Zenith troposphere delay estimation method, equipment and medium

Abstract

The invention relates to the technical field of GNSS positioning, in particular to a zenith troposphere delay estimation method and device and a medium, and the method comprises the following steps: constructing a zenith dry delay calculation module for calculating a GNSS site; selecting a combination of a correlation model and a regression model which are most suitable for the current GNSS data set; determining a spatial distribution scheme of an optimal GNSS station; carrying out zenith troposphere delay spatial interpolation modeling and obtaining an interpolation result by adopting the selected combination of the correlation model and the regression model and a spatial distribution scheme, and constructing a regional zenith wet delay interpolation model; and acquiring a troposphere delay estimated value by using the regional zenith wet delay interpolation model. According to the method, the elevation influence of zenith troposphere delay space interpolation modeling is removed, and the GNSS positioning precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of GNSS positioning technology, and in particular to a zenith tropospheric delay estimation method, device and medium. Background Art

[0002] Tropospheric delay error is a major error in Global Navigation Satellite System (GNSS) positioning applications. If not refined and corrected, the impact on positioning results can reach the meter level. The high-precision Zenith Tropospheric Delay (ZTD) product can effectively correct the tropospheric error of GNSS stations and improve the accuracy of coordinate solutions.

[0003] The existing zenith tropospheric delay interpolation process does not separate the effects of plane and elevation. All spatial interpolation modeling is directly based on the ZTD observation data of multiple GNSS stations. However, the correlation between ZTD data and elevation is significantly stronger than that between plane coordinates. As a result, the existing interpolation model methods have shortcomings such as overly complex modeling process and insufficient applicability of the models in different regions.

[0004] In summary, there is an urgent need for a zenith tropospheric delay estimation method, equipment and medium to solve the existing problems. Summary of the Invention

[0005] The present invention aims to provide a method, device and medium for estimating zenith tropospheric delay. The specific technical solutions are as follows:

[0006] A method for estimating the zenith tropospheric delay is as follows:

[0007] S1: Build a module for calculating the zenith dry delay of GNSS stations;

[0008] S2: Select multiple groups of different GNSS station distributions, select different correlation model and regression model combinations for each group of GNSS station distributions, perform independent calculations, and calculate the changes in the root mean square value of the spatial interpolation error of the correlation model and regression model combinations corresponding to different GNSS station distributions. Based on the changes in the root mean square value of the spatial interpolation error, select the correlation model and regression model combination that best suits the current GNSS dataset;

[0009] S3: Using the selected correlation model and regression model combination, spatial interpolation modeling is performed on multiple different GNSS station combinations. The zenith tropospheric delay estimation results and the change in the root mean square error of the zenith tropospheric delay observation data of the GNSS stations not involved in the modeling are calculated. The optimal GNSS station spatial distribution scheme is determined from the multiple GNSS station distributions selected in S1.

[0010] S4: Using the selected correlation model and regression model combination and spatial distribution scheme, perform spatial interpolation modeling of zenith tropospheric delay and obtain interpolation results to construct a regional zenith wet delay interpolation model;

[0011] S5: Substitute the latitude and longitude coordinates of the site to be measured into the regional zenith wet delay interpolation model to obtain the zenith wet delay estimate, call the zenith dry delay calculation module to calculate the zenith dry delay estimate, and calculate the zenith tropospheric delay estimate through the zenith wet delay estimate and the zenith dry delay estimate.

[0012] Optionally, in S1, the zenith dry delay calculation module includes the GPT3 model and the Saastamoinen model. The process of calculating the zenith dry delay of the GNSS station is as follows:

[0013] The spatial coordinates and observation time information of the GNSS station are obtained. The observation time information is the annual cumulative day information. The spatial coordinates and annual cumulative day information are input into the GPT3 model to calculate the air pressure parameters at the location of the GNSS station and the observation time. The air pressure parameters are substituted into the Saastamoinen model to calculate the zenith dry delay estimate of the GNSS station.

