Water vapor density determination method, apparatus, and computer readable storage medium
By combining a GNSS chip and a barometric pressure sensor, the tropospheric delay and wet delay are calculated, solving the problems of high cost and low efficiency in traditional water vapor detection, and achieving accurate, stable and efficient determination of water vapor density.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ALLYSTAR TECH (BEIJING) CO LTD
- Filing Date
- 2026-04-28
- Publication Date
- 2026-07-03
AI Technical Summary
Traditional water vapor detection relies on weather balloons, which are costly and have limited coverage, resulting in low efficiency in determining water vapor density.
A water vapor density determination device employing multiple GNSS chips and barometric pressure sensors is used to calculate the tropospheric delay and zenith tropospheric wet delay of the GNSS chips by acquiring target information, and to determine the water vapor density by combining it with barometric pressure data. Regional water vapor inversion is then performed using the path wet delay information from multiple observation devices.
It has achieved accurate, stable, and efficient determination of water vapor density, improved detection accuracy and spatiotemporal resolution, reduced costs, and expanded coverage.
Smart Images
Figure CN122331024A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of meteorological detection technology, and in particular to methods, apparatus and computer-readable storage media for determining water vapor density. Background Technology
[0002] Water vapor is the direct source of precipitation, and its distribution and changes directly affect the amount of rainfall. Accurate water vapor density detection plays an important role in early warning of weather disasters such as rainstorms.
[0003] Traditional water vapor detection mainly relies on weather balloons, using meteorological sensors mounted on the balloons for precise measurements. However, weather balloons are expensive and weather balloon stations are extremely rare, making them unusable in areas far from weather balloons. Therefore, existing methods for determining water vapor density are costly and inefficient. Summary of the Invention
[0004] This application provides a method, apparatus, and computer-readable storage medium for determining water vapor density, which is costly and inefficient.
[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for determining water vapor density is provided. The method is applied to a water vapor density determination device within a water vapor density determination system, which further includes multiple observation devices. The water vapor density determination device is connected to each observation device, which includes a GNSS chip and a barometric pressure sensor. The method includes: acquiring target information for each of the multiple observation devices; the target information includes first information and barometric pressure data from the barometric pressure sensor; the first information includes the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip; determining the tropospheric delay of the GNSS chip based on the first information; determining the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay of the GNSS chip and the barometric pressure data; using the product of the zenith tropospheric delay and the projection function of the tropospheric wet delay as the ray path tropospheric wet delay corresponding to the observation device; and determining the water vapor density of the target region based on the ray path tropospheric wet delay corresponding to the multiple observation devices; the target region is the area where the water vapor density determination device is located.
[0006] In conjunction with the first aspect, in some embodiments of the first aspect, determining the tropospheric delay of the GNSS chip based on first information includes: determining the tropospheric delay of the GNSS chip based on the first information, Formula 1, and Formula 2. in, These are ionospherically-free combined phase observations from a GNSS chip. To determine the geometric distance between the satellite and the GNSS chip, The speed of light in a vacuum. For the clock bias of the GNSS chip, For the clock bias of the satellite, For the tropospheric delay of the GNSS chip, The wavelength of the ionosphere-free combination corresponding to the GNSS chip. For ionospheric-free combined ambiguities corresponding to GNSS chips, The observation noise corresponding to the GNSS chip. and These are the dual-frequency points corresponding to the GNSS chip.
[0007] In conjunction with the first aspect, in certain embodiments of the first aspect, determining the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay and barometric pressure data of the GNSS chip includes: determining the zenith tropospheric delay corresponding to the observation device based on the tropospheric delay of the GNSS chip, barometric pressure data, formulas 3 and 4. in, For the tropospheric delay of the GNSS chip, The zenith tropospheric dry delay corresponds to the observation device. The wet tropospheric delay corresponding to the observation device. For tropospheric dry delay projection function, This is the tropospheric wet delay projection function. The latitude of the GNSS chip. This refers to the atmospheric pressure at the GNSS chip. The ground elevation of the GNSS chip.
[0008] In conjunction with the first aspect, in some embodiments of the first aspect, the target region includes multiple pixels. Determining the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices includes: determining the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to the multiple observation devices, formulas 6, 7, 8, and 9. in, Here, SWV represents the total water vapor volume along the oblique path corresponding to the observation device, and SWD represents the tropospheric wet delay along the ray path corresponding to the observation device. Let be the horizontal constraint weight coefficient of the i-th pixel. Let be the water vapor density of the i-th pixel, and j be the total number of pixels in the target region. Let i be the elevation of the i-th pixel. The distance matrix for the signal ray from the satellite to the GNSS chip traversing each pixel. V is the coefficient matrix for horizontal constraints, and V is the coefficient matrix for vertical constraints. This is a water vapor density matrix for multiple pixels. This is the SWV matrix corresponding to the observation device. This is the noise vector of the observations.
[0009] Secondly, a water vapor density determination apparatus is provided for implementing the water vapor density determination method of the first aspect described above. This water vapor density determination apparatus includes modules, units, or means corresponding to the above method. These modules, units, or means can be implemented in hardware, software, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the above functions.
