A method and device for constructing vertical constraint equations with an adaptive function selection mechanism

Abstract

The present invention discloses a method and device for constructing a vertical constraint equation with an adaptive function selection mechanism, which belongs to the application of global navigation satellite system technology in the field of meteorology. The present invention summarizes three typical water vapor distribution characteristics on the basis of extensive analysis of water vapor vertical distribution characteristics, namely exponential function type, Gaussian function type and S-type function type distribution, and specifically proposes a Beidou water vapor tomography vertical constraint construction method with an additional adaptive function selection mechanism, which solves the problem of automatically selecting the optimal function to construct vertical constraints under different water vapor vertical structures, improves the accuracy of vertical constraint construction, and further improves the accuracy and reliability of Beidou water vapor tomography results.

Description

Technical Field

[0001] The present invention relates to the application of global navigation satellite system technology in the field of meteorology, and in particular to a method for constructing vertical constraint equations with an additional adaptive function selection mechanism when performing three-dimensional water vapor inversion using Beidou or GNSS technology. Specifically, the present invention provides a method and device for constructing vertical constraint equations with an adaptive function selection mechanism. Background Art

[0002] BeiDou / GNSS (Global Navigation Satellite System) 3D water vapor inversion technology is an emerging method for monitoring 3D atmospheric water vapor content. It is a key application of BeiDou / GNSS technology in meteorology and one of the few existing 3D water vapor monitoring methods. When using BeiDou / GNSS technology to invert 3D water vapor, the signal rays are excessively concentrated in the zenith direction, causing the basic observation equations used in BeiDou 3D water vapor inversion to be ill-posed. This results in multiple solutions for the tomographic results, necessitating the addition of constraint equations to address rank deficiency and ill-posedness. Of these constraint equations, the vertical constraint equation is the most important, often crucial for accurately and stably capturing the vertical structure of water vapor.

[0003] Because in most cases the vertical distribution of water vapor decreases with increasing altitude, showing a change form similar to an exponential curve, the existing Beidou water vapor tomography technology generally adopts a single function, such as an exponential function or a Gaussian function, to construct vertical constraints. However, the vertical changes of water vapor are complex and the vertical structure is changeable. A single function cannot describe the water vapor distribution characteristics in all situations. Inaccurate vertical constraints will cause the results to deviate from the actual situation, that is, distort the solution space, and greatly reduce the accuracy and reliability of the vertical constraints. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for constructing a vertical constraint equation with an adaptive function selection mechanism. The new vertical constraint equation can more accurately express the conventional and unconventional water vapor vertical distribution characteristics than a single exponential function method. The vertical constraint equation continuously approaches the actual situation, and ultimately improves the accuracy and stability of the Beidou three-dimensional water vapor inversion results by refining the vertical constraint equation.

[0005] According to one aspect of the present invention, a method for constructing a vertical constraint equation of an adaptive function selection mechanism is provided, comprising:

[0006] Based on the sounding or numerical weather forecast data in the target area, the three designed vertical constraint functions are fitted respectively, and the initial coefficients of each vertical constraint function are calculated to obtain the initial optimal fitting function;

[0007] Constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group;

[0008] The vertical constraint equations are combined with the basic observation equations of BeiDou 3D water vapor inversion to solve the equations and obtain the root mean square error of the residual of the vertical constraint equations. The optimal vertical constraint function is determined based on the minimum root mean square error, and the corresponding solution is used as the initial tomographic solution.

[0009] The initial tomographic solution is combined with the sounding or numerical weather forecast data for constructing vertical constraints, and the coefficients of the optimal vertical constraint function are solved again to form a set of vertical constraint equations again. The tomographic solution is obtained by combining the solution with the observation equation until the iteration stop condition is met.

[0010] As a further technical solution, satisfying the iteration stopping condition includes: the absolute value of the minimum difference between two adjacent tomographic solutions is less than a preset value.

[0011] As a further technical solution, three vertical constraint functions are designed to simulate three typical water vapor vertical distribution structures, including:

[0012] Sigmoid function:

[0013] Exponential function:

[0014] Gaussian function:

[0015] Where x represents elevation, f(x) represents water vapor density or wet refractive index, e is a natural constant, and a, b, and c are coefficients of the equation.

[0016] As a further technical solution, a proportional relationship model is constructed between vertically adjacent grids based on the initial optimal fitting function to form a vertical constraint equation group, including:

[0017] Perform BeiDou 3D water vapor inversion and discretize the continuous space into a finite number of grids;

[0018] The optimal fitting function is used to construct a proportional relationship model between grids at the same horizontal position but at different heights to form a vertical constraint equation group.

