Global real-time troposphere delay determination method and device for low atmosphere

Abstract

The invention provides a global real-time troposphere delay determination method and device for low atmosphere. The method comprises the following steps: according to troposphere delay data provided by ERA5 atmosphere reanalysis data, constructing a troposphere delay gridding model oriented to low-layer atmosphere; a global region is divided into regular windows with consistent sizes, so that the calculation efficiency reduction caused by the fact that the number of models exceeds a preset threshold value and the model calculation effect reduction caused by the sparse modeling data size of a single grid point are avoided; training the troposphere delay data provided based on the ERA5 atmosphere reanalysis data, and constructing a troposphere delay gridding model oriented to the lower atmosphere; and troposphere delay data with preset precision are determined by adopting the troposphere delay gridding model facing the lower atmosphere, and the troposphere delay data with the preset precision are used for GNSS positioning and GNSS water vapor detection. According to the invention, the performance and precision of the GNSS troposphere delayed atmosphere service are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of satellite navigation and positioning technology, and in particular to a global real-time tropospheric delay determination method and device for the lower atmosphere. Background Art

[0002] High-precision tropospheric delay (ZTD) can effectively improve the positioning accuracy of the Global Navigation Satellite System (GNSS) and meet the needs of applications such as weather forecasting and geological monitoring. However, the existing ZTD model has some limitations in modeling the lower atmosphere, such as relying solely on a single grid point for modeling (a large amount of grid point information will affect the user's computing efficiency, and using a single grid point for modeling may lead to poor model performance due to insufficient data), and traditional mathematical models are difficult to effectively capture the nonlinear relationship of tropospheric information. Therefore, there is an urgent need to develop a global real-time tropospheric delay determination method and equipment for the lower atmosphere that can effectively overcome the defects in the above-mentioned related technologies, which has become a technical problem that needs to be solved urgently in the industry. Summary of the Invention

[0003] In response to the above-mentioned problems existing in the prior art, embodiments of the present invention provide a global real-time tropospheric delay determination method and device for the lower atmosphere.

[0004] In a first aspect, an embodiment of the present invention provides a global real-time tropospheric delay determination method for the lower atmosphere, comprising: constructing a tropospheric delay grid model for the lower atmosphere based on tropospheric delay data provided by ERA5 atmospheric reanalysis data; dividing the global region into regular windows of uniform size to avoid a decrease in computational efficiency due to the number of models exceeding a predetermined threshold, and a decrease in model calculation effect due to sparse data volume of modeling of a single grid point; training the tropospheric delay data provided by the ERA5 atmospheric reanalysis data to construct a tropospheric delay grid model for the lower atmosphere; and using the tropospheric delay grid model for the lower atmosphere to determine tropospheric delay data of a predetermined accuracy, wherein the tropospheric delay data of the predetermined accuracy is used for GNSS positioning and GNSS water vapor detection.

[0005] Based on the contents of the above method embodiments, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiments of the present invention includes: the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, including: a time resolution of one hour and a spatial resolution of 0.25°×0.25°; extracting grid point layered meteorological data from the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, including potential height, specific humidity and temperature, performing data format conversion and potential height conversion on the meteorological data, calculating vertical profile information of different isobaric layers and tropospheres at each grid point, and using the vertical profile information as model training samples.

[0006] Based on the contents of the above method embodiments, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiments of the present invention calculates the vertical profile information of different isobaric layers and tropospheres at each grid point, including: calculating the atmospheric refractive index based on the meteorological parameters provided by the standard atmospheric pressure profile, integrating the refractive index at each profile height, and layering and combining the tropospheric delay data for each grid point.

[0007] Based on the content of the above method embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention integrates the refractive index at each profile height and hierarchically combines the tropospheric delay data of each grid point, including:

[0008]

[0009] k1=77.604K / hPa

[0010] k2=64.79K / hPa

[0011] k3=375463K 2 / hPa

[0012] Wherein, k1 is the first refractive index coefficient; k2 is the second refractive index coefficient; k3 is the third refractive index coefficient; K / hPa is Kelvin per hectopascals; P is atmospheric pressure; e is water vapor pressure; N is the total refractive index of the atmosphere; T is temperature; K 2 / hPa is Kelvin squared per hectopascal; Sh is specific humidity; h is height; dh is the predetermined change in height h; ZTD is tropospheric delay data; h L The bottom height calculated by integrating atmospheric data; h top Bottom height calculated for the atmospheric data integration.

