A water vapor real-time tomography monitoring system based on characteristics of beidou satellite signal attenuation

Abstract

The present application belongs to the technical field of satellite meteorological monitoring, and particularly relates to a water vapor real-time tomography monitoring system based on the signal attenuation characteristics of Beidou satellites, which comprises four modules of data acquisition, feature calculation, model construction and inversion warning. The data acquisition module is used for collecting meteorological parameters of a monitoring area and statistical variance of Beidou satellite signals. The feature calculation module is used for calculating thermal anomaly values and signal purity rights. The model construction module is used for obtaining dynamic projection coefficients and constructing a tomography observation equation set. The inversion warning module is used for iteratively solving the tomography observation equation set to obtain grid water vapor density, and outputting a warning when the grid water vapor density is out of limit and the thermal anomaly value is continuously high. The present application combines thermal features and signal quality constraints, and improves the water vapor inversion accuracy and warning reliability.

Description

Technical Field

[0001] This invention relates to the field of satellite meteorological monitoring technology. More specifically, this invention relates to a real-time water vapor tomography monitoring system based on the signal attenuation characteristics of the BeiDou satellite system. Background Technology

[0002] Atmospheric water vapor is a key driver of geological disasters such as rainstorms and mudslides, and rapid changes in its three-dimensional spatial distribution often contain precursory information for disasters. Utilizing the signal propagation characteristics of China's BeiDou Navigation Satellite System for water vapor tomography has become an important technique for obtaining high spatiotemporal resolution atmospheric water vapor fields. This technology receives BeiDou satellite signals through a ground-based receiver network and inverts the water vapor density distribution over the monitored area, effectively compensating for the shortcomings of traditional meteorological detection methods in vertical resolution. It has significant application value in improving the accuracy of meteorological risk warnings for geological disasters.

[0003] Existing water vapor tomography monitoring technologies typically employ spatial discretization modeling, dividing the atmospheric space of the monitoring area into several regular three-dimensional grids. During the calculation process, the geometric relationship between the satellite and the station is determined, a set of tomographic observation equations is constructed, and mathematical algorithms such as algebraic reconstruction techniques or least squares methods are used for inversion and solution to obtain the water vapor density values ​​within each grid. This data serves as the basis for determining the air humidity conditions in the monitoring area.

[0004] However, existing water vapor tomography techniques still have limitations in complex geological environments. On the one hand, traditional models lack constraints on the atmospheric thermodynamic state, typically assuming the atmosphere is in an ideal adiabatic or uniformly changing state, neglecting the physical connection between non-adiabatic atmospheric changes and water vapor phase transitions during the disaster incubation period, leading to decreased model inversion accuracy under severe convective weather. On the other hand, existing observation weighting strategies are relatively simplistic, mostly relying on simple geometric weighting based solely on satellite elevation angles. This fails to effectively identify and eliminate non-physical signal jitter caused by multipath effects in mountainous areas or environmental noise, easily introducing high-noise data into the inversion equations and resulting in spurious anomalous clusters in the solution results. Summary of the Invention

[0005] To address the aforementioned technical problems of low tomographic accuracy and poor anti-interference, this invention provides a real-time water vapor tomographic monitoring system based on the attenuation characteristics of BeiDou satellite signals. The system includes the following modules:

[0006] The data acquisition module is used to determine the area to be analyzed as the monitoring area, collect surface air pressure, surface temperature, dew point temperature, and calculate the statistical variance of each BeiDou satellite signal. The feature calculation module is used to calculate the thermal anomaly value based on the deviation of surface temperature and surface air pressure from the standard atmospheric value, and the difference between surface temperature and dew point temperature. The monitoring area is discretized into a three-dimensional grid, and the statistical variance is adjusted using the satellite elevation angle of the BeiDou satellite signal passing through each three-dimensional grid to calculate the signal purity weight of each three-dimensional grid. The model construction module is used to geometrically weight the thermal anomaly value after the signal purity weight correction using the satellite elevation angle to obtain the dynamic projection coefficient of each three-dimensional grid. Based on the dynamic projection coefficient, the tomographic observation equations of each BeiDou satellite signal are constructed and combined to form a set of tomographic observation equations. The inversion and early warning module is used to iteratively solve the set of tomographic observation equations to obtain the grid water vapor density of each three-dimensional grid. When the grid water vapor density of any three-dimensional grid exceeds a preset danger threshold and the thermal anomaly value remains at a high level, an early warning signal is output.

