Method and device for constructing vertical constraint equation of adaptive function selection mechanism

Abstract

The invention discloses a method and a device for constructing a vertical constraint equation of an adaptive function selection mechanism, belongs to the application of a global navigation satellite system technology in the field of meteorology, and summarizes three typical water vapor distribution characteristics on the basis of widely analyzing water vapor vertical distribution characteristics. The invention provides a Beidou water vapor chromatography vertical constraint construction method with an additional adaptive function selection mechanism in a targeted manner, solves the problem of automatically selecting an optimal function to construct a vertical constraint under different water vapor vertical structures, improves the construction precision of the vertical constraint, and improves the construction efficiency of the vertical constraint. Therefore, the precision and reliability of the Beidou water vapor chromatography result are improved.

Description

Technical Field

[0001] The present invention belongs 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 three-dimensional water vapor inversion is performed using Beidou or GNSS technology, specifically a method and device for constructing vertical constraint equations with an adaptive function selection mechanism. Background Art

[0002] BeiDou / GNSS (Global Navigation Satellite System) three-dimensional water vapor inversion technology is an emerging three-dimensional atmospheric water vapor content monitoring method. It is an important application of BeiDou / GNSS technology in the field of meteorology and one of the few existing three-dimensional water vapor monitoring methods. When using BeiDou / GNSS technology to invert three-dimensional water vapor, the signal rays are overly concentrated in the zenith direction, which causes the basic observation equations in BeiDou three-dimensional water vapor inversion to be ill-posed, which determines the multi-solution nature of the tomography results. Therefore, it is usually necessary to add some constraint equations to solve the equation rank deficiency and ill-posed problems. Among all kinds of constraint equations, the vertical constraint equation is the most important, which often plays a decisive role in whether the vertical structure of water vapor can be obtained stably and accurately.

[0003] Because in most cases the vertical distribution of water vapor decreases with increasing height, presenting 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 under all circumstances. 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 the accuracy and stability of the Beidou three-dimensional water vapor inversion results are improved 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: 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 coefficient of each vertical constraint function is calculated to obtain the initial optimal fitting function; Constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group; 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.

[0006] As a further technical solution, the iteration stopping condition is satisfied, including: the absolute value of the minimum difference between two adjacent chromatographic solutions is less than a preset value.

[0007] As a further technical solution, three vertical constraint functions are designed to simulate three typical water vapor vertical distribution structures, including: Sigmoid function: Exponential function: Gaussian function: Among them, 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.

[0008] As a further technical solution, a proportional relationship model is constructed between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group, including: Perform Beidou 3D water vapor inversion and discretize the continuous space into a finite number of grids; The optimal fitting function is used to construct a proportional relationship model between grids at different heights at the same horizontal position to form a vertical constraint equation group.

[0009] As a further technical solution, the vertical constraint equations are: , in, Indicates height The undetermined parameters at Indicates height The undetermined parameters at .

[0010] 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: 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.

[0011] According to one aspect of the present invention, there is provided a vertical constraint equation construction device with an adaptive function selection mechanism, comprising: 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.

[0012] According to one aspect of the present invention, there is provided a computer device, 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.

[0013] According to one aspect of the present specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the method described above.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention introduces a two-step adaptive mechanism to construct vertical constraints. The first is an adaptive selection mechanism for the optimal vertical constraint function, and the second is a 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 deviations.

[0015] (2) The present invention can better express the vertical distribution structure of water vapor under conventional and unconventional conditions.

[0016] (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 minimum deviation.

[0017] (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 result is good, the constraint will become more and more accurate, and there will be a benign iteration between the constraint and the solution, but when the tomographic result is not good, the reconstructed constraint equation will also become worse, resulting in a vicious iteration between the constraint 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 hidden dangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is 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.

[0019] Figure 1 A schematic flow chart of a method for constructing a vertical constraint equation with an adaptive function selection mechanism provided in an embodiment of the present invention.

[0020] Figure 2 A schematic diagram of the vertical structure of water vapor under normal conditions and the fitting effect of an exponential function on the vertical distribution of water vapor provided by an embodiment of the present invention.

[0021] Figure 3 A schematic diagram of the vertical structure of water vapor under unconventional conditions and the fitting effect of a new function on the vertical distribution of water vapor provided in an embodiment of the present invention.

[0022] Figure 4 A schematic diagram of the constraint completion effect provided by an embodiment of the present invention.

[0023] Figure 5 A schematic structural diagram of a vertical constraint equation construction device with an adaptive function selection mechanism provided in an embodiment of the present invention.

