High-precision edge detection meteorological correction method based on three-dimensional meteorological field and calculus

Abstract

The application provides a high-precision edge measurement meteorological correction method based on a three-dimensional meteorological field and calculus, and the method is characterized in that: meteorological stations are arranged according to the topography, landform, section and elevation characteristics of a measurement area, original sample values of temperature and water vapor pressure are corrected through a terrain dynamic factor and then taken as input fitting of a basic semi-variogram function, a three-dimensional meteorological field model is constructed by solving the input fitting through Kriging weight, and an elevation linear fitting model is used for air pressure; meteorological elements of an arbitrary point in a measurement line direction are obtained based on the model, the measurement line is scattered at a fixed interval distance, and then each differential segment meteorological correction amount is calculated through an Edlen model, and a cumulative correction amount is obtained through numerical integration, so that the edge measurement meteorological correction precision under long-distance complex topography is significantly improved, the method has strong environmental adaptability, superior expansibility and all-weather reliable monitoring capability, and can meet the millimeter-level precision requirement of high-precision deformation monitoring.

Description

Technical Field

[0001] This invention belongs to the field of surveying and mapping technology, and relates to a high-precision meteorological correction method for boundary measurement based on three-dimensional meteorological fields and calculus. Background Technology

[0002] In the field of precision engineering surveying, surveying robots (such as total stations) perform high-precision distance measurement by emitting and receiving electromagnetic waves, and have become an indispensable technical means in modern engineering construction, geological disaster monitoring, and large-scale structure installation. When surveying robots perform precise distance measurement, atmospheric environmental factors such as temperature, air pressure, and water vapor pressure affect the propagation speed of electromagnetic waves, leading to deviations in the measurement results. Therefore, meteorological corrections are necessary. The core of traditional meteorological correction methods is to deploy one set of meteorological sensors at each of the measuring and observation stations, collect meteorological elements (temperature, air pressure, and water vapor pressure; water vapor pressure can be used to characterize humidity) from both stations, take the arithmetic mean of the meteorological elements measured at both stations, and substitute it into a classic atmospheric refractive index model—such as the internationally recognized highly accurate Ciddor model or the improved Edlén formula—to calculate the atmospheric refractive index along the electromagnetic wave propagation path, thereby obtaining the meteorological correction for the distance measurement and correcting the original observation values.

[0003] However, this method has obvious drawbacks: when the measurement line distance is long (e.g., >1000 meters) or the meteorological distribution in the measurement area is uneven, such as the presence of temperature gradients or airflow disturbances, the average values ​​of the measuring station and the mirror station cannot reflect the actual meteorological conditions in the middle area of ​​the measurement line. The error introduced by the meteorological correction itself may reach several millimeters or even more, resulting in a large deviation between the correction model and the real atmospheric environment, which ultimately affects the measurement accuracy and cannot meet the millimeter-level accuracy requirements of engineering construction, geological monitoring and other scenarios. Summary of the Invention

[0004] To address the problems of large errors in meteorological correction methods, which affect the accuracy of boundary measurement and fail to meet millimeter-level precision requirements, as described in the background art, this invention provides a high-precision boundary measurement meteorological correction method based on three-dimensional meteorological fields and calculus. By finely capturing the meteorological distribution characteristics and dynamic changes along the measurement line, this method overcomes the problem of meteorological interference, improves the accuracy of meteorological correction, and provides reliable data support for deformation monitoring. It is particularly suitable for high-precision measurement scenarios involving long distances, complex terrain, and meteorological environments, such as deformation monitoring, precision engineering surveying, and the installation and positioning of large equipment.

[0005] The first aspect of this application provides a high-precision meteorological correction method for boundary measurement based on three-dimensional meteorological fields and calculus, including the following steps: Meteorological stations are set up based on the topography, landforms, cross sections, and elevation of the survey area, combined with the potential path of the survey line and areas of sudden meteorological changes. Based on meteorological elements collected from meteorological stations, a three-dimensional meteorological field model of the survey area is constructed: for temperature and water vapor pressure, topographic dynamic factors including slope, aspect, elevation difference and valley airflow disturbance terms are constructed by normalization and weighted summation. The original meteorological sample values ​​are corrected by the topographic dynamic factors, and then the corrected meteorological sample values ​​are used as the input of the basic semivariogram for fitting. For air pressure, an elevation linear fitting model is used for fitting. Based on the three-dimensional meteorological field model of the survey area, the temperature and water vapor pressure meteorological elements at any point along the survey line are calculated by ordinary Kriging weight solution, and the air pressure meteorological elements at any point along the survey line are obtained by solving the elevation linear fitting model. The survey line direction is discretized at fixed intervals to obtain several differential segments. The meteorological elements of any point in the survey line direction corresponding to the endpoints of each differential segment are input into the atmospheric refractive index calculation model based on the simplified engineering expression of the Edlén model to calculate a series of meteorological corrections for fixed-interval differential segments. Based on the above series of fixed-interval meteorological corrections, the cumulative meteorological correction for the entire survey line is calculated using a numerical integration method over the survey line distance.

[0006] Furthermore, in the deployment of the meteorological stations, sensor deployment should focus on the canyon orientation, slope gradient, airflow channels, and deformation monitoring section characteristics, adopting a "baseline grid + key densification" pattern. All sensor spacing must meet interpolation accuracy requirements. Baseline grids with preset side lengths are deployed at the canyon floor and on gently sloping areas. These preset side lengths are determined based on the terrain complexity and interpolation accuracy requirements of the survey area. The baseline grids are densified at steep slopes, convex and concave terrain, and airflow convergence areas on both sides of the canyon. Sensors are added along contour lines at locations of abrupt elevation changes. Sensors are simultaneously deployed within a certain range on both sides of the deformation monitoring section. The sensors are synchronously connected to the BeiDou positioning system to obtain three-dimensional coordinates (X, Y, Z), and sampling is performed at a frequency no less than that required for capturing the time-varying characteristics of meteorological elements, sampling temperature, air pressure, and relative humidity.