[0013] Based on the contents of the above method embodiments, the present invention provides a global real-time tropospheric delay determination method for the lower atmosphere, which divides the global area into regular windows of uniform size to avoid reduced computational efficiency due to the number of models exceeding a predetermined threshold, and reduced model calculation effect due to sparse modeling data at a single grid point. The method includes: determining the window size and step size, and determining the grid size based on the integer number of divisions, the continuity of the window, and the solvability of the model parameters within the window; dividing the world into several regular windows of uniform size, the input layer in model training is longitude, latitude, annual cumulative day, and altitude, the output layer is tropospheric delay data ZTD, storing tropospheric delay data information in the form of window geometric center grid points, and constructing a tropospheric delay grid model for the lower atmosphere.

[0014] Based on the contents of the above method embodiments, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiments of the present invention, the training of the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, and the construction of a tropospheric delay gridded model for the lower atmosphere, include: according to the data structure, using parallel processing and maximizing memory utilization methods to train a predetermined scale data set at a predetermined speed; constructing a model by integrating multiple weak learners to capture data relationships of predetermined complexity in the data, using the input and output parameters of the data set for modeling, improving residuals by incrementally integrating multiple base learners to solve the regression problem; reducing the complexity of the model by regularization terms to prevent overfitting and improve the generalization of the model; calculating the importance of features to determine the contribution of each feature in the data to the model, and selecting features based on the contribution.

[0015] Based on the contents of the above method embodiments, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiments of the present invention uses the tropospheric delay gridding model for the lower atmosphere to determine tropospheric delay data of a predetermined accuracy, including: inputting the annual cumulative day, altitude, and longitude and latitude into the tropospheric delay gridding model for the lower atmosphere, identifying the gridding model closest to the user's location, and obtaining high-precision tropospheric delay information data.

[0016] In a second aspect, an embodiment of the present invention provides a global real-time tropospheric delay determination device for the lower atmosphere, comprising: a first main module, configured to construct a tropospheric delay gridding model for the lower atmosphere based on the tropospheric delay data provided by the ERA5 atmospheric reanalysis data; a second main module, configured to divide the global region into regular windows of uniform size, thereby avoiding a decrease in computational efficiency due to the number of models exceeding a predetermined threshold, and a decrease in model computational effect due to sparse data volume of modeling of a single grid point; a third main module, configured to train the tropospheric delay data provided by the ERA5 atmospheric reanalysis data to construct a tropospheric delay gridding model for the lower atmosphere; and a fourth main module, configured to determine tropospheric delay data of a predetermined accuracy using the tropospheric delay gridding model for the lower atmosphere, wherein the tropospheric delay data of the predetermined accuracy is used for GNSS positioning and GNSS water vapor detection.

[0017] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0018] At least one processor, at least one memory and a communication interface; wherein,

[0019] The processor, memory and communication interface communicate with each other;

[0020] The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the global real-time tropospheric delay determination method for the lower atmosphere provided by any one of the various implementation methods of the first aspect.

[0021] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute the global real-time tropospheric delay determination method for the lower atmosphere provided by any one of the various implementation methods of the first aspect.

[0022] The global real-time tropospheric delay determination method and device for the lower atmosphere provided by the embodiments of the present invention adopt the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, adopt the XGBoost algorithm and introduce the gridding algorithm window to establish a tropospheric delay gridding model for the lower atmosphere. Only the annual accumulated day, altitude and longitude and latitude need to be provided, and the gridding model closest to the target point is searched according to the target point position information to obtain high-precision tropospheric delay information, thereby ensuring the accuracy of the tropospheric delay atmospheric service and improving the timeliness of the GNSS tropospheric delay atmospheric service parameters, thereby improving the performance and accuracy of the GNSS tropospheric delay atmospheric service. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A schematic flow chart of a method for determining global real-time tropospheric delay in the lower atmosphere provided by an embodiment of the present invention;

[0025] Figure 2 A schematic structural diagram of a global real-time tropospheric delay determination device for the lower atmosphere provided by an embodiment of the present invention;

