A Method for Constructing a Two-Dimensional Dynamic Grid Non-Uniform Atmospheric Parameter Optimization and Correction Model

Abstract

This invention discloses a method for constructing a two-dimensional dynamic grid non-uniform atmospheric parameter optimization and correction model. The method includes dividing the terrain into horizontal regions according to land cover type, collecting meteorological data from different horizontal regions to construct a meteorological data variation model, calculating the atmospheric refractive index above the land cover in the corresponding horizontal region, performing vertical layering to obtain a two-dimensional dynamic grid, constructing a two-dimensional spatial nonlinear atmospheric refractive index matrix, dividing the grid into radial distances according to the two-dimensional dynamic grid to obtain the grid transmission distance, performing atmospheric correction and discrete summation to obtain the radial distance correction value, calculating atmospheric disturbance error and performing atmospheric compensation to obtain the line-of-sight deformation, and calculating the micro-deformation of urban infrastructure through the spatial relationship between a microwave radar interferometer and the observation point. This method can consider changes in meteorological factors in both the vertical and horizontal directions, adapting to complex terrain and variable meteorological conditions, thereby improving the measurement accuracy of microwave reflectometers for micro-deformation of urban infrastructure.

Description

Technical Field

[0001] This invention relates to the field of microwave remote sensing measurement technology, and in particular to a method for constructing a two-dimensional dynamic grid non-uniform atmospheric parameter optimization and correction model. Background Technology

[0002] With the acceleration of urbanization, the demand for structural health monitoring of infrastructure is becoming increasingly urgent. Microwave interferometry, with its advantages of non-contact operation, high precision, high sampling frequency, and all-weather monitoring, has become the mainstream method for obtaining information on micro-deformations of urban infrastructure. However, electromagnetic waves are affected by the spatiotemporal nonlinear variations of meteorological factors during propagation in the troposphere, resulting in local heterogeneity of atmospheric refractive index and atmospheric disturbance errors, which limit the accuracy and reliability of long-term, large-scale micro-deformation monitoring. Constructing a correction model that can finely characterize the spatiotemporal distribution characteristics of non-uniform atmosphere is key to improving the practical value of microwave interferometry in engineering.

[0003] Current atmospheric correction techniques have significant limitations: First, they only consider changes in meteorological factors in a single dimension, neglecting the differences in the thermal properties of different surface cover materials in the horizontal direction and the coupling effect of meteorological gradients in the vertical direction. Second, the boundary determination of piecewise linear models relies on empirical knowledge, which easily leads to compensation errors in areas with poor meteorological continuity. Furthermore, existing methods are mostly static grids and do not incorporate grid-by-grid refraction tracking based on the actual propagation path of radar waves, making it difficult to accurately characterize the cumulative impact of non-uniform atmosphere on the radial transmission path of electromagnetic waves. Therefore, this invention proposes a method for constructing a two-dimensional dynamic grid non-uniform atmospheric parameter optimization correction model. This method involves dividing the horizontal region into a two-dimensional dynamic grid, constructing a meteorological data change model, calculating the atmospheric refractive index above ground features in the horizontal region, constructing a two-dimensional spatial nonlinear atmospheric refractive index matrix, dividing the grid transmission distance and performing atmospheric correction, calculating atmospheric disturbance errors, performing atmospheric compensation, and finally calculating the micro-deformation of urban infrastructure. This achieves refined correction of nonlinear spatiotemporal atmospheric disturbance errors within the monitoring field of view, significantly improving the stability and accuracy of micro-deformation monitoring by microwave interferometry in complex urban environments, and has important engineering significance for ensuring the structural safety of urban infrastructure. Summary of the Invention

[0004] The purpose of this invention is to provide a method for constructing a two-dimensional dynamic grid non-uniform atmospheric parameter optimization and correction model.