[0014] Optionally, in S1, the calculation expression of the air pressure parameter is as follows:

[0015]

[0016] Among them, P s is the air pressure parameter at the station, DoY represents the accumulated days per year, A0 represents the annual mean air pressure, A1 and B1 represent the annual amplitude variation coefficient of air pressure, and A2 and B2 represent the semiannual amplitude variation coefficient of air pressure.

[0017] Optionally, in S1, the zenith interference delay estimate is calculated as follows:

[0018] ZWD s =0.0022768×P s / [1-0.00266×cos(2B)-0.00028×H s ];

[0019] Among them, ZWD s represents the zenith dry delay, B represents the latitude, H s Indicates the site elevation.

[0020] Optionally, in S2, the spatial interpolation model is Kriging interpolation, and different correlation models and regression models are used to perform nonlinear least squares fitting on a given data set to obtain a fitting function, and predict the attribute value of the interpolation point.

[0021] Optionally, in S2, the Kriging interpolation calculation process is as follows:

[0022]

[0023] F(β :,l ,x)=β 1,l f1(x)+β 2,l f2(x)+...β p,l f p (x)=[f1(x)...f p (x)]·β :,l ;

[0024]

[0025] in, represents the valuation of the lth attribute, f p (x) represents the regression function, β :,l represents the regression function coefficient, represents the variance of the lth attribute value, and R(θ,w,x) represents the correlation model with θ as the parameter.

[0026] Optionally, in S4, the process of constructing a regional zenith wet delay interpolation model is as follows:

[0027] The interpolation result is the zenith tropospheric delay of the GNSS station. The zenith dry delay is subtracted from the zenith tropospheric delay of the GNSS station to obtain the zenith wet delay. The zenith wet delay is projected to the elevation of the GNSS station using the UNB3m model to obtain the station elevation estimate of the zenith wet delay. At the same time, the three-dimensional spatial interpolation process of the zenith wet delay is simplified to a two-dimensional plane interpolation process of the zenith wet delay to obtain the regional zenith wet delay interpolation model.

[0028] Optionally, in S4, the zenith wet delay station elevation estimate is calculated as follows:

[0029]

[0030] Among them, ZWD0 and ZWD s They represent the estimated elevation of the zenith wet delay station and the estimated elevation of the zenith wet delay at sea level, β is the temperature drop rate, T0 is the sea level temperature, λ is the water vapor pressure elevation drop factor, g is the acceleration of gravity, and R d is the gas constant for dry air.

[0031] Additionally, the present invention also includes a computer device comprising a memory and a processor;

[0032] The memory is used to store a computer program that can be executed on the processor;

[0033] The processor is configured to implement the steps of the above-mentioned zenith tropospheric delay estimation method when executing the computer program.

[0034] In addition, the present invention also includes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned zenith tropospheric delay estimation method are implemented.

[0035] The application of the technical solution of the present invention has the following beneficial effects:

[0036] (1) The present invention provides a method, device and medium for estimating zenith tropospheric delay. The method adopts a ZTD spatial interpolation modeling method that removes the influence of elevation. In view of the characteristic that the ZTD data of a GNSS station is highly correlated with the elevation, the ZHD calculated using the GPT3 model and the Saastamoinen model is subtracted from the ZTD data to obtain the ZWD data. The ZWD data at the station elevation is then transferred to the sea level in combination with the UNB3m model, thereby simplifying the three-dimensional spatial interpolation process into a two-dimensional plane interpolation process.

[0037] (2) The method of the present invention determines and selects the correlation model and regression model by analyzing the changes in the root mean square values ​​of spatial interpolation errors corresponding to various combinations of correlation models and regression models, thereby determining the selection of the correlation model and regression model for spatial interpolation. In addition, the method of the present invention also determines the spatial distribution scheme of the optimal GNSS station by comparing the root mean square values ​​of the errors of spatial interpolation modeling performed by multiple groups of different GNSS station combinations. The root mean square value of the error is calculated based on the difference between the ZTD observation data and the interpolation results of the GNSS stations not involved in the modeling.

[0038] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only 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.