[0007] This invention utilizes signal purity weighting to reduce the weight of low-quality observation data severely affected by multipath effects or environmental noise, and leverages thermal anomalies to enhance the model's sensitivity to atmospheric thermal instability regions. Thus, when solving for grid water vapor density in the inversion and early warning module, it can suppress non-physical interference and retain precursory disaster characteristics. By jointly judging the state of grid water vapor density exceeding limits and persistently high thermal anomalies, it reduces false alarms caused by fluctuations in single meteorological parameters, thereby improving the accuracy and reliability of judging the spatial distribution and changing trends of water vapor in geological disaster meteorological risk monitoring.

[0008] Preferably, the statistical variances of surface air pressure, surface temperature, dew point temperature, and various BeiDou satellite signals in the data collection and monitoring area include:

[0009] Atmospheric pressure changes are sensed by a piezoresistive pressure sensor to obtain the surface air pressure of the monitoring area; the environmental thermal state is sensed by a platinum resistance temperature sensor to obtain the surface temperature of the monitoring area; and the water vapor saturation is sensed by a polymer humidity-sensitive capacitive sensor and combined with the surface temperature to calculate the dew point temperature of the monitoring area.

[0010] Based on a preset sampling frequency, within a preset time window, the signal-to-noise ratio (SNR) observation sequence of each BeiDou satellite signal within the preset time window is obtained by measuring the ratio of the received carrier signal power to the ambient noise power density, and the statistical variance of the SNR observation sequence in the monitoring area is calculated.

[0011] Preferably, the thermal anomaly value satisfies the following relationship:

[0012] ;

[0013] In the formula, Indicates the thermal anomaly value of the monitored area; P represents the surface temperature of the monitored area; P represents the surface air pressure of the monitored area. The reference temperature representing the standard atmosphere; The reference pressure representing the standard atmosphere; Indicates the ratio of gas constants; Indicates the dew point temperature of the monitored area; Indicates the humidity sensitivity coefficient; Represents an exponential function with the natural constant as the base; Represents the absolute value symbol.

[0014] This invention utilizes the deviations of surface temperature and surface air pressure relative to standard atmospheric values ​​to assess the distance between the atmospheric state and the theoretical adiabatic state in the monitored area, reflecting the intensity of thermal activity within the atmosphere. It uses an exponential function with a natural constant as the base to describe the nonlinear growth trend of water vapor when it approaches saturation, thereby capturing atmospheric stratification instability and water vapor phase transition trends caused by severe convective weather. This enables the system to locate areas of active thermal activity when facing complex and changeable meteorological environments, and helps improve the ability to identify precursors of geological disasters.

[0015] Preferably, the signal purity weight satisfies the following relationship:

[0016] ;

[0017] In the formula, Indicates the first The signal purity of a three-dimensional grid; Indicates the noise adjustment factor; Indicates passing through the first Statistical variance of BeiDou satellite signals in a three-dimensional grid; Indicates passing through the first The satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid; This represents the cosine function.

[0018] This invention takes into account that BeiDou satellite signals at low satellite elevation angles are easily affected by ground multipath effects, and that a large statistical variance directly reflects the severe jitter caused by environmental noise interference. Therefore, it performs geometric correction on the satellite elevation angle and combines it with the statistical variance, thereby automatically reducing the weight of observation paths from low satellite elevation angles and with severe jitter. This reduces the negative impact of low-confidence data on the solution results during the tomographic model inversion process, and ensures that the final obtained grid water vapor density is mainly obtained from high-purity satellite signals, thus improving the numerical stability and physical authenticity of the inversion results.

