BDS deformation monitoring method and system in strong shielding environment

Abstract

The invention provides a BDS deformation monitoring method and system in a strong shielding environment, and the method comprises the following steps: S1, obtaining the observed quantity data of a base station receiver and the reference coordinates of a base station, and obtaining the observed quantity data of a monitoring station all-in-one machine and the MEMS sensor data of the monitoring station all-in-one machine; s2, obtaining the motion resultant velocity and resultant acceleration variation of the monitoring station; s3, the motion state of the monitoring station is judged according to the motion resultant velocity and the resultant acceleration variation, and if the judgment result is a rapid displacement state, the step S4 is executed; if the judgment result is a static state, turning to step S5; s4, performing real-time differential calculation to obtain a first displacement amount; s5, performing static multi-epoch large-interval period three-difference observed quantity differential positioning to obtain a second displacement quantity; and S6, performing linear regression fitting according to the first displacement amount / the second displacement amount to obtain a displacement deviation.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a BDS deformation monitoring method and system in a strong shielding environment. Background Art

[0002] For Beidou navigation satellites, Beidou measurement has the characteristics of short cycle, rapid network deployment, and all-weather, and is widely used in monitoring geological disasters such as landslides and subsidence. The areas with high incidence of geological disasters are generally located in mountainous areas, lakes and other areas with harsh environments. The monitoring stations located in the area are constrained by the actual terrain environment and often face severe obstruction. In a strong obstruction environment, the positioning accuracy of Beidou monitoring equipment cannot meet the monitoring requirements, and it cannot even be positioned normally in some special scenarios. Summary of the invention

[0003] The object of the present invention is to provide a BDS deformation monitoring method and system in a strong occlusion environment. The BDS deformation monitoring method mainly selects real-time differential decomposition and static multi-epoch large interval period triple difference observation differential positioning to solve the displacement of the monitoring station according to the motion state of the monitoring station, and performs linear regression processing on the displacement, which can improve the displacement measurement accuracy and can also be applied to an environment with severe occlusion to meet the positioning accuracy in an environment with severe occlusion.

[0004] In order to solve the above technical problems, the present invention adopts the following solutions:

[0005] A BDS deformation monitoring method in a strong occlusion environment, the method comprising the following steps:

[0006] S1. Obtain the observation data of the base station receiver and the reference coordinates of the base station, and obtain the observation data of the monitoring station integrated machine and the MEMS sensor data of the monitoring station integrated machine;

[0007] S2, calculate the combined velocity and combined acceleration change of the monitoring station according to the observation data of the monitoring station integrated machine and the MEMS sensor data;

[0008] S3, judging the motion state of the monitoring station according to the combined motion velocity and the combined acceleration change, if the judgment result is a fast displacement state, go to step S4; if the judgment result is a stationary state, go to step S5;

[0009] S4, performing real-time differential calculation according to the reference coordinates of the reference station, the observation data of the reference station receiver, and the observation data of the monitoring station integrated machine to obtain a first displacement;

[0010] S5, performing static multi-epoch large-interval period triple-difference observation differential positioning according to the reference coordinates of the reference station, the observation data of the reference station receiver and the observation data of the monitoring station integrated machine to obtain a second displacement;

[0011] S6. Perform linear regression fitting based on the first displacement amount / the second displacement amount to obtain a displacement deviation.

[0012] Furthermore, the step S2 includes the following steps:

[0013] S21, calculating the speed of the integrated monitoring station machine according to the observed data of the integrated monitoring station machine, and taking the speed of the integrated monitoring station machine as the combined motion speed;

[0014] S22, calculating the average value of the MEMS sensor data within an epoch time, subtracting the average value within the current epoch time from the average value within the previous epoch time, and taking the difference as the total acceleration change.

[0015] Furthermore, in S3, the motion state of the monitoring station is determined according to the combined motion velocity and the combined acceleration change, and the determination process is as follows:

[0016] Determine whether the combined velocity and combined acceleration changes of the movement meet the rapid displacement condition. If so, the determination result is a rapid displacement state; determine whether the combined velocity and combined acceleration changes of the movement meet the static condition. If so, the determination result is a static state;

[0017] The fast displacement conditions are: the combined velocity of movement is greater than n times the combined velocity threshold and the combined acceleration change is greater than n times the combined acceleration change threshold / the combined velocity of movement is greater than the combined velocity threshold for k consecutive epochs / the combined acceleration change is greater than the combined acceleration change threshold for k consecutive epochs.