[0007] Furthermore, in the construction of the three-dimensional meteorological field model of the survey area, the spatial continuity of meteorological elements is achieved through the Kriging interpolation algorithm, and the meteorological elements at any coordinate (x, y, z) are calculated: temperature, water vapor pressure, and air pressure, as follows: Definition of the first The terrain dynamic factors of each sensor are: ; In the formula, For the normalized slope factor, This is the directional consistency factor corresponding to the angle between the slope aspect and the canyon's main axis. For the first Elevation of each sensor, For reference elevation of the survey area, The normalization constant for the elevation difference in the survey area is... The airflow disturbance factor in the canyon. to These are empirical weighting coefficients; The temperature sample values ​​were corrected for terrain features to obtain the following: , In the formula, These are the original observations. These are the sample values ​​after terrain correction. These are the correction factors for the corresponding meteorological elements; The water vapor pressure sample values ​​were corrected for topographic features to obtain: , In the formula, These are the original observations. These are the sample values ​​after terrain correction. These are correction coefficients for the corresponding meteorological elements; where water vapor pressure is calculated from relative humidity (or dew point) and temperature using the Magnus formula; For temperature and water vapor pressure, the original sample values ​​are first corrected based on topographic dynamic factors. Then, the corrected sample values ​​are used as the calculation samples for the basic semivariogram, allowing the influence of slope, aspect, elevation difference, and canyon airflow disturbance on the spatial correlation of meteorological elements to enter the semivariogram fitting process. The basic semivariogram... The expression is as follows: ; In the formula, Based on the semi-variogram function, The spatial distance between the sampling points of the two sensors. The distance is The number of sensor sampling points, For the first The measurement values ​​of each sensor, For distance from the first The distance between the sensors is The measured value; The basic semivariogram adopts a spherical model, and its expression is: , In the formula, The value of the nugget reflects the measurement error; The sill value reflects the total spatial variation; For variable range, the distance of maximum spatial correlation of meteorological elements is adjusted according to the regional meteorological complexity; For air pressure, the calculation of air pressure elements at any point adopts an elevation linear fitting model. When the elevation difference in the survey area is no greater than 300 m and the residual obtained by fitting the linear model based on measured air pressure samples from meteorological stations is no greater than 0.3 hPa, according to... ;in, The pressure value at the point to be estimated. For the elevation of the point to be estimated, The slope of the fitted equation for air pressure as a function of elevation is given. The intercept is the fitting angle. When the elevation difference in the survey area is greater than 300 m or the linear fitting residual is greater than 0.3 hPa, the survey area is divided into at least two zones according to the elevation interval, and a piecewise linear model is established for each zone. The air pressure value at the point to be estimated is calculated according to the following formula: ;in, and The first The fitting intercept and fitting slope corresponding to each elevation zone.

[0008] Furthermore, in the linear or piecewise fitting of the air pressure with elevation, the intercept of the linear fitting is... and linear fitting slope The calculation method is as follows: , , In the formula, For the first The readings from the barometer sensor. To correspond to the elevation, The average air pressure, The average elevation is used. When the elevation difference of the survey area within a single sampling period is not greater than the preset threshold and the fitting residual does not exceed the preset threshold, a single fitting is used; otherwise, segmented fitting is performed according to elevation zones.

[0009] Furthermore, the method for calculating meteorological elements at any point along the survey line includes: Based on the three-dimensional meteorological field model of the survey area, calculate the temperature at any coordinate (x0, y0, z0). The formula is as follows: , In the formula, m represents the number of sensors involved in the interpolation. Let be the weighting coefficient of the i-th sensor, satisfying ; Based on the three-dimensional meteorological field model of the survey area, the water vapor pressure at any coordinate (x0, y0, z0) is calculated. The formula is as follows: , In the formula, Let be the water vapor pressure at any coordinate (x0, y0, z0), and m be the number of sensors involved in the interpolation. Let be the weighting coefficient of the i-th sensor, satisfying ; Air pressure is obtained directly from elevation through linear fitting, as shown in the following formula: , In the formula, Let be the air pressure value at any spatial coordinate (x, y, z). The intercept of the linear fit. denoted as the slope of the linear fit, and z represents the elevation of the spatial location to be calculated.

[0010] Furthermore, the method for calculating the meteorological correction at the fixed interval includes: First, the actual meteorological elements (P, T, e) are converted into refractive index increments N, and then simplified using the Edlén model: ; In the formula, Absolute temperature For air pressure, For water vapor pressure, and Empirical coefficients obtained from the working wavelength calibration of the perimeter; atmospheric refractive index. satisfy ; After discretizing the survey line direction at fixed intervals, first calculate the first... segment starting point refractive index With the endpoint refractive index average : ; No. The weather correction for a fixed-interval differential segment is: .

[0011] In the formula, The interval is fixed for the differential segments; For the first The average atmospheric refractive index of each differential segment is the average of the refractive indices at the beginning and end of the differential segment.

[0012] Furthermore, the method for calculating the cumulative meteorological correction for the entire survey line is as follows: Let the total length of the entire survey line be It can be broken down into Each differential segment, This indicates rounding up; the cumulative meteorological correction D for the entire survey line is the sum of all differential segment corrections, as shown in the following formula: .

[0013] The second aspect of this application provides a high-precision boundary-finding meteorological correction system based on three-dimensional meteorological fields and calculus, applicable to the aforementioned high-precision boundary-finding meteorological correction method based on three-dimensional meteorological fields and calculus, including a meteorological station layout module, a three-dimensional meteorological field model construction module, an arbitrary point meteorological element calculation module, a meteorological correction calculation module, and a cumulative meteorological correction calculation module.

[0014] The meteorological station deployment module deploys meteorological stations based on the topography, landforms, cross-sections, and elevation of the survey area, combined with the potential path of the survey line and areas of sudden meteorological changes.

[0015] The three-dimensional meteorological field model construction module, based on meteorological elements collected by meteorological stations, constructs a topographic dynamic factor that includes slope, aspect, elevation difference, and valley airflow disturbance terms through normalization and weighted summation. The original meteorological sample values ​​are corrected by the topographic dynamic factor, and then the corrected meteorological sample values ​​are used as the input of the basic semivariogram to construct a three-dimensional meteorological field model of the survey area.

[0016] The arbitrary point meteorological element calculation module, based on the three-dimensional meteorological field model of the survey area, calculates the temperature and water vapor pressure meteorological elements at any point along the survey line by ordinary Kriging weighting, and calculates the air pressure meteorological elements at any point along the survey line by using an elevation linear fitting model.

[0017] The meteorological correction calculation module discretizes the survey line direction at fixed intervals to obtain several differential segments. The meteorological elements at any point along the survey line direction corresponding to the endpoints of each differential segment are input into the atmospheric refractive index calculation model based on the simplified engineering expression of the Edlén model to calculate a series of meteorological corrections for fixed-interval differential segments.

[0018] The cumulative meteorological correction calculation module calculates the cumulative meteorological correction for the entire survey line by using a numerical integration method within the survey line distance, based on the aforementioned series of fixed-interval meteorological corrections.

[0019] A third aspect of this application provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to realize the high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus as described above.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-precision boundary measurement meteorological correction method based on three-dimensional meteorological fields and calculus as described above.

