GNSS co-seismic displacement rapid monitoring method and system

Abstract

The invention relates to the technical field of satellite navigation technology application, and particularly discloses a GNSS co-seismic displacement rapid monitoring method and system, and the method comprises the steps: obtaining real-time orbit and satellite clock error data based on GNSS observation data; constructing a receiver carrier motion model; constructing a GNSS co-seismic displacement monitoring function model based on a receiver carrier motion model, real-time orbit and satellite clock error data, and a GNSS pseudo-range, carrier phase and Doppler observation value linear equation; constructing a GNSS co-seismic displacement monitoring random model based on the GNSS pseudo range, the carrier phase and the Doppler observation value; acquiring real-time GNSS observation data; constructing a dynamic PPP model based on the GNSS co-seismic displacement monitoring function model and the random model; and inputting real-time GNSS observation data into the dynamic PPP model to obtain GNSS co-seismic displacement. According to the method, the precision and real-time performance of seismic co-seismic displacement monitoring are improved, and high-precision and broadband surface displacement information is provided for seismic early warning and seismic source parameter inversion.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology application technology, specifically to a GNSS coseismic displacement rapid monitoring method and system. Background Technology

[0002] Traditionally, earthquake data is recorded using seismographs or accelerometers. However, seismograph data suffers from problems such as saturation, missing data, or distortion. This means they cannot fully record the amplitude of coseismic displacement, easily leading to inaccurate magnitude estimation and source parameter inversion. While accelerometer data is not missing, the seismic displacement obtained by integrating accelerometer data may be distorted due to instrument tilt and rotation. Therefore, obtaining large-scale, high-precision broadband surface displacement data is a critical problem that urgently needs to be solved in current earthquake monitoring.

[0003] High-sampling-rate GNSS (Global Navigation Satellite System) observations can estimate broadband seismic displacements, including static and dynamic displacements caused by large earthquakes, effectively supplementing traditional earthquake monitoring methods. High-frequency GNSS observation networks can serve as a powerful complement to traditional seismic networks, improving the performance of earthquake early warning systems by expanding the monitoring range of ground motion. Furthermore, displacements estimated using GNSS technology can be used to calculate earthquake magnitudes, invert fault distributions, and issue earthquake or tsunami warnings.

[0004] Traditional GNSS earthquake monitoring methods include Real-time Dynamic Differential (RTK), Precise Point Positioning (PPP), Variometric, and Time-Domain Point Positioning (TPP). However, each method has its own drawbacks. For example, the RTK method cannot use a reference station in the epicenter area, resulting in high operating costs; PPP technology has long convergence times; Variometric methods suffer from baseline drift; and TPP methods require sufficiently accurate prior coordinates. In some scenarios, these methods may not meet users' needs for real-time, high-precision GNSS earthquake monitoring. Therefore, given the complex satellite status under various external environments, establishing a reasonable GNSS high-frequency coseismic monitoring method is of great significance for improving the application of GNSS technology in seismology. Summary of the Invention

[0005] To address the aforementioned problems, the first objective of this invention is to provide a rapid GNSS coseismic displacement monitoring method. This method integrates pseudorange, carrier phase, and Doppler observations, aiming to utilize the high sampling rate of GNSS observation data to rapidly and accurately monitor coseismic displacement, improve the accuracy and real-time performance of coseismic displacement monitoring, and provide high-precision, wide-bandwidth surface displacement information for earthquake early warning and source parameter inversion.

[0006] The second objective of this invention is to provide a GNSS coseismic displacement rapid monitoring system.

[0007] The first technical solution adopted in this invention is: a GNSS coseismic displacement rapid monitoring method, comprising the following steps: S100: Acquire GNSS observation data, perform precise orbit determination and precise satellite clock error estimation based on the GNSS observation data, and obtain real-time orbit and satellite clock error data; wherein, the GNSS observation data includes GNSS pseudorange, carrier phase and Doppler observation values; S200: Construct a receiver carrier motion model; based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and linear equations of GNSS pseudorange, carrier phase and Doppler observations, construct a GNSS coseismic displacement monitoring function model; S300: Constructing a stochastic model for GNSS coseismic displacement monitoring based on GNSS pseudorange, carrier phase, and Doppler observations; S400: Acquire real-time GNSS observation data; and construct a constant velocity dynamic PPP model and a constant acceleration dynamic PPP model based on the GNSS coseismic displacement monitoring function model and the GNSS coseismic displacement monitoring stochastic model; input the real-time GNSS observation data into the constant velocity dynamic PPP model or the constant acceleration dynamic PPP model to obtain GNSS coseismic displacement.