[0019] As a further technical solution, the vertical constraint equations are

[0020] ,

[0021] in, Indicates height The undetermined parameters at Indicates height The parameters to be determined at .

[0022] As a further technical solution, the vertical constraint equations are combined with the basic observation equations of BeiDou 3D water vapor inversion to solve the equations, including:

[0023] Combine the vertical constraint equations with the basic observation equations of BeiDou 3D water vapor inversion;

[0024] The generalized cross-validation method is used to determine the weighted relationship between the constraint equation and the observation equation, and the weighted least squares principle is used to solve the equation system.

[0025] According to one aspect of the present invention, there is provided a device for constructing a vertical constraint equation with an adaptive function selection mechanism, comprising:

[0026] The first main module is used to fit the three designed vertical constraint functions based on the sounding or numerical weather forecast data in the target area, calculate the initial coefficient of each vertical constraint function, and obtain the initial optimal fitting function;

[0027] A second main module is used to construct a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group;

[0028] The third main module is used to combine the vertical constraint equations with the basic observation equations of Beidou 3D water vapor inversion, solve the equations, and obtain the root mean square error of the residual of the vertical constraint equation. The optimal vertical constraint function is determined based on the minimum root mean square error, and the corresponding solution is used as the initial tomographic solution.

[0029] The fourth main module is used to combine the initial tomographic solution with the sounding or numerical weather forecast data for constructing vertical constraints, solve the coefficients of the optimal vertical constraint function again, form the vertical constraint equation group again, and solve it together with the observation equation to obtain the tomographic solution until the iteration stop condition is met.

[0030] According to one aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.

[0031] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions cause the computer to execute the method described above.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) The present invention introduces a two-step adaptive mechanism to construct vertical constraints. The first is the adaptive selection of the optimal vertical constraint function mechanism, and the second is the constraint function optimization adjustment mechanism. Through these two-step mechanisms, a scientific, reasonable, accurate and reliable vertical constraint equation can be constructed, which can reduce the introduction of system deviation.

[0034] (2) The present invention can better express the vertical distribution structure of water vapor under conventional and unconventional conditions.

[0035] (3) The present invention can construct a more accurate and reliable vertical constraint equation for water vapor tomography, thereby improving the accuracy and stability of Beidou three-dimensional water vapor inversion while introducing minimal deviation.

[0036] (4) In the second step of adaptive adjustment, the present invention adopts a method of jointly solving the coefficients of the vertical constraint equation by using the solution results and external data (sounding / numerical weather data). Compared with the prior art that only uses the tomographic solution to solve the coefficients of the vertical constraint equation (the hidden danger of the prior art is that when the tomographic results are good, the constraints will become more and more accurate, and there will be a benign iteration between the constraints and the solution, but when the tomographic results are not good, the reconstructed constraint equation will also deteriorate, resulting in a vicious iteration between the constraints and the solution), the present invention reconstructs the vertical constraint equation by jointly using external data and tomographic results, which can effectively suppress the above-mentioned hidden dangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flowchart of a method for constructing a vertical constraint equation with an adaptive function selection mechanism provided by an embodiment of the present invention.

[0039] Figure 2 A schematic diagram of the vertical structure of water vapor under normal conditions and the fitting effect of the exponential function on the vertical distribution of water vapor provided by an embodiment of the present invention.

[0040] Figure 3 A schematic diagram of the vertical structure of water vapor under unconventional conditions and the fitting effect of the new function on the vertical distribution of water vapor provided by an embodiment of the present invention.

[0041] Figure 4 A schematic diagram of the constraint completion effect provided by an embodiment of the present invention.

[0042] Figure 5A schematic structural diagram of a vertical constraint equation construction device with an adaptive function selection mechanism provided by an embodiment of the present invention.

[0043] Figure 6 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In view of the fact that a single function cannot describe the water vapor distribution characteristics in all situations, which greatly reduces the accuracy and reliability of vertical constraints, this paper summarizes three typical water vapor distribution characteristics based on an extensive analysis of water vapor vertical distribution characteristics, namely exponential function, Gaussian function and S-type function distribution, and proposes a Beidou water vapor tomography vertical constraint construction method with an additional adaptive function selection mechanism. This solves the problem of automatically selecting the optimal function to construct vertical constraints under different water vapor vertical structures, improves the accuracy of vertical constraint construction, and thus improves the accuracy and reliability of Beidou / GNSS water vapor tomography results.