[0021] Compared with the prior art, the present invention has the following advantages: (1) Significantly improved correction accuracy: In view of the problem that the traditional two-point average meteorological correction method is not accurate enough under long distance or uneven meteorological distribution conditions, this invention scientifically sets up meteorological stations by combining the topography, landform, cross section and elevation characteristics of the survey area. The temperature and water vapor pressure are normalized and weighted to construct a topographic dynamic factor including slope, aspect, elevation difference and valley airflow disturbance terms. After correcting the original meteorological sample values, the corrected values ​​are used as the input of the basic semivariogram for fitting. Then, the high-precision spatial distribution is obtained by solving the ordinary Kriging weight. The air pressure is solved by the elevation linear fitting model, which can accurately capture the meteorological gradient along the survey line. At the same time, the fixed-interval differential calculation is performed on the survey line direction, and the cumulative correction is obtained by numerical integration. This avoids the large error introduced by the deviation of meteorological mean in the traditional method and can fully meet the millimeter-level accuracy requirement of high-precision deformation monitoring. (2) Strong adaptability to complex environments: Traditional meteorological correction methods are poorly adapted to complex areas with large temperature gradients and frequent airflow disturbances. However, the sensor deployment and modeling algorithm of this invention fully integrates the topography and airflow characteristics of the survey area. Temperature and water vapor pressure are corrected by topographic dynamic factors and then incorporated into the semi-variogram fitting process to reflect the influence of topography on the spatial correlation of meteorological elements. An elevation linear fitting model is used for air pressure to adapt to elevation change characteristics. It can effectively cope with the drastic differences in temperature and air pressure between the top and bottom of valleys in high mountain and canyon areas and short-term meteorological fluctuations. Moreover, the three-dimensional meteorological field model can be dynamically updated, which solves the problem of "incompatibility" of traditional methods in complex meteorological environments from a technical perspective. (3) Superior scalability: The three-dimensional meteorological field data constructed by this invention can provide services to multiple measurement robots simultaneously, breaking the limitation of a single sensor corresponding to a single measurement device in the traditional way. It supports large-scale cluster measurement scenarios and effectively reduces the deployment cost of meteorological sensors when measuring multiple devices. At the same time, the entire technical process relies on automated data acquisition and model calculation, which greatly reduces the cost of manual observation. (4) Reliable all-weather monitoring: Traditional side measurement correction relies on manual observation of meteorological data, which is easily affected by weather conditions and on-site operation limitations. Under extreme weather conditions, it is difficult to guarantee the accuracy and real-time performance of monitoring. This invention, through the fully automated design of automatic data acquisition, wireless data transmission and automatic model calculation, can overcome the influence of severe weather and complex terrain, and realize continuous monitoring around the clock and in all weather conditions. At the same time, it can stably acquire meteorological elements in the measurement area, significantly improving the reliability and sustainability of meteorological correction, and ensuring the continuity of high-precision deformation monitoring under extreme climate conditions.

[0022] In summary, this invention deploys meteorological stations based on the topography, landforms, cross-sections, and elevation characteristics of the survey area. Temperature and water vapor pressure are corrected for their original sample values ​​using topographic dynamic factors and then used as the input for fitting a basic semi-variogram. A Kriging weighted solution is used, and an elevation linear fitting model is employed for air pressure to construct a three-dimensional meteorological field model. Based on this model, meteorological elements at any point along the survey line are obtained. The survey line is discretized at fixed intervals, and meteorological corrections for each differential segment are calculated using the Edlén model. The cumulative correction is obtained through numerical integration. This significantly improves the accuracy of meteorological corrections for long-distance, complex terrain surveys. It possesses strong environmental adaptability, superior scalability, and reliable all-weather monitoring capabilities, meeting the millimeter-level accuracy requirements for high-precision deformation monitoring. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method of the present invention.

[0024] Figure 2 A flowchart for setting up meteorological stations.

[0025] Figure 3 Flowchart for building a 3D meteorological field model.

[0026] Figure 4 This is a schematic diagram of a three-dimensional meteorological field model of a hydropower station.

[0027] Figure 5 The graph shows the fitting of the semivariograms of temperature and humidity.

[0028] Figure 6 This is a linear fit diagram of air pressure and elevation.

[0029] Figure 7 This is a flowchart of the discretization of survey lines and the calculation of meteorological corrections.

[0030] Figure 8 A schematic diagram showing the spatial relationship between the meteorological stations and survey lines in the canyon survey area.

[0031] Figure 9 This is a profile of meteorological elements along the survey route.

[0032] Figure 10 This is a distribution diagram of refractive index and segmented correction along the measurement line.

[0033] Figure 11 This is a statistical chart of interpolation errors for meteorological elements at any point.

[0034] Figure 12 This is a graph showing the effect of the differential step size on the cumulative correction result.

[0035] Figure 13 This is a comparison chart of the error correction methods of the present invention and the traditional two-point averaging method.

[0036] Figure 14This is a system architecture diagram of the present invention. Detailed Implementation

[0037] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0038] Example 1

[0039] A high-precision meteorological correction method for boundary measurement based on three-dimensional meteorological fields and calculus, flowchart as follows: Figure 1 As shown, the specific steps are as follows.

[0040] The first step is to set up meteorological stations based on the topography, landforms, cross sections, and elevation of the survey area, combined with the potential path of the survey line and areas of sudden meteorological changes.

[0041] Meteorological station deployment flowchart as follows Figure 2 As shown, in the deployment of meteorological stations, sensor deployment should focus on the canyon orientation, slope gradient, airflow channels, and deformation monitoring section characteristics, adopting a "baseline grid + key densification" mode for sensor deployment. The spacing between all sensors should meet the interpolation accuracy requirements. In the canyon bottom and gently sloping areas, a baseline grid with a preset side length should be deployed, the preset side length being determined based on the terrain complexity of the survey area and the interpolation accuracy requirements. The baseline grid should be densified on steep slopes, convex and concave terrain, and airflow convergence areas on both sides of the canyon. Sensors should be added along the contour line direction at locations of abrupt elevation changes. Sensors should be deployed synchronously within a certain range on both sides of the deformation monitoring section extension direction. The sensors should be synchronously connected to the BeiDou positioning system to obtain three-dimensional coordinates (X, Y, Z), and sampling should be performed at a sampling frequency no less than that required for capturing the time-varying characteristics of meteorological elements, sampling temperature, air pressure, and relative humidity.