[0008] Preferably, the receiver carrier motion model in step S200 includes a constant velocity motion model and a constant acceleration motion model; When the data sampling rate interval of the GNSS receiver If the interval is less than 1 s, the receiver carrier is considered to follow a constant velocity motion model; if the data sampling interval of the GNSS receiver is 1.0~3.0 s, the receiver carrier is considered to follow a constant acceleration motion model.

[0009] Preferably, the constant velocity motion model is represented by the following formula: In the formula, , They are respectively The three-dimensional position and velocity state vector of the receiver carrier at any given time; The time interval for the receiver data sampling rate; It is a three-dimensional identity matrix; , They are respectively The three-dimensional position and velocity state vector of the receiver carrier at any given time; for The process noise matrix at each time step; This refers to speed dynamic noise.

[0010] Preferably, the constant acceleration motion model is represented by the following formula: In the formula, , , They are respectively The three-dimensional position, velocity state vector, and three-dimensional acceleration state vector of the real-time receiver carrier; The time interval for the receiver data sampling rate; It is a three-dimensional identity matrix; , , They are respectively The three-dimensional position, velocity state vector, and three-dimensional acceleration state vector of the real-time receiver carrier; for The process noise matrix at each time step; This refers to acceleration dynamic noise.

[0011] Preferably, the linear equations for GNSS pseudorange, carrier phase, and Doppler observations in step S200 are expressed by the following formula: In the formula, These are pseudorange observations; The linear combination coefficients for the line of sight direction; Incremental correction for receiver position; For receiver clock bias parameters; This is the wetted projection function of the troposphere; For tropospheric wet delay; This is the ionospheric coefficient; For ionospheric delay; This represents noise in pseudorange observations; These are phase observations; For ambiguity parameters; This represents noise in the phase observations. These are Doppler observations; Incremental correction for receiver speed; For receiver clock drift; This represents noise in Doppler observations.

[0012] Preferably, the GNSS coseismic displacement monitoring stochastic model in step S300 is expressed by the following formula: In the formula, This represents observation noise. Coefficients for observed values; This is the elevation angle scaling factor; , E is a constant, and E is the satellite elevation angle; This refers to the bandwidth of the phase tracking loop. S Signal-to-noise ratio; λ is the wavelength.

[0013] Preferably, step S400 includes: when the sampling rate time interval for acquiring real-time GNSS observation data is less than 1 s, inputting the real-time GNSS observation data into the constant velocity dynamic PPP model to obtain GNSS coseismic displacement; when the sampling rate time interval for acquiring real-time GNSS observation data is 1.0~3.0 s, inputting the real-time GNSS observation data into the constant acceleration dynamic PPP model to obtain GNSS coseismic displacement.

[0014] Preferably, the constant-velocity dynamic PPP model is represented by the following formula: In the formula, For designing the matrix; The linear combination coefficients for the line of sight direction; This is the wetted projection function of the troposphere; This is the ionospheric coefficient; This refers to the location information of the receiver carrier; Incremental correction for receiver position; Incremental correction for receiver speed; For tropospheric wet delay; For receiver clock bias; For receiver clock drift; For ambiguity parameters; For transpose; The constant-acceleration dynamic PPP model is expressed by the following formula: In the formula, These are acceleration parameters.

[0015] Preferably, the GNSS coseismic displacement mentioned in step S400 is calculated using the following formula: In the formula, The integral obtained from the dynamic PPP model t GNSS coseismic displacement at time t; For time indexing; For speed parameters; The time interval for the receiver data sampling rate; These are acceleration parameters.