[0019] Preferably, the dynamic projection coefficients satisfy the following relationship:

[0020] ;

[0021] In the formula, Indicates the first The dynamic projection coefficients of a 3D grid; Indicates the first The signal purity of a three-dimensional grid; Indicates the thermal anomaly value of the monitored area; Indicates passing through the first The satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid; Indicates anomaly correction factor; Indicates a preset micro value; Represent the natural logarithm function; This represents the sine function.

[0022] This invention utilizes a logarithmic term incorporating thermal anomalies as a correction factor, combined with a sine function of satellite elevation angle for geometric mapping, thereby achieving multidimensional adaptive adjustment of the contribution of the observation path. It uses a sine function term to geometrically normalize the path length at different inclination angles, employs signal purity weights to gate and suppress noise interference, and uses the logarithmic form of thermal anomalies to nonlinearly amplify high-thermal-risk regions. This ensures that the established tomographic observation equations conform to both geometric projection laws and physical driving characteristics, enabling the model to automatically focus on key regions with good signal quality and active thermal activity during iterative solving. This improves the accuracy of traditional tomographic models in capturing local extreme water vapor accumulation phenomena.

[0023] Preferably, the tomographic observation equation satisfies the following relationship:

[0024] ;

[0025] Indicates the first The total attenuation observed along each BeiDou satellite signal path; Indicates the first A set of three-dimensional grids through which BeiDou satellite signals pass; Indicates the first The first of the three-dimensional grid sets through which the BeiDou satellite signal passes. The dynamic projection coefficients of a 3D grid; Indicates the first The first of the three-dimensional grid sets through which the BeiDou satellite signal passes. The water vapor density of a three-dimensional mesh to be solved.

[0026] Preferably, the step of iteratively solving the tomographic observation equations to obtain the water vapor density of each three-dimensional grid includes:

[0027] The tomographic observation equations are solved iteratively using algebraic reconstruction techniques until the solution residuals of the tomographic observation equations are less than a preset threshold or the maximum number of iterations is reached. Then, the water vapor density of the corresponding three-dimensional grid is output.

[0028] Preferably, the step of outputting a warning signal when the water vapor density of any three-dimensional grid exceeds a preset danger threshold and the thermal anomaly value remains at a high level includes:

[0029] When the water vapor density of any three-dimensional grid exceeds a preset danger threshold, the corresponding three-dimensional grid is judged to be an abnormal cluster;

[0030] If abnormal clusters are identified in the monitoring area and the thermal anomaly value in the monitoring area remains at a high level, it is determined that there is a risk of geological disaster meteorology, and an early warning signal is issued.

[0031] This invention employs a dual-judgment logic when outputting early warning signals. First, it identifies three-dimensional grids where the water vapor density exceeds a preset danger threshold and judges them as abnormal clusters. Then, it performs a comprehensive evaluation based on the thermal anomaly status of the monitored area. Only when abnormal clusters are identified and the thermal anomaly value remains at a high level is it determined that there is a risk of geological disaster meteorology. This invention uses the spatial morphological characteristics of abnormal clusters to eliminate isolated numerical noise and uses the temporal persistence characteristics of thermal anomalies to verify the thermodynamic background of water vapor accumulation. This distinguishes between regular non-hazardous high humidity weather and geological disaster meteorological risks caused by atmospheric thermal instability, reducing the false alarm rate of the system caused by a single threshold judgment.

[0032] Preferably, obtaining the satellite elevation angle includes:

[0033] Based on the three-dimensional coordinates of the BeiDou monitoring station and the instantaneous spatial coordinates of the satellite that launched the observation signal, a straight transmission path for the BeiDou satellite signal is constructed; the angle between the straight transmission path and the horizontal plane where the BeiDou monitoring station is located is calculated as the satellite elevation angle.

[0034] Preferably, the gas constant ratio is .