[0042] Specifically, in the deployment of meteorological stations, sensor deployment should focus on the canyon orientation, slope gradient, airflow channels, and deformation monitoring section characteristics, adopting a "baseline grid + key densification" pattern for sensor deployment, with all sensor spacing controlled within 100m; the canyon bottom and gently sloping areas should be deployed using a 100m×100m base grid; steep slopes on both sides of the canyon, convex and concave terrain, and airflow convergence areas should be densified to 50-80m; at elevation abrupt changes, one sensor should be added every 50m along the contour line direction; sensors should be deployed synchronously within 30m on both sides of the deformation monitoring section extension direction; the sensors should have measurement accuracy of ±0.1℃ for temperature, ±0.1hPa for air pressure, and ±1%RH for relative humidity (or ±0.15℃ for dew point temperature), and the relative error of water vapor pressure calculated from relative humidity (or dew point) and temperature using the Magnus formula should not exceed ±1%, synchronously connected to the BeiDou positioning system to obtain three-dimensional coordinates (X,Y,Z), with a sampling frequency of 2Hz and data transmission delay controlled within 100ms.

[0043] In this implementation, an industrial-grade meteorological sensor (such as the Vaisala WXT series, HMP155 series, or equivalent products) with integrated measurement of three parameters—temperature, relative humidity, and atmospheric pressure—is used. The relative humidity is measured using a capacitive element, and the temperature is measured using a PT100 platinum resistance thermometer or a temperature sensor of equivalent accuracy.

[0044] In this embodiment, the meteorological differences in high mountain and canyon areas are large and the changes are drastic. The sensor deployment needs to focus on the canyon direction, slope gradient (>10° is considered a steep slope), airflow channels, and deformation monitoring section characteristics. The sensor deployment adopts a "baseline grid + key densification" mode, and the spacing between all sensors is strictly controlled within 100m. The canyon bottom and gentle slope areas are deployed according to a 100m×100m base grid; the density is increased to 50-80m on steep slopes on both sides of the canyon, convex and concave terrain, and airflow convergence areas; at elevation change points (such as slopes with a gradient >15°), one sensor is added every 50m along the contour line direction; sensors need to be deployed simultaneously within 30m on both sides of the deformation monitoring section extension direction to ensure coverage of potential survey path and areas of meteorological change. The sensor must have a measurement accuracy of ±0.1℃ for temperature, ±0.1hPa for air pressure, and ±1%RH for relative humidity (or ±0.15℃ for dew point temperature). The relative error of water vapor pressure calculated from relative humidity (or dew point) and temperature using the Magnus formula should not exceed ±1%. It should be synchronously connected to the BeiDou positioning system to obtain three-dimensional coordinates (X,Y,Z), with the sampling frequency increased to 2Hz to capture short-term meteorological fluctuations, and the data transmission delay controlled within 100ms.

[0045] The second step is to construct a three-dimensional meteorological field model of the survey area based on meteorological elements collected from meteorological stations: For temperature and water vapor pressure, topographic dynamic factors including slope, aspect, elevation difference and valley airflow disturbance terms are constructed by normalization and weighted summation. The original meteorological sample values ​​are corrected by the topographic dynamic factors, and then the corrected meteorological sample values ​​are used as the input of the basic semivariogram for fitting. For air pressure, an elevation linear fitting model is used for fitting.

[0046] The flowchart for constructing a 3D meteorological field model is as follows: Figure 3 As shown.

[0047] In this embodiment, the weather in high mountain canyons changes drastically. Based on temperature, relative humidity, and air pressure data collected by sensors with close spacing within 100 m, a topographic dynamic factor is constructed, which includes slope, aspect, elevation difference, and canyon airflow disturbance terms. That is, the topographic dynamic factor of each station is calculated first. The slope factor was obtained from the DEM of the station's neighborhood, the aspect factor was obtained by normalizing the angle between the station's aspect and the canyon's main axis, the elevation term was obtained by normalizing the station's relative reference elevation, and the airflow disturbance term was determined by the distance from the station to the valley axis, the valley mouth contraction, and the location of the prevailing wind direction. Subsequently, the temperature and humidity samples were topographically corrected, and the Kriging weights were solved based on the corrected semivariogram to form a three-dimensional meteorological grid.

[0048] Figure 4 The diagram shows a schematic of a three-dimensional meteorological field model of a hydropower station in this embodiment. As can be seen from the figure, the temperature, water vapor pressure and air pressure data of any measuring point can be calculated by interpolation. The coordinates of the measuring line can be calculated at uniform intervals along the measuring line direction. Then, the meteorological element values ​​in the measuring line direction can be calculated based on the coordinate values. By comparing with the measured values, the required accuracy of the model can be achieved.

[0049] Specifically, the process of constructing the three-dimensional meteorological field model of the survey area is as follows: First, a topographic dynamic factor is constructed for each sensor to characterize the additional influence of topography on the evolution of the local temperature and humidity field. The first... Terrain dynamics factors of individual sensors for: ; In the formula, For the normalized slope factor, This is the directional consistency factor corresponding to the angle between the slope aspect and the canyon's main axis. For the first Elevation of each sensor, For reference elevation of the survey area, Let be the normalization constant for the elevation difference of the survey area. The airflow disturbance factor in the canyon. to These are empirical weighting coefficients.

[0050] Normalized slope factor Determined according to common practices in the fields of surveying and topographic analysis: Step 1: Calculate the original slope: Based on the DEM (Digital Elevation Model) or measured terrain data near the sensor location, calculate the slope angle at that point using the ratio of the difference in adjacent elevations to the horizontal distance. (Unit: degrees or radians); The second step is normalization: The original slope is mapped to the [0, 1] interval. A common method is as follows: , in, , These represent the minimum and maximum slope values ​​for all sensor locations within the survey area, respectively.

[0051] Directional consistency factor corresponding to the angle between slope aspect and canyon principal axis Determined according to common methods in the fields of topography, climatology, and surveying: Step 1: Determine the aspect angle Using the DEM surrounding the sensor location, calculate the projection direction of the ground surface normal vector onto the horizontal plane, i.e., the azimuth angle of the slope orientation (with true north as 0°, measured clockwise): ; Step 2: Determine the main axis direction of the canyon : The main axis direction of a canyon can be obtained in the following ways: extract the valley direction from a topographic map or DEM; perform linear fitting on the valley floor centerline and take its azimuth angle as the valley's orientation. Alternatively, the direction of the survey line extension can be used as an approximate substitute. Step 3: Calculate the included angle : , The 180° modulus is used because the "consistency" between the slope and the principal axis is symmetrical (the forward and reverse directions have the same physical meaning). Step 4: Calculate the directional consistency factor The included angle is transformed into a consistency score within the interval [0, 1] using cosine mapping. , When the slope is parallel to the main axis of the canyon ( ), The airflow guiding effect is strongest when the two are perpendicular ( ), There is no directional consistency.

[0052] Normalized constant of elevation difference in the survey area Defined as the difference in elevation between the highest and lowest points within the survey area, i.e., H = z max -z min ;z ref To determine the reference elevation for the survey area, the arithmetic mean of the elevations of all meteorological stations within the survey area is taken, i.e., z. ref = (1 / n)Σz i .