[0016] The second technical solution adopted in this invention is: a GNSS coseismic displacement rapid monitoring system, including a orbit determination and clock error estimation module, a monitoring function model construction module, a monitoring stochastic model construction module, and a GNSS coseismic displacement calculation module; The orbit determination and clock error estimation module is used to acquire GNSS observation data, perform precise orbit determination and precise satellite clock error estimation based on the GNSS observation data, and obtain real-time orbit and satellite clock error data; wherein, the GNSS observation data includes GNSS pseudorange, carrier phase and Doppler observation values; The monitoring function model construction module is used to construct a receiver carrier motion model; based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and linear equations of GNSS pseudorange, carrier phase and Doppler observations, a GNSS coseismic displacement monitoring function model is constructed. The monitoring stochastic model construction module is used to construct a GNSS coseismic displacement monitoring stochastic model based on GNSS pseudorange, carrier phase and Doppler observations; The GNSS coseismic displacement calculation module is used to acquire real-time GNSS observation data; and to construct a constant velocity dynamic PPP model and a constant acceleration dynamic PPP model based on the GNSS coseismic displacement monitoring function model and the GNSS coseismic displacement monitoring stochastic model; and to input the real-time GNSS observation data into the constant velocity dynamic PPP model or the constant acceleration dynamic PPP model to obtain the GNSS coseismic displacement.

[0017] The beneficial effects of the above technical solution are as follows: (1) This invention can monitor coseismic displacement in real time and quickly. It comprehensively processes pseudorange, carrier phase and Doppler observations and constructs a stochastic model that is applicable and compatible with various observations. By differentiating and integrating GNSS observation data, the obtained location information is not directly related to the ambiguity parameters. The GNSS coseismic displacement results are reliable, reducing the influence of various complex external environments and making up for the shortcomings of traditional schemes. This method enables the coseismic monitoring technology to have high integrity and high availability, and can flexibly meet the needs of different scenarios, providing strong technical support for GNSS earthquake monitoring services. Even in complex external scenarios such as satellite signal blockage and frequent external cycle slips, it can still provide users with high-precision GNSS coseismic displacement monitoring services.

[0018] (2) The GNSS coseismic displacement rapid monitoring method disclosed in this invention integrates pseudorange, carrier phase and Doppler observations, aiming to use the high sampling rate observation data of GNSS to quickly and accurately monitor the coseismic displacement of earthquakes, improve the accuracy and real-time performance of coseismic displacement monitoring, and provide high-precision, wide-bandwidth surface displacement information for earthquake early warning and source parameter inversion.

[0019] (3) This invention provides the real-time precision orbit and precision clock difference server required by GNSS users to ensure the real-time earthquake monitoring needs of users; on this basis, a set of GNSS coseismic displacement rapid monitoring methods that take into account various observation values ​​are developed to overcome the shortcomings of traditional methods and improve the accuracy and real-time performance of earthquake coseismic displacement monitoring. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a rapid GNSS coseismic displacement monitoring method according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the earthquake real-time monitoring server and user terminal processing flow provided in one embodiment of the present invention. Detailed Implementation

[0021] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of the present invention by way of example, but should not be used to limit the scope of the present invention. That is, the present invention is not limited to the described preferred embodiments, and the scope of the present invention is defined by the claims.

[0022] In the description of this invention, it should be noted that, unless otherwise stated, "a plurality of" means two or more; the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance; those skilled in the art can understand the specific meaning of the above terms in this invention as appropriate.

[0023] Example 1 like Figure 1 As shown, one embodiment of the present invention provides a rapid GNSS coseismic displacement monitoring method considering elastic power, comprising the following steps: S100: Acquire GNSS observation data, perform precise orbit determination and precise satellite clock error estimation based on the GNSS observation data, and obtain real-time orbit and satellite clock error data; like Figure 2 As shown, by receiving real-time streaming GNSS observation data broadcast by a certain number of evenly distributed global GNSS stations, precise orbit determination (POD) and precise satellite clock error estimation (PCE) are performed based on the real-time streaming GNSS observation data to obtain real-time orbit and satellite clock error data.

[0024] GNSS observation data includes, but is not limited to, GNSS pseudorange, carrier phase, and Doppler observations.

[0025] Among them, precise orbit determination (POD) and precise satellite clock error estimation (PCE) can be achieved using existing technical methods.

[0026] S200: Construct a receiver carrier motion model; based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and linear equations of GNSS pseudorange, carrier phase and Doppler observations, construct a GNSS coseismic displacement monitoring function model; (1) Construct a motion model of the receiver carrier; The motion model of the receiver carrier (i.e., the dynamic model of the moving carrier) can be divided into a dynamic model and a kinematic model. The dynamic model describes the motion state of the satellite or receiver based on Newton's second law, such as satellite dynamic orbit determination. The kinematic model is generally used to describe the state of the moving carrier. This invention comprehensively considers the constant velocity (CV) motion model and the constant acceleration (CA) motion model.