[0018] The stationary condition is: when the motion state of the monitoring station is a fast displacement state, the combined velocity of the motion is less than the combined velocity threshold for k consecutive epochs and the combined acceleration change is less than the combined acceleration change threshold for k consecutive epochs;

[0019] Among them, n≥3, k≥10.

[0020] Furthermore, the combined motion velocity threshold is calculated by continuously collecting a plurality of combined motion velocities in a stationary state, and the calculation process is:

[0021] When the motion state of the monitoring station is a stationary state, multiple motion combined velocities are continuously collected, and the root mean square values ​​of the multiple motion combined velocities are calculated, and n times the root mean square value is used as the motion combined velocity threshold.

[0022] Furthermore, the combined acceleration change threshold is calculated by continuously collecting a plurality of combined acceleration changes in a stationary state, and the calculation process is:

[0023] When the motion state of the monitoring station is a stationary state, a plurality of combined acceleration changes are continuously collected, and the root mean square values ​​of the plurality of combined acceleration changes are calculated, and the n-fold root mean square value is used as the combined acceleration change threshold.

[0024] Furthermore, in S4, the process of real-time differential decomposition is:

[0025] Dual-frequency cycle slip detection is performed on the observation data of the base station receiver and the observation data of the monitoring station integrated machine, and a carrier phase double-difference model is constructed. The carrier phase double-difference model is differentially analyzed according to the reference coordinates of the base station to obtain the first displacement.

[0026] Further, in S5, the process of the static multi-epoch large-interval period triple-difference observation differential positioning is:

[0027] Polynomial cycle slip detection is performed on the observation data of the base station receiver and the observation data of the monitoring station integrated machine, and then polynomial fitting interpolation is performed on the observation data according to the preset interval to construct a linearized model of the three-difference observation equations. The linearized model of the three-difference observation equations is differentially positioned according to the reference coordinates of the base station to obtain the second displacement.

[0028] Furthermore, in S6, the linear regression fitting process is:

[0029] A linear regression fitting is performed on the first displacement / second displacement, and the linear regression fitting adopts a long-period linear function model and a short-period quadratic function model for fitting respectively to obtain the corresponding first displacement deviation and second displacement deviation. At the same time, the model errors of the current long-period linear function model and the current short-period quadratic function model are judged respectively, and the corresponding first displacement deviation or second displacement deviation is selected and output according to the model error.

[0030] A BDS deformation monitoring system in a strong occlusion environment, using the BDS deformation monitoring method in a strong occlusion environment, comprising:

[0031] Data acquisition module: obtains the observation data of the base station receiver and the reference coordinates of the base station, and obtains the observation data of the monitoring station integrated machine and the MEMS sensor data of the monitoring station integrated machine;

[0032] Observation data processing module: calculates the combined velocity and combined acceleration change of the monitoring station based on the observation data of the monitoring station integrated machine and the MEMS sensor data;

[0033] Monitoring station motion state determination module: determines the motion state of the monitoring station according to the combined velocity and combined acceleration changes;

[0034] Real-time differential calculation module: performs real-time differential calculation according to the reference coordinates of the base station, the observation data of the base station receiver and the observation data of the monitoring station integrated machine to obtain the first displacement;

[0035] Static multi-epoch large-interval period triple-difference observation differential positioning module: Static multi-epoch large-interval period triple-difference observation differential positioning is performed according to the reference coordinates of the base station, the observation data of the base station receiver and the observation data of the monitoring station integrated machine to obtain the second displacement;

[0036] Linear regression fitting displacement deviation module: linear regression fitting is performed according to the first displacement / second displacement to obtain the displacement deviation.

[0037] Beneficial effects of the present invention:

[0038] The present invention provides a BDS deformation monitoring method and system in a strong occlusion environment. The BDS deformation monitoring method mainly judges the motion state of the current monitoring station by the combined motion velocity and combined acceleration change of the monitoring station. In a fast displacement state, real-time differential analysis can be used to monitor the displacement in real time. In a stationary state, static multi-epoch large interval period triple difference observation differential positioning can be used to calculate the displacement, thereby effectively improving the calculation accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of a strong shielding environment in Example 1 of the present invention;

[0040] Figure 2 Schematic diagram of the flow of a BDS deformation monitoring method in a strong occlusion environment in Example 1 of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0043] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0044] Additionally, descriptions of well-known structures, functions, and configurations may be omitted for clarity and conciseness.One of ordinary skill in the art will recognize that various changes and modifications may be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0045] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0046] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0047] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments:

[0048] Example 1

[0049] In this embodiment, when the monitoring station is located in a harsh environment, it will face severe obstruction due to the constraints of the actual terrain environment, such as Figure 1 As shown, in Figure 1 It can be seen that the base station is deployed in an open and geologically stable area, and the integrated monitoring station is deployed below the base station. However, affected by the terrain of the area, the integrated monitoring station is located at a monitoring point with strong obstruction. Through the monitoring of the number of satellites and DOP values ​​throughout the day at the monitoring point, it can be seen that the obstruction is very serious. If the real-time differential positioning method is directly used to solve it, the accuracy of the obtained position is very poor and cannot meet the millimeter-level accuracy index requirements.