[0005] To achieve the above objectives, the present invention is implemented according to the following technical solution: This invention includes the following steps: The study area was divided into multiple horizontal regions according to land cover type. Meteorological data of land cover and the airspace above land cover were collected in different horizontal regions. A meteorological data change model was constructed by fitting a function of the difference in meteorological data. Meteorological data of land cover in each horizontal region are input into the meteorological data change model and the Coddor-Owens empirical formula to calculate the atmospheric refractive index above the land cover in the corresponding horizontal region. Two-dimensional dynamic grids are obtained by vertically layering each horizontal region. The atmospheric refractive index of each two-dimensional dynamic grid is calculated based on the atmospheric refractive index above the ground features in the horizontal region, and a two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​constructed. The radial distance from the microwave radar interferometer to the observation point is obtained, and the distance is divided into grid transmission distances according to a two-dimensional dynamic grid. The grid transmission distance is then corrected by atmospheric correction using a two-dimensional spatial nonlinear atmospheric refractive index matrix. The radial distance correction value is obtained by discrete summation. Atmospheric disturbance error is calculated based on radial distance correction value. Line-of-sight deformation is obtained by atmospheric compensation of monitored deformation. Micro-deformation of urban infrastructure is calculated by the spatial relationship between microwave radar interferometer and observation point.

[0006] Furthermore, the method for constructing a meteorological data change model includes: The study area was divided into multiple horizontal zones according to land cover types, including water bodies, grasslands, bare land, buildings, and concrete surfaces. Meteorological data of land cover and the airspace above land cover in different horizontal regions are collected. The meteorological data of land cover is used as input and the meteorological data of the airspace above land cover is used as output. The meteorological data difference variation function is fitted according to the type of horizontal region. The meteorological data includes temperature, relative humidity and atmospheric pressure. A meteorological data variation model is constructed based on meteorological data above ground features and a function for the variation of meteorological data differences. The expression is as follows: ; in , , These are the temperature, relative humidity, and atmospheric pressure of the land cover, respectively. , , They are respectively Temperature, relative humidity, and atmospheric pressure above ground features in a horizontal region. for Function for variation of meteorological data differences in horizontal regions.

[0007] Furthermore, the method for calculating the atmospheric refractive index above ground features in the corresponding horizontal region includes: Meteorological data of land cover in the study area were obtained, and meteorological data of the airspace above land cover in different horizontal areas were obtained by inputting the meteorological data change model according to the horizontal area. Meteorological data above ground features in different horizontal regions are input into the Coddor-Owens empirical formula to calculate the atmospheric refractive index above ground features in the corresponding horizontal regions. The Ciddor-Owens empirical formula is specifically expressed as follows: ; ; ; in for Atmospheric refractive index above horizontal features. , , They are respectively Temperature, relative humidity, and atmospheric pressure above ground features in a horizontal region. Atmospheric dry pressure, This refers to atmospheric water vapor pressure. The saturated vapor pressure, The temperature is measured in Celsius by meteorological instruments.

[0008] Furthermore, the method for constructing a two-dimensional spatial nonlinear atmospheric refractive index matrix includes: A two-dimensional dynamic grid is obtained by vertically layering the troposphere above each horizontal region along the vertical direction; the layer height of the vertical layer is the empirical range resolution length of the microwave radar interferometer. The atmospheric refractive index of each two-dimensional dynamic grid is calculated using the atmospheric refractive index and atmospheric refractive index gradient formulas above ground features in each horizontal region. A two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​constructed according to spatial relationships. The row elements of the two-dimensional spatial nonlinear atmospheric refractive index matrix are the atmospheric refractive indices of two-dimensional dynamic grids in different horizontal regions at the same altitude. The column elements of the two-dimensional spatial nonlinear atmospheric refractive index matrix are the atmospheric refractive indices of two-dimensional dynamic grids in different altitude regions at the same horizontal region.

