5G water vapor monitoring method and system based on Beidou satellite timing

Abstract

The present invention provides a 5G water vapor monitoring method and system based on Beidou satellite timing, comprising: receiving Beidou satellite signals at each 5G base station in a study area; performing precise single-point positioning based on the Beidou satellite precise orbit and clock correction products, extracting the atmospheric delay above the base station; extracting the atmospheric delay between stations; dividing the atmospheric water vapor in the study area into a three-dimensional grid; constructing a unified atmospheric parameter vertical variation constraint model and an atmospheric parameter horizontal constraint model; discretizing the atmospheric delay, establishing an observation model between the atmospheric delay and the atmospheric parameter to be estimated, and a random model of other influencing factors; solving and estimating the gridded atmospheric refractive index grid product of the study area using the comprehensively constructed constraint observation model and broadcasting it; and user terminals determining the grid point information passed by the measurement signal based on their own approximate position and the precise coordinates of the reference station, and receiving the atmospheric grid product using a 5G communication link. The present invention can provide high-precision, high temporal and spatial resolution, near-real-time, all-weather atmospheric water vapor monitoring.

Description

Technical Field

[0001] The present invention relates to the fields of communication technology and meteorology, and in particular to water vapor inversion technology. Background Art

[0002] At present, the temporal and spatial resolution of water vapor observations is far from meeting the requirements of the next generation of numerical weather forecast models, and cannot reveal the occurrence process of some important mesoscale disastrous weather. There is still a large gap in monitoring and forecasting boundary layer water vapor convergence and mesoscale weather processes, which has become one of the important reasons why such disastrous weather is easily missed.

[0003] Currently, GNSS satellite signal inversion of water vapor utilizes carrier phase for pseudorange measurement. Compared to traditional water vapor detection methods, this method offers the advantages of low cost, all-weather capability, high precision, high temporal resolution, and good uniformity. However, due to its sensitivity to integer ambiguity and signal multipath, the near-surface and boundary layer topography and interference cause diffraction and multipath interference in signal transmission. The atmospheric refractive index derived from carrier phase difference has poor anti-interference and anti-multipath capabilities, resulting in detection errors.

[0004] Given this situation, there is an urgent need to expand current atmospheric water vapor monitoring methods, particularly high-resolution boundary-layer atmospheric water vapor monitoring methods, to obtain water vapor observation data with a temporal resolution of 10 to 30 minutes, a horizontal scale of 1 km to 5 km, a detection accuracy of 1 mm to 2 mm, and the ability to provide information such as profile distribution. This will compensate for the shortcomings in temporal and spatial resolution of radiosonde and GPS water vapor observations and provide the high-precision, large-capacity, near-real-time atmospheric water vapor information required for refined numerical weather forecasting.

[0005] Research has been conducted on using 4G signals to infer water vapor. However, 4G signals have limited coverage and are not effective for real-time, high-precision detection. Compared to previous 3G and 4G communication systems, which primarily meet communication needs, 5G introduces large-scale array antenna technology, which offers greater freedom and can further improve ranging and angle measurement accuracy. 5G can also fully leverage the advantages of millimeter waves, dense deployment of small base stations, and beamforming positioning to achieve more accurate water vapor monitoring. However, using millimeter wave water vapor monitoring can only capture low-level water vapor conditions horizontally in the area where the base station is located. Summary of the Invention

[0006] The present invention provides a 5G+Beidou satellite atmospheric water vapor monitoring method, which uses a 5G base station after high-precision time synchronization to transmit and receive signals, and at the same time sets up a Beidou receiver to receive Beidou satellite signals and their precise orbit and clock correction products, estimates the atmospheric water vapor content, builds a ground water vapor monitoring network and broadcasts atmospheric water vapor grid products, providing high-precision, high temporal and spatial resolution, near real-time, all-weather atmospheric water vapor monitoring.

[0007] The present invention provides a 5G water vapor monitoring method based on Beidou satellite timing, comprising the following steps:

[0008] Step 1: Each 5G base station in the study area receives BeiDou satellite signals and performs relevant processing to obtain BeiDou time information. If there is any inconsistency, it is synchronized to BeiDou time and its precise coordinates are fixed.

[0009] Step 2: Perform precise point positioning based on the BeiDou satellite precise orbit and clock correction products to extract the atmospheric delay above the base station.