[0010] In conjunction with the second aspect, in some embodiments of the second aspect, the water vapor density determination device includes multiple observation devices; each observation device includes a GNSS chip and a barometric pressure sensor, and the device includes: an acquisition module and a processing module; the acquisition module is used to acquire target information for each of the multiple observation devices; the target information includes first information and barometric pressure data from the barometric pressure sensor, the first information including the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip; the processing module is used to determine the tropospheric delay of the GNSS chip based on the first information; the processing module is also used to determine the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay of the GNSS chip and the barometric pressure data; the processing module is also used to multiply the zenith tropospheric delay by the projection function of the tropospheric wet delay as the ray path tropospheric wet delay corresponding to the observation device; the processing module is also used to determine the water vapor density of the target area based on the ray path tropospheric wet delay corresponding to the multiple observation devices; the target area is the area where the water vapor density determination device is located.
[0011] In conjunction with the second aspect, in some embodiments of the second aspect, the processing module is configured to determine the tropospheric delay of the GNSS chip based on the first information, including: determining the tropospheric delay of the GNSS chip based on the first information, Formula 1, and Formula 2. in, These are ionospherically-free combined phase observations from a GNSS chip. To determine the geometric distance between the satellite and the GNSS chip, The speed of light in a vacuum. For the clock bias of the GNSS chip, For the clock bias of the satellite, For the tropospheric delay of the GNSS chip, The wavelength of the ionosphere-free combination corresponding to the GNSS chip. For ionospheric-free combined ambiguities corresponding to GNSS chips, The observation noise corresponding to the GNSS chip. and These are the dual-frequency points corresponding to the GNSS chip.
[0012] In conjunction with the second aspect, in some embodiments of the second aspect, the processing module is further configured to determine the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay and pressure data of the GNSS chip, including: determining the zenith tropospheric delay corresponding to the observation device based on the tropospheric delay of the GNSS chip, pressure data, formulas 3 and 4. in, For the tropospheric delay of the GNSS chip, The zenith tropospheric dry delay corresponds to the observation device. The wet tropospheric delay corresponding to the observation device. For tropospheric dry delay projection function, This is the tropospheric wet delay projection function. The latitude of the GNSS chip. This refers to the atmospheric pressure at the GNSS chip. The ground elevation of the GNSS chip.
[0013] In conjunction with the second aspect, in some embodiments of the second aspect, the target region includes multiple pixels. The processing module is further configured to determine the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, including: determining the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, formula 6, formula 7, formula 8, and formula 9. in, Here, SWV represents the total water vapor volume along the oblique path corresponding to the observation device, and SWD represents the tropospheric wet delay along the ray path corresponding to the observation device. Let be the horizontal constraint weight coefficient of the i-th pixel. Let be the water vapor density of the i-th pixel, and j be the total number of pixels in the target region. Let i be the elevation of the i-th pixel. The distance matrix for the signal ray from the satellite to the GNSS chip traversing each pixel. V is the coefficient matrix for horizontal constraints, and V is the coefficient matrix for vertical constraints. This is a water vapor density matrix for multiple pixels. This is the SWV matrix corresponding to the observation device. This is the noise vector of the observations.
[0014] Thirdly, a water vapor density determination apparatus is provided, comprising: at least one water vapor density determination apparatus and a memory for storing instructions executable by the water vapor density determination apparatus; wherein the water vapor density determination apparatus is configured to execute the instructions to implement the method provided by the first aspect and any possible embodiment thereof.
[0015] Fourthly, a computer-readable storage medium is provided, which, when instructions in the computer-readable storage medium are executed by a water vapor density determining device, enables the water vapor density determining device to perform the method provided by the first aspect and any possible embodiment thereof.
[0016] Fifthly, a computer program product containing instructions is provided that, when run on a computer, enables the computer to perform the methods provided in the first aspect and any possible implementation thereof.
[0017] The technical effects of any one of the second to fifth aspects can be found in the technical effects of the different embodiments of the first aspect described above, and will not be repeated here. Attached Figure Description
[0018] Figure 1 This application provides a schematic diagram of the architecture of a water vapor density determination system. Figure 2 A flowchart illustrating a method for determining water vapor density provided in this application; Figure 3 A schematic diagram of the structure of a water vapor density determination device provided in this application; Figure 4 A schematic diagram of the architecture of another water vapor density determination device provided in this application. Detailed Implementation
[0019] In the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.
[0020] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.
[0021] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.
[0022] It is understood that the term "embodiment" used throughout the specification means that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, various embodiments throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It is understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0023] It is understood that in this application, “when…”, “if” and “if” all refer to the corresponding processing that will be carried out under certain objective circumstances, and are not limited to a time, nor do they require that there must be a judgment action when implemented, nor do they mean that there are other limitations.