[0009] Furthermore, the method for obtaining the radial distance correction value includes: The radial distance from the microwave radar interferometer to the observation point is obtained, and the radial distance is divided into multiple grid transmission distances according to a two-dimensional dynamic grid to obtain a radial distance matrix; the radial distance from the microwave radar interferometer to the observation point reflects the radial distance of electromagnetic wave propagation. The grid transmission distance correction value is obtained by performing atmospheric correction on the corresponding grid transmission distance using a two-dimensional nonlinear atmospheric refractive index matrix. The radial distance correction value from the microwave radar interferometer to the observation point is then calculated using a discrete method, expressed as: ; ; in for Radial distance correction value at any time, This is a correction value for grid transmission distance. for time Horizontal area Vertical layer grid transmission distance, for time Horizontal area The atmospheric refractive index of the vertical layer, for time Atmospheric refractive index above horizontal features. The absolute height of the vertical layer This is the angle between the radar wave and the vertical direction when the radar wave is emitted.

[0010] Furthermore, the method for calculating the micro-deformation of urban infrastructure includes: The atmospheric disturbance error is calculated based on the radial distance correction value. Line-of-sight deformation is obtained by atmospheric compensation of the monitored deformation, expressed as follows: ; ; in for Linear deformation after atmospheric compensation at any time The line-of-sight deformation obtained by a microwave radar interferometer for monitoring target points. for Constant atmospheric disturbance error. for Radial distance correction value at any time, This is the initial radial distance correction value between the microwave radar interferometer and the monitoring point; The micro-deformation of urban infrastructure is calculated using the spatial relationship between a microwave radar interferometer and the observation point, expressed as follows: ; in for Micro-deformation of urban infrastructure corrected by nonlinear atmospheric parameters over time The height of the microwave radar interferometer above the target point.

[0011] The beneficial effects of this invention are: This invention is a method for constructing a two-dimensional dynamic mesh non-uniform atmospheric parameter optimization and correction model. Compared with the prior art, this invention has the following technical advantages: This invention constructs a two-dimensional dynamic grid by dividing the horizontal region and vertically layering the data according to the land cover category and the empirical distance resolution length of the microwave radar interferometer, and calculates the refractive index of each grid to construct a two-dimensional grid refractive index matrix, which significantly improves the environmental adaptability and stability of this invention. This invention maps the radial transmission path of radar waves onto a two-dimensional dynamic grid, uses the atmospheric refractive index within each grid to refine the local transmission distance, and obtains an accurate radial distance correction value through discrete summation, which can improve the inversion accuracy of micro-deformation of urban infrastructure. This invention continuously collects meteorological data from regions with different land cover types, dynamically reconstructs a two-dimensional spatial nonlinear atmospheric refractive index matrix at monitoring times, and then calculates atmospheric disturbance errors based on radial distance correction values ​​at each time point. Dynamic atmospheric compensation is then implemented for line-of-sight deformation. This mechanism effectively separates the actual structural deformation from atmospheric disturbance errors, ensuring the reliable extraction of deformation information from long-term monitoring data and providing high-precision data support for the long-term structural health assessment of urban infrastructure. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating the steps of constructing a two-dimensional dynamic mesh non-uniform atmospheric parameter optimization and correction model according to the present invention. Figure 2 This is a schematic diagram of two-dimensional dynamic grid division for three types of land features provided in an embodiment of the present invention; Figure 3 A geometric diagram showing the relationship between the microwave interferometer and the monitoring point provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will be further described below through specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0014] The present invention provides a method for constructing a two-dimensional dynamic mesh non-uniform atmospheric parameter optimization and correction model, comprising the following steps: like Figure 1 As shown, this embodiment includes the following steps: The study area was divided into multiple horizontal regions according to land cover type. Meteorological data of land cover and the airspace above land cover were collected in different horizontal regions. A meteorological data change model was constructed by fitting a function of the difference in meteorological data. Meteorological data of land cover in each horizontal region are input into the meteorological data change model and the Coddor-Owens empirical formula to calculate the atmospheric refractive index above the land cover in the corresponding horizontal region. Two-dimensional dynamic grids are obtained by vertically layering each horizontal region. The atmospheric refractive index of each two-dimensional dynamic grid is calculated based on the atmospheric refractive index above the ground features in the horizontal region, and a two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​constructed. The radial distance from the microwave radar interferometer to the observation point is obtained, and the distance is divided into grid transmission distances according to a two-dimensional dynamic grid. The grid transmission distance is then corrected by atmospheric correction using a two-dimensional spatial nonlinear atmospheric refractive index matrix. The radial distance correction value is obtained by discrete summation. Atmospheric disturbance error is calculated based on radial distance correction value. Line-of-sight deformation is obtained by atmospheric compensation of monitored deformation. Micro-deformation of urban infrastructure is calculated by the spatial relationship between microwave radar interferometer and observation point.