[0010] Step 3: Extract the inter-station atmospheric delay using the inter-station ranging observation value of each 5G base station;

[0011] Step 4: Divide the atmospheric water vapor three-dimensional grid in the study area according to the distribution characteristics of 5G base stations;

[0012] Step 5: Based on the multi-source meteorological observation data of the study area, a unified atmospheric parameter vertical variation constraint model is constructed, and a horizontal constraint model of atmospheric parameters is constructed using a Gaussian filter template;

[0013] Step 6: Discretize the atmospheric delay, establish an observation model between the atmospheric delay and the atmospheric parameters to be estimated, and a random model of other influencing factors;

[0014] Step 7: Using the constrained observation model, the gridded atmospheric refractivity product of the study area is calculated and estimated and broadcasted via the 5G communication link.

[0015] In step 8, the user terminal determines the grid point information that the measurement signal passes through based on its own approximate location and the precise coordinates of the reference station, and uses the 5G communication link to receive the atmospheric grid product.

[0016] Moreover, step 3 is implemented by introducing the precise position coordinates and clock error of the 5G base station as known parameters into the TOA observation equation between 5G base stations, eliminating the influence of other error terms, and inversely calculating the atmospheric delay between 5G base stations.

[0017] Moreover, in step 5, the meteorological reanalysis data products or radiosonde data of the study area in recent years are used to analyze the spatiotemporal variation characteristics of the atmospheric parameters in the study area. The exponential function model is used to fit the refractive index profiles at each grid point in the study area to obtain the model coefficients. The coefficients are then analyzed in the frequency domain to establish a coefficient model that takes into account the seasonal variation characteristics, and finally a unified vertical variation constraint model of atmospheric parameters is constructed.

[0018] Furthermore, in step 6, the atmospheric delay is discretized using the Newton-Cotes interpolation quadrature method.

[0019] Moreover, in step 8, the atmospheric delay is calculated according to the Gauss-Cotes integral formula based on the received atmospheric grid product, and is eliminated in the positioning observation equation, or atmospheric water vapor related information is obtained through existing water vapor inversion technology.

[0020] On the other hand, the present invention also provides a 5G water vapor monitoring system based on Beidou satellite timing, which is used to implement the 5G water vapor monitoring method based on Beidou satellite timing as described above.

[0021] Furthermore, the following modules are included,

[0022] The first module is used to study the 5G base stations in the area that receive BeiDou satellite signals and perform related processing to obtain BeiDou time information. If there is any inconsistency, the BeiDou time is synchronized and the precise coordinates are fixed.

[0023] The second module is used to perform precise point positioning based on the BeiDou satellite precise orbit and clock correction products and extract the atmospheric delay above the base station;

[0024] The third module is used to extract the inter-station atmospheric delay using the inter-station ranging observation values of each 5G base station;

[0025] The fourth module is used to divide the atmospheric water vapor three-dimensional grid in the study area according to the distribution characteristics of 5G base stations;

[0026] The fifth module is used to build a unified atmospheric parameter vertical variation constraint model based on multi-source meteorological observation data in the study area, and to build an atmospheric parameter horizontal constraint model using a Gaussian filter template;

[0027] The sixth module is used to discretize the atmospheric delay, establish the observation model between the atmospheric delay and the atmospheric parameters to be estimated, and the stochastic model of other influencing factors;

[0028] The seventh module is used to comprehensively construct the constrained observation model, solve and estimate the gridded atmospheric refractivity grid product of the study area and broadcast it through the 5G communication link;

[0029] The eighth module is used for the user terminal to determine the grid point information passed by the measurement signal based on its own approximate location and the precise coordinates of the base station, and use the 5G communication link to receive the atmospheric grid product.

[0030] Alternatively, it includes a processor and a memory, the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute the 5G water vapor monitoring method based on Beidou satellite timing as described above.

[0031] Alternatively, it includes a readable storage medium having a computer program stored thereon, and when the computer program is executed, it implements the 5G water vapor monitoring method based on Beidou satellite timing as described above.