[0024] It is understood that some optional features in the embodiments of this application can be implemented independently in certain scenarios without relying on other features, such as the current solution on which they are based, to solve the corresponding technical problems and achieve the corresponding effects. Alternatively, they can be combined with other features as needed in certain scenarios. Correspondingly, the apparatus given in the embodiments of this application can also implement these features or functions, which will not be elaborated here.
[0025] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments and implementation methods of the various embodiments in this application, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the implementation methods of the various embodiments are consistent and can be mutually referenced. The technical features in different embodiments and between the implementation methods of the various embodiments can be combined according to their inherent logical relationships to form new embodiments, implementation methods, implementation methods, or implementation approaches. The following embodiments of this application do not constitute a limitation on the scope of protection of this application.
[0026] Water vapor is the direct source of precipitation, and its distribution and changes directly affect the amount of rainfall. Accurate water vapor density detection plays an important role in early warning of weather disasters such as rainstorms.
[0027] Traditional water vapor detection mainly relies on weather balloons, using meteorological sensors mounted on the balloons for precise measurements. However, weather balloons are expensive and weather balloon stations are extremely rare, making them unusable in areas far from weather balloons. Therefore, existing methods for determining water vapor density are costly and inefficient.
[0028] To address the aforementioned problems, this application provides a method for determining water vapor density. The method is applied to a water vapor density determination device located in a target area. The device includes multiple observation devices, each comprising a GNSS chip and a barometric pressure sensor. The method includes: acquiring target information for each of the multiple observation devices; the target information includes first information and barometric pressure data from the barometric pressure sensor; the first information includes the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip; determining the tropospheric delay of the GNSS chip based on the first information; determining the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay of the GNSS chip and the barometric pressure data; using the product of the zenith tropospheric delay and the projection function of the tropospheric wet delay as the ray path tropospheric wet delay corresponding to the observation device; and determining the water vapor density of the target area based on the ray path tropospheric wet delay corresponding to the multiple observation devices.
[0029] Based on this scheme, on the one hand, the water vapor density determination device located in the target area includes multiple observation devices. These devices include low-cost GNSS chips and barometric pressure sensors. The number of observation devices can be added or reduced arbitrarily according to different needs and environments, ensuring that rays pass through the vast majority of pixels, thereby achieving high temporal and spatial resolution water vapor detection. Simultaneously, because the GNSS chip is small, it can integrate radio frequency front-ends and communication modules, allowing data to be integrated and aggregated into the water vapor density determination device for real-time calculation without affecting the GNSS chip's operational performance. On the other hand, by acquiring target information such as the ionospheric-free combined phase observation values, satellite-to-ground distance, dual-frequency points, observation noise, and barometric pressure data from each observation device, ionospheric errors can be effectively eliminated and observation reliability improved. Based on the aforementioned first information, accurate GNSS calculations can be performed. The total tropospheric delay is used to separate the zenith tropospheric wet delay from the pressure data. Then, the zenith wet delay is converted into the ray path tropospheric wet delay using the tropospheric wet delay projection function. By fully integrating the path wet delay information from multiple observation devices, regional water vapor inversion is carried out, thereby significantly improving the detection accuracy and spatiotemporal resolution of water vapor density in the target area and achieving accurate, stable, and efficient determination of water vapor density in the target area.
[0030] Figure 1 This is a schematic diagram of the architecture of a water vapor density determination system provided in this application. The technical solutions of the embodiments of this application can be applied to... Figure 1 The water vapor density determination system shown is as follows: Figure 1 As shown, the water vapor density determination system 10 includes a water vapor density determination device 11 and an observation device 12.
[0031] The water vapor density determining device 11 is directly or indirectly connected to the observation device 12. This connection can be wired or wireless, and this embodiment of the application does not limit the connection.
[0032] The observation device 12 includes a GNSS chip 121 and a barometric pressure sensor 122.
[0033] The GNSS chip 121 is an integrated circuit that can receive signals from global navigation satellite devices (including GPS, BeiDou, GLONASS, Galileo, etc.). It calculates the device's location, speed, and time information by capturing, tracking, and demodulating the radio signals emitted by the satellites.
[0034] The barometric pressure sensor 122 is a miniature sensor that can detect the external atmospheric pressure. It can convert changes in atmospheric pressure into electrical signals and calculate altitude, relative altitude, or ambient air pressure from changes in air pressure.
[0035] Furthermore, the observation device may also include a communication device that can send the GNSS chip 121 and the barometric pressure sensor 122 to the water vapor density determination device 11.
[0036] The water vapor density determination device 11 and the observation device 12 can exchange data.
[0037] It should be noted that the water vapor density determining device 11 and the observation device 12 can be independent devices or integrated into the same device; this application does not make any specific limitation in this regard.
[0038] When the water vapor density determining device 11 and the observation device 12 are integrated into the same device, the communication method between the water vapor density determining device 11 and the observation device 12 is the same as the communication method between the modules within the device. In this case, the communication process between the two is the same as the communication process between the water vapor density determining device 11 and the observation device 12 when they are independent of each other.