[0035] The beneficial effects of this invention are as follows: This invention constructs a water vapor tomography monitoring system that integrates atmospheric thermodynamic characteristics and signal quality assessment. Addressing the challenges of traditional tomography models failing under severe convective weather conditions due to the adiabatic assumption and severe multipath interference, it proposes an adaptive constraint mechanism based on thermal anomalies and signal purity weights. The system utilizes surface temperature, pressure, and humidity parameters to construct thermal anomaly indicators, enabling the perception of atmospheric stratification instability and water vapor phase transition potential, introducing crucial thermodynamic prior information into the inversion process. Simultaneously, it uses statistical variance and satellite elevation angle to construct signal purity weights, eliminating low-quality observation data contaminated by surface environmental noise. By integrating these two types of physical constraints in the projection matrix, the solution of the tomography equations automatically focuses on key areas with high signal-to-noise ratios and intense thermal activity, resolving the ill-conditioned nature of inversion results under complex environments. Furthermore, it employs dual early warning criteria of anomalous cluster morphology and thermal background persistence to physically distinguish between regular water vapor fluctuations and hazardous accumulation, reducing the false alarm rate of geological disaster meteorological risk warnings and enhancing the application value and data reliability of the BeiDou ground-based augmentation system in extreme weather monitoring. Attached Figure Description

[0036] Figure 1 This schematic diagram illustrates a system block diagram of a real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to the present invention.

[0037] Figure 2 A schematic diagram illustrating the statistical variance distribution of signals from various BeiDou satellites is provided.

[0038] Figure 3 A schematic diagram illustrating the signal purity weight distribution of each 3D grid;

[0039] Figure 4 A schematic diagram of the grid water vapor density obtained based on the dynamic projection coefficient inversion is shown. Detailed Implementation

[0040] This invention provides a real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals. For example... Figure 1 As shown, a real-time water vapor tomography monitoring system based on the attenuation characteristics of Beidou satellite signals includes a data acquisition module 100, a feature calculation module 200, a model building module 300, and an inversion early warning module 400, which are described in detail below.

[0041] The data acquisition module 100 is used to determine the area to be analyzed as the monitoring area, collect the surface air pressure, surface temperature, dew point temperature of the monitoring area, and calculate the statistical variance of each Beidou satellite signal.

[0042] It should be noted that, considering that environmental interference is usually superimposed on normal BeiDou satellite signals in the form of high-frequency jitter, in order to characterize the physical confidence of the observation data, this invention calculates the statistical variance of BeiDou satellite signals, thereby capturing the degree of discretization of BeiDou satellite signals caused by multipath effects or environmental noise, and thus distinguishing between normal physical attenuation and non-physical interference.

[0043] Specifically, the area to be analyzed is defined as the monitoring area, and meteorological parameters and satellite observation data within the monitoring area are extracted, including:

[0044] Atmospheric pressure changes are sensed using a piezoresistive pressure sensor to obtain the surface air pressure of the monitored area. The surface temperature of the monitored area is obtained by sensing the ambient thermal state using a platinum resistance temperature sensor. The dew point temperature of the monitored area is calculated by sensing the water vapor saturation level using a polymer humidity-sensitive capacitive sensor and combining this with the surface temperature. .

[0045] Based on a preset sampling frequency and within a preset time window, the signal-to-noise ratio (SNR) observation sequence of all BeiDou satellite signals belonging to the monitoring area within the preset time window is obtained by measuring the ratio of the received carrier signal power to the ambient noise power density, and the statistical variance of the SNR observation sequence of the monitoring area is calculated. For example, the sampling frequency is 1 Hz; the preset time window size is 30 seconds to cover a complete BeiDou satellite signal fluctuation cycle.

[0046] For example, Figure 2 This is a schematic diagram of the statistical variance distribution of various BeiDou satellite signals, which shows the degree of signal discretization caused by multipath effects or environmental noise interference of different BeiDou satellite signals, and is used to distinguish between normal physical attenuation and non-physical interference.

[0047] Thus, the surface air pressure, surface temperature, dew point temperature, and statistical variance were obtained.

[0048] The feature calculation module 200 is used to calculate thermal anomaly values ​​based on the deviations of surface temperature, surface air pressure and standard atmospheric values, as well as the difference between surface temperature and dew point temperature; the monitoring area is discretized into a three-dimensional grid, and the statistical variance is adjusted by the satellite elevation angle of the Beidou satellite signals passing through each three-dimensional grid, and the signal purity weight of each three-dimensional grid is calculated.