[0050] Therefore, in order to solve the problem that it is currently impossible to effectively and accurately locate Beidou monitoring equipment in a strong occlusion environment, a BDS deformation monitoring method in a strong occlusion environment is proposed. Figure 2 As shown, the method comprises the following steps:

[0051] S1. Obtain the observation data of the base station receiver and the reference coordinates of the base station, and obtain the observation data of the monitoring station integrated machine and the MEMS sensor data of the monitoring station integrated machine;

[0052] S2, calculate the combined velocity and combined acceleration change of the monitoring station according to the observation data of the monitoring station integrated machine and the MEMS sensor data;

[0053] S3, judging the motion state of the monitoring station according to the combined motion velocity and the combined acceleration change, if the judgment result is a fast displacement state, go to step S4; if the judgment result is a stationary state, go to step S5;

[0054] S4, performing real-time differential calculation according to the reference coordinates of the reference station, the observation data of the reference station receiver, and the observation data of the monitoring station integrated machine to obtain a first displacement;

[0055] S5, performing static multi-epoch large-interval period triple-difference observation differential positioning according to the reference coordinates of the reference station, the observation data of the reference station receiver and the observation data of the monitoring station integrated machine to obtain a second displacement;

[0056] S6. Perform linear regression fitting based on the first displacement amount / the second displacement amount to obtain a displacement deviation.

[0057] Preferably, step S2 includes the following steps:

[0058] S21, calculating the speed of the integrated monitoring station machine according to the observed data of the integrated monitoring station machine, and taking the speed of the integrated monitoring station machine as the combined motion speed;

[0059] S22, calculating the average value of the MEMS sensor data within an epoch time, subtracting the average value within the current epoch time from the average value within the previous epoch time, and taking the difference as the total acceleration change.

[0060] Specifically, for example, the sampling rate of the observation data of the current base station receiver is 15 seconds, the sampling frequency of the observation data of the monitoring station integrated machine is 1 second, and the sampling rate of the MEMS sensor data is 10 Hz.

[0061] The combined velocity and combined acceleration change of the monitoring station are calculated based on the above data. At this time, the Doppler observation of the monitoring station integrated machine is used to calculate the speed of the monitoring station integrated machine in real time, and the speed of the monitoring station integrated machine is used as the combined velocity of the movement; the combined acceleration change is then calculated using the MEMS sensor data of the monitoring station integrated machine. The process is as follows:

[0062] (1) Calculate the average value of MEMS sensor data within one epoch time as follows:

[0063]

[0064] In another embodiment, if the sampling rate of the current MEMS sensor data is greater than 10 Hz, and the acceleration is calculated at a frequency of 1 Hz, the average value of the MEMS sensor data within 1 second can be calculated according to the above formula.

[0065] (2) Subtract the average value in the current epoch from the average value in the previous epoch, as follows:

[0066]

[0067] According to the above formula, the difference between the average values ​​in two adjacent epochs can be calculated, thereby obtaining the current total acceleration change.

[0068] Preferably, in S3, the motion state of the monitoring station is determined according to the combined motion velocity and the combined acceleration change, and the determination process is:

[0069] Determine whether the combined velocity and combined acceleration changes of the movement meet the rapid displacement condition. If so, the determination result is a rapid displacement state; determine whether the combined velocity and combined acceleration changes of the movement meet the static condition. If so, the determination result is a static state;

[0070] The fast displacement conditions are: the combined velocity of movement is greater than n times the combined velocity threshold and the combined acceleration change is greater than n times the combined acceleration change threshold / the combined velocity of movement is greater than the combined velocity threshold for k consecutive epochs / the combined acceleration change is greater than the combined acceleration change threshold for k consecutive epochs.

[0071] The stationary condition is: when the motion state of the monitoring station is a fast displacement state, the combined velocity of the motion is less than the combined velocity threshold for k consecutive epochs and the combined acceleration change is less than the combined acceleration change threshold for k consecutive epochs;

[0072] Among them, n≥3, k≥10.