[0039] In practical applications, the water vapor density determination method provided in this application embodiment can be applied to the water vapor density determination device 11 in the water vapor density determination device 10, or to the devices included in the water vapor density determination device 11.
[0040] The method for determining water vapor density provided in this application will be described below with reference to the accompanying drawings, taking the application of the water vapor density determination method to a water vapor density determination device as an example.
[0041] Figure 2 A flowchart illustrating a method for determining water vapor density provided in this application is shown below. Figure 2 As shown, the method includes the following steps: S201, The water vapor density determination device acquires target information for each of the multiple observation devices.
[0042] The target information includes first information and barometric pressure data from a barometric sensor. The first information includes the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip.
[0043] As one possible implementation method, combined Figure 1 For each of the multiple observation devices, the water vapor density determination device sends a data request message to each observation device to request target information.
[0044] After receiving the data request message, the observation device encapsulates the target information, including the first information and the air pressure data from the air pressure sensor, into a data response message and sends the data response message to the water vapor density determination device.
[0045] After receiving the data response message from the observation device, the water vapor density determination device obtains the target information from the data response message.
[0046] S202, The water vapor density determination device determines the tropospheric delay of the GNSS chip based on the first information.
[0047] Tropospheric delay is an error in satellite navigation and positioning (such as GPS and BeiDou) caused by the slowing of electromagnetic wave signals as they pass through the Earth's troposphere due to atmospheric temperature, pressure, humidity, and other meteorological factors. This results in a longer signal propagation time than in a vacuum, ultimately leading to an overestimation of the distance measurement result. It is one of the main atmospheric errors that must be corrected in satellite positioning.
[0048] As one possible implementation, the water vapor density determination device determines the tropospheric delay of the GNSS chip based on the first information, Formula 1, and Formula 2: in, These are ionospherically-free combined phase observations from a GNSS chip. To determine the geometric distance between the satellite and the GNSS chip, The speed of light in a vacuum. For the clock bias of the GNSS chip, For the clock bias of the satellite, For the tropospheric delay of the GNSS chip, The wavelength of the ionosphere-free combination corresponding to the GNSS chip. For ionospheric-free combined ambiguities corresponding to GNSS chips, The observation noise corresponding to the GNSS chip. and These are the dual-frequency points corresponding to the GNSS chip.
[0049] Understandably, for each of the multiple observation devices, the water vapor density determination device substitutes the first information of each observation device into Formulas 1 and 2 to obtain the tropospheric delay of the GNSS chip in the observation device.
[0050] Based on this possible implementation, the scheme eliminates the influence of ionospheric delay on phase observation through a dual-frequency ionospheric-free combination (Formula 2), obtaining a pure ionospheric-free combined phase observation value LIF; then, substituting this observation value into Formula (1), given the geometric distance ρ, the speed of light c, and the receiver and satellite clock difference (dtr)... dts), ionospheric-free combined wavelength λIF, ambiguity NIF, and observation noise Under the premise of parameters such as LIF, the tropospheric delay term T can be separated by solving the equation, realizing the accurate determination of the tropospheric delay of GNSS, while avoiding the interference of ionospheric delay, and improving the reliability and accuracy of tropospheric delay calculation.
[0051] S203. The water vapor density determination device determines the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay and air pressure data of the GNSS chip.
[0052] Zenith Tropospheric Wet Delay (ZWD) is the delay component caused by the slowing of electromagnetic wave propagation speed and the lengthening of the propagation path due to changes in the refractive index of atmospheric water vapor molecules as satellite navigation signals pass through the troposphere along the zenith direction (the line connecting the receiver and the satellite is perpendicular to the ground). It is the wet component remaining after subtracting the static delay (ZHD) from the total zenith tropospheric delay (ZTD). Primarily determined by the spatial distribution and density of atmospheric water vapor, it is characterized by dramatic spatiotemporal variations and is difficult to model accurately using conventional meteorological parameters. In high-precision GNSS positioning and navigation, wet delay cannot be precisely corrected for using conventional meteorological elements such as air pressure and temperature as dry delay. It is typically calculated as an unknown parameter along with position, ambiguity, and clock error. Accurate estimation of wet delay not only significantly improves positioning accuracy but is also a core physical quantity for GNSS inversion of atmospheric precipitable water and the realization of meteorological remote sensing.
[0053] As one possible implementation, the water vapor density determination device determines the zenith tropospheric delay corresponding to the observation device based on the tropospheric delay of the GNSS chip, air pressure data, and Equations 3 and 4: in, For the tropospheric delay of the GNSS chip, The zenith tropospheric dry delay corresponds to the observation device. The wet tropospheric delay corresponding to the observation device. For tropospheric dry delay projection function, This is the tropospheric wet delay projection function. The latitude of the GNSS chip. This refers to the atmospheric pressure at the GNSS chip. The ground elevation of the GNSS chip.
[0054] Understandably, for each of the multiple observation devices, the water vapor density determination device substitutes the tropospheric delay and air pressure data of the GNSS chip in each observation device into Formulas 3 and 4 to obtain the zenith tropospheric delay corresponding to the observation device.