[0027] The CV motion model is a relatively idealized and simple motion model that assumes the position of the moving vehicle changes randomly over time, and its state vector follows a first-order Gaussian Markov process; when the data sampling rate of the GNSS receiver is at a certain time interval... If the time is less than 1 second, the receiver carrier is considered to follow the CV motion model, which is expressed by the following formula: In the formula, , They are respectively The three-dimensional position and velocity state vector of the receiver carrier at any given time; The time interval for the receiver data sampling rate; It is a three-dimensional identity matrix; , They are respectively The three-dimensional position and velocity state vector of the receiver carrier at any given time; for The process noise matrix at each time step; This refers to speed dynamic noise.

[0028] When the receiver carrier is in a stable acceleration state, the CA motion model can be constructed. Compared with the CV motion model, the CA motion model adds three acceleration components to the CV motion model.

[0029] The CA motion model assumes that the state vector follows a second-order Gaussian Markov process. When the data sampling interval of the GNSS receiver is 1.0~3.0 s, the receiver carrier is considered to follow the CA motion model, which is expressed by the following formula: In the formula, , , They are respectively The three-dimensional position, velocity state vector, and three-dimensional acceleration state vector of the real-time receiver carrier; The time interval for the receiver data sampling rate; It is a three-dimensional identity matrix; , , They are respectively The three-dimensional position, velocity state vector, and three-dimensional acceleration state vector of the real-time receiver carrier; for The process noise matrix at each time step; This refers to acceleration dynamic noise.

[0030] (2) Construct a GNSS coseismic displacement monitoring function model; Based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and combined with the linear equations of GNSS pseudorange, carrier phase and Doppler observations, a GNSS coseismic displacement monitoring function model (i.e., DPPP, Dynamic Precise Point Positioning (PPP) function model) is constructed. This GNSS coseismic displacement monitoring function model can comprehensively consider the motion state of the GNSS receiver and the characteristics of GNSS observation data, thereby improving the accuracy of coseismic displacement monitoring.

[0031] In constructing the GNSS coseismic displacement monitoring function model, the linear equations for GNSS pseudorange, carrier phase, and Doppler observations are expressed in a specific form, which includes the tropospheric wet projection function and tropospheric wet delay related parameters. The Doppler observations provide receiver velocity and clock speed, and atmospheric delay has a small impact on Doppler observations; the effects of ionospheric and tropospheric delay rates are negligible. Therefore, in DPPP, the linear equations for GNSS pseudorange, carrier phase, and Doppler observations are expressed by the following formula: In the formula, These are pseudorange observations; The linear combination coefficients for the line of sight direction; Incremental correction for receiver position; For receiver clock bias parameters; This is the wetted projection function of the troposphere; For tropospheric wet delay; This is the ionospheric coefficient; For ionospheric delay; This represents noise in pseudorange observations; These are phase observations; For ambiguity parameters; This represents noise in the phase observations. These are Doppler observations; Incremental correction for receiver speed; For receiver clock drift; This represents noise in Doppler observations.

[0032] The constructed GNSS coseismic displacement monitoring function model includes a receiver carrier motion model and linear equations for GNSS pseudorange, carrier phase, and Doppler observations. The GNSS coseismic displacement monitoring function model constructed in this invention comprehensively considers the receiver motion state and observation characteristics, which can effectively improve the accuracy of seismic coseismic displacement monitoring.

[0033] S300: A stochastic model for GNSS coseismic displacement monitoring is constructed based on GNSS pseudorange, carrier phase, and Doppler observations.

[0034] A stochastic model for GNSS coseismic displacement monitoring is constructed based on GNSS pseudorange, carrier phase, and Doppler observations. This stochastic model is compatible with various observation types. Based on the characteristics of GNSS observation data, a stochastic model is designed to estimate the signal-to-noise ratio and elevation angle, accurately reflecting the statistical characteristics of GNSS observation data and providing reliable weight information for subsequent coseismic displacement extraction. In other words, the stochastic model for GNSS coseismic displacement monitoring comprehensively considers the influence of elevation angle and signal-to-noise ratio. The stochastic model for GNSS coseismic displacement monitoring is expressed by the following formula: In the formula, This represents observation noise. Coefficients for observed values; This is the elevation angle scaling factor; , E is a constant, and E is the satellite elevation angle; This refers to the bandwidth of the phase tracking loop. S Signal-to-noise ratio; λ is the wavelength.