[0073] Specifically, in S3, a method is proposed to assist in detecting the motion state of the monitoring station by combining MEMS sensor data. The method mainly determines whether the motion state of the current monitoring station is a fast displacement state or a static state by the combined velocity and combined acceleration change. The determination process includes the following:

[0074] (1) When the combined speed of movement is greater than 3 times the combined speed threshold and the combined acceleration change is greater than 3 times the combined acceleration change threshold, the movement state of the current monitoring station is determined to be a rapid displacement state;

[0075] (2) When the combined velocity of movement is greater than the combined velocity threshold for 10 consecutive epochs, the movement state of the current monitoring station is determined to be a rapid displacement state;

[0076] (3) When the total acceleration change is greater than the total acceleration change threshold for 10 consecutive epochs, the motion state of the current monitoring station is determined to be a rapid displacement state;

[0077] (4) When the motion state of the monitoring station is a rapid displacement state, when the combined velocity of the motion is less than the combined velocity threshold for 10 consecutive epochs and the combined acceleration change is less than the combined acceleration change threshold for 10 consecutive epochs, the motion state of the current monitoring station is changed from a rapid displacement state to a stationary state.

[0078] The combined motion velocity threshold and the combined acceleration change threshold can be calculated by continuously collecting multiple combined motion velocities in a stationary state, and the calculation process is:

[0079] When the motion state of the monitoring station is static, continuously collect the combined velocity and combined acceleration change for 1 hour, statistically calculate the root mean square value of the combined velocity and the root mean square value of the combined acceleration change for 1 hour, and take 3 times the root mean square value of the combined velocity as the current combined velocity threshold, and take 3 times the root mean square value of the combined acceleration change as the current combined acceleration change threshold.

[0080] Preferably, in S4, the process of real-time differential decomposition is:

[0081] Dual-frequency cycle slip detection is performed on the observation data of the base station receiver and the observation data of the monitoring station integrated machine, and a carrier phase double-difference model is constructed. The carrier phase double-difference model is differentially analyzed according to the reference coordinates of the base station to obtain the first displacement.

[0082] Specifically, when the monitoring station moves rapidly, that is, the motion state of the monitoring station is a rapid displacement state, real-time differential analysis is selected to calculate the displacement, and the carrier phase double difference model is as follows:

[0083]

[0084] Among them, the subscript r represents the observation of the monitoring station, the subscript b represents the observation of the reference station, the superscript k represents the observation of the kth satellite, and the superscript j represents the observation of the reference satellite;

[0085] Since the baseline length between the reference station and the monitoring station is generally less than 5 km, which is an ultra-short baseline, the errors of the transmission path can be ignored, and the linearized form of the double difference observation equation can be obtained:

[0086]

[0087] Where λ is the carrier wavelength, is the double difference ambiguity, is the inter-satellite difference of the first-order term of the Taylor series expansion of the satellite-ground line of satellite k in the ECEF coordinate system, is the distance from the satellite to the base station and monitoring station.

[0088] If there are n synchronous public satellites, according to the above formula, after the double difference ambiguity initialization is completed, the error equation group is as follows:

[0089]

[0090] Thus, the corresponding equation and its solution are obtained:

[0091] δX r =-(A T PA) -1 (A T PL);

[0092] And, in the above formula, P is the corresponding double difference observation weight matrix, δX r =[δx r δy r δz r ], L is a column vector consisting of n-1 v, and A is expressed as follows:

[0093]

[0094] Preferably, in S5, the process of static multi-epoch large-interval period triple-difference observation differential positioning is:

[0095] Polynomial cycle slip detection is performed on the observation data of the base station receiver and the observation data of the monitoring station integrated machine, and then polynomial fitting interpolation is performed on the observation data according to the preset interval to construct a linearized model of the three-difference observation equations. The linearized model of the three-difference observation equations is differentially positioned according to the reference coordinates of the base station to obtain the second displacement.

[0096] Specifically, when the monitoring station does not undergo rapid displacement, that is, the motion state of the monitoring station is static, the static multi-epoch large interval period triple difference observation differential positioning is selected to obtain the displacement, and the solution frequency is once every 1h. The process is as follows:

[0097] Firstly, cycle slip detection and cycle slip repair are performed on the observation data of the base station receiver and the observation data of the monitoring station integrated device, and the cycle slip detection and cycle slip repair method adopts the polynomial fitting method;

[0098] Then, the original observations were interpolated by polynomial fitting, and the observations were extracted by interpolation at 10-minute intervals. The non-GEO satellite observations were fitted by a quadratic term model, and the GEO satellite observations were fitted by a linear term model.

