined based on the distance, and a weight coefficient matrix is ​​obtained to facilitate inverse distance weighting. The station residual matrix, composed of the station residuals, is rank-transformed to facilitate interpolation; The grid residuals at each grid point are determined based on the weight coefficient matrix and the station residual matrix after rank transformation.

6. A device for optimizing the distribution of measuring stations, characterized in that, include: An acquisition and generation module is used to acquire the location of each station and generate CRDF functions to determine various distribution attributes of the stations, wherein the stations include reference stations and monitoring stations; The module is invoked and determined to invoke the grid ionospheric model constructed based on the reference station and determine the secondary residual of the station. The first obtaining module is used to obtain a distribution attribute table based on the distribution attributes and the quadratic residual, so as to determine the distribution of the station using the distribution attribute table; The mapping module is used to map the location of each station onto the grid. Create a module to establish a rectangular coordinate system based on the grid; The first acquisition module is used to acquire the coordinates of the stations after they have been mapped to the grid. The generation module is used to generate CRDF functions based on the coordinates of the station. ； Where n is the number of monitoring stations. and These are the coordinates of each station in the regional grid coordinate system. and These represent the length and height of the grid area, respectively. The transformation module is used to establish the position of a rectangular coordinate system based on the grid through multiple transformations in order to determine various distribution attributes of the station.

7. A device for optimizing the distribution of measuring stations, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the method for optimizing station distribution as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for optimizing station distribution as described in any one of claims 1 to 5.

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