[0040] Figure 1 is a flowchart of the steps of the zenith tropospheric delay estimation method in a preferred embodiment of the present invention;

[0041] FIG2( a ) is a diagram showing the experimental results of the PARK site of the zenith tropospheric delay estimation method in a preferred embodiment of the present invention;

[0042] FIG2( b ) is a diagram showing the experimental results of the ULAB site of the zenith tropospheric delay estimation method in a preferred embodiment of the present invention;

[0043] FIG2( c ) is a diagram showing the BOGT site experimental results of the zenith tropospheric delay estimation method in a preferred embodiment of the present invention;

[0044] FIG2( d ) is a diagram showing the experimental results of the MKEA site of the zenith tropospheric delay estimation method in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0046] like Figure 1 As shown, this embodiment provides a method for estimating zenith tropospheric delay, and the process is as follows:

[0047] S1: Build a module for calculating the zenith dry delay of GNSS stations;

[0048] S2: Select multiple groups of different GNSS station distributions, select different correlation model and regression model combinations for each group of GNSS station distributions, perform independent calculations, and calculate the changes in the root mean square value of the spatial interpolation error of the correlation model and regression model combinations corresponding to different GNSS station distributions. Based on the changes in the root mean square value of the spatial interpolation error, select the correlation model and regression model combination that best suits the current GNSS dataset;

[0049] S3: Using the selected correlation model and regression model combination, spatial interpolation modeling is performed on multiple different GNSS station combinations. The zenith tropospheric delay estimation results and the change in the root mean square error of the zenith tropospheric delay observation data of the GNSS stations not involved in the modeling are calculated. The optimal GNSS station spatial distribution scheme is determined from the multiple GNSS station distributions selected in S1.

[0050] S4: Using the selected correlation model and regression model combination and spatial distribution scheme, perform spatial interpolation modeling of zenith tropospheric delay and obtain interpolation results to construct a regional zenith wet delay interpolation model;

[0051] S5: Substitute the latitude and longitude coordinates of the site to be measured into the regional zenith wet delay interpolation model to obtain the zenith wet delay estimate, call the zenith dry delay calculation module to calculate the zenith dry delay estimate, and calculate the zenith tropospheric delay estimate through the zenith wet delay estimate and the zenith dry delay estimate.

[0052] Optionally, in S1, the zenith dry delay calculation module includes the GPT3 model and the Saastamoinen model. The process of calculating the zenith dry delay of the GNSS station is as follows:

[0053] The spatial coordinates and observation time information of the GNSS station are obtained. The observation time information is the annual cumulative day information. The spatial coordinates and annual cumulative day information are input into the GPT3 model to calculate the air pressure parameters at the location of the GNSS station and the observation time. The air pressure parameters are substituted into the Saastamoinen model to calculate the zenith dry delay estimate of the GNSS station.

[0054] Optionally, in S1, the calculation expression of the air pressure parameter is as follows:

[0055]

[0056] Among them, P s is the air pressure parameter at the station, DoY represents the accumulated days per year, A0 represents the annual mean air pressure, A1 and B1 represent the annual amplitude variation coefficient of air pressure, and A2 and B2 represent the semiannual amplitude variation coefficient of air pressure.

[0057] Optionally, in S1, the zenith interference delay estimate is calculated as follows:

[0058] ZWD s =0.0022768×P s / [1-0.00266×cos(2B)-0.00028×H s ];

[0059] Among them, ZWD s represents the zenith dry delay, B represents the latitude, H s Indicates the site elevation.

[0060] Optionally, in S2, the spatial interpolation model is Kriging interpolation, and different correlation models and regression models are used to perform nonlinear least squares fitting on a given data set to obtain a fitting function, and predict the attribute value of the interpolation point.

[0061] Optionally, in S2, the Kriging interpolation calculation process is as follows:

[0062]

[0063] F(β:,l ,x)=β 1,l f1(x)+β 2,l f2(x)+...β p,l f p (x)=[f1(x)...f p (x)]·β :,l ;

[0064]

[0065] in, represents the valuation of the lth attribute, f p (x) represents the regression function, β :,l represents the regression function coefficient, represents the variance of the lth attribute value, and R(θ,w,x) represents the correlation model with θ as the parameter.