[0099] Next, the displacement is calculated using the static multi-epoch large-interval period triple-difference observation differential positioning method, where the linearized model of the triple-difference observation equation is as follows:

[0100]

[0101] Where δ represents the epoch difference operator; if there are m epochs, then there are n1, n2, ... n common satellites between two adjacent epochs. m-1 The error equations are as follows:

[0102]

[0103] Among them, the rest of the definitions are consistent with the description of the double difference model, and the corresponding normal equations and their solutions are also consistent with the double difference model;

[0104] In addition, the epoch time interval selected when constructing the triple-difference model is 10 minutes, and the observations are the values ​​of the original observations after polynomial fitting interpolation. The non-GEO satellite observations are fitted by a quadratic model, and the GEO satellite observations are fitted by a linear model.

[0105] Preferably, in S6, the process of linear regression fitting is:

[0106] A linear regression fitting is performed on the first displacement / second displacement, and the linear regression fitting adopts a long-period linear function model and a short-period quadratic function model for fitting respectively to obtain the corresponding first displacement deviation and second displacement deviation. At the same time, the model errors of the current long-period linear function model and the current short-period quadratic function model are judged respectively, and the corresponding first displacement deviation or second displacement deviation is selected and output according to the model error.

[0107] Specifically, in this embodiment, the position deviation is also calculated by linear regression. The linear regression model adopts a long-period linear function model and a short-period quadratic function model respectively. If the fitting accuracy of the quadratic function model is significantly higher than that of the linear function model, it indicates that the displacement characteristics of the current monitoring station have changed significantly. The fitting result of the quadratic function model is used as the final output result, and the linear function model is reinitialized to improve the displacement measurement accuracy. It can also be applied to environments with severe occlusion to meet the positioning accuracy in environments with severe occlusion.

[0108] Furthermore, when the position deviation is greater than a threshold or the monitoring station integrated machine determines that it is in a fast displacement state, the output frequency of the position deviation can be increased and the output warning information can be increased.

[0109] Example 2

[0110] A BDS deformation monitoring system in a strong occlusion environment, using the BDS deformation monitoring method in a strong occlusion environment, comprising:

[0111] Data acquisition module: obtains the observation data of the base station receiver and the reference coordinates of the base station, and obtains the observation data of the monitoring station integrated machine and the MEMS sensor data of the monitoring station integrated machine;

[0112] Observation data processing module: calculates the combined velocity and combined acceleration change of the monitoring station based on the observation data of the monitoring station integrated machine and the MEMS sensor data;

[0113] Monitoring station motion state determination module: determines the motion state of the monitoring station according to the combined velocity and combined acceleration changes;

[0114] Real-time differential calculation module: performs real-time differential calculation according to the reference coordinates of the base station, the observation data of the base station receiver and the observation data of the monitoring station integrated machine to obtain the first displacement;

[0115] Static multi-epoch large-interval period triple-difference observation differential positioning module: Static multi-epoch large-interval period triple-difference observation differential positioning is performed according to the reference coordinates of the base station, the observation data of the base station receiver and the observation data of the monitoring station integrated machine to obtain the second displacement;

[0116] Linear regression fitting displacement deviation module: linear regression fitting is performed according to the first displacement / second displacement to obtain the displacement deviation.

[0117] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. According to the technical essence of the present invention, within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A BDS deformation monitoring method in a strong occlusion environment, characterized in that: The method comprises the following steps: S1. Obtain the observation data of the base station receiver and the reference coordinates of the base station, and obtain the observation data of the monitoring station integrated machine and the MEMS sensor data of the monitoring station integrated machine; S2, calculate the combined velocity and combined acceleration change of the monitoring station according to the observation data of the monitoring station integrated machine and the MEMS sensor data; S3, judging the motion state of the monitoring station according to the combined motion velocity and the combined acceleration change, if the judgment result is a fast displacement state, go to step S4; if the judgment result is a stationary state, go to step S5; S4, performing real-time differential calculation according to the reference coordinates of the reference station, the observation data of the reference station receiver, and the observation data of the monitoring station integrated machine to obtain a first displacement; S5, performing static multi-epoch large-interval period triple-difference observation differential positioning according to the reference coordinates of the reference station, the observation data of the reference station receiver and the observation data of the monitoring station integrated machine to obtain a second displacement; S6. Perform linear regression fitting based on the first displacement amount / the second displacement amount to obtain a displacement deviation.

2. The BDS deformation monitoring method in a strong shielding environment according to claim 1 is characterized in that: The step S2 includes the following steps: S21, calculating the speed of the integrated monitoring station