[0015] In this embodiment, the method for constructing a meteorological data change model includes: The study area was divided into multiple horizontal zones according to land cover types, including water bodies, grasslands, bare land, buildings, and concrete surfaces. Meteorological data of land cover and the airspace above land cover in different horizontal regions are collected. The meteorological data of land cover is used as input and the meteorological data of the airspace above land cover is used as output. The meteorological data difference variation function is fitted according to the type of horizontal region. The meteorological data includes temperature, relative humidity and atmospheric pressure. A meteorological data variation model is constructed based on meteorological data above ground features and a function for the variation of meteorological data differences. The expression is as follows: ; in , , These are the temperature, relative humidity, and atmospheric pressure of the land cover, respectively. , , They are respectively Temperature, relative humidity, and atmospheric pressure above ground features in a horizontal region. for Function for variation of meteorological data differences in horizontal regions; In the actual assessment, taking City A as an example, typical urban infrastructure and its surrounding areas were selected as the research scope. Using high-resolution remote sensing images and geographic information system data, the surface cover of the research area was divided into types such as water bodies, grasslands, bare land, buildings, and concrete. For each type of land cover, typical areas were selected as meteorological parameter collection points. Portable meteorological instruments were deployed to continuously collect temperature, relative humidity, and atmospheric pressure data for different seasons and time of day and night, in order to explore the distribution characteristics of meteorological parameters above different land cover, the diurnal variation characteristics of meteorological parameters, and the seasonal variation characteristics of meteorological parameters. Using meteorological data of near-surface land cover actually collected by meteorological instruments as input and meteorological data of the airspace above different land cover as output, the system fits the variation function of meteorological data difference according to the horizontal region type to quantify the impact of land cover on near-surface meteorological parameters; among them, the meteorological data of land cover is collected by the meteorological station located on the land cover closest to the microwave radar interferometer. A meteorological data variation model is constructed by combining meteorological data above ground features and the meteorological data variation function.

[0016] In this embodiment, the method for calculating the atmospheric refractive index above ground features in the corresponding horizontal region includes: Meteorological data of land cover in the study area were obtained, and meteorological data of the airspace above land cover in different horizontal areas were obtained by inputting the meteorological data change model according to the horizontal area. Meteorological data above ground features in different horizontal regions are input into the Coddor-Owens empirical formula to calculate the atmospheric refractive index above ground features in the corresponding horizontal regions. The Ciddor-Owens empirical formula is specifically expressed as follows: ; ; ; in for Atmospheric refractive index above horizontal features. , , They are respectively Temperature (in K), relative humidity, and atmospheric pressure (in hPa) above ground features in a horizontal region. This refers to atmospheric dry pressure (unit: hPa). Atmospheric water vapor pressure (unit: hPa). This is the saturated water vapor pressure (in hPa). The temperature is measured in Celsius by meteorological instruments.