[0055] Based on this possible implementation method, and based on the dry / wet component decomposition model of tropospheric delay, firstly, using formula (4), combined with the air pressure P and latitude at the GNSS chip, The method accurately calculates the zenith tropospheric dry delay (ZHD) using the geodetic height (H). Then, by substituting the known tropospheric delay (T), the calculated ZHD, and the dry / wet delay projection functions (Mh and Mw) into formula (3), the zenith tropospheric wet delay (ZWD) can be obtained by solving the equation, thereby determining the total zenith tropospheric delay (ZHD+ZWD) corresponding to the observation device. This method, through meteorological parameter modeling and dry / wet component separation, eliminates the dry delay modeling error while achieving accurate calculation of the zenith tropospheric delay, providing reliable basic data for subsequent water vapor density inversion.
[0056] S204. The water vapor density determination device uses the product of the zenith tropospheric delay and the tropospheric wet delay projection function as the tropospheric wet delay of the ray path corresponding to the observation device.
[0057] The tropospheric wet delay of the ray path refers to the delay component caused by the decrease in electromagnetic wave propagation speed and the extension of the propagation path due to the spatial distribution and density changes of atmospheric water vapor molecules along the actual oblique propagation path between the receiver and the satellite when the satellite navigation signal passes through the troposphere. Its value is equal to the product of the zenith tropospheric wet delay and the wet delay mapping function of the corresponding ray path. Compared with the zenith tropospheric wet delay, it can truly reflect the water vapor delay effect under oblique propagation conditions of the signal and can effectively improve the precision of tropospheric delay correction.
[0058] Understandably, after obtaining the zenith tropospheric delay corresponding to each observation device, the water vapor density determination device multiplies the zenith tropospheric delay by the tropospheric wet delay projection function, and the resulting product can be used as the tropospheric wet delay of the ray path corresponding to the observation device.
[0059] S205, The water vapor density determination device determines the water vapor density of the target area based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices.
[0060] The target area is the region where the water vapor density determination device is located.
[0061] The target region comprises multiple pixel volumes. A pixel volume is a three-dimensional spatial unit formed by regularly meshing the three-dimensional atmospheric space according to preset latitude, longitude, and altitude ranges. Each pixel volume corresponds to an independent, tiny cubic spatial region in the atmosphere. During the inversion process, the water vapor refractive index or water vapor density within each pixel volume is treated as an independent unknown parameter. Combining the path lengths of multiple satellite signal ray paths passing through the corresponding pixel volume with the observed tropospheric wet delay of the ray paths, an observation equation is constructed and solved. This enables tomographic imaging and quantitative determination of the three-dimensional spatial distribution of atmospheric water vapor, providing a spatially discretized basic modeling unit for high-precision water vapor inversion.
[0062] As one possible implementation, the water vapor density determination device determines the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, and Equations 6, 7, 8, and 9. in, Here, SWV represents the total water vapor volume along the oblique path corresponding to the observation device, and SWD represents the tropospheric wet delay along the ray path corresponding to the observation device. Let be the horizontal constraint weight coefficient of the i-th pixel. Let be the water vapor density of the i-th pixel, and j be the total number of pixels in the target region. Let i be the elevation of the i-th pixel. The distance matrix for the signal ray from the satellite to the GNSS chip traversing each pixel. V is the coefficient matrix for horizontal constraints, and V is the coefficient matrix for vertical constraints. This is a water vapor density matrix for multiple pixels. This is the SWV matrix corresponding to the observation device. This is the noise vector of the observations.
[0063] Based on this possible implementation method, the tropospheric wet delay (SWD) of the ray path is first converted into the total water vapor volume (SWV) of the oblique path through formula (6), realizing the physical quantity mapping from observation delay to total water vapor volume; then, based on the preset three-dimensional pixel discretization model, the spatial correlation constraint matrix of water vapor density is constructed by combining the horizontal constraint of formula (7) and the vertical constraint of formula (8); finally, the distance matrix of the signal ray passing through each pixel, the horizontal / vertical constraint matrix and the total water vapor volume matrix of the oblique path are integrated into the joint observation equation of formula (9). By solving this equation, the water vapor density of each pixel in the target area can be obtained. While making full use of the redundant information of the ray path of multiple observation devices, the introduction of physical constraints in the horizontal and vertical directions improves the solution stability, realizing the three-dimensional fine determination of atmospheric water vapor density.