[0026] Figure 3 A schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention can be combined with each other arbitrarily to form a feasible technical solution. This combination is not subject to the constraints of the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention. If there are step numbers in the following embodiments, they are only set for the convenience of explanation and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0028] The embodiment of the present invention provides a global real-time tropospheric delay determination method for the lower atmosphere, see Figure 1 The method includes: constructing a tropospheric delay grid model for the lower atmosphere based on tropospheric delay data provided by ERA5 atmospheric reanalysis data; dividing the global area into regular windows of uniform size to avoid a decrease in computational efficiency due to the number of models exceeding a predetermined threshold, and a decrease in model computational effect due to sparse modeling data at a single grid point; training the tropospheric delay data provided by the ERA5 atmospheric reanalysis data to construct a tropospheric delay grid model for the lower atmosphere; using the tropospheric delay grid model for the lower atmosphere to determine tropospheric delay data of a predetermined accuracy, wherein the tropospheric delay data of the predetermined accuracy is used for GNSS positioning and GNSS water vapor detection.

[0029] Specifically, meteorological parameters such as grid point layered potential height, specific humidity, and temperature are extracted from the tropospheric delay data provided by the ERA5 atmospheric reanalysis data. The data are preprocessed (data format conversion, potential height conversion, etc.). The integral method is used to calculate the vertical profile information of the tropospheric layer at different isobaric layers of each grid point as model training samples. A gridding algorithm is introduced to divide the world into several regular windows of equal size. The input layer of the model training is longitude, latitude, annual cumulative day and altitude, and the output layer is the tropospheric delay (ZTD). The tropospheric delay information is stored in the form of window geometric center grid points (gridding), and a gridded tropospheric delay model for the lower atmosphere is constructed.

[0030] Based on the content of the above method embodiment, as an optional embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention, the tropospheric delay data provided by the ERA5 atmospheric reanalysis data includes: a time resolution of one hour and a spatial resolution of 0.25°×0.25°; extracting grid point layered meteorological data from the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, including potential height, specific humidity and temperature, performing data format conversion and potential height conversion on the meteorological data, calculating vertical profile information of different isobaric layers and tropospheres at each grid point, and using the vertical profile information as a model training sample.

[0031] Based on the content of the above method embodiment, as an optional embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention, wherein the vertical profile information of different isobaric layers and tropospheres at each grid point is calculated, including: calculating the atmospheric refractive index based on the meteorological parameters provided by the standard atmospheric pressure profile, integrating the refractive index at each profile height, and layering and combining the tropospheric delay data of each grid point.

[0032] Based on the content of the above method embodiment, as an optional embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention, wherein the refractive index at each profile height is integrated and the tropospheric delay data of each grid point is layered and combined, includes:

[0033]

[0034]

[0035] k1=77.604K / hPa

[0036] k2=64.79K / hPa

[0037] k3=375463K 2 / hPa

[0038] Wherein, k1 is the first refractive index coefficient; k2 is the second refractive index coefficient; k3 is the third refractive index coefficient; K / hPa is Kelvin per hectopascals; P is atmospheric pressure; e is water vapor pressure; N is the total refractive index of the atmosphere; T is temperature; K 2 / hPa is Kelvin squared per hectopascal; Sh is specific humidity; h is height; dh is the predetermined change in height h; ZTD is tropospheric delay data; h L The bottom height calculated by integrating atmospheric data; h top Bottom height calculated for atmospheric data integration.

[0039] Specifically, the total tropospheric zenith delay (ZTD2) is calculated using the Saastamoinen model based on the ZTD1 within the ERA5 atmospheric reanalysis data altitude range. The sum of these two ZTD values is the total tropospheric zenith delay. The integration method works as follows: First, the atmospheric refractivity index is calculated based on meteorological parameters provided by the standard atmospheric pressure profile. The refractivity at each profile height is then integrated. Finally, the ZTD values for each grid point are layered and combined. The calculation formulas for N, e, and ZTD are shown below.