[0053] In this embodiment, the elevation z of the highest point max = 1280 m, lowest point elevation z min = 680 m, therefore H = 600 m; z ref The average elevation of the survey area is 980 m.

[0054] Canyon airflow disturbance factor : Dimensionless normalized quantity, calculated based on the digital elevation model of the survey area, including but not limited to the weighted combination of the following components: normalized proximity of the i-th sensor to the main axis of the canyon, normalized topographic curvature of the location, normalized proximity to the confluence of the tributary gullies, and sky visibility factor; the value range of each component is [0,1].

[0055] In terms of specific operations, It is decomposed into multiple sub-items that can be directly calculated by the digital elevation model, and explicitly normalized to the interval [0, 1]: , In the formula, the weights to satisfy and Recommended value It can be calibrated by the least squares method using measured data.

[0056] The sub-items are as follows: Canyon main axis proximity factor This reflects the distance of the sensor location from the main axis of the canyon. The airflow channel effect is strongest near the main axis, and the expression is: , In the formula, Let be the horizontal and vertical distance from the i-th sensor to the canyon's main axis (the valley floor centerline extracted from the DEM). This is half the width of the canyon cross-section; Topographic curvature factor This reflects the disturbance of airflow caused by uneven terrain. Concave terrain results in stronger converging and convex terrain in stronger diverging. The expression is: , In the formula, The plane curvature (or profile curvature) calculated from the DEM for the sensor location. This represents the maximum curvature of the measurement area, used for normalization.

[0057] airflow convergence factor : Reflects the proximity of the sensor to the branch ditch / confluence, expressed as: , In the formula, The distance of the sensor from the nearest busbar. The characteristic length of the confluence effect (recommended value: 100-200 m); Topographic openness factor : Reflects the visibility factor of the sky around the sensor (Sky View Factor) The lower the openness and the stronger the valley wall confinement, the more significant the airflow disturbance. The expression is: .

[0058] In this embodiment, the airflow disturbance factor in the canyon is addressed. The main axis of the canyon was extracted from the digital elevation model of the survey area: m, m, m -1 Taking the fifth sensor, located in the center of the valley floor, as an example, m、 m、 m -1 , Substituting into the calculation, we get Similarly, the 18th sensor located on the ridge corresponds to... .

[0059] Empirical weighting coefficient to The following method can be used to determine the sample: Select measured temperature data from N meteorological stations within the survey area as the sample, and aim to minimize the cross-validation error of the reserved n validation stations. Use the least squares method / genetic algorithm / grid search method to... to Perform parameter optimization with the following constraints: and It can also be other reasonable constraints.

[0060] In mountainous and canyon terrain, the recommended value is [value to be filled in]. ∈ [0.2, 0.4] (slope weight) ∈ [0.1,0.2] (slope weight) ∈ [0.3, 0.5] (elevation weight) ∈ [0.1, 0.3] (airflow disturbance weight); in flat terrain, and A smaller value can be taken.

[0061] In this embodiment, the following can be taken: = 0.30、 = 0.15、 = 0.40、 = 0.15.

[0062] The temperature sample values ​​were corrected for terrain features to obtain the following: , In the formula, These are the original observations. These are the sample values ​​after terrain correction. This is the correction factor for the corresponding meteorological element.

[0063] Regarding temperature, Primarily determined by the temperature lapse rate of the measurement area, it is recommended under standard atmospheric conditions. Mainly = -0.6 × H / 100 (°C). In this embodiment, -3.6℃ is acceptable; The water vapor pressure sample values ​​were corrected for topographic features to obtain: , In the formula, These are the original observations. These are the sample values ​​after terrain correction. This is the correction factor for the corresponding meteorological element; where water vapor pressure is calculated from relative humidity (or dew point) and temperature using the Magnus formula.

[0064] The Magnus formula expression is as follows: , , in, The saturated vapor pressure is (hPa). Temperature in Celsius Relative humidity (%) This is the actual water vapor pressure.

[0065] Regarding water vapor pressure This characterizes the change in water vapor pressure caused by a unit change in topographic dynamic factors, reflecting the intensity of topography's modulation of the spatial distribution of water vapor pressure, and is expressed in hPa. In this embodiment, based on the partial derivative of the Magnus formula with respect to temperature, a... and Relationship: , At typical temperatures hour: , therefore: , by , For example, hPa: .

[0066] For temperature and water vapor pressure, the original sample values ​​are first corrected based on topographic dynamic factors. Then, the corrected sample values ​​are used as the calculation samples for the basic semivariogram, allowing the influence of slope, aspect, elevation difference, and canyon airflow disturbance on the spatial correlation of meteorological elements to enter the semivariogram fitting process. The basic semivariogram... The expression is as follows: ; In the formula, Based on the semi-variogram function, The spatial distance between the sampling points of the two sensors. The distance is The number of sensor sampling points, For the first The measurement values ​​of each sensor, For distance from the first The distance between the sensors is The measured value; The basic semivariogram adopts a spherical model, and its expression is: , In the formula, The value of the nugget reflects the measurement error; The sill value reflects the total spatial variation; For variable range, the distance of maximum spatial correlation of meteorological elements is adjusted according to the regional meteorological complexity.

[0067] In this embodiment, the temperature semivariogram nugget value is taken as 0.02 to 0.05, the sill value as 0.10 to 0.30, and the range as 300 to 500 m; humidity adopts similar spherical model parameters and is finely adjusted based on the verification residuals. The fitting graph of the temperature and humidity semivariograms is shown below. Figure 5 As shown.

[0068] For air pressure, the calculation of air pressure elements at any point adopts an elevation linear fitting model. When the elevation difference in the survey area is no greater than 300 m and the residual obtained by fitting the linear model based on measured air pressure samples from meteorological stations is no greater than 0.3 hPa, according to... ;in, The pressure value at the point to be estimated. For the elevation of the point to be estimated, The slope of the fitted equation for air pressure as a function of elevation is given. The intercept is the fitting distance. When the elevation difference in the survey area is greater than 300 m or the linear fitting residual is greater than 0.3 hPa, the survey area is divided into at least two zones according to the elevation interval, and a piecewise linear model is established for each zone. The air pressure value at the point to be estimated is calculated according to the following formula: ;in, and The first The fitting intercept and fitting slope corresponding to each elevation zone.

[0069] The barometric pressure-elevation linear fitting graph is shown below. Figure 6 As shown.

[0070] More specifically, in a linear or piecewise fitting model of air pressure versus elevation, the linear fitting intercept... and linear fitting slope The calculation method is as follows: , , In the formula, For the first The readings from the barometer sensor. To correspond to the elevation, The average air pressure, The average elevation is used. When the elevation difference of the survey area within a single sampling period is not greater than the preset threshold and the fitting residual does not exceed the preset threshold, a single fitting is used; otherwise, segmented fitting is performed according to elevation zones.