[0049] It should be noted that during the formation of geological disasters, the atmosphere is often in a non-adiabatic state of change, and real-time thermodynamic parameters deviate from the theoretical values ​​of the ideal model. This deviation is highly correlated with water vapor phase change activities. In order to correct the systematic errors caused by the failure of the adiabatic assumption in traditional tomographic models, this invention combines the surface temperature and pressure deviation with the water vapor saturation trend to calculate the thermodynamic anomaly value, thereby characterizing the thermodynamic instability inside the atmosphere, introducing key thermodynamic prior constraints for the inversion process, and enhancing the model's response to severe convective weather.

[0050] Specifically, based on the deviation of real-time atmospheric conditions from the ideal adiabatic state and the saturation trend of water vapor, the thermal anomaly values ​​of the monitoring area are calculated, including:

[0051] Set gas constant ratio Exemplary It is a recognized physical constant calculated based on the ratio of the dry air gas constant to the specific heat capacity at constant pressure in the Poisson equation of atmospheric thermodynamics, and is used to establish the theoretical adiabatic benchmark.

[0052] Set humidity sensitivity coefficient For example, the humidity sensitivity coefficient The aim is to balance the sensitivity to minute changes in water vapor with the numerical stability of the index calculation, and to avoid numerical divergence.

[0053] The thermal anomaly values ​​satisfy the following relationship:

[0054] ;

[0055] In the formula, Indicates the thermal anomaly value of the monitored area; P represents the surface temperature of the monitored area; P represents the surface air pressure of the monitored area. The reference temperature representing the standard atmosphere; The reference pressure representing the standard atmosphere; Indicates the ratio of gas constants; Indicates the dew point temperature of the monitored area; Indicates the humidity sensitivity coefficient; Represents an exponential function with the natural constant as the base; Represents the absolute value symbol.

[0056] In this relation, This indicates the distance between the current atmospheric state and the theoretical steady state within the monitoring area. The larger the value, the more intense the thermal activity within the atmosphere of the monitoring area; conversely, The smaller the value, the more the atmospheric state within the monitoring area conforms to the physical adiabatic assumption, and the atmospheric stratification is in a relatively static or uniformly changing stable stage. Humidity sensitivity coefficient Control, when near When this occurs, it indicates that the air in the monitoring area is close to saturation. The exponential growth of thermal anomalies makes them highly sensitive to high humidity environments; conversely, when... Higher than This indicates that the air in the monitoring area is dry and far from saturation, thereby suppressing the response amplitude of thermal anomalies under non-hazardous dry weather and reducing false alarms caused by background noise.

[0057] Thus, the thermal anomaly values ​​for the monitored area were obtained.

[0058] It should be noted that multipath effects in complex environments can cause non-physical fluctuations in signal-to-noise ratio data. In order to suppress noise while fully preserving effective observation information, this invention uses statistical variance to characterize jitter intensity and combines it with satellite elevation angle for geometric correction, dynamically reducing the weight contribution of low-quality data, thereby achieving adaptive suppression of environmental noise without losing spatial coverage.

[0059] Preferably, the signal purity weight of the monitoring area is calculated based on the statistical jitter amplitude during signal transmission and the geometric observation angle of the satellite, including:

[0060] The monitoring area is discretized spatially into A three-dimensional grid is constructed, and a grid coordinate system is established. The three-dimensional coordinates of the BeiDou monitoring station are obtained, and the spatial position mapping relationship of the BeiDou monitoring station in the three-dimensional grid is determined.

[0061] Based on the three-dimensional coordinates of the BeiDou monitoring station and the instantaneous spatial coordinates of the corresponding satellite transmitting observation signals, a straight-line transmission path for the BeiDou satellite signal is constructed; the angle between the straight-line transmission path and the horizontal plane where the BeiDou monitoring station is located is calculated as the satellite elevation angle; the path passing through the first... The straight-line transmission path of the three-dimensional grid is used as the corresponding satellite elevation angle to pass through the first three-dimensional grid. The satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid.