[0024] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In view of the fact that a single function cannot describe the water vapor distribution characteristics under all situations, which greatly reduces the accuracy and reliability of vertical constraints, the present invention, based on an extensive analysis of the vertical distribution characteristics of water vapor, summarizes three typical water vapor distribution characteristics, namely, exponential function, Gaussian function and S-type function 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 / GNSS water vapor tomography results.

[0026] The terms "including" and "having" and any variations thereof in the specification 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 comprising 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.

[0027] 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 in the present invention, all other embodiments obtained by ordinary technicians in this field without 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 the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0028] In view of 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.

[0029] like Figure 1 As shown, the method for constructing a vertical constraint equation of an adaptive function selection mechanism provided by an embodiment of the present invention includes: Step 1: 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.

[0030] Step 1.1, using a large amount of radiosonde data, the typical structure of water vapor vertical distribution is analyzed, three typical water vapor distribution structures are summarized, and three alternative vertical constraint functions are designed to simulate the three typical water vapor vertical distribution structures. The expressions of the three vertical constraint functions are as follows: Sigmoid function: (1) Exponential function: (2) Gaussian function: (3) Where x represents the elevation, f(x) represents the water vapor density or wet refractive index, e is a natural constant, and a, b, and c are coefficients of the equation. Specifically, the initial values ​​of the coefficients a, b, and c of the above equation can be fitted based on the measured data such as the water vapor density or wet refractive index and the corresponding elevation obtained by radiosonde or numerical weather forecast.

[0031] Step 1.2: When performing BeiDou / GNSS three-dimensional water vapor tomography, the three alternative vertical constraint functions in step 1.1 are fitted respectively 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 of 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 , .

[0032] 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.

[0033] 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 different heights at the same horizontal position. 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, , 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 between 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 function relationships are constructed between all vertically adjacent grids to form a vertical constraint equation set.

[0034] 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.

[0035] 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, and 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 minimum root mean square error, and the corresponding solution is used as the initial tomographic solution.

[0036] Step 4: Combine the initial tomographic solution with the radiosonde or numerical prediction 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 the above 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.

[0037] The above process of the embodiment of the present invention introduces a two-step adaptive mechanism to construct the vertical constraint. The first is the adaptive selection of the optimal vertical constraint function mechanism in step 3, and the second is the constraint function optimization adjustment mechanism in step 4. Through these two-step mechanisms, a scientific, reasonable, accurate and reliable vertical constraint equation can be constructed.

[0038] 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 shown. According to the root mean square error of each function, the root mean square error of the exponential function is 11.1 mm / km, while the root mean square errors of the Gaussian function and the S-type function are 8.6 mm / km and 8.7 mm / km respectively. It can be seen that using a single function cannot well reflect the specific inversion of water vapor. After adaptive selection, the function with the least root mean square error can be selected for subsequent calculations.

[0039] Figure 2The vertical structure of water vapor under unconventional conditions and the fitting effect of the new function on the vertical distribution of water vapor are shown. In the face of unconventional situations, adaptive correction of functions is more necessary. The root mean square error of the conventional, single exponential function is 6.5 mm / km, while the root mean square error of the Gaussian function is 3.9 mm / km, and the root mean square error of the S-type function is 6.4 mm / km. After adaptive adjustment, the Gaussian function is selected for fitting and subsequent calculations.

[0040] 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 under the premise of introducing minimum deviation.

[0041] As a preferred embodiment, the embodiment of the present invention uses the Beidou three-dimensional water vapor inversion experiment in Beijing at 0:00 UTC on the 206th day of 2023 as an example to illustrate the specific implementation of the present invention, including the following steps: In step 1, the radiosonde data or numerical weather forecast data of Beijing area at 0:00 UTC for 206 days are used to fit the three alternative vertical constraint functions of equations (1), (2) and (3), respectively. The coefficients of the equations are calculated using the nonlinear least squares method, and the exponential function f1(h) is a=95.4641, b=-0.0004, the Gaussian function f2(h) is a=311.7688, b=-7.5588e+03, c=6.8353e+03, and the S-type function f3(h) is a=267.7522, b=-5.0055e-04, c=-1.2848e+03.

[0042] 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 kilometers is divided into a grid every 1000 meters, so the elevations of the grid center points are 500 meters, 1500 meters, 2500 meters, 3500 meters, 4500 meters, 5500 meters, 6500 meters, 7500 meters, 8500 meters, and 9500 meters, respectively, and the distance between any two adjacent grid center points is 1000 meters. At this time, if the three initial candidate functions all 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 500m and 1500m 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).