[0071] The third step involves using a three-dimensional meteorological field model of the survey area to calculate the temperature and water vapor pressure meteorological elements at any point along the survey line using ordinary Kriging weighted solutions, and to obtain the air pressure meteorological elements at any point along the survey line using an elevation linear fitting model.

[0072] Specifically, the calculation methods for meteorological elements at any point along the survey line include: Based on the three-dimensional meteorological field model of the survey area, calculate the temperature at any coordinate (x0, y0, z0). The formula is as follows: , In the formula, m represents the number of sensors involved in the interpolation. Let be the weighting coefficient of the i-th sensor, satisfying .

[0073] In this embodiment, the number of effective neighboring sensors participating in the interpolation is 60 to 80.

[0074] Based on the three-dimensional meteorological field model of the survey area, the water vapor pressure at any coordinate (x0, y0, z0) is calculated. The formula is as follows: , In the formula, Let be the water vapor pressure at any coordinate (x0, y0, z0), and m be the number of sensors involved in the interpolation. Let be the weighting coefficient of the i-th sensor, satisfying .

[0075] Air pressure is obtained directly from elevation through linear fitting, as shown in the following formula: , In the formula, Let be the air pressure value at any spatial coordinate (x, y, z). The intercept of the linear fit. is the slope of the linear fit, and z is the elevation of the spatial location to be calculated.

[0076] In this embodiment, the three-dimensional meteorological field model generates a three-dimensional meteorological grid with a resolution of 0.5m. The query error of meteorological elements at any spatial point (X,Y,Z) is controlled within ±0.1℃ for temperature, ±0.2hPa for air pressure, and ±1% for humidity, which can accurately capture the meteorological gradient and short-term fluctuations at mountain tops, hillsides, and valley bottoms. Based on the unbiased optimal estimation characteristics of Kriging interpolation, a grid of arbitrary resolution can be mathematically output, with 0.5m being the corresponding resolution for the discretization of the survey line.

[0077] The flowchart for discretization of survey lines and calculation of meteorological corrections is as follows: Figure 7 As shown, that is, steps four and five below, are described in detail below.

[0078] The fourth step is to discretize the survey line direction at fixed intervals to obtain several differential segments. The meteorological elements of any point in the survey line direction corresponding to the endpoints of each differential segment are input into the atmospheric refractive index calculation model based on the simplified engineering expression of the Edlén model to calculate a series of meteorological corrections for fixed-interval differential segments.

[0079] A schematic diagram of the spatial relationship between the meteorological station layout and the survey lines in the canyon survey area is shown below. Figure 8 As shown, specifically, the calculation method for meteorological corrections at fixed intervals includes: First, the actual meteorological elements (P, T, e) are converted into refractive index increments N, and then simplified using the Edlén model: ; In the formula, Absolute temperature For air pressure, For water vapor pressure, and Empirical coefficients obtained from the working wavelength calibration of the perimeter; atmospheric refractive index. satisfy .

[0080] The above expression is used for rapid refractive index calculation in engineering boundary measurement. Its core temperature-pressure relationship uses the P / T form, and the water vapor term uses the e / T form to maintain consistency with the physical dimensions of the refractive index. Electromagnetic waves travel slower in the atmosphere than in a vacuum, so the initial boundary measurement value L0 will be too large and needs to be corrected. Correction.

[0081] After discretizing the survey line direction at fixed intervals, first calculate the first... segment starting point refractive index With the endpoint refractive index average : ; No. The weather correction for a fixed-interval differential segment is: .

[0082] In the formula, The interval is fixed for the differential segments; For the first The average atmospheric refractive index of each differential segment is the average of the refractive indices at the beginning and end of the differential segment.

[0083] In this embodiment, the fixed spacing between the differential segments is 1m.

[0084] Meteorological element profile along the survey line as shown in the figure Figure 9 As shown, the distribution of refractive index and piecewise correction along the survey line is as follows: Figure 10 As shown.

[0085] Step 5: Based on the above series of fixed-interval meteorological corrections, the cumulative meteorological correction for the entire survey line is calculated using the numerical integration method within the survey line distance.

[0086] Specifically, the method for calculating the cumulative meteorological correction for the entire survey line is as follows: Let the total length of the entire survey line be The total length of the entire survey line in this embodiment =1500m, can be broken down into Each differential segment, This indicates rounding up; the cumulative meteorological correction D for the entire survey line is the sum of all differential segment corrections, as shown in the following formula: .

[0087] This embodiment addresses the characteristics of large and drastic differences in meteorological conditions in high mountains and canyons. The sensor deployment and modeling algorithm fully integrate the terrain and airflow characteristics of high mountains and canyons, which can effectively cope with the drastic differences in temperature and air pressure between the top and bottom of the valley and short-term meteorological fluctuations. It can dynamically update the three-dimensional meteorological field model, solving the problem of traditional methods being "unsuitable" in such complex areas. It is applicable to complex meteorological environments with large temperature gradients and frequent airflow disturbances.

[0088] To verify the technical effectiveness of this method, a 1 km check line was selected in the same canyon survey area, and the calculation results of independent check sensor profiles deployed every 20 m along the line were used as reference true values ​​for comparison across 40 sets of synchronous observation periods. The statistical diagram of interpolation errors for meteorological elements at any point is shown in Figure 11, and the influence of the differential step size on the cumulative correction results is shown in Figure 12. Figure 12 As shown in the figure, the error correction method of this invention is compared with that of the traditional two-point averaging method. Figure 13 As shown, the results indicate that the average corrected residual of the method of this invention is 0.42 mm / km, and the maximum residual is 0.48 mm / km; the average corrected residual of the traditional two-endpoint averaging method is 2.31 mm / km, and the maximum residual is 2.87 mm / km. The corresponding three-dimensional meteorological field query residual statistics are: average absolute error of temperature 0.08 ℃, average absolute error of air pressure 0.17 hPa, and average absolute error of humidity 2.6%. Under three typical terrain types—valley area, slope break area, and near-ridge area—the corrected residuals of this method are 0.31 mm / km, 0.44 mm / km, and 0.49 mm / km, respectively, all meeting the requirements for millimeter-level error control in high-precision deformation monitoring.

[0089] Traditional boundary measurement correction relies on manual observation of meteorological data, which is limited by weather conditions and on-site operations. Especially under extreme weather conditions such as heavy rain and floods, the monitoring accuracy and real-time performance are often difficult to guarantee. In contrast, this application, based on automatic data acquisition, wireless data transmission, and automatic model calculation, can overcome harsh environments such as weather and terrain, achieving all-day, all-weather monitoring. It can also stably acquire meteorological elements in the survey area, greatly improving the reliability and sustainability of high-precision boundary measurement data meteorological correction, and ensuring continuous high-precision deformation monitoring capabilities under extreme weather conditions.