[0064] Based on S201-S205, on the one hand, the water vapor density determination device located in the target area includes multiple observation devices. These devices include low-cost GNSS chips and barometric pressure sensors. The number of observation devices can be added or reduced arbitrarily according to different needs and environments, ensuring that rays pass through the vast majority of pixels, thereby achieving high temporal and spatial resolution water vapor detection. Simultaneously, because the GNSS chip is small, it can integrate a radio frequency front-end and communication module, integrating data into the water vapor density determination device for real-time calculation without affecting the GNSS chip's operational performance. On the other hand, by acquiring target information such as the ionospheric-free combined phase observation values, satellite-to-ground distance, dual-frequency point frequencies, observation noise, and barometric pressure data from each observation device, ionospheric errors can be effectively eliminated and observation reliability improved. Based on the aforementioned first information, GNSS data can be accurately calculated. The total tropospheric delay is used to separate the zenith tropospheric wet delay from the pressure data. Then, the zenith wet delay is converted into the ray path tropospheric wet delay using the tropospheric wet delay projection function. By fully integrating the path wet delay information from multiple observation devices, regional water vapor inversion is carried out, thereby significantly improving the detection accuracy and spatiotemporal resolution of water vapor density in the target area and achieving accurate, stable, and efficient determination of water vapor density in the target area.
[0065] The above mainly describes the solution provided by the embodiments of this application from the perspective of the water vapor density determination method performed by the water vapor density determination device. To achieve the above functions, the water vapor density determination device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0066] This application embodiment can divide the water vapor density determining device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation. Furthermore, "module" here can refer to an application-specific integrated circuit (ASIC), a circuit, a processor and memory executing one or more software or firmware programs, integrated logic circuits, and / or other devices that can provide the above functions.
[0067] When using functional module division Figure 3 A schematic diagram of a device for determining water vapor density is shown. Figure 3 As shown, the water vapor density determination device 30 includes an acquisition module 301 and a processing module 302.
[0068] In some embodiments, the water vapor density determining device 30 may further include a storage module ( Figure 3 (not shown in the image) is used to store program instructions and data.
[0069] The acquisition module 301 is used to acquire target information for each of the multiple observation devices. The target information includes first information and barometric pressure data from a barometric pressure sensor. The first information includes the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip. The processing module 302 is used to determine the tropospheric delay of the GNSS chip based on the first information. The processing module 302 is also used to determine the zenith tropospheric wet delay corresponding to the observation device based on the tropospheric delay of the GNSS chip and the barometric pressure data. The processing module 302 is also used to use the product of the zenith tropospheric delay and the tropospheric wet delay projection function as the ray path tropospheric wet delay corresponding to the observation device. The processing module 302 is also used to determine the water vapor density of the target area based on the ray path tropospheric wet delay corresponding to the multiple observation devices. The target area is the area where the water vapor density determination device is located.
[0070] Optionally, the processing module 302 is used to determine the tropospheric delay of the GNSS chip based on the first information, including: determining the tropospheric delay of the GNSS chip based on the first information, Formula 1, and Formula 2. in, These are ionospherically-free combined phase observations from a GNSS chip. To determine the geometric distance between the satellite and the GNSS chip, The speed of light in a vacuum. For the clock bias of the GNSS chip, For the clock bias of the satellite, For the tropospheric delay of the GNSS chip, The wavelength of the ionosphere-free combination corresponding to the GNSS chip. For ionospheric-free combined ambiguities corresponding to GNSS chips, The observation noise corresponding to the GNSS chip. and These are the dual-frequency points corresponding to the GNSS chip.
[0071] Optionally, the processing module 302 is further configured to determine the zenith wet tropospheric delay corresponding to the observation device based on the tropospheric delay and pressure data of the GNSS chip, including: determining the zenith wet tropospheric delay corresponding to the observation device based on the tropospheric delay of the GNSS chip, pressure data, formulas 3 and 4. in, For the tropospheric delay of the GNSS chip, The zenith tropospheric dry delay corresponds to the observation device. The wet tropospheric delay corresponding to the observation device. For tropospheric dry delay projection function, This is the tropospheric wet delay projection function. The latitude of the GNSS chip. This refers to the atmospheric pressure at the GNSS chip. The ground elevation of the GNSS chip.
[0072] Optionally, the target region includes multiple pixel volumes. The processing module 302 is further configured to determine the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to the multiple observation devices, including: determining the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to the multiple observation devices, formula 6, formula 7, formula 8, and formula 9. in, Here, SWV represents the total water vapor volume along the oblique path corresponding to the observation device, and SWD represents the tropospheric wet delay along the ray path corresponding to the observation device. Let be the horizontal constraint weight coefficient of the i-th pixel. Let be the water vapor density of the i-th pixel, and j be the total number of pixels in the target region. Let i be the elevation of the i-th pixel. The distance matrix for the signal ray from the satellite to the GNSS chip traversing each pixel. V is the coefficient matrix for horizontal constraints, and V is the coefficient matrix for vertical constraints. This is a water vapor density matrix for multiple pixels. This is the SWV matrix corresponding to the observation device. This is the noise vector of the observations.
[0073] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.
[0074] When the functions of the above modules are implemented in hardware... Figure 4 A schematic diagram of yet another device for determining water vapor density is shown. Figure 4As shown, the water vapor density determining device 40 includes a processor 401, a memory 402, and a bus 403. The processor 401 and the memory 402 can be connected via the bus 403.
[0075] Processor 401 is the control center of the water vapor density determining device 40. It can be a single processor or a collective term for multiple processing elements. For example, processor 401 can be a general-purpose central processing unit (CPU) or other general-purpose processors. Among them, the general-purpose processor can be a microprocessor or any conventional processor.