[0035] The GNSS coseismic displacement monitoring stochastic model constructed in this invention is compatible with various observation values ​​and reflects their statistical characteristics, making the final extracted coseismic displacement stable and reliable.

[0036] S400: Acquire real-time GNSS observation data, and construct a CV dynamic PPP model and a CA dynamic PPP model based on the GNSS coseismic displacement monitoring function model and the GNSS coseismic displacement monitoring stochastic model; input the real-time GNSS observation data into the CV dynamic PPP model or the CA dynamic PPP model to obtain the GNSS coseismic displacement.

[0037] Based on the constructed GNSS coseismic displacement monitoring function model and stochastic model, a dynamic PPP model is built. The dynamic PPP model is applicable to single-frequency, dual-frequency, and multi-frequency observations. It obtains the velocity or acceleration information of the receiver carrier by differentiating the real-time GNSS observation data, and then integrates this velocity and acceleration information to obtain the GNSS coseismic displacement. Because the dynamic PPP model comprehensively considers the characteristics of various observations and the dynamic model of the moving carrier, the extracted coseismic displacement is a stable and reliable result.

[0038] The dynamic PPP model includes a CV dynamic PPP model and a CA dynamic PPP model. The CV dynamic PPP model or the CA dynamic PPP model is selected based on the sampling rate when acquiring real-time GNSS observation data. When the sampling rate interval for acquiring real-time GNSS observation data is less than 1 s, the real-time GNSS observation data is input into the CV dynamic PPP model to obtain the GNSS coseismic displacement. When the sampling rate interval for acquiring real-time GNSS observation data is 1.0~3.0 s, the real-time GNSS observation data is input into the CA dynamic PPP model to obtain the GNSS coseismic displacement.

[0039] For the CV dynamic PPP model, the position information, state transition matrix, and process noise are based on its motion model, while other parameters remain consistent with those of the traditional PPP model. The CV dynamic PPP model (i.e., the CV dynamic PPP design matrix and the parameters to be estimated) can be expressed by the following formula: In the formula, For designing the matrix; The linear combination coefficients for the line of sight direction; This is the wetted projection function of the troposphere; This is the ionospheric coefficient; This refers to the location information of the receiver carrier; Incremental correction for receiver position; Incremental correction for receiver speed; For tropospheric wet delay; For receiver clock bias; For receiver clock drift; For ambiguity parameters; This is a transpose.

[0040] For the CA dynamic PPP model, the location information state transition matrix and process noise are similar to those of the CV dynamic PPP model; therefore, the CA dynamic PPP model (i.e., the CA dynamic PPP design matrix and the parameters to be estimated) can be expressed by the following formula: In the formula, These are acceleration parameters.

[0041] Based on the functional relationship between the CV dynamic PPP design matrix and the CA dynamic PPP design matrix, the CV dynamic PPP model and the CA dynamic PPP model can directly obtain the acceleration and velocity information of the receiver carrier. The accuracy of the dynamic PPP model depends on the sufficiency of the assumptions of the kinematic model of the observation. Therefore, the setting of process noise is an important factor affecting the accuracy of velocity and acceleration.

[0042] Furthermore, dynamic PPP models are also commonly used in coseismic displacement monitoring. Considering the influence of dynamic noise in CV and CA motion models, the location parameters directly estimated from the PPP (Precise Point Positioning) equations are usually not directly selected. The actual location information provided can be obtained through velocity or acceleration integration. In dynamic PPP models... t The GNSS coseismic displacement at time t can be expressed as follows: In the formula, The integral obtained from the dynamic PPP model t GNSS coseismic displacement at time t; For time indexing; For speed parameters; The time interval for the receiver data sampling rate; These are acceleration parameters.

[0043] The accuracy of the velocity and acceleration values ​​obtained by the dynamic PPP model mainly depends on the accuracy of the real-time GNSS observation data and the sufficiency of the kinematic model assumptions. The dynamic PPP model can actually be viewed as two steps. The first step is to calculate the velocity or acceleration of the carrier using the carrier's position information. This first step is actually a differentiation process of the position information, which is an unstable process. If the assumed kinematic model is insufficient, the obtained velocity and acceleration results will be meaningless. Therefore, the process noise setting of the dynamic PPP model must be reasonable and cautious. The second step is to obtain the GNSS coseismic displacement through the integration of acceleration and velocity. Although the integration process is a relatively stable process, the obtained position information will be affected by the initial state error, and some systematic errors may occur.