[0045] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0047] Aiming at the shortcomings of the vertical constraint construction method in the existing Beidou three-dimensional water vapor inversion technology, namely (1) the constraint equation is single and cannot accurately characterize the vertical structure of water vapor under conventional and unconventional conditions; (2) the vertical constraint does not have an adaptive selection mechanism, the present invention provides a vertical constraint equation construction method with an adaptive function selection mechanism to realize the adaptive selection and optimization mechanism of the vertical constraint function.

[0048] like Figure 1 As shown, the method for constructing a vertical constraint equation of the adaptive function selection mechanism provided by the embodiment of the present invention includes:

[0049] Step 1: Fit the three designed vertical constraint functions based on the sounding or numerical weather forecast data in the target area, calculate the initial coefficient of each vertical constraint function, and obtain the initial optimal fitting function.

[0050] In step 1.1, we analyzed the typical structures of vertical water vapor distribution using a large amount of radiosonde data. We summarized three typical water vapor distribution structures and designed three alternative vertical constraint functions to simulate the three typical water vapor vertical distribution structures. The three vertical constraint functions are expressed as follows:

[0051] Sigmoid function: (1)

[0052] Exponential function: (2)

[0053] Gaussian function: (3)

[0054] Where x represents elevation, f(x) represents water vapor density or wet refractive index, e is a natural constant, and a, b, and c are the coefficients of the equation. Specifically, the initial values ​​of the coefficients a, b, and c in the equation can be fitted based on measured data such as water vapor density or wet refractive index and the corresponding elevation obtained from radiosonde or numerical weather forecasting.

[0055] Step 1.2: When performing BeiDou / GNSS 3D water vapor tomography, the three alternative vertical constraint functions in step 1.1 are fitted separately using the sounding data or numerical weather forecast data in the study area. The coefficients of the equations are calculated using the nonlinear least squares method to determine the optimal fitting functions for the three typical water vapor distribution structures. These three optimal fitting functions are used as alternative functions for constructing vertical constraints in the region, denoted as , .

[0056] Step 2: construct a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group.

[0057] When performing Beidou 3D water vapor inversion, the continuous space is discretized into a finite number of grids. The embodiment of the present invention needs to use the optimal fitting function obtained in step 1 to construct a vertical constraint equation between grids at the same horizontal position but at different heights. The specific method is to use the vertical constraint equation determined in step 1 to construct a proportional relationship model between vertically adjacent grids, that is,

[0058] ,

[0059] Among them, the values ​​on the right side of the equal sign are all known values, and and represent known heights. The above formulas are equivalent to constructing the proportional relationship of and. Indicates height The undetermined parameters at Indicates height The unknown parameter at can be the wet refractive index or water vapor density. Similar linear functional relationships are constructed between all vertically adjacent grids to form a vertical constraint equation system.

[0060] Step 3: Combine the vertical constraint equations with the basic observation equations of Beidou 3D water vapor inversion, solve the equations, and obtain the root mean square error of the residual of the vertical constraint equation. Determine the optimal vertical constraint function based on the minimum root mean square error, and use the corresponding solution as the initial tomographic solution.

[0061] Specifically, one of the three vertical constraint equations constructed in step 2 is combined with the basic observation equation of Beidou 3D water vapor inversion. The generalized cross-validation method is used to determine the weighted relationship between the constraint equation and the observation equation. Then, the weighted least squares principle is used to solve the system of equations to obtain the root mean square error of the residual of the vertical constraint equation. The optimal vertical constraint function is determined based on the criterion of minimizing the root mean square error, and the corresponding solution is used as the initial tomographic solution.

[0062] In step 4, the initial tomographic solution is combined with the radiosonde or numerical forecast data used to construct the vertical constraint to re-solve the coefficients of the optimal vertical constraint function, reform the vertical constraint equations, and then return to step 3 to solve the tomographic solution. Repeat these steps until the absolute value of the minimum difference between the two tomographic results (measured in wet refractive index) is less than 0.5 mm / km.

[0063] The above process of the embodiment of the present invention introduces a two-step adaptive mechanism to construct vertical constraints. The first is the adaptive selection of the optimal vertical constraint function mechanism in step 3, and the second is the constraint function optimization and adjustment mechanism in step 4. Through these two-step mechanisms, a scientific, reasonable, accurate and reliable vertical constraint equation can be constructed.