[0066] Optionally, in S4, the process of constructing a regional zenith wet delay interpolation model is as follows:

[0067] The interpolation result is the zenith tropospheric delay of the GNSS station. The zenith dry delay is subtracted from the zenith tropospheric delay of the GNSS station to obtain the zenith wet delay. The zenith wet delay is projected to the elevation of the GNSS station using the UNB3m model to obtain the station elevation estimate of the zenith wet delay. At the same time, the three-dimensional spatial interpolation process of the zenith wet delay is simplified to a two-dimensional plane interpolation process of the zenith wet delay to obtain the regional zenith wet delay interpolation model.

[0068] Optionally, in S4, the zenith wet delay station elevation estimate is calculated as follows:

[0069]

[0070] Among them, ZWD0 and ZWD s They represent the estimated elevation of the zenith wet delay station and the estimated elevation of the zenith wet delay at sea level, β is the temperature drop rate, T0 is the sea level temperature, λ is the water vapor pressure elevation drop factor, g is the acceleration of gravity, and R d is the gas constant for dry air.

[0071] The method of this embodiment is different from the traditional method of directly selecting the correlation model and regression model for spatial interpolation. It uses the ZTD data of all available GNSS stations in the time period to interpolate and model the ZTD data in three-dimensional space. The method of this embodiment proposes a simple and feasible method for selecting correlation models and regression models and determining the spatial distribution scheme of GNSS stations. The error RMS value (root mean square value) between the ZTD observation data of the GNSS stations not involved in the modeling and the interpolation result is used as the judgment basis. The changes in the RMS values ​​of the Kriging spatial interpolation error corresponding to various different correlation model and regression model combinations are analyzed to select the optimal correlation model and regression model combination. The spatial interpolation error RMS corresponding to multiple groups of different GNSS station combinations are compared to determine the spatial distribution scheme of the optimal GNSS station. For ZTD interpolation modeling application scenarios where the spatial distribution of GNSS stations is uneven, it can effectively weaken the influence of the selection of correlation models and regression models, enhance the representativeness of the GNSS station spatial distribution scheme in describing the spatiotemporal variation characteristics of the ZTD data in the study area, and suppress the influence of the uneven spatial distribution of GNSS stations.

[0072] In addition, the Kriging spatial interpolation parameters and GNSS station distribution of the method proposed in this embodiment are determined with reference to the RMS value of the error of multiple randomly selected GNSS stations in the spatial interpolation modeling. In the process of multiple interpolation modeling, the influence of the randomness of the selection of a single GNSS station is weakened, and the reliability and stability of the spatial interpolation model are improved. The selection of the relevant model and regression model of Kriging spatial interpolation and the determination of the spatial distribution scheme of GNSS stations are determined by verifying that the RMS error of the GNSS station is minimized, reducing the dependence on data processing experience. Experimental verification shows that the method proposed in this embodiment has a good application effect in the spatial interpolation modeling of ZTD data of large-scale, high-precision GNSS stations. Compared with GNSS stations that do not participate in the interpolation modeling, the model interpolation results are in good agreement with the ZTD data of the GNSS stations. The difference between the two is mostly within 10mm, and the RMS of the ZTD time series error is about 15mm.

[0073] The data of the experiment using the method of this embodiment are shown in FIG2 , and are as follows:

[0074] The method of this embodiment is applied to the spatial interpolation modeling of ZTD data from actual GNSS stations. The ZTD estimation results of 573 GNSS stations and 132,685 observation epochs from the time span of 1997-01-01 to 2025-02-22 are used as input. The model obtains the corresponding ZTD estimation results for any position at the observation time, verifying the stability of the method in obtaining high-precision ZTD interpolation results.

[0075] In this experiment, the RMS error values ​​and station information of the measured values ​​of four GNSS checkpoints over 132,685 observation epochs and the calculated results of different models were statistically analyzed. The statistical results are shown in Table 1.