[0044] The method disclosed in this invention can quickly monitor high-precision coseismic displacement during earthquakes, reduce the impact of complex external environments, make up for the shortcomings of traditional methods, and provide strong technical support for GNSS earthquake monitoring.

[0045] Example 2 One embodiment of the present invention provides a GNSS coseismic displacement rapid monitoring system, including an orbit determination and clock error estimation module, a monitoring function model construction module, a monitoring stochastic model construction module, and a GNSS coseismic displacement calculation module; The orbit determination and clock error estimation module is used to acquire GNSS observation data, perform precise orbit determination and precise satellite clock error estimation based on the GNSS observation data, and obtain real-time orbit and satellite clock error data; wherein, the GNSS observation data includes GNSS pseudorange, carrier phase and Doppler observation values; The monitoring function model construction module is used to construct a receiver carrier motion model; based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and linear equations of GNSS pseudorange, carrier phase and Doppler observations, a GNSS coseismic displacement monitoring function model is constructed. The monitoring stochastic model construction module is used to construct a GNSS coseismic displacement monitoring stochastic model based on GNSS pseudorange, carrier phase and Doppler observations; The GNSS coseismic displacement calculation module is used to acquire real-time GNSS observation data; and to construct a constant velocity dynamic PPP model and a constant acceleration dynamic PPP model based on the GNSS coseismic displacement monitoring function model and the GNSS coseismic displacement monitoring stochastic model; and to input the real-time GNSS observation data into the constant velocity dynamic PPP model or the constant acceleration dynamic PPP model to obtain the GNSS coseismic displacement.

[0046] The real-time earthquake monitoring service includes three basic functions: precise orbit determination (POD), precise satellite clock error estimation (PCE), and precise positioning. By acquiring real-time orbit data and satellite clock errors, real-time GNSS coseismic displacement rapid monitoring (i.e., precise positioning) can be implemented, providing users with high-precision coseismic displacement monitoring services.

[0047] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software 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 implementations should not be considered beyond the scope of this invention.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for rapid monitoring of coseismic displacement using GNSS, characterized in that, Includes the following steps: S100: Acquire GNSS observation data, perform precise orbit determination and precise satellite clock error estimation based on the GNSS observation data, and obtain real-time orbit and satellite clock error data; wherein, the GNSS observation data includes GNSS pseudorange, carrier phase and Doppler observation values; S200: Construct a receiver carrier motion model; based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and linear equations of GNSS pseudorange, carrier phase and Doppler observations, construct a GNSS coseismic displacement monitoring function model; S300: Constructing a stochastic model for GNSS coseismic displacement monitoring based on GNSS pseudorange, carrier phase, and Doppler observations; S400: Acquires real-time GNSS observation data; Based on the GNSS coseismic displacement monitoring function model and the GNSS coseismic displacement monitoring stochastic model, a constant velocity dynamic PPP model and a constant acceleration dynamic PPP model are constructed; the real-time GNSS observation data are input into the constant velocity dynamic PPP model or the constant acceleration dynamic PPP model to obtain the GNSS coseismic displacement.

2. The GNSS coseismic displacement rapid monitoring method according to claim 1, characterized in that, The receiver carrier motion model mentioned in step S200 includes a constant velocity motion model and a constant acceleration motion model; When the data sampling rate interval of the GNSS receiver If the interval is less than 1 s, the receiver carrier is considered to follow a constant velocity motion model; if the data sampling interval of the GNSS receiver is 1.0~3.0 s, the receiver carrier is considered to follow a constant acceleration motion model.

3. The GNSS coseismic displacement rapid monitoring method according to claim 2, characterized in that, The constant velocity motion model is expressed by the following formula: In the formula, , They are respectively The three-dimensional position and velocity state vector of the receiver carrier at any given time; The time interval for the receiver data sampling rate; It is a three-dimensional identity matrix; , They are respectively The three-dimensional position and velocity state vector of the receiver carrier at any given time; for The process noise matrix at each time step; This refers to speed dynamic noise.