[0064] Figure 1 The vertical structure of water vapor under normal conditions and the fitting effect of the exponential function on the vertical distribution of water vapor are demonstrated. The root mean square error (RMS) of the exponential function is 11.1 mm / km, while the Gaussian and S-shaped functions have RMS errors of 8.6 mm / km and 8.7 mm / km, respectively. This shows that using a single function cannot effectively reflect the specific water vapor inversion. Through adaptive selection, the function with the lowest RMS error can be selected for subsequent calculations.

[0065] Figure 2 The vertical structure of water vapor under unconventional conditions and the effectiveness of the new function in fitting the vertical distribution of water vapor are demonstrated. Adaptive correction of the function is even more necessary for unconventional conditions. The root mean square error (RMS) of the conventional, single exponential function is 6.5 mm / km, while the RMS error of the Gaussian function is 3.9 mm / km, and the RMS error of the S-shaped function is 6.4 mm / km. After adaptive adjustment, the Gaussian function was selected for fitting and subsequent calculations.

[0066] Based on the above embodiments, the present invention can be used to construct a more accurate and reliable water vapor tomography vertical constraint equation, thereby improving the accuracy and stability of Beidou / GNSS three-dimensional water vapor inversion while introducing minimum deviation.

[0067] As a preferred embodiment, the embodiment of the present invention uses the Beidou three-dimensional water vapor inversion experiment in the Beijing area at 0:00 UTC on the 206th day of 2023 as an example to illustrate the specific implementation method of the present invention, including the following steps:

[0068] In step 1, the three alternative vertical constraint functions (Equations (1), (2), and (3)) are fitted using 206 days of 0:00 UTC radiosonde data or numerical weather forecast data in the Beijing area. The coefficients of the equations are calculated using the nonlinear least squares method. The results show that a=95.4641 and b=-0.0004 are obtained for the exponential function f1(h), a=311.7688, b=-7.5588e+03, and c=6.8353e+03 are obtained for the Gaussian function f2(h), and a=267.7522, b=-5.0055e-04, and c=-1.2848e+03 are obtained for the S-type function f3(h).

[0069] In step 2, the three initial candidate function coefficients solved in step 1 are used to construct a proportional relationship model between vertically adjacent grids. In this example, the vertical tropospheric space at an altitude of 0-10 km is divided into a grid every 1000 meters, so the elevations of the grid centers are 500 meters, 1500 meters, 2500 meters, 3500 meters, 4500 meters, 5500 meters, 6500 meters, 7500 meters, 8500 meters, and 9500 meters, respectively. The distance between any two adjacent grid centers is 1000 meters. At this point, if all three initial candidate functions need to construct the wet refractive index parameters ( N 1 (h j ) and N 1 (h j+1 )), we only need to substitute their elevations of 500 meters and 1500 meters into Equations (4), (5), and (6) respectively. The specific calculations are as follows: in the exponential function, N1(h1) = 0.67N1(h2); in the Gaussian function, N2(h1) = 2446.181N2(h2); in the S-type function, N3(h1) = 0.832N3(h2).

[0070] In step 3, the generalized cross-validation method is used to determine the weighted relationship between the constraint equation and the observation equation. Then, the weighted least squares principle is used to solve the system of equations to obtain the root mean square error of the residual of the vertical constraint equation. The root mean square error (RMS) of the exponential function is 11.1 mm / km, the root mean square error (RMS) of the Gaussian function is 8.6 mm / km, and the root mean square error (RMS) of the S-type function is 8.7 mm / km.

[0071] In step 4, the initial tomographic solution is combined with the radiosonde or numerical forecast data used to construct the vertical constraint to re-solve the coefficients of the optimal vertical constraint function, and then return to step 3 to solve the tomographic solution. The above steps are repeated until the absolute value of the minimum difference between the two tomographic results (measured in wet refractive index) is less than 0.5 mm / km. The constraint is completed as shown in the figure below. Figure 4 shown.

[0072] The implementation basis of each embodiment of the present invention is achieved through programmed processing by a device with processor functionality. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above-mentioned embodiments, an embodiment of the present invention provides a device for constructing vertical constraint equations using an adaptive function selection mechanism. This device is used to execute a method for constructing vertical constraint equations using an adaptive function selection mechanism described in the above-mentioned method embodiments.