[0076] Table 1. RMS error values ​​and station information statistics of GNSS checkpoint measurements and calculation results of different models

[0077] Verification station name PARK ULAB BOGT MKEA Kriging 17.3 15.7 16.1 13.7 GPT3 48.8 22.7 25.0 24.4 UNB3m 51.9 25.4 55.8 21.8 Longitude (°) 148.2646 107.0523 -74.0809 -155.4564 Latitude (°) -32.9988 47.8651 4.6401 19.8014 Elevation (m) 397.4 1575.5 2576.2 3754.6

[0078] Referring to Figures 2(a), 2(b), 2(c), and 2(d), the numerical variations of the 2024 ZTD data for the verification sites (blue line) and the calculated results using the proposed model (red line), the GPT3 model (green line), and the UNB3m model (black line) show that for the four verification sites (PARK, ULAB, BOGT, and MKEA) at elevations of 397.4 m, 1575.5 m, 2576.2 m, and 3754.6 m, respectively, the Kriging interpolation model can well reflect the numerical variations of the zenith tropospheric delay, including subtle changes and significant jumps in time. The difference between the ZTD values ​​of the interpolation model and the GNSS sites remains generally within 20 mm. Compared with the UNB3m model and the GPT3 model, the Kriging interpolation model has better consistency with the GNSS measured values, with a smaller difference between the two. The model can accurately reflect the numerical fluctuations of the ZTD within a small range. For the BOGT stations near the equator, the Kriging interpolation model has better consistency with the GNSS measured values, and the GPT3 model can basically match the overall trend of the ZTD numerical changes.

[0079] In addition, this embodiment also includes a computer device, including a memory and a processor;

[0080] The memory is used to store a computer program that can be executed on the processor;

[0081] The processor is configured to implement the steps of the above-mentioned zenith tropospheric delay estimation method when executing the computer program.

[0082] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0083] The computer device may be a mobile phone, desktop computer, laptop, PDA, cloud server, or other computing device. The computer device may include, but is not limited to, a processor and memory. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0084] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0085] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0086] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0087] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned zenith tropospheric delay estimation method are implemented.

[0088] This embodiment provides a method for estimating zenith tropospheric delay, which adopts a ZTD spatial interpolation modeling method that removes the influence of elevation. In view of the characteristic that the ZTD data of GNSS stations are highly correlated with the elevation, the ZHD calculated using the GPT3 model and the Saastamoinen model is subtracted from the ZTD data to obtain the ZWD data. The ZWD data at the station elevation is then transferred to the sea level in combination with the UNB3m model, simplifying the three-dimensional spatial interpolation process into a two-dimensional plane interpolation process. In addition, the method of this embodiment determines and selects the correlation model and regression model by analyzing the changes in the root mean square value of the spatial interpolation error corresponding to a variety of different correlation models and regression model combinations, and determines the selection of the correlation model and regression model for spatial interpolation. In addition, the method of the present invention also determines the spatial distribution scheme of the optimal GNSS station by comparing the root mean square error of spatial interpolation modeling of multiple groups of different GNSS station combinations. The root mean square error value is calculated based on the difference between the ZTD observation data and the interpolation result of the GNSS station not participating in the modeling.

[0089] It should be noted that the device embodiments described above are merely illustrative, wherein 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, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0090] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for estimating zenith tropospheric delay, characterized in that: The process is as follows: S1: Build a module for calculating the zenith dry delay of GNSS stations; S2: Select multiple groups of different GNSS station distributions, select different correlation model and regression model combinations for each group of GNSS station distributions, perform independent calculations, and calculate the changes in the root mean square value of the spatial interpolation error of the correlation model and regression model combinations corresponding to different GNSS station distributions. Based on the changes in the root mean square value of the spatial interpolation error, select the correlation model and regression model combination that best suits the current GNSS dataset; S3: Using the selected correlation model and regression model combination, spatial interpolation modeling is performed on multiple different GNSS station combinations. The zenith tropospheric delay estimation results and the change in the root mean square error of the zenith tropospheric delay observation data of the GNSS stations not involved in the modeling are calculated. The optimal GNSS station spatial distribution scheme is determined from the multiple GNSS station distributions selected in S1. S4: Using the selected correlation model and regression model combination and spatial distribution scheme, perform spatial interpolation modeling of zenith tropospheric delay and obtain interpolation results to construct a regional zenith wet delay interpolation model; S5: Substitute the latitude and longitude coordinates of the site to be measured into the regional zenith wet delay interpolation model to obtain the zenith wet delay estimate, call the zenith dry delay calculation module to calculate the zenith dry delay estimate, and calculate the zenith tropospheric delay estimate through the zenith wet delay estimate and the zenith dry delay estimate.