[0017] In this embodiment, the method for constructing a two-dimensional spatial nonlinear atmospheric refractive index matrix includes: A two-dimensional dynamic grid is obtained by vertically layering the troposphere above each horizontal region along the vertical direction; the layer height of the vertical layer is the empirical range resolution length of the microwave radar interferometer. The atmospheric refractive index of each two-dimensional dynamic grid is calculated using the atmospheric refractive index and atmospheric refractive index gradient formulas above ground features in each horizontal region. A two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​constructed according to spatial relationships. The row elements of the two-dimensional spatial nonlinear atmospheric refractive index matrix are the atmospheric refractive indices of two-dimensional dynamic grids in different horizontal regions at the same altitude layer. The column elements of the two-dimensional spatial nonlinear atmospheric refractive index matrix are the atmospheric refractive indices of two-dimensional dynamic grids in different altitude layers at the same horizontal region. In actual assessments, meteorological parameters typically change continuously in the vertical direction, varying with altitude in the troposphere. The monitoring field is divided into multiple media layers, using horizontal regions to delineate different land cover types and vertical regions based on the empirical range resolution of the microwave radar interferometer. This is represented by a two-dimensional dynamic grid (taking three types of land cover as an example, corresponding to three horizontal regions). Figure 2 As shown; By using the near-surface horizontal atmospheric refractive index value and combining it with the atmospheric refractive index gradient formula, the atmospheric refractive index in each grid is calculated. The number of rows in the two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​set to 3 according to the three types of land cover, and the number of columns in the two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​set according to the vertical stratification results. Finally, the two-dimensional spatial nonlinear atmospheric refractive index matrix within the monitoring field of view of the microwave radar interferometer at each time is calculated according to the time series data. That is, by combining the distribution characteristics of meteorological parameters above different land cover, the diurnal variation and seasonal variation characteristics of meteorological parameters, and using geographic information technology, the nonlinear atmospheric refractive index that changes with time and space can be finely captured. The formula for the atmospheric refractive index gradient is: ; in for Horizontal area The atmospheric refractive index of the vertical layer, for Atmospheric refractive index above horizontal features. for The height of the vertical layer, This refers to the altitude near the Earth's surface.

[0018] In this embodiment, the method for obtaining the radial distance correction value includes: The radial distance from the microwave radar interferometer to the observation point is obtained, and the radial distance is divided into multiple grid transmission distances according to a two-dimensional dynamic grid to obtain a radial distance matrix; the radial distance from the microwave radar interferometer to the observation point reflects the radial distance of electromagnetic wave propagation. The grid transmission distance correction value is obtained by performing atmospheric correction on the corresponding grid transmission distance using a two-dimensional nonlinear atmospheric refractive index matrix. The radial distance correction value from the microwave radar interferometer to the observation point is then calculated using a discrete method, expressed as: ; ; in for Radial distance correction value at any time, This is a correction value for grid transmission distance. for time Horizontal area Vertical layer grid transmission distance, for time Horizontal area The atmospheric refractive index of the vertical layer, for time Atmospheric refractive index above horizontal features. The absolute height of the vertical layer This is the angle between the radar wave and the vertical direction when the radar wave is emitted.