[0032] The beneficial effects of the present invention are:

[0033] 1.5G signals acquire horizontal atmospheric delays in the atmospheric boundary layer, complementing the BeiDou satellite signal's atmospheric delay inversion of water vapor at the zenith, enabling higher-precision, high-temporal and high-resolution atmospheric water vapor monitoring. GNSS has only IGS stations worldwide, and since station antennas are primarily oriented toward the zenith, while 5G base stations transmit and receive signals horizontally, they can improve water vapor resolution in the boundary layer, complementing BeiDou satellite water vapor monitoring to create a uniformly dense spatial distribution grid of water vapor.

[0034] 2. Combining the high-precision timing of Beidou satellites with the millimeter wave technology and ultra-dense networking technology of the 5G system can improve ranging accuracy and thus obtain more accurate water vapor inversion results and finer grid resolution.

[0035] 3. Simple and convenient implementation, strong practicality. Utilizing the existing 5G communication network, there is no need to re-wire the network, which has a great advantage in construction cost and has important market value. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a water vapor monitoring method provided in an embodiment of the present invention.

[0037] Figure 2 Schematic diagram of the principle of the water vapor monitoring method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following describes the technical solution of the present invention in conjunction with specific embodiments and with reference to the accompanying drawings. These descriptions are merely illustrative and are not intended to limit the scope of the present invention. Descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessary confusion of the concepts of the present invention.

[0039] The present invention provides a 5G+Beidou satellite atmospheric water vapor monitoring method, which uses a 5G base station after high-precision time synchronization to transmit and receive signals, and at the same time sets up a Beidou receiver to receive Beidou satellite signals and their precise orbit and clock correction products, estimates the atmospheric water vapor content, builds a ground water vapor monitoring network and broadcasts atmospheric water vapor grid products, providing high-precision, high temporal and spatial resolution, near real-time, all-weather atmospheric water vapor monitoring.

[0040] See also Figure 1 and Figure 2 , an embodiment of the present invention provides a 5G water vapor monitoring method based on Beidou satellite timing, comprising the following steps:

[0041] (1) The 5G base station receives BeiDou satellite signals and performs relevant processing to obtain BeiDou time information. If there is any inconsistency, it synchronizes to BeiDou time and fixes its precise coordinates.

[0042] The present invention utilizes 5G base stations to receive Beidou satellite signals for related processing and obtains Beidou satellite precise orbit and clock correction products. It can conveniently fix the precise coordinates of the base station through the existing precise single-point positioning technology, and utilize the existing Beidou satellite timing technology to obtain Beidou time information for time synchronization, thereby ensuring the accuracy of the atmospheric delay between each 5G base station in subsequent steps.

[0043] (2) Beidou satellite precise single point positioning extracts the atmospheric delay above the base station;

[0044] In specific implementation, based on the precise coordinates of the base station obtained in step (1) and the Beidou satellite precise orbit and clock correction results, the atmospheric delay of the Beidou satellite signal above the base station can be inversely calculated using the existing precise single-point positioning technology.

[0045] (3) 5G base station extraction of atmospheric delay between base stations

[0046] By transmitting and receiving PRS signals between 5G base stations, the inter-station atmospheric delay can be extracted using the inter-station ranging observation values.

[0047] The embodiment further uses step (1) to determine the spatiotemporal reference of the 5G base station, including the precise position coordinates and clock difference of the 5G base station, and introduces them as known parameters into the TOA observation equation between 5G base stations, eliminates the influence of other error terms through existing models or parameter estimation methods, and reversely calculates the atmospheric delay between 5G base stations; in order to improve the robustness of the estimation system and the reliability of the results, it can be assumed that there is no obvious change in the atmospheric parameters within 5 minutes, that is, using a 5-minute window, extracting the average atmospheric delay between stations as the input observation quantity for subsequent atmospheric product construction.

[0048] (4) Regional atmospheric water vapor three-dimensional grid division

[0049] During specific implementation, the study area can be reasonably divided into grids based on the geographical distribution characteristics of 5G base stations and Beidou satellites, and each 5G observation base station can be dispersed as evenly as possible in the grid area. The preferred recommendation is: unlike GNSS observation stations, the horizontal distance of 5G stations is maintained at 1-5km, and the altitude cutoff angle is at least 10°; the top-level height of the grid is generally not more than 10km, and the vertical layered grid resolution cannot be less than 300m, and 800m is generally selected.