2. The method for estimating zenith tropospheric delay according to claim 1, wherein: In S1, the zenith dry delay calculation module includes the GPT3 model and the Saastamoinen model. The process of calculating the zenith dry delay of a GNSS station is as follows: The spatial coordinates and observation time information of the GNSS station are obtained. The observation time information is the annual cumulative day information. The spatial coordinates and annual cumulative day information are input into the GPT3 model to calculate the air pressure parameters at the location of the GNSS station and the observation time. The air pressure parameters are substituted into the Saastamoinen model to calculate the zenith dry delay estimate of the GNSS station.

3. The method for estimating zenith tropospheric delay according to claim 2, wherein: In S1, the calculation expression of the air pressure parameter is as follows: Among them, P s is the air pressure parameter at the station, DoY represents the accumulated days per year, A0 represents the annual mean air pressure, A1 and B1 represent the annual amplitude variation coefficient of air pressure, and A2 and B2 represent the semiannual amplitude variation coefficient of air pressure.

4. The method for estimating zenith tropospheric delay according to claim 2, wherein: In S1, the calculation expression of the zenith interference delay estimate is as follows: ZWD s =0.0022768×P s / [1-0.00266×cos(2B)-0.00028×H s ]; Among them, ZWD s represents the zenithal delay, P s is the air pressure parameter at the station, B represents the latitude, H s Indicates the site elevation.

5. The method for estimating zenith tropospheric delay according to claim 1, wherein: In S2, spatial interpolation is modeled as Kriging interpolation. Different correlation models and regression models are used to perform nonlinear least squares fitting on the given data set to obtain the fitting function and predict the attribute values ​​of the interpolation points.

6. The method for estimating zenith tropospheric delay according to claim 5, wherein: In S2, the calculation process of Kriging interpolation is as follows: F(β :,l ,x)=β 1,l f1(x)+β 2,l f2(x)+...β p,l f p (x)=[f1(x)...f p (x)]·β :,l ; in, represents the valuation of the lth attribute, f p (x) represents the regression function, β :,l represents the regression function coefficient, represents the variance of the lth attribute value, and R(θ,w,x) represents the correlation model with θ as the parameter.

7. The method for estimating zenith tropospheric delay according to claim 1, wherein: In S4, the process of constructing the regional zenith wet delay interpolation model is as follows: The interpolation result is the zenith tropospheric delay of the GNSS station. The zenith dry delay is subtracted from the zenith tropospheric delay of the GNSS station to obtain the zenith wet delay. The zenith wet delay is projected to the elevation of the GNSS station using the UNB3m model to obtain the station elevation estimate of the zenith wet delay. At the same time, the three-dimensional spatial interpolation process of the zenith wet delay is simplified to a two-dimensional plane interpolation process of the zenith wet delay to obtain the regional zenith wet delay interpolation model.

8. The method for estimating zenith tropospheric delay according to claim 7, wherein: In S4, the calculation formula for the zenith wet delay station elevation estimate is as follows: Among them, ZWD0 and ZWD s They represent the estimated elevation of the zenith wet delay station and the estimated elevation of the zenith wet delay at sea level, β is the temperature drop rate, T0 is the sea level temperature, λ is the water vapor pressure elevation drop factor, g is the acceleration of gravity, and R d is the gas constant for dry air.

9. A computer device, characterized in that: including memory and processor; The memory is used to store a computer program that can be executed on the processor; The processor is configured to implement the steps of the zenith tropospheric delay estimation method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the zenith tropospheric delay estimation method according to any one of claims 1 to 8.

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