[0040] Based on the content of the above method embodiment, as an optional embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention divides the global area into regular windows of uniform size to avoid the reduction in computational efficiency caused by the number of models exceeding a predetermined threshold, and the reduction in model calculation effect caused by the sparse amount of modeling data of a single grid point. The method includes: determining the size and step size of the window, and determining the size of the grid based on the integer number of divisions, the continuity of the window, and the solvability of the model parameters within the window; dividing the world into several regular windows of equal size, the input layer in the model training is longitude, latitude, annual cumulative day, and altitude, the output layer is tropospheric delay data ZTD, storing tropospheric delay data information in the form of window geometric center grid points, and constructing a tropospheric delay grid model for the lower atmosphere.

[0041] Based on the content of the above method embodiment, as an optional embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention, the training of the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, and the construction of a tropospheric delay gridded model for the lower atmosphere include: according to the data structure, using parallel processing and maximizing memory utilization methods to train a predetermined scale data set at a predetermined speed; constructing a model by integrating multiple weak learners to capture data relationships of predetermined complexity in the data, using the input and output parameters of the data set for modeling, improving the residual by incrementally integrating multiple base learners to solve the regression problem; reducing the complexity of the model by regularization terms to prevent overfitting and improve the generalization of the model; calculating the importance of features to determine the contribution of each feature in the data to the model, and selecting features according to the contribution.

[0042] In another embodiment, the XGBoost algorithm can be used to achieve parallel processing and efficient memory utilization based on the data structure, has a very high training speed, and performs well on large-scale data sets; the XGBoost algorithm uses a boosting tree method to build a powerful model by integrating multiple weak learners, which can effectively capture complex relationships in the data, can use the input and output parameters of the data set for modeling, and improve the residual by incrementally integrating multiple base learners, which is suitable for solving regression problems; the XGBoost algorithm controls the complexity of the model through regularization terms, prevents overfitting, and improves the generalization ability of the model; the XGBoost algorithm can calculate the importance of features to help users understand the contribution of each feature in the data to the model, thereby performing feature selection and optimization.

[0043] Based on the content of the above method embodiment, as an optional embodiment, the global real-time tropospheric delay determination method for the lower atmosphere provided in the embodiment of the present invention adopts the tropospheric delay gridding model for the lower atmosphere to determine the tropospheric delay data of a predetermined accuracy, including: inputting the annual cumulative day, altitude, and longitude and latitude into the tropospheric delay gridding model for the lower atmosphere, identifying the gridding model closest to the user's location, and obtaining high-precision tropospheric delay information data.

[0044] The global real-time tropospheric delay determination method for the lower atmosphere provided by the embodiment of the present invention adopts the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, adopts the XGBoost algorithm and introduces the gridding algorithm window to establish a tropospheric delay gridding model for the lower atmosphere. Only the annual accumulated day, altitude and longitude and latitude need to be provided, and the gridding model closest to the target point is searched according to the position information of the target point to obtain high-precision tropospheric delay information, thereby ensuring the accuracy of the tropospheric delay atmospheric service and improving the timeliness of the GNSS tropospheric delay atmospheric service parameters, thereby improving the performance and accuracy of the GNSS tropospheric delay atmospheric service.

[0045] Through real data, the technical effect of the global real-time tropospheric delay determination method for the lower atmosphere is scientifically demonstrated. Taking the ZTD of the sounding station as the reference value, the effect of the comparison of the tropospheric delay accuracy of the gridded model of the tropospheric delay for the lower atmosphere and the GPT3 model on a global scale is demonstrated. It can be seen that the technical solutions of the various embodiments of the present invention are aimed at GNSS atmospheric correction services, and utilize the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, adopt the XGBoost algorithm and introduce the grid partitioning algorithm window to establish a tropospheric delay gridded model for the lower atmosphere. The user only needs to provide the annual accumulated day, altitude and longitude and latitude, and search for the gridded model closest to the target point according to the target point location information to obtain high-precision tropospheric delay information, which ensures the accuracy of the tropospheric delay atmospheric service and improves the timeliness of the GNSS tropospheric delay atmospheric service parameters. It is of great significance to the improvement of the performance of the GNSS tropospheric delay atmospheric service and high-precision applications.