[0019] In this embodiment, the method for calculating micro-deformations of urban infrastructure includes: The atmospheric disturbance error is calculated based on the radial distance correction value. Line-of-sight deformation is obtained by atmospheric compensation of the monitored deformation, expressed as follows: ; ; in for Linear deformation after atmospheric compensation at any time The line-of-sight deformation obtained by a microwave radar interferometer for monitoring target points. for Constant atmospheric disturbance error. for Radial distance correction value at any time, This is the initial radial distance correction value between the microwave radar interferometer and the monitoring point; The micro-deformation of urban infrastructure is calculated using the spatial relationship between a microwave radar interferometer and the observation point, expressed as follows: ; in for Micro-deformation of urban infrastructure corrected by nonlinear atmospheric parameters over time The height of the microwave radar interferometer from the target point; In practical assessments, during the monitoring of micro-deformations of urban infrastructure using microwave radar interferometers, the nonlinear change in atmospheric refractive index causes refraction of the radar wave propagation path, introducing atmospheric disturbance errors and affecting monitoring accuracy. This invention calculates the electromagnetic wave propagation distance in a two-dimensional grid, uses the law of refraction to obtain the radial distance of the electromagnetic wave within the monitoring field of view, and utilizes the geometric relationship between the microwave interferometer and the monitoring point to obtain the micro-deformations of urban infrastructure corrected for nonlinear atmospheric parameters. The geometric relationship between the microwave interferometer and the monitoring point is as follows: Figure 3 As shown; according to Figure 2 The radial distance from the microwave radar interferometer to the observation point is divided into multiple grid transmission distances, and a radial distance matrix is ​​constructed. When the radial distance is divided into a two-dimensional dynamic grid, the matrix element corresponding to the two-dimensional dynamic grid is non-zero. If the radial distance is not divided into a two-dimensional dynamic grid, the corresponding matrix element is zero. According to the law of refraction, the grid transmission distance correction value is taken as the ratio of the grid transmission distance to the atmospheric refractive index of the grid, and the radial distance correction value from the microwave radar interferometer to the monitoring point is calculated by summing using a discrete method. ; When using a microwave radar interferometer to perform long-term monitoring of micro-deformations in urban infrastructure, the atmosphere undergoes nonlinear changes over time and space, with the initial monitoring time as the baseline. The atmospheric disturbance error and the line-of-sight deformation after atmospheric compensation are calculated sequentially based on the radial distance corrected by the nonlinear atmospheric parameters. The initial radial distance correction value is then calculated. At that time, the initial radial distance between the microwave radar interferometer and the monitoring point is... The radial distance matrix is ​​obtained by projecting onto a two-dimensional dynamic grid, and the initial radial distance correction value between the microwave radar interferometer and the monitoring point is obtained by performing atmospheric correction through a two-dimensional spatial nonlinear atmospheric refractive index matrix. .

[0020] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a two-dimensional dynamic grid non-uniform atmospheric parameter optimization correction model, characterized in that, Includes the following steps: S1. Divide the study area into multiple horizontal regions according to land cover type, collect meteorological data of land cover and the airspace above land cover in different horizontal regions, and fit the meteorological data difference variation function to construct a meteorological data variation model. S2. Input the meteorological data of the land cover in each horizontal region into the meteorological data change model and the Coddor-Owens empirical formula to calculate the atmospheric refractive index above the land cover in the corresponding horizontal region. S3. Vertically layer each horizontal region to obtain a two-dimensional dynamic grid. Calculate the atmospheric refractive index of each two-dimensional dynamic grid based on the atmospheric refractive index above the ground features in the horizontal region, and construct a two-dimensional spatial nonlinear atmospheric refractive index matrix. S4. Obtain the radial distance from the microwave radar interferometer to the observation point, and divide it into grid transmission distances according to the two-dimensional dynamic grid. Use the two-dimensional spatial nonlinear atmospheric refractive index matrix to perform atmospheric correction on the grid transmission distance to obtain the grid transmission distance correction value, and then discretize and sum to obtain the radial distance correction value. S5. Calculate atmospheric disturbance error based on radial distance correction value, obtain line-of-sight deformation by atmospheric compensation of monitored deformation, and calculate micro-deformation of urban infrastructure by spatial relationship between microwave radar interferometer and observation point.

2. The method according to claim 1, wherein, The method for constructing a meteorological data change model includes: The study area was divided into multiple horizontal zones according to land cover types, including water bodies, grasslands, bare land, buildings, and concrete surfaces. Meteorological data of land cover and the airspace above land cover in different horizontal regions are collected. The meteorological data of land cover is used as input and the meteorological data of the airspace above land cover is used as output. The meteorological data difference variation function is fitted according to the type of horizontal region. The meteorological data includes temperature, relative humidity and atmospheric pressure. A meteorological data variation model is constructed based on meteorological data above ground features and a function for the variation of meteorological data differences. The expression is as follows: ； in , , These are the temperature, relative humidity, and atmospheric pressure of the land cover, respectively. , , They are respectively Temperature, relative humidity, and atmospheric pressure above ground features in a horizontal region. for Function for variation of meteorological data differences in horizontal regions.