[0076] As one embodiment, processor 401 may include one or more CPUs, for example Figure 4 CPU 0 and CPU 1 are shown in the diagram.
[0077] The memory 402 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.
[0078] As one possible implementation, the memory 402 can exist independently of the processor 401. The memory 402 can be connected to the processor 401 via a bus 403 and is used to store instructions or program code. When the processor 401 calls and executes the instructions or program code stored in the memory 402, it can implement the water vapor density determination method provided in the embodiments of this application.
[0079] In another possible implementation, the memory 402 can also be integrated with the processor 401.
[0080] Bus 403 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0081] It should be pointed out that, Figure 4 The structure shown does not constitute a limitation on the water vapor density determining device 40. Except... Figure 4 In addition to the components shown, the water vapor density determining device 40 may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0082] As an example, combined Figure 3 The functions implemented by the acquisition module 301 and processing module 302 in the water vapor density determination device 30 are the same as those of the acquisition module 301 and processing module 302. Figure 4 The processor 401 in it has the same function.
[0083] Optional, such as Figure 4 As shown, the water vapor density determination device 40 provided in this application embodiment may also include a communication interface 404.
[0084] Communication interface 404 is used to connect with other devices via a communication network. This communication network can be Ethernet, a wireless access network, a wireless local area network (WLAN), etc. Communication interface 404 may include a receiving unit for receiving data and a transmitting unit for transmitting data.
[0085] In one possible implementation, the communication interface 404 in the water vapor density determination device 40 provided in this application embodiment can also be integrated into the processor 401, and this application embodiment does not specifically limit this.
[0086] As a possible product form, the water vapor density determination device of this application embodiment can also be implemented using one or more field programmable gate arrays (FPGAs), programmable logic devices (PLDs), controllers, state machines, gate logic, discrete hardware components, any other suitable circuits, or any combination of circuits capable of performing the various functions described throughout this application.
[0087] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0088] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed, causes a computer to perform the various steps in the method flow shown in the above method embodiments.
[0089] Embodiments of this application provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the various steps in the method flow shown in the above-described method embodiments.
[0090] This application provides a chip system, including: a processor and an interface circuit; the interface circuit is used to receive computer programs or instructions and transmit them to the processor; the processor is used to execute the computer programs or instructions so that the chip system performs each step in the method flow shown in the above method embodiments.
[0091] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing, or any other form of computer-readable storage medium in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in a purpose-specific ASIC. In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0092] Since the water vapor density determination device, computer-readable storage medium, and computer program product provided in this embodiment can be applied to the water vapor density determination method provided in this embodiment, the technical effects that can be obtained can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.
[0093] Although this application has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed application.
[0094] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative examples of this application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of equivalent technology of this application, this application also intends to include such modifications and modifications.
Claims
1. A method for determining water vapor density, characterized in that, The method is applied to the water vapor density determination device in a water vapor density determination system, which also includes multiple observation devices. A water vapor density determination device is connected to each observation device, which includes a GNSS chip and a barometric pressure sensor. The method includes: For each of the multiple observation devices, acquire the target information of the observation device; the target information includes first information and air pressure data from the barometric pressure sensor. The first information includes the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip. The tropospheric delay of the GNSS chip is determined based on the first piece of information; The wet zenith tropospheric delay corresponding to the observation device is determined based on the tropospheric delay and barometric pressure data from the GNSS chip. The product of the zenith tropospheric delay and the tropospheric wet delay projection function is used as the tropospheric wet delay of the ray path corresponding to the observation device. The water vapor density in the target region is determined by the tropospheric wet delay of the ray paths corresponding to multiple observation devices; the target region is the area where the water vapor density determination device is located.
2. The method according to claim 1, characterized in that, The tropospheric delay of the GNSS chip is determined based on the first information, including: The tropospheric delay of the GNSS chip is determined based on the first information, Formula 1, and Formula 2: in, These are ionospherically-free combined phase observations from a GNSS chip. To determine the geometric distance between the satellite and the GNSS chip, The speed of light in a vacuum. For the clock bias of the GNSS chip, For the clock bias of the satellite, For the tropospheric delay of the GNSS chip, The wavelength of the ionosphere-free combination corresponding to the GNSS chip. For ionospheric-free combined ambiguities corresponding to GNSS chips, The observation noise corresponding to the GNSS chip. and These are the dual-frequency points corresponding to the GNSS chip.
3. The method according to claim 1, characterized in that, The wet zenith tropospheric delay corresponding to the observation device is determined based on the tropospheric delay and pressure data from the GNSS chip, including: The zenith tropospheric delay corresponding to the observation device is determined based on the tropospheric delay of the GNSS chip, air pressure data, and Equations 3 and 4: in, For the tropospheric delay of the GNSS chip, The zenith tropospheric dry delay corresponds to the observation device. The wet tropospheric delay corresponding to the observation device. For tropospheric dry delay projection function, This is the tropospheric wet delay projection function. The latitude of the GNSS chip. This refers to the atmospheric pressure at the GNSS chip. The ground elevation of the GNSS chip.