[0046] The implementation basis of each embodiment of the present invention is to implement it through programmed processing by a device with processor functions. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention can be encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a global real-time tropospheric delay determination device for the lower atmosphere, which is used to execute the global real-time tropospheric delay determination method for the lower atmosphere in the above method embodiment. Figure 2 The device includes: a first main module, which is used to build a tropospheric delay grid model for the lower atmosphere based on the tropospheric delay data provided by the ERA5 atmospheric reanalysis data; a second main module, which is used to divide the global area into regular windows of uniform size, so as to avoid the reduction of computing efficiency due to the number of models exceeding a predetermined threshold, and the reduction of model computing effect due to the sparse amount of modeling data of a single grid point; a third main module, which is used to train the tropospheric delay data provided by the ERA5 atmospheric reanalysis data to build a tropospheric delay grid model for the lower atmosphere; and a fourth main module, which is used to determine tropospheric delay data of predetermined accuracy by using the tropospheric delay grid model for the lower atmosphere, and the tropospheric delay data of predetermined accuracy is used for GNSS positioning and GNSS water vapor detection.

[0047] The embodiment of the present invention provides a global real-time tropospheric delay determination device for the lower atmosphere, which adopts Figure 2Several modules in it use the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, adopt the XGBoost algorithm and introduce the gridding algorithm window to establish a tropospheric delay gridded model for the lower atmosphere. Only the annual accumulated day, altitude and longitude and latitude are required. According to the location information of the target point, the gridded model closest to the target point is searched to obtain high-precision tropospheric delay information, which ensures the accuracy of the tropospheric delay atmospheric service and improves the timeliness of the GNSS tropospheric delay atmospheric service parameters, thereby improving the performance and accuracy of the GNSS tropospheric delay atmospheric service.

[0048] It should be noted that the device in the device embodiment provided by the present invention can be used to implement the method in the above-mentioned method embodiment as well as the method in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set. The principle is basically the same as the principle of the above-mentioned device embodiment provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned device embodiment, obtain the corresponding technical means and the technical solutions composed of these technical means by combining technical features, and ensure the practicality of the technical solutions, they can improve the device in the above-mentioned device embodiment to obtain the corresponding device class embodiment, thereby obtaining the corresponding device class embodiment for implementing the methods in other method class embodiments. For example:

[0049] Based on the content of the above-mentioned device embodiment, as an optional embodiment, the global real-time tropospheric delay determination device for the lower atmosphere provided in the embodiment of the present invention further includes: a first submodule, used to implement the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, including: a time resolution of one hour and a spatial resolution of 0.25°×0.25°; extracting grid point layered meteorological data from the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, including potential height, specific humidity and temperature, performing data format conversion and potential height conversion on the meteorological data, calculating the vertical profile information of different isobaric layers and tropospheres at each grid point, and using the vertical profile information as a model training sample.

[0050] Based on the content of the above-mentioned device embodiment, as an optional embodiment, the global real-time tropospheric delay determination device for the lower atmosphere provided in the embodiment of the present invention further includes: a second submodule, which is used to realize the calculation of the vertical profile information of different isobaric layers and tropospheres at each grid point, including: calculating the atmospheric refractive index based on the meteorological parameters provided by the standard atmospheric pressure profile, integrating the refractive index at each profile height, and layering and combining the tropospheric delay data of each grid point.

[0051] Based on the content of the above device embodiment, as an optional embodiment, the global real-time tropospheric delay determination device for the lower atmosphere provided in the embodiment of the present invention further includes: a third submodule, which is used to implement the integration of the refractive index at each profile height and hierarchically combine the tropospheric delay data of each grid point, including:

[0052]

[0053] k1=77.604K / hPa

[0054] k2=64.79K / hPa

[0055] k3=375463K 2 / hPa

[0056] Wherein, k1 is the first refractive index coefficient; k2 is the second refractive index coefficient; k3 is the third refractive index coefficient; K / hPa is Kelvin per hectopascals; P is atmospheric pressure; e is water vapor pressure; N is the total refractive index of the atmosphere; T is temperature; K 2 / hPa is Kelvin squared per hectopascal; Sh is specific humidity; h is height; dh is the predetermined change in height h; ZTD is tropospheric delay data; h L The bottom height calculated by integrating atmospheric data; h top Bottom height calculated for atmospheric data integration.