[0090] Example 2

[0091] A high-precision boundary measurement meteorological correction system based on three-dimensional meteorological fields and calculus is illustrated in the following diagram. Figure 14 As shown, it consists of a meteorological station layout module, a three-dimensional meteorological field model construction module, an arbitrary point meteorological element calculation module, a meteorological correction calculation module, and a cumulative meteorological correction calculation module.

[0092] The meteorological station deployment module deploys meteorological stations based on the topography, landforms, cross sections, and elevation of the survey area, combined with the potential path of the survey line and areas of sudden meteorological changes.

[0093] The three-dimensional meteorological field model construction module constructs a three-dimensional meteorological field model of the survey area based on meteorological elements collected from meteorological stations. For temperature and water vapor pressure, topographic dynamic factors including slope, aspect, elevation difference, and valley airflow disturbance terms are constructed through normalization and weighted summation. The original meteorological sample values ​​are corrected by the topographic dynamic factors, and then the corrected meteorological sample values ​​are used as the input of the basic semivariogram for fitting. For air pressure, an elevation linear fitting model is used for fitting.

[0094] The arbitrary point meteorological element calculation module, based on the three-dimensional meteorological field model of the survey area, calculates the temperature and water vapor pressure meteorological elements at any point along the survey line by ordinary Kriging weight solution, and obtains the air pressure meteorological elements at any point along the survey line by solving the elevation linear fitting model.

[0095] The meteorological correction calculation module discretizes the survey line direction at fixed intervals to obtain several differential segments. The meteorological elements of any point in the survey line direction corresponding to the endpoints of each differential segment are input into the atmospheric refractive index calculation model based on the simplified engineering expression of the Edlén model to calculate a series of meteorological corrections for fixed-interval differential segments.

[0096] The cumulative meteorological correction calculation module calculates the cumulative meteorological correction for the entire survey line by using a numerical integration method within the survey line distance, based on the aforementioned series of fixed-interval meteorological corrections.

[0097] Specifically, in this system, the output end of the meteorological station deployment module is connected to the input end of the three-dimensional meteorological field model construction module, the output end of the three-dimensional meteorological field model construction module is connected to the input end of the arbitrary point meteorological element calculation module, the output end of the arbitrary point meteorological element calculation module is connected to the input end of the meteorological correction calculation module, and the output end of the meteorological correction calculation module is connected to the input end of the cumulative meteorological correction calculation module.

[0098] The specific implementation methods of each module in this system are the same as those described in Example 1, and will not be repeated here.

[0099] Example 3

[0100] An electronic device includes a memory and a processor, the memory and the processor being communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus as described in Embodiment 1 above, and the high-precision boundary measurement meteorological correction system based on three-dimensional meteorological field and calculus as described in Embodiment 2.

[0101] Example 4

[0102] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus as described in Embodiment 1 above, and the high-precision boundary measurement meteorological correction system based on three-dimensional meteorological field and calculus as described in Embodiment 2.

[0103] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as object-oriented programming languages ​​like Java, C++, Python, and interpreted scripting languages ​​like JavaScript.

[0104] This application is described with reference to flowchart illustrations and / or block diagrams of methods, electronic devices (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing electronic device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing electronic device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing electronic device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing electronic device to cause a series of operational steps to be performed on the computer or other programmable electronic device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable electronic device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0108] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A high-precision meteorological correction method for boundary measurement based on three-dimensional meteorological fields and calculus, characterized in that, Includes the following steps: Meteorological stations are set up based on the topography, landforms, cross sections, and elevation of the survey area, combined with the potential path of the survey line and areas of sudden meteorological changes. Based on meteorological elements collected from meteorological stations, a three-dimensional meteorological field model of the survey area is constructed: for temperature and water vapor pressure, topographic dynamic factors including slope, aspect, elevation difference and valley airflow disturbance terms are constructed by normalization and weighted summation. The original meteorological sample values ​​are corrected by the topographic dynamic factors, and then the corrected meteorological sample values ​​are used as the input of the basic semivariogram for fitting. For air pressure, a linear fitting model based on elevation was used for fitting. Based on the three-dimensional meteorological field model of the survey area, the temperature and water vapor pressure meteorological elements at any point along the survey line are calculated by ordinary Kriging weight solution, and the air pressure meteorological elements at any point along the survey line are obtained by solving the elevation linear fitting model. The survey line direction is discretized at fixed intervals to obtain several differential segments. The meteorological elements of any point in the survey line direction corresponding to the endpoints of each differential segment are input into the atmospheric refractive index calculation model based on the simplified engineering expression of the Edlén model to calculate a series of meteorological corrections for fixed-interval differential segments. Based on the above series of fixed-interval meteorological corrections, the cumulative meteorological correction for the entire survey line is calculated using a numerical integration method over the survey line distance.

2. The high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus according to claim 1, characterized in that: In the deployment of the meteorological stations, the sensor deployment should take into account the valley orientation, slope gradient, airflow channels and deformation monitoring section characteristics, and adopt the "baseline grid + key densification" mode to deploy the sensors, and the spacing between all sensors should meet the interpolation accuracy requirements. The baseline grid is laid out according to a preset side length in the valley bottom and gentle slope areas. The preset side length is determined according to the terrain complexity and interpolation accuracy requirements of the survey area. The baseline grid is densified in the steep slopes, convex and concave terrain and airflow confluence areas on both sides of the valley. Sensors are added along the contour line direction at the elevation change points. Sensors are synchronously laid out within a certain range on both sides of the deformation monitoring section in the extension direction of the section. The sensor synchronously connects to the BeiDou positioning system to obtain three-dimensional coordinates (X,Y,Z), and samples at a sampling frequency no less than that required for capturing the time-varying characteristics of meteorological elements, sampling temperature, air pressure, and relative humidity.