[0043] In step 3, the generalized cross-validation method is used to determine the weighted ratio relationship between the constraint equation and the observation equation, and then the weighted least squares principle is used to solve the equation group to obtain the root mean square error of the residual of the vertical constraint equation. The root mean square error of the exponential function is RMS = 11.1 mm / km, the root mean square error of the Gaussian function is RMS = 8.6 mm / km, and the root mean square error of the S-type function is RMS = 8.7 mm / km.

[0044] 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. Repeat the above 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. The constraint is completed as shown in the figure below. Figure 4 shown.

[0045] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. 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 embodiments, an embodiment of the present invention provides a vertical constraint equation construction device of an adaptive function selection mechanism, and the device is used to execute a vertical constraint equation construction method of an adaptive function selection mechanism in the above method embodiment.

[0046] 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 initial tomographic solution; a fourth main module, which is used to combine the initial tomographic solution with the sounding or numerical weather forecast data for constructing vertical constraints, 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.

[0047] The embodiment of the present invention provides a vertical constraint equation construction device with an adaptive function selection mechanism, which is aimed at (1) the problem that the constraint equation is single and cannot accurately describe the vertical structure of water vapor under normal and unconventional conditions; (2) the problem that the vertical constraint does not have an adaptive selection mechanism. Figure 5 Several modules in the model can 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.

[0048] It should be noted that the device embodiments provided by the present invention are used to implement the methods in the above method embodiments as well as the methods in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set, and the principle is basically the same as the principle of the above device embodiments provided by the present invention. As long as the technical personnel in the field refer to the specific technical solutions in other method embodiments on the basis of the above device embodiments, obtain the corresponding technical means and the technical solutions composed of these technical means by combining the technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above device embodiments to obtain the corresponding device class embodiments, which are used to implement the methods in other method class embodiments. For example: Based on the content of the above device embodiment, as a preferred embodiment, in a vertical constraint equation construction device of an adaptive function selection mechanism provided in an embodiment of the present invention, the fourth main module is further used to execute the following instructions: When the absolute value of the minimum difference between two adjacent chromatography solutions is less than a preset value, the iteration is stopped.

[0049] Based on the content of the above device embodiment, as a preferred embodiment, a vertical constraint equation construction device of an adaptive function selection mechanism is provided in the embodiment of the present invention, and the three vertical constraint functions designed in the first main module are respectively used to simulate three typical water vapor vertical distribution structures, including: Sigmoid function: Exponential function: Gaussian function: Among them, 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.

[0050] Based on the content of the above device embodiment, as a preferred embodiment, in a vertical constraint equation construction device of an adaptive function selection mechanism provided in an embodiment of the present invention, the second main module is further used to execute the following instructions: Perform Beidou 3D water vapor inversion and discretize the continuous space into a finite number of grids; The optimal fitting function is used to construct a proportional relationship model between grids at different heights at the same horizontal position to form a vertical constraint equation group.

[0051] Based on the content of the above device embodiment, as a preferred embodiment, a vertical constraint equation construction device with an adaptive function selection mechanism provided in an embodiment of the present invention, the vertical constraint equation group in the second main module is: , in, Indicates height The undetermined parameters at Indicates height The undetermined parameters at .

[0052] Based on the content of the above device embodiment, as a preferred embodiment, in a vertical constraint equation construction device of an adaptive function selection mechanism provided in an embodiment of the present invention, the third main module is further used to execute the following instructions: 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.

[0053] 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 6As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, 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 the 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 vertical constraint equation construction method of 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 shell, or an external keyboard, touchpad or mouse.

[0054] 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0055] 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: 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 coefficient of each vertical constraint function is calculated to obtain the initial optimal fitting function; Constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group; 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.

[0056] An embodiment of the present invention further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the method, including the following steps: 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 coefficient of each vertical constraint function is calculated to obtain the initial optimal fitting function; Constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group; 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.

[0057] 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 coefficient of each vertical constraint function is calculated to obtain the initial optimal fitting function; Constructing a proportional relationship model between vertically adjacent grids according to the initial optimal fitting function to form a vertical constraint equation group; 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 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 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.

4. The method for constructing a vertical constraint equation with an adaptive function selection mechanism according to claim 1, characterized in that: 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: Perform Beidou 3D water vapor inversion and discretize the continuous space into a finite number of grids; The optimal fitting function is used to construct a proportional relationship model between grids at different heights at the same horizontal position to form a vertical constraint equation group.

5. The method for constructing a vertical constraint equation with an adaptive function selection mechanism according to claim 4, characterized in that: The vertical constraint equations are: , in, Indicates height The undetermined parameters at Indicates height The undetermined parameters at .

6. 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.

7. A vertical constraint equation construction device with an adaptive function selection mechanism, 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.

8. 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 6 are implemented.

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

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