[0050] (5) Constraint model construction

[0051] This step constructs a unified vertical variation constraint model of atmospheric parameters based on multi-source meteorological observation data in the study area, and uses a Gaussian filter template to construct a horizontal constraint model of atmospheric parameters.

[0052] Due to the spatial distribution characteristics of 5G base stations and Beidou satellites, there is no guarantee that sufficient signals can pass through all divided grids. Therefore, estimating atmospheric parameters based on atmospheric delay is rank-deficient, and a reasonable constraint model needs to be introduced.

[0053] In terms of the vertical constraint model, a coefficient model can be constructed based on specific meteorological conditions. In this embodiment, meteorological reanalysis data products (such as ERA5 released by the European ECMWF or CRA-40 released by the China Meteorological Administration) or radiosonde data from the study area over the past five years are used to analyze the spatiotemporal variation characteristics of atmospheric parameters (such as the total tropospheric refractivity) in the study area. An exponential function model is used to fit the refractivity profiles at each grid point in the study area to obtain model coefficients. The coefficients are then analyzed in the frequency domain to establish a coefficient model that takes into account seasonal variation characteristics. Ultimately, a unified vertical variation constraint model for atmospheric parameters is constructed.

[0054] In terms of the horizontal constraint model, a Gaussian filter template is used for horizontal constraint. Gaussian filtering is a smoothing technique widely used in the field of image processing, and its implementation is mainly discrete window sliding convolution and Fourier transform. This method can use a 3×3 or 5×5 Gaussian template to scan each grid point, assign the weight coefficient within the template to the neighborhood around the grid point, and use the weighted average of this neighborhood as the smoothed value of the point. The formula for calculating the parameters within the Gaussian template is:

[0055]

[0056] Where s is the distance between the central grid point and the surrounding grid points, σ is the standard deviation, the smaller the standard deviation, the greater the weight of the central point, and the less obvious the smoothing effect; the larger the standard deviation, the smaller the weight of the central point, and the more obvious the smoothing effect, and e is a mathematical constant.

[0057] (6) Construction of atmospheric grid products

[0058] The atmospheric delay is discretized using the Newton-Cotes interpolation quadrature method, and an observation model between the atmospheric delay and the estimated atmospheric parameters and a random model of other influencing factors are established.

[0059] The atmospheric delay (STD) can be expressed as:

[0060] STD=10 -6 ∫ s N w ds (Equation 2)

[0061] where N w represents the atmospheric refractive index on the signal propagation path s, and ds represents the corresponding differential sign of the signal propagation path s.

[0062] There are two main common discretization methods. One is the classic model proposed by Flores et al. (2000). This method assumes that the refractive index within each grid cell is uniform and constant, and the slant path atmospheric delay is expressed as the product of a single refractive index and the slant path length. This method is prone to discretization errors. Specifically, because the actual water vapor varies both horizontally and vertically within the cell grid, the modeled STD differs from the actual value. In addition, this method artificially introduces cell grid boundaries, resulting in discontinuous refractive indices. The other is the node-based parameterization method proposed by Perler et al. (2000), which uses the Newton-Cotes formula to interpolate and quadrature to estimate the wet refractive index at the grid nodes. This method takes into account the heterogeneous characteristics of water vapor within the grid and provides a more reasonable model parameterization. Therefore, the latter method is preferred in this paper.

[0063] The Newton-Cotes formula is a commonly used interpolation quadrature formula that uses Lagrange polynomials to approximate the integrand. Assuming that the intersection points of the ray passing through two tomographic height layers are a and b respectively, and the order of the Lagrange polynomial is set to 4, the Newton-Cotes formula used for Nw is integrated between [a, b] and substituted into formula (2), which can be expressed as:

[0064]

[0065] Where S ab is the length of the line segment; N w (a),N w (b) and N w (x) are the wet refractive indices at the endpoints and the equally divided points, respectively. The values at the endpoints and the equally divided points are obtained by horizontal bilinear interpolation and three-dimensional linear interpolation, respectively, x = x1, x2, x3.