[0062] It should be noted that, Expressed in radians, its corresponding value range is: .

[0063] Set noise adjustment factor For example, the noise adjustment coefficient It is used to balance the ability to suppress noise interference with the retention rate of effective satellite signals at low satellite elevation angles.

[0064] No. The signal purity weights of a 3D grid satisfy the following relation:

[0065] ;

[0066] In the formula, Indicates the first The signal purity of a three-dimensional grid; Indicates the noise adjustment factor; Indicates passing through the first Statistical variance of BeiDou satellite signals in a three-dimensional grid; Indicates passing through the first The satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid; This represents the cosine function.

[0067] In this relationship, the larger the statistical variance, the more severe the jitter of the BeiDou satellite signal, leading to an increase in the denominator and causing the signal purity weight to approach 0, indicating that the... The BeiDou satellite signals within the three-dimensional grid are subject to multipath contamination or environmental noise interference, resulting in low reliability. Used to introduce satellite elevation angle correction, considering that low satellite elevation angles are easily affected by ground multipath propagation. This makes the satellite more sensitive to noise at low elevation angles.

[0068] For example, Figure 3 This is a schematic diagram of the signal purity weight distribution of each 3D grid, reflecting the physical confidence level of the BeiDou satellite signal obtained by combining the satellite elevation angle and statistical variance. The lower the signal purity weight, the more serious the noise pollution of the observation data in the corresponding 3D grid.

[0069] At this point, the signal purity of each 3D grid was obtained.

[0070] The model building module 300 is used to geometrically weight the thermal anomaly values ​​after the signal purity weight correction using the satellite elevation angle to obtain the dynamic projection coefficients of each three-dimensional grid; based on the dynamic projection coefficients, tomographic observation equations for each BeiDou satellite signal are constructed and combined to form a set of tomographic observation equations.

[0071] It should be noted that the contribution of the BeiDou satellite signal transmission path should also be affected by the joint modulation of atmospheric activity and BeiDou satellite signal reliability. In order to achieve physical-driven adaptive inversion, this invention integrates thermal anomaly values ​​and signal purity weights into the projection matrix, so that the model can automatically enhance the constraint weights of high thermal anomaly regions and high signal-to-noise ratio BeiDou satellite signal transmission paths during the iterative solution process, thereby improving the capture accuracy of local extreme water vapor accumulation areas.

[0072] Specifically, based on signal purity weights and atmospheric thermodynamic activity levels, the dynamic projection coefficients of each 3D grid are calculated, including:

[0073] Set an anomaly correction factor Exemplary It is used to control the degree of nonlinear amplification of thermal anomalies, which can both highlight the characteristics of disaster precursors and reduce iterative divergence caused by over-correction.

[0074] No. The dynamic projection coefficients of the three-dimensional grid satisfy the following relationship:

[0075] ;

[0076] In the formula, Indicates the first The dynamic projection coefficients of a 3D grid; Indicates the first The signal purity of a three-dimensional grid; Indicates the thermal anomaly value of the monitored area; Indicates passing through the first The satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid; Indicates anomaly correction factor; This represents a preset microvalue to prevent the denominator from being 0; it can be set to 0.001. Represent the natural logarithm function; This represents the sine function.

[0077] In this relationship, the signal purity weight acts as a gate, reducing the magnitude of the dynamic projection coefficient when the signal quality is poor. The dynamic projection coefficient is nonlinearly amplified using an anomaly correction factor. When the thermal anomaly value is large... This increases the volume, thus enabling the capture of high concentrations of water vapor. This represents the proportionality coefficient between the BeiDou satellite signal transmission path length and the vertical path length. The smaller the satellite elevation angle corresponding to the satellite observation signal within a 3D grid, the longer the path of the BeiDou satellite signal through the atmosphere, and the more obvious the geometric projection effect; conversely, the smaller the elevation angle, the longer the path of the BeiDou satellite signal through the atmosphere, and the more obvious the geometric projection effect. The larger the satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid, the closer the path of the BeiDou satellite signal through the atmosphere is to the vertical direction, and the weaker the geometric projection effect.