3. The high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus according to claim 2, characterized in that: In constructing the three-dimensional meteorological field model of the survey area, the spatial continuity of meteorological elements is achieved through the Kriging interpolation algorithm, and the meteorological elements at any coordinate (x, y, z) are calculated: temperature, water vapor pressure, and air pressure, as detailed below: Definition of the first The terrain dynamic factors of each sensor are: ； In the formula, For the normalized slope factor, This is the directional consistency factor corresponding to the angle between the slope aspect and the canyon's main axis. For the first Elevation of each sensor, For reference elevation of the survey area, The normalization constant for the elevation difference in the survey area is... The airflow disturbance factor in the canyon. to These are empirical weighting coefficients; The temperature sample values ​​were corrected for terrain features to obtain the following: , In the formula, These are the original observations. These are the sample values ​​after terrain correction. These are the correction factors for the corresponding meteorological elements; The water vapor pressure sample values ​​were corrected for topographic features to obtain: , In the formula, These are the original observations. These are the sample values ​​after terrain correction. These are correction coefficients for the corresponding meteorological elements; where water vapor pressure is calculated from relative humidity or dew point and temperature using the Magnus formula; For temperature and water vapor pressure, the original sample values ​​are first corrected based on topographic dynamic factors, and then the corrected sample values ​​are used as the calculation samples for the basic semivariogram, so that the influence of slope, aspect, elevation difference and canyon airflow disturbance on the spatial correlation of meteorological elements enters the semivariogram fitting process. The basic semivariogram The expression is as follows: ； In the formula, Based on the semi-variogram function, The spatial distance between the sampling points of the two sensors. The distance is The number of sensor sampling points, For the first The measurement values ​​of each sensor, For distance from the first The distance between the sensors is The measured value; The basic semivariogram adopts a spherical model, and its expression is: ， In the formula, The value of the nugget reflects the measurement error; The sill value reflects the total spatial variation; For variable range, the distance of maximum spatial correlation of meteorological elements is adjusted according to the regional meteorological complexity; For air pressure, the calculation of air pressure elements at any point adopts an elevation linear fitting model. When the elevation difference in the survey area is no greater than 300 m and the residual obtained by fitting the linear model based on measured air pressure samples from meteorological stations is no greater than 0.3 hPa, according to... ;in, The pressure value at the point to be estimated. For the elevation of the point to be estimated, The slope of the fitted equation for air pressure as a function of elevation is given. The intercept is the fitting angle. When the elevation difference in the survey area is greater than 300 m or the linear fitting residual is greater than 0.3 hPa, the survey area is divided into at least two zones according to the elevation interval, and a piecewise linear model is established for each zone. The air pressure value at the point to be estimated is calculated according to the following formula: ;in, and The first The fitting intercept and fitting slope corresponding to each elevation zone.

4. The high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus according to claim 3, characterized in that: In the linear or piecewise fitting of air pressure with elevation, the intercept of the linear fitting is... and linear fitting slope The calculation method is as follows: ， ， In the formula, For the first The readings from the barometer sensor. To correspond to the elevation, The average air pressure, The average elevation is used. When the elevation difference of the survey area within a single sampling period is not greater than the preset threshold and the fitting residual does not exceed the preset threshold, a single fitting is used; otherwise, segmented fitting is performed according to elevation zones.

5. The high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus according to claim 3, characterized in that: The calculation method for meteorological elements at any point along the survey line includes: Based on the three-dimensional meteorological field model of the survey area, calculate the temperature at any coordinate (x0, y0, z0). The formula is as follows: ， In the formula, m represents the number of sensors involved in the interpolation. Let be the weighting coefficient of the i-th sensor, satisfying ; Based on the three-dimensional meteorological field model of the survey area, the water vapor pressure at any coordinate (x0, y0, z0) is calculated. The formula is as follows: ， In the formula, Let be the water vapor pressure at any coordinate (x0, y0, z0), and m be the number of sensors involved in the interpolation. Let be the weighting coefficient of the i-th sensor, satisfying ; Air pressure is obtained directly from elevation through linear fitting, as shown in the following formula: ， In the formula, Let be the air pressure value at any spatial coordinate (x, y, z). The intercept of the linear fit. is the slope of the linear fit, and z is the elevation of the spatial location to be calculated.

6. The high-precision meteorological correction method for boundary measurement based on three-dimensional meteorological field and calculus according to claim 5, characterized in that: The calculation method for the fixed-interval meteorological correction includes: First, the actual meteorological elements (P, T, e) are converted into refractive index increments N, and then simplified using the Edlén model: ; In the formula, Absolute temperature For air pressure, For water vapor pressure, and Empirical coefficients obtained from the working wavelength calibration of the perimeter; atmospheric refractive index. satisfy ; After discretizing the survey line direction at fixed intervals, first calculate the first... segment starting point refractive index With the endpoint refractive index average : ； No. The weather correction for a fixed-interval differential segment is: ； In the formula, The interval between the differential segments is fixed. For the first The average atmospheric refractive index of each differential segment is the average of the refractive indices at the beginning and end of the differential segment.

7. The high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus according to claim 6, characterized in that: The method for calculating the cumulative meteorological correction for the entire survey line is as follows: Let the total length of the entire survey line be It can be broken down into Each differential segment, This indicates rounding up; the cumulative meteorological correction D for the entire survey line is the sum of all differential segment corrections, as shown in the following formula: 。 8. A high-precision boundary-finding meteorological correction system based on three-dimensional meteorological fields and calculus, applicable to the high-precision boundary-finding meteorological correction method based on three-dimensional meteorological fields and calculus described in any one of claims 1-7, characterized in that: It includes modules for meteorological station deployment, three-dimensional meteorological field model construction, calculation of meteorological elements at any point, calculation of meteorological corrections, and calculation of cumulative meteorological corrections. The meteorological station deployment module deploys meteorological stations based on the topography, landforms, cross sections, and elevation of the survey area, combined with the potential path of the survey line and areas of sudden meteorological changes. The three-dimensional meteorological field model construction module constructs a three-dimensional meteorological field model of the survey area based on meteorological elements collected by meteorological stations: for temperature and water vapor pressure, a topographic dynamic factor including slope, aspect, elevation difference and valley airflow disturbance terms is constructed by normalization and weighted summation. The original meteorological sample values ​​are corrected by the topographic dynamic factor, and then the corrected meteorological sample values ​​are used as the input of the basic semivariogram for fitting. For air pressure, a linear fitting model based on elevation was used for fitting. The arbitrary point meteorological element calculation module, based on the three-dimensional meteorological field model of the survey area, calculates the temperature and water vapor pressure meteorological elements at any point in the survey line direction through ordinary Kriging weight solution, and calculates the air pressure meteorological elements at any point in the survey line direction through the elevation linear fitting model. The meteorological correction calculation module discretizes the survey line direction at fixed intervals to obtain several differential segments. The meteorological elements of any point in the survey line direction corresponding to the endpoints of each differential segment are input into the atmospheric refractive index calculation model based on the simplified engineering expression of the Edlén model to calculate a series of meteorological corrections for fixed-interval differential segments. The cumulative meteorological correction calculation module calculates the cumulative meteorological correction for the entire survey line by using a numerical integration method within the survey line distance, based on the aforementioned series of fixed-interval meteorological corrections.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes these computer instructions to implement the high-precision boundary measurement meteorological correction method based on three-dimensional meteorological fields and calculus as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the high-precision boundary measurement meteorological correction method based on three-dimensional meteorological field and calculus as described in any one of claims 1-7.

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