[0066] After discretizing the STD between all stations, the observation equation can be obtained:

[0067]

[0068] Where H STD is the input slope path wet delay STD, STD1, STD2, ...STD m They represent the slant path wet delay extracted by each 5G observation base station, including the STD in the zenith direction of the satellite signal and the horizontal direction of the 5G base station; A STD Is the coefficient matrix, by a 11 、a 12 ,…,a 1n ,…,a m1 、a m2 ,…,a mn Represents the intercept of each STD observation value passing through each grid, a mn is the intercept of the mth extracted STD observation passing through the nth grid point; X is the parameter matrix, N w1 、N w2 ,…N wn They represent the wet refractive index of each grid point respectively; m is the number of observation equation rows, that is, the STD observation values involved in discretization; n is the number of observation equation columns, that is, the number of unknown grid points to be discretized.

[0069] (7) A comprehensive constrained observation model is constructed to solve and estimate the gridded atmospheric refractivity products in the study area and broadcast them through 5G communication links.

[0070] Considering factors related to inter-station signal quality and path propagation, a reasonable stochastic model for atmospheric parameter estimation is established. Combining the observation model obtained in step (6) and the constraint model constructed in step (5), the existing least squares or Kalman filter estimator technology can be used to solve and obtain the atmospheric refractivity grid product of the study area. In specific implementation, the atmospheric grid product can be broadcasted through the 5G communication link by selecting an existing reasonable encoding scheme.

[0071] (8) User terminal atmospheric correction

[0072] The user terminal determines the grid points through which the measurement signal passes based on its approximate location and the precise coordinates of the reference station. Based on the received atmospheric grid product, the user terminal calculates the atmospheric delay using the Gauss-Cotes integral formula and eliminates it in the positioning observation equation, or obtains atmospheric water vapor information using existing water vapor inversion techniques. In practice, any terminal device that receives the atmospheric grid product over a 5G link can serve as a user terminal.

[0073] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0074] In some possible embodiments, a 5G water vapor monitoring system based on Beidou satellite timing is provided, including the following modules:

[0075] The first module is used to study the 5G base stations in the area that receive BeiDou satellite signals and perform related processing to obtain BeiDou time information. If there is any inconsistency, the BeiDou time is synchronized and the precise coordinates are fixed.

[0076] The second module is used to perform precise point positioning based on the BeiDou satellite precise orbit and clock correction products and extract the atmospheric delay above the base station;

[0077] The third module is used to extract the inter-station atmospheric delay using the inter-station ranging observation values of each 5G base station;

[0078] The fourth module is used to divide the atmospheric water vapor three-dimensional grid in the study area according to the distribution characteristics of 5G base stations;

[0079] The fifth module is used to build a unified atmospheric parameter vertical variation constraint model based on multi-source meteorological observation data in the study area, and to build an atmospheric parameter horizontal constraint model using a Gaussian filter template;

[0080] The sixth module is used to discretize the atmospheric delay, establish the observation model between the atmospheric delay and the atmospheric parameters to be estimated, and the stochastic model of other influencing factors;

[0081] The seventh module is used to comprehensively construct the constrained observation model, solve and estimate the gridded atmospheric refractivity grid product of the study area and broadcast it through the 5G communication link;

[0082] The eighth module is used for the user terminal to determine the grid point information passed by the measurement signal based on its own approximate location and the precise coordinates of the base station, and use the 5G communication link to receive the atmospheric grid product.

[0083] In some possible embodiments, a 5G water vapor monitoring system based on Beidou satellite timing is provided, including a processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute a 5G water vapor monitoring method based on Beidou satellite timing as described above.

[0084] In some possible embodiments, a 5G water vapor monitoring system based on Beidou satellite timing is provided, including a readable storage medium, on which a computer program is stored. When the computer program is executed, a 5G water vapor monitoring method based on Beidou satellite timing as described above is implemented.