[0078] Preferably, the first is constructed based on dynamic projection coefficients. The tomographic observation equations for the BeiDou satellite signals satisfy the following relationship:

[0079] ;

[0080] Indicates the first The total attenuation observed along each BeiDou satellite signal path; Indicates the first A set of three-dimensional grids through which BeiDou satellite signals pass; Indicates the first The first of the three-dimensional grid sets through which the BeiDou satellite signal passes. The dynamic projection coefficients of a 3D grid; Indicates the first The first of the three-dimensional grid sets through which the BeiDou satellite signal passes. The water vapor density of a three-dimensional mesh to be solved.

[0081] Furthermore, the tomographic observation equations of each BeiDou satellite signal within the monitoring area are combined to form a set of tomographic observation equations.

[0082] Thus, the dynamic projection coefficients of each 3D grid were obtained and the tomographic observation equations were established.

[0083] The inversion early warning module 400 is used to iteratively solve the tomographic observation equations to obtain the water vapor density of each three-dimensional grid; when the water vapor density of any three-dimensional grid exceeds the preset danger threshold and the thermal anomaly value remains at a high level, an early warning signal is output.

[0084] It should be noted that, considering the large sparsity and ill-conditioned characteristics of the tomographic observation equations, direct solutions are easily affected by observation errors, and the simple water vapor density value lacks intuitive physical indication. In order to improve the numerical stability of the inversion results, this invention adopts algebraic reconstruction technology for solving the problem, and uses its iterative mechanism to suppress noise amplification. At the same time, by establishing a mapping logic from the numerical field to risk warning, and combining water vapor density and thermal anomaly values ​​for comprehensive judgment, a geological disaster meteorological risk warning signal is output.

[0085] Specifically, the tomographic observation equations are iteratively solved until the solution residuals are less than a preset threshold or the maximum number of iterations is reached, at which point the water vapor density of each 3D grid is output. For example, the iterative solution employs algebraic reconstruction technology, and the preset threshold is... The maximum number of iterations is 200. Algebraic reconstruction techniques are existing technologies and will not be elaborated upon here.

[0086] For example, Figure 4 A schematic diagram of the spatial distribution of grid water vapor density obtained by inversion under dynamic projection coefficient constraints is shown. This diagram is used to characterize the identification effect of local high water vapor accumulation areas and to provide a basis for meteorological disaster risk early warning.

[0087] When the water vapor density of any three-dimensional mesh exceeds a preset danger threshold, the corresponding three-dimensional mesh is determined to be an abnormal cluster. For example, the preset danger threshold is 15. .

[0088] If abnormal clusters are identified in the monitoring area and the thermal anomaly value in the monitoring area remains at a high level, it is determined that there is a risk of geological disaster meteorology, and an early warning signal is issued.

[0089] Thus, real-time water vapor tomography monitoring and meteorological disaster early warning based on the attenuation characteristics of BeiDou satellite signals have been completed.