4. The GNSS coseismic displacement rapid monitoring method according to claim 2, characterized in that, The constant acceleration motion model is expressed by the following formula: In the formula, , , They are respectively The three-dimensional position, velocity state vector, and three-dimensional acceleration state vector of the real-time receiver carrier; The time interval for the receiver data sampling rate; It is a three-dimensional identity matrix; , , They are respectively The three-dimensional position, velocity state vector, and three-dimensional acceleration state vector of the real-time receiver carrier; for The process noise matrix at each time step; This refers to acceleration dynamic noise.

5. The GNSS coseismic displacement rapid monitoring method according to claim 1, characterized in that, The linear equations for GNSS pseudorange, carrier phase, and Doppler observations described in step S200 are expressed by the following formula: In the formula, These are pseudorange observations; The linear combination coefficients for the line of sight direction; Incremental correction for receiver position; For receiver clock bias parameters; This is the wetted projection function of the troposphere; For tropospheric wet delay; This is the ionospheric coefficient; For ionospheric delay; This represents noise in pseudorange observations; These are phase observations; For ambiguity parameters; This represents noise in the phase observations. These are Doppler observations; Incremental correction for receiver speed; For receiver clock drift; This represents noise in Doppler observations.

6. The GNSS coseismic displacement rapid monitoring method according to claim 1, characterized in that, The GNSS coseismic displacement monitoring stochastic model described in step S300 is expressed by the following formula: In the formula, This represents observation noise. Coefficients for observed values; This is the elevation angle scaling factor; , E is a constant, and E is the satellite elevation angle; This refers to the bandwidth of the phase tracking loop. S Signal-to-noise ratio; λ is the wavelength.

7. The GNSS coseismic displacement rapid monitoring method according to claim 1, characterized in that, Step S400 includes: when the sampling rate time interval for acquiring real-time GNSS observation data is less than 1 s, inputting the real-time GNSS observation data into the constant velocity dynamic PPP model to obtain GNSS coseismic displacement; when the sampling rate time interval for acquiring real-time GNSS observation data is 1.0~3.0 s, inputting the real-time GNSS observation data into the constant acceleration dynamic PPP model to obtain GNSS coseismic displacement.

8. The GNSS coseismic displacement rapid monitoring method according to claim 7, characterized in that, The constant-velocity dynamic PPP model is expressed by the following formula: In the formula, For designing the matrix; The linear combination coefficients for the line of sight direction; This is the wetted projection function of the troposphere; This is the ionospheric coefficient; This refers to the location information of the receiver carrier; Incremental correction for receiver position; Incremental correction for receiver speed; For tropospheric wet delay; For receiver clock bias; For receiver clock drift; For ambiguity parameters; For transpose; The constant-acceleration dynamic PPP model is expressed by the following formula: In the formula, These are acceleration parameters.

9. The GNSS coseismic displacement rapid monitoring method according to claim 1, characterized in that, The GNSS coseismic displacement mentioned in step S400 is calculated using the following formula: In the formula, The integral obtained from the dynamic PPP model t GNSS coseismic displacement at time t; For time indexing; For speed parameters; The time interval for the receiver data sampling rate; These are acceleration parameters.

10. A GNSS coseismic displacement rapid monitoring system, characterized in that, It includes a orbit determination and clock error estimation module, a monitoring function model construction module, a monitoring stochastic model construction module, and a GNSS coseismic displacement calculation module; The orbit determination and clock error estimation module is used to acquire GNSS observation data, perform precise orbit determination and precise satellite clock error estimation based on the GNSS observation data, and obtain real-time orbit and satellite clock error data; wherein, the GNSS observation data includes GNSS pseudorange, carrier phase and Doppler observation values; The monitoring function model construction module is used to construct a receiver carrier motion model; based on the receiver carrier motion model, real-time orbit and satellite clock bias data, and linear equations of GNSS pseudorange, carrier phase and Doppler observations, a GNSS coseismic displacement monitoring function model is constructed. The monitoring stochastic model construction module is used to construct a GNSS coseismic displacement monitoring stochastic model based on GNSS pseudorange, carrier phase and Doppler observations; The GNSS coseismic displacement calculation module is used to acquire real-time GNSS observation data; and to construct a constant velocity dynamic PPP model and a constant acceleration dynamic PPP model based on the GNSS coseismic displacement monitoring function model and the GNSS coseismic displacement monitoring stochastic model; and to input the real-time GNSS observation data into the constant velocity dynamic PPP model or the constant acceleration dynamic PPP model to obtain the GNSS coseismic displacement.

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