[0057] Based on the content of the above-mentioned device embodiment, as an optional embodiment, the global real-time tropospheric delay determination device for the lower atmosphere provided in the embodiment of the present invention further includes: a fourth submodule, which is used to realize the division of the global area into regular windows of uniform size, avoid the reduction of computational efficiency due to the number of models exceeding a predetermined threshold, and the reduction of model calculation effect due to the sparse amount of modeling data of a single grid point, including: determining the size and step size of the window, and determining the size of the grid based on the integer number of divisions, the continuity of the window, and the solvability of the model parameters within the window; dividing the world into several regular windows of the same size, the input layer in the model training is longitude, latitude, annual cumulative day and altitude, the output layer is tropospheric delay data ZTD, storing tropospheric delay data information in the form of window geometric center grid points, and constructing a tropospheric delay gridded model for the lower atmosphere.

[0058] Based on the content of the above-mentioned device embodiment, as an optional embodiment, the global real-time tropospheric delay determination device for the lower atmosphere provided in the embodiment of the present invention further includes: a fifth submodule, used to implement the training of the tropospheric delay data provided by the ERA5 atmospheric reanalysis data, and construct a tropospheric delay gridded model for the lower atmosphere, including: according to the data structure, using parallel processing and maximizing memory utilization methods to train a predetermined scale data set at a predetermined speed; constructing a model by integrating multiple weak learners to capture data relationships of predetermined complexity in the data, using the input and output parameters of the data set for modeling, improving the residual by incrementally integrating multiple base learners to solve the regression problem; reducing the complexity of the model by regularization terms to prevent overfitting and improve the generalization of the model; calculating the importance of features to determine the contribution of each feature in the data to the model, and selecting features according to the contribution.

[0059] Based on the contents of the above-mentioned device embodiment, as an optional embodiment, the global real-time tropospheric delay determination device for the lower atmosphere provided in the embodiment of the present invention further includes: a sixth submodule, which is used to implement the use of the tropospheric delay gridding model for the lower atmosphere to determine tropospheric delay data of a predetermined accuracy, including: inputting the annual accumulated day, altitude, and longitude and latitude into the tropospheric delay gridding model for the lower atmosphere, identifying the gridding model closest to the user's location, and obtaining high-precision tropospheric delay information data.

[0060] The method of the embodiment of the present invention is implemented by electronic devices, so it is necessary to introduce the relevant electronic devices. Based on this purpose, the embodiment of the present invention provides an electronic device, such as Figure 3 As shown, the electronic device includes: at least one processor, a communications interface, at least one memory, and a communications bus, wherein the at least one processor, the communications interface, and the at least one memory communicate with each other via the communications bus. The at least one processor can call logic instructions in the at least one memory to execute all or part of the steps of the methods provided in the aforementioned method embodiments.

[0061] In addition, the logic instructions in the at least one memory mentioned above can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each method embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0062] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0063] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0064] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. Based on this understanding, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or sometimes in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0065] It should be noted that the terms "include", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements. Any "predetermined threshold", "preset threshold" or similar expressions that do not indicate a specific value can be determined by a person of ordinary skill in the art through simple experiments or corresponding debugging.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A global real-time tropospheric delay determination method for the lower atmosphere, characterized in that: include: A gridded tropospheric delay model for the lower atmosphere is constructed based on the tropospheric delay data provided by the ERA5 atmospheric reanalysis data. The global region is divided into regular windows of uniform size to avoid a decrease in computational efficiency due to the number of models exceeding a predetermined threshold, and a decrease in model computational effect due to sparse modeling data at a single grid point. The tropospheric delay data provided by the ERA5 atmospheric reanalysis data are trained to construct a gridded tropospheric delay model for the lower atmosphere. Tropospheric delay data of a predetermined accuracy are determined using the gridded tropospheric delay model for the lower atmosphere. The tropospheric delay data of the predetermined accuracy are used for GNSS positioning and GNSS water vapor detection.

2. The global real-time tropospheric delay determination method for the lower atmosphere according to claim 1, characterized in that: The tropospheric delay data provided by the ERA5 atmospheric reanalysis data includes: a time resolution of one hour and a spatial resolution of 0.25°×0.25°; grid point layered meteorological data in the tropospheric delay data provided by the ERA5 atmospheric reanalysis data is extracted, including potential height, specific humidity, and temperature; the meteorological data is converted into a data format and a potential height, and vertical profile information of different isobaric layers and tropospheres at each grid point is calculated, and the vertical profile information is used as a model training sample.