[0073] See also Figure 5The device includes: a first main module, which is used to fit the three designed vertical constraint functions based on the sounding or numerical weather forecast data in the target area, calculate the initial coefficient of each vertical constraint function, and obtain the initial optimal fitting function; a second main module, which is used to construct a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group; a third main module, which is used to combine the vertical constraint equation group with the basic observation equation of Beidou three-dimensional water vapor inversion, solve the equation group, obtain the root mean square error of the residual of the vertical constraint equation, determine the optimal vertical constraint function based on the minimum root mean square error as the criterion, and use the corresponding solution as the tomographic initial solution; a fourth main module, which is used to combine the tomographic initial solution with the sounding or numerical weather forecast data for constructing the vertical constraint, solve the coefficient of the optimal vertical constraint function again, form the vertical constraint equation group again, and solve it together with the observation equation to obtain the tomographic solution until the iteration stop condition is met.

[0074] The embodiment of the present invention provides a device for constructing a vertical constraint equation with an adaptive function selection mechanism, which is designed to solve the following problems: (1) the constraint equation is single and cannot accurately describe the vertical structure of water vapor under conventional and unconventional conditions; (2) the vertical constraint does not have an adaptive selection mechanism. Figure 5 Several modules in the model construct vertical constraints by introducing a two-step adaptive mechanism, which can reduce the introduction of system deviations and better express the vertical distribution structure of water vapor under conventional and unconventional conditions.

[0075] It should be noted that the device embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference is the setting of corresponding functional modules. The principles thereof are basically the same as those of the above-mentioned device embodiments provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned device embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above-mentioned device embodiments to obtain corresponding device-type embodiments, which are used to implement the methods in other method-type embodiments. For example:

[0076] Based on the content of the above device embodiment, as a preferred embodiment, in an embodiment of the present invention, a device for constructing a vertical constraint equation with an adaptive function selection mechanism is provided, wherein the fourth main module is further configured to execute the following instructions:

[0077] When the absolute value of the minimum difference between two adjacent chromatography solutions is less than a preset value, the iteration is stopped.

[0078] Based on the content of the above device embodiment, as a preferred embodiment, the embodiment of the present invention provides a device for constructing a vertical constraint equation with an adaptive function selection mechanism. The three vertical constraint functions designed in the first main module are respectively used to simulate three typical water vapor vertical distribution structures, including:

[0079] Sigmoid function:

[0080] Exponential function:

[0081] Gaussian function:

[0082] Where x represents elevation, f(x) represents water vapor density or wet refractive index, e is a natural constant, and a, b, and c are coefficients of the equation.

[0083] Based on the content of the above device embodiment, as a preferred embodiment, in an embodiment of the present invention, a device for constructing a vertical constraint equation with an adaptive function selection mechanism is provided, wherein the second main module is further configured to execute the following instructions:

[0084] Perform BeiDou 3D water vapor inversion and discretize the continuous space into a finite number of grids;

[0085] The optimal fitting function is used to construct a proportional relationship model between grids at the same horizontal position but at different heights to form a vertical constraint equation group.

[0086] Based on the content of the above device embodiment, as a preferred embodiment, the vertical constraint equation construction device of an adaptive function selection mechanism provided in the embodiment of the present invention, the vertical constraint equation group in the second main module is

[0087] ,

[0088] in, Indicates height The undetermined parameters at Indicates height The parameters to be determined at .

[0089] Based on the content of the above device embodiment, as a preferred embodiment, in an embodiment of the present invention, a device for constructing a vertical constraint equation with an adaptive function selection mechanism is provided, wherein the third main module is further configured to execute the following instructions:

[0090] Combine the vertical constraint equations with the basic observation equations of BeiDou 3D water vapor inversion;

[0091] The generalized cross-validation method is used to determine the weighted relationship between the constraint equation and the observation equation, and the weighted least squares principle is used to solve the equation system.

[0092] The embodiment of the present invention further provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for constructing a vertical constraint equation with an adaptive function selection mechanism is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0093] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0094] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0095] Based on the sounding or numerical weather forecast data in the target area, the three designed vertical constraint functions are fitted respectively, and the initial coefficients of each vertical constraint function are calculated to obtain the initial optimal fitting function;

[0096] Constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group;

[0097] The vertical constraint equations are combined with the basic observation equations of BeiDou 3D water vapor inversion to solve the equations and obtain the root mean square error of the residual of the vertical constraint equations. The optimal vertical constraint function is determined based on the minimum root mean square error, and the corresponding solution is used as the initial tomographic solution.