[0085] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A 5G water vapor monitoring method based on Beidou satellite timing, characterized by: The following steps are included: Step 1: Each 5G base station in the study area receives BeiDou satellite signals and performs relevant processing to obtain BeiDou time information. If there is any inconsistency, it is synchronized to BeiDou time and its precise coordinates are fixed. Step 2: Perform precise point positioning based on the BeiDou satellite precise orbit and clock correction products to extract the atmospheric delay above the base station. Step 3: Extract the inter-station atmospheric delay using the inter-station ranging observation value of each 5G base station; Step 4: Divide the atmospheric water vapor three-dimensional grid in the study area according to the distribution characteristics of 5G base stations; Step 5: Based on the multi-source meteorological observation data of the study area, a unified atmospheric parameter vertical variation constraint model is constructed, and a horizontal constraint model of atmospheric parameters is constructed using a Gaussian filter template; Step 6: Discretize the atmospheric delay, establish an observation model between the atmospheric delay and the atmospheric parameters to be estimated, and a random model of other influencing factors; Step 7: Using the constrained observation model, the gridded atmospheric refractivity product of the study area is calculated and estimated and broadcasted via the 5G communication link. In step 8, the user terminal determines the grid point information that the measurement signal passes through based on its own approximate location and the precise coordinates of the reference station, and uses the 5G communication link to receive the atmospheric grid product.

2. The 5G water vapor monitoring method based on BeiDou satellite timing according to claim 1 is characterized in that: Step 3 is implemented by introducing the precise location coordinates and clock error of the 5G base station as known parameters into the TOA observation equation between 5G base stations, eliminating the influence of other error terms, and inversely calculating the atmospheric delay between 5G base stations.

3. The 5G water vapor monitoring method based on BeiDou satellite timing according to claim 1 is characterized in that: In step 5, the meteorological reanalysis data products or radiosonde data of the study area in recent years are used to analyze the spatiotemporal variation characteristics of the atmospheric parameters in the study area. The exponential function model is used to fit the refractive index profiles at each grid point in the study area to obtain the model coefficients. The coefficients are then analyzed in the frequency domain to establish a coefficient model that takes into account the seasonal variation characteristics. Finally, a unified vertical variation constraint model of atmospheric parameters is constructed.

4. The 5G water vapor monitoring method based on BeiDou satellite timing according to claim 1 is characterized in that: In step 6, the atmospheric delay is discretized using the Newton-Cotes interpolation quadrature method.

5. The 5G water vapor monitoring method based on BeiDou satellite timing according to claim 1, 2, 3 or 4, characterized in that: In step 8, based on the received atmospheric grid product, the atmospheric delay is calculated according to the Gauss-Cotes integral formula and eliminated in the positioning observation equation, or atmospheric water vapor related information is obtained through existing water vapor inversion technology.

6. A 5G water vapor monitoring system based on Beidou satellite timing, characterized by: Used to implement a 5G water vapor monitoring method based on Beidou satellite timing as described in any one of claims 1-5.

7. The 5G water vapor monitoring system based on BeiDou satellite timing according to claim 6 is characterized in that: It includes the following modules: The first module is used to study the 5G base stations in the area that receive BeiDou satellite signals and perform related processing to obtain BeiDou time information. If there is any inconsistency, it is synchronized to BeiDou time and its precise coordinates are fixed at the same time; The second module is used to perform precise point positioning based on the BeiDou satellite precise orbit and clock correction products and extract the atmospheric delay above the base station; The third module is used to extract the inter-station atmospheric delay using the inter-station ranging observation values of each 5G base station; The fourth module is used to divide the atmospheric water vapor three-dimensional grid in the study area according to the distribution characteristics of 5G base stations; The fifth module is used to build a unified atmospheric parameter vertical variation constraint model based on multi-source meteorological observation data in the study area, and to build an atmospheric parameter horizontal constraint model using a Gaussian filter template; The sixth module is used to discretize the atmospheric delay, establish the observation model between the atmospheric delay and the atmospheric parameters to be estimated, and the stochastic model of other influencing factors; The seventh module is used to comprehensively construct the constrained observation model, solve and estimate the gridded atmospheric refractivity grid product of the study area and broadcast it through the 5G communication link; The eighth module is used for the user terminal to determine the grid point information passed by the measurement signal based on its own approximate location and the precise coordinates of the base station, and use the 5G communication link to receive the atmospheric grid product.

8. The 5G water vapor monitoring system based on BeiDou satellite timing according to claim 6 is characterized in that: It includes a processor and a memory, the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a 5G water vapor monitoring method based on Beidou satellite timing as described in any one of claims 1-5.

9. The 5G water vapor monitoring system based on BeiDou satellite timing according to claim 6 is characterized in that: It includes a readable storage medium, on which a computer program is stored. When the computer program is executed, a 5G water vapor monitoring method based on Beidou satellite timing as described in any one of claims 1 to 5 is implemented.

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