3. The global real-time tropospheric delay determination method for the lower atmosphere according to claim 2, characterized in that: The calculation of vertical profile information of different isobaric layers and tropospheres at each grid point includes: calculating the atmospheric refractive index based on meteorological parameters provided by the standard atmospheric pressure profile, integrating the refractive index at each profile height, and layering and combining the tropospheric delay data at each grid point.

4. The method for determining global real-time tropospheric delay in the lower atmosphere according to claim 3, wherein: The step of integrating the refractive index at each profile height and combining the tropospheric delay data at each grid point in layers includes: k1=77.604K / hPa k2=64.79K / hPa k3=375463K 2 / hPa Wherein, k1 is the first refractive index coefficient; k2 is the second refractive index coefficient; k3 is the third refractive index coefficient; K / hPa is Kelvin per hectopascals; P is atmospheric pressure; e is water vapor pressure; N is the total refractive index of the atmosphere; T is temperature; K 2 / hPa is Kelvin squared per hectopascal; Sh is specific humidity; h is height; dh is the predetermined change in height h; ZTD is tropospheric delay data; h L The bottom height calculated by integrating atmospheric data; h top Bottom height calculated for the atmospheric data integration.

5. The method for determining global real-time tropospheric delay in the lower atmosphere according to claim 4, wherein: The method of dividing the global area into regular windows of uniform size avoids a decrease in computational efficiency due to the number of models exceeding a predetermined threshold, and a decrease in model computational effect due to sparse modeling data at a single grid point. The method includes: determining the window size and step size, and determining the grid size based on the integer number of divisions, the continuity of the window, and the solvability of the model parameters within the window; dividing the global area into a number of regular windows of uniform size, using longitude, latitude, annual accumulated days, and altitude as input layers in model training, and using tropospheric delay data ZTD as output layers. Tropospheric delay data information is stored in the form of window geometric center grid points, and a tropospheric delay grid model for the lower atmosphere is constructed.

6. The method for determining global real-time tropospheric delay in the lower atmosphere according to claim 5, characterized in that: The training is based on the tropospheric delay data provided by the ERA5 atmospheric reanalysis data to construct a tropospheric delay gridded model for the lower atmosphere, including: according to the data structure, using parallel processing and maximizing memory utilization methods to train a predetermined size data set at a predetermined speed; by integrating multiple weak learners to build a model, capturing data relationships of predetermined complexity in the data, using the input and output parameters of the data set for modeling, and improving residuals by incrementally integrating multiple base learners to solve regression problems; reducing the complexity of the model through regularization terms to prevent overfitting and improve the generalization of the model; calculating the importance of features, determining the contribution of each feature in the data to the model, and selecting features based on the contribution.

7. The method for determining global real-time tropospheric delay in the lower atmosphere according to claim 6, wherein: The method of using the tropospheric delay gridding model for the lower atmosphere to determine tropospheric delay data of a predetermined accuracy includes: inputting the annual accumulated day, altitude, and longitude and latitude into the tropospheric delay gridding model for the lower atmosphere, identifying the gridding model closest to the user's location, and obtaining high-precision tropospheric delay information data.

8. A global real-time tropospheric delay determination device for the lower atmosphere, characterized in that: include: The first main module is used to construct a tropospheric delay gridded model for the lower atmosphere based on the tropospheric delay data provided by the ERA5 atmospheric reanalysis data; the second main module is used to divide the global area into regular windows of uniform size to avoid the reduction in computational efficiency caused by the number of models exceeding a predetermined threshold, and the reduction in model calculation effect caused by the sparse amount of modeling data at a single grid point; the third main module is used to train the tropospheric delay data provided by the ERA5 atmospheric reanalysis data to construct a tropospheric delay gridded model for the lower atmosphere; the fourth main module is used to determine tropospheric delay data of a predetermined accuracy using the tropospheric delay gridded model for the lower atmosphere, and the tropospheric delay data of the predetermined accuracy is used for GNSS positioning and GNSS water vapor detection.

9. An electronic device, characterized in that: include: At least one processor, at least one memory and a communication interface; wherein, The processor, memory and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which cause a computer to execute the method of any one of claims 1 to 7.

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