[0098] The initial tomographic solution is combined with the sounding or numerical weather forecast data for constructing vertical constraints, and the coefficients of the optimal vertical constraint function are solved again to form a set of vertical constraint equations again. The tomographic solution is obtained by combining the solution with the observation equation until the iteration stop condition is met.

[0099] An embodiment of the present invention further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions cause the computer to execute the method, including the following steps:

[0100] Based on the sounding or numerical weather forecast data in the target area, the three designed vertical constraint functions are fitted respectively, and the initial coefficients of each vertical constraint function are calculated to obtain the initial optimal fitting function;

[0101] constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group;

[0102] The vertical constraint equations are combined with the basic observation equations of BeiDou 3D water vapor inversion to solve the equations and obtain the root mean square error of the residual of the vertical constraint equations. The optimal vertical constraint function is determined based on the minimum root mean square error, and the corresponding solution is used as the initial tomographic solution.

[0103] The initial tomographic solution is combined with the sounding or numerical weather forecast data for constructing vertical constraints, and the coefficients of the optimal vertical constraint function are solved again to form a set of vertical constraint equations again. The tomographic solution is obtained by combining the solution with the observation equation until the iteration stop condition is met.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a vertical constraint equation with an adaptive function selection mechanism, characterized in that: include: Based on the sounding or numerical weather forecast data in the target area, the three designed vertical constraint functions are fitted respectively, and the initial coefficients of each vertical constraint function are calculated to obtain the initial optimal fitting function; the three designed vertical constraint functions are used to simulate three typical water vapor vertical distribution structures, including: Sigmoid function: Exponential function: Gaussian function: Among them, x represents the elevation, f(x) represents the water vapor density or wet refractive index, e is a natural constant, and a, b, c are the coefficients of the equation; According to the initial optimal fitting function, a proportional relationship model is constructed between vertically adjacent grids to form a vertical constraint equation group, including: performing Beidou three-dimensional water vapor inversion to discretize the continuous space into a finite number of grids; using the optimal fitting function to construct a proportional relationship model between grids at different heights at the same horizontal position to form a vertical constraint equation group; The vertical constraint equations are: ， in, Indicates height The undetermined parameters at Indicates height Undetermined parameters at ; Combine the vertical constraint equations with the basic observation equations of Beidou 3D water vapor inversion, solve the equations, and obtain the root mean square error of the residual of the vertical constraint equation. Determine the optimal vertical constraint function based on the minimum root mean square error, and use the corresponding solution as the initial tomographic solution. The initial tomographic solution is combined with the sounding or numerical weather forecast data for constructing vertical constraints, and the coefficients of the optimal vertical constraint function are solved again to form a set of vertical constraint equations again. The tomographic solution is obtained by combining it with the observation equations until the iteration stop condition is met.

2. According to the method for constructing a vertical constraint equation of an adaptive function selection mechanism according to claim 1, it is characterized in that: The iteration stopping condition is met, including: the absolute value of the minimum difference between two adjacent chromatography solutions is less than a preset value.

3. The method for constructing a vertical constraint equation with an adaptive function selection mechanism according to claim 1, characterized in that: The vertical constraint equations are combined with the basic observation equations of Beidou 3D water vapor inversion to solve the equations, including: Combine the vertical constraint equations with the basic observation equations of Beidou 3D water vapor inversion; The generalized cross-validation method is used to determine the weighted relationship between the constraint equation and the observation equation, and the weighted least squares principle is used to solve the equation system.

4. A vertical constraint equation construction device with an adaptive function selection mechanism, used to implement the method described in any one of claims 1 to 3, characterized in that: include: The first main module is used to fit the three designed vertical constraint functions respectively based on the sounding or numerical weather forecast data in the target area, calculate the initial coefficient of each vertical constraint function, and obtain the initial optimal fitting function; The second main module is used to construct a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group; The third main module is used to combine the vertical constraint equations with the basic observation equations of Beidou 3D water vapor inversion, solve the equations, obtain the root mean square error of the residual of the vertical constraint equation, determine the optimal vertical constraint function based on the minimum root mean square error, and use the corresponding solution as the initial tomographic solution; The fourth main module is used to combine the initial tomographic solution with the sounding or numerical weather forecast data for constructing vertical constraints, solve the coefficients of the optimal vertical constraint function again, form the vertical constraint equation group again, and solve it together with the observation equation to obtain the tomographic solution until the iteration stop condition is met.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method of any one of claims 1 to 3.

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