[0090] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals, characterized in that, include: The data acquisition module is used to determine the area to be analyzed as the monitoring area, collect the surface air pressure, surface temperature, dew point temperature of the monitoring area, and calculate the statistical variance of each Beidou satellite signal; The feature calculation module is used to calculate thermal anomaly values ​​based on the deviations of surface temperature, surface air pressure and standard atmospheric values, as well as the difference between surface temperature and dew point temperature. The monitoring area is discretized into a three-dimensional grid. The statistical variance is adjusted by the satellite elevation angle of the BeiDou satellite signals passing through each three-dimensional grid, and the signal purity weight of each three-dimensional grid is calculated. The model building module is used to geometrically weight the thermal anomaly values ​​after the signal purity weight correction using the satellite elevation angle to obtain the dynamic projection coefficients of each three-dimensional grid; based on the dynamic projection coefficients, tomographic observation equations for each BeiDou satellite signal are constructed and combined to form a set of tomographic observation equations. The inversion early warning module is used to iteratively solve the tomographic observation equations to obtain the water vapor density of each three-dimensional grid; when the water vapor density of any three-dimensional grid exceeds the preset danger threshold and the thermal anomaly value remains at a high level, an early warning signal is output. Thermal anomalies satisfy the following relationship: In the formula, Indicates the thermal anomaly value of the monitored area; P represents the surface temperature of the monitored area; P represents the surface air pressure of the monitored area. The reference temperature representing the standard atmosphere; The reference pressure representing the standard atmosphere; Indicates the ratio of gas constants; Indicates the dew point temperature of the monitored area; Indicates the humidity sensitivity coefficient; Represents an exponential function with the natural constant as the base; Indicates the absolute value symbol; The signal purity weight satisfies the following relation: In the formula, Indicates the first The signal purity of a three-dimensional grid; Indicates the noise adjustment factor; Indicates passing through the first Statistical variance of BeiDou satellite signals in a three-dimensional grid; Indicates passing through the first The satellite elevation angle corresponding to the satellite observation signal within a three-dimensional grid; Represents the cosine function; The dynamic projection coefficients satisfy the following relationship: In the formula, Indicates the first The dynamic projection coefficients of a 3D grid; Indicates anomaly correction factor; Indicates a preset micro value; Represent the natural logarithm function; This represents the sine function.

2. The real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to claim 1, characterized in that, The surface air pressure, surface temperature, dew point temperature, and statistical variance of each BeiDou satellite signal in the monitored area are collected, including: Atmospheric pressure changes are sensed by a piezoresistive pressure sensor to obtain the surface air pressure of the monitoring area; the environmental thermal state is sensed by a platinum resistance temperature sensor to obtain the surface temperature of the monitoring area; and the water vapor saturation is sensed by a polymer humidity-sensitive capacitive sensor and combined with the surface temperature to calculate the dew point temperature of the monitoring area. Based on a preset sampling frequency, within a preset time window, the signal-to-noise ratio (SNR) observation sequence of each BeiDou satellite signal within the preset time window is obtained by measuring the ratio of the received carrier signal power to the ambient noise power density, and the statistical variance of the SNR observation sequence in the monitoring area is calculated.

3. The real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to claim 1, characterized in that, The tomographic observation equations satisfy the following relationship: ； Indicates the first The total attenuation observed along each BeiDou satellite signal path; Indicates the first A set of three-dimensional grids through which BeiDou satellite signals pass; Indicates the first The first of the three-dimensional grid sets through which the BeiDou satellite signal passes. The dynamic projection coefficients of a 3D grid; Indicates the first The first of the three-dimensional grid sets through which the BeiDou satellite signal passes. The water vapor density of a three-dimensional mesh to be solved.

4. The real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to claim 1, characterized in that, The iterative solution of the tomographic observation equations to obtain the water vapor density of each three-dimensional grid includes: The tomographic observation equations are solved iteratively using algebraic reconstruction techniques until the solution residuals of the tomographic observation equations are less than a preset threshold or the maximum number of iterations is reached. Then, the water vapor density of the corresponding three-dimensional grid is output.

5. A real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to claim 1, characterized in that, When the water vapor density of any three-dimensional grid exceeds a preset danger threshold and the thermal anomaly value remains high, an early warning signal is output, including: When the water vapor density of any three-dimensional grid exceeds a preset danger threshold, the corresponding three-dimensional grid is judged to be an abnormal cluster; If abnormal clusters are identified in the monitoring area and the thermal anomaly value in the monitoring area remains at a high level, it is determined that there is a risk of geological disaster meteorology, and an early warning signal is issued.

6. A real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to claim 1, characterized in that, The acquisition of the satellite elevation angle includes: Based on the three-dimensional coordinates of the BeiDou monitoring station and the instantaneous spatial coordinates of the satellite that launched the observation signal, a straight transmission path for the BeiDou satellite signal is constructed; the angle between the straight transmission path and the horizontal plane where the BeiDou monitoring station is located is calculated as the satellite elevation angle.

7. A real-time water vapor tomography monitoring system based on the attenuation characteristics of BeiDou satellite signals according to claim 1, characterized in that, The gas constant ratio is .

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