4. The method according to any one of claims 1-3, characterized in that, The target region comprises multiple pixels. The water vapor density of the target region is determined based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, including: The water vapor density in the target region is determined based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, and formulas 6, 7, 8, and 9. in, Here, SWV represents the total water vapor volume along the oblique path corresponding to the observation device, and SWD represents the tropospheric wet delay along the ray path corresponding to the observation device. Let be the horizontal constraint weight coefficient of the i-th pixel. Let be the water vapor density of the i-th pixel, and j be the total number of pixels in the target region. Let i be the elevation of the i-th pixel. The distance matrix for the signal ray from the satellite to the GNSS chip traversing each pixel. V is the coefficient matrix for horizontal constraints, and V is the coefficient matrix for vertical constraints. This is a water vapor density matrix for multiple pixels. This is the SWV matrix corresponding to the observation device. This is the noise vector of the observations.
5. A device for determining water vapor density, characterized in that, The water vapor density determination device is included in the water vapor density determination system, which further includes multiple observation devices. The water vapor density determination device is connected to each observation device, and each observation device includes a GNSS chip and a barometric pressure sensor. The device includes an acquisition module and a processing module. The acquisition module is used to acquire target information for each of the multiple observation devices. The target information includes first information and air pressure data from the barometer. The first information includes the ionospheric combination phase observation value of the GNSS chip, the distance between the GNSS chip and the satellite, the wavelength of the ionospheric combination corresponding to the GNSS chip, the observation noise corresponding to the GNSS chip, and the frequency of the dual-frequency point corresponding to the GNSS chip. The processing module is used to determine the tropospheric delay of the GNSS chip based on the first information; The processing module is also used to determine the zenith wet tropospheric delay corresponding to the observation device based on the tropospheric delay and barometric pressure data of the GNSS chip. The processing module is also used to take the product of the zenith tropospheric delay and the tropospheric wet delay projection function as the tropospheric wet delay of the ray path corresponding to the observation device. The processing module is also used to determine the water vapor density of the target area based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices; the target area is the area where the water vapor density determining device is located.
6. The water vapor density determining device according to claim 5, characterized in that, The processing module, used to determine the tropospheric delay of the GNSS chip based on the first information, includes: The tropospheric delay of the GNSS chip is determined based on the first information, Formula 1, and Formula 2: in, These are ionospherically-free combined phase observations from a GNSS chip. To determine the geometric distance between the satellite and the GNSS chip, The speed of light in a vacuum. For the clock bias of the GNSS chip, For the clock bias of the satellite, For the tropospheric delay of the GNSS chip, The wavelength of the ionosphere-free combination corresponding to the GNSS chip. For ionospheric-free combined ambiguities corresponding to GNSS chips, The observation noise corresponding to the GNSS chip. and These are the dual-frequency points corresponding to the GNSS chip.
7. The water vapor density determining device according to claim 5, characterized in that, The processing module is also used to determine the zenith wet tropospheric delay corresponding to the observation device based on the tropospheric delay and pressure data from the GNSS chip, including: The zenith tropospheric delay corresponding to the observation device is determined based on the tropospheric delay of the GNSS chip, air pressure data, and Equations 3 and 4: in, For the tropospheric delay of the GNSS chip, The zenith tropospheric dry delay corresponds to the observation device. The wet tropospheric delay corresponding to the observation device. For tropospheric dry delay projection function, This is the tropospheric wet delay projection function. The latitude of the GNSS chip. This refers to the atmospheric pressure at the GNSS chip. The ground elevation of the GNSS chip.
8. The water vapor density determining device according to any one of claims 5-7, characterized in that, The target region comprises multiple pixels. The processing module is also used to determine the water vapor density of the target region based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, including: The water vapor density in the target region is determined based on the tropospheric wet delay of the ray paths corresponding to multiple observation devices, and formulas 6, 7, 8, and 9. in, Here, SWV represents the total water vapor volume along the oblique path corresponding to the observation device, and SWD represents the tropospheric wet delay along the ray path corresponding to the observation device. Let be the horizontal constraint weight coefficient of the i-th pixel. Let be the water vapor density of the i-th pixel, and j be the total number of pixels in the target region. Let i be the elevation of the i-th pixel. The distance matrix for the signal ray from the satellite to the GNSS chip traversing each pixel. V is the coefficient matrix for horizontal constraints, and V is the coefficient matrix for vertical constraints. This is a water vapor density matrix for multiple pixels. This is the SWV matrix corresponding to the observation device. This is the noise vector of the observations.
9. A device for determining water vapor density, characterized in that, The water vapor density determination device includes: a water vapor density determination device coupled to a memory, the memory being used to store programs or instructions, which, when executed by the water vapor density determination device, cause the device to perform the method as described in any one of claims 1 to 4.
10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they cause the computer to perform the method as described in any one of claims 1 to 4.