3. The method according to claim 1, wherein, The method for calculating the atmospheric refractive index above ground features in a corresponding horizontal region includes: Meteorological data of land cover in the study area were obtained, and meteorological data of the airspace above land cover in different horizontal areas were obtained by inputting the meteorological data change model according to the horizontal area. Meteorological data above ground features in different horizontal regions are input into the Coddor-Owens empirical formula to calculate the atmospheric refractive index above ground features in the corresponding horizontal regions. The Ciddor-Owens empirical formula is specifically expressed as follows: ； ； ； wherein is the atmospheric refractive index above the horizontal area ground object, , , are respectively the temperature, the relative humidity and the atmospheric pressure above the horizontal area ground object, is the atmospheric dry pressure, is the atmospheric water vapor pressure, is the saturated water vapor pressure, is the Celsius temperature actually collected by the meteorological instrument.

4. The method for constructing a two-dimensional dynamic mesh non-uniform atmospheric parameter optimization and correction model according to claim 1, characterized in that, The method for constructing a two-dimensional spatial nonlinear atmospheric refractive index matrix includes: A two-dimensional dynamic grid is obtained by vertically layering the troposphere above each horizontal region along the vertical direction; the layer height of the vertical layer is the empirical range resolution length of the microwave radar interferometer. The atmospheric refractive index of each two-dimensional dynamic grid is calculated using the atmospheric refractive index and atmospheric refractive index gradient formulas above ground features in each horizontal region. A two-dimensional spatial nonlinear atmospheric refractive index matrix is ​​constructed according to spatial relationships. The row elements of the two-dimensional spatial nonlinear atmospheric refractive index matrix are the atmospheric refractive indices of two-dimensional dynamic grids in different horizontal regions at the same altitude. The column elements of the two-dimensional spatial nonlinear atmospheric refractive index matrix are the atmospheric refractive indices of two-dimensional dynamic grids in different altitude regions at the same horizontal region.

5. The method for constructing a two-dimensional dynamic grid non-uniform atmospheric parameter optimization and correction model according to claim 1, characterized in that, The method for obtaining the radial distance correction value includes: The radial distance from the microwave radar interferometer to the observation point is obtained, and the radial distance is divided into multiple grid transmission distances according to a two-dimensional dynamic grid to obtain a radial distance matrix; the radial distance from the microwave radar interferometer to the observation point reflects the radial distance of electromagnetic wave propagation. The grid transmission distance correction value is obtained by performing atmospheric correction on the corresponding grid transmission distance using a two-dimensional nonlinear atmospheric refractive index matrix. The radial distance correction value from the microwave radar interferometer to the observation point is then calculated using a discrete method, expressed as: ； ； in for Radial distance correction value at any time, This is a correction value for grid transmission distance. for time Horizontal area Vertical layer grid transmission distance, for time Horizontal area The atmospheric refractive index of the vertical layer, for time Atmospheric refractive index above ground features in a horizontal region. The absolute height of the vertical layer This is the angle between the radar wave and the vertical direction when the radar wave is emitted.

6. The method for constructing a two-dimensional dynamic mesh non-uniform atmospheric parameter optimization and correction model according to claim 1, characterized in that, The method for calculating micro-deformations of urban infrastructure includes: The atmospheric disturbance error is calculated based on the radial distance correction value. Line-of-sight deformation is obtained by atmospheric compensation of the monitored deformation, expressed as follows: ； ； in for Linear deformation after atmospheric compensation at any time The line-of-sight deformation obtained by a microwave radar interferometer for monitoring target points. for Constant atmospheric disturbance error. for Radial distance correction value at any time, This is the initial radial distance correction value between the microwave radar interferometer and the monitoring point; The micro-deformation of urban infrastructure is calculated using the spatial relationship between a microwave radar interferometer and the observation point, expressed as follows: ； in for Micro-deformation of urban infrastructure corrected by nonlinear atmospheric parameters over time The height of the microwave radar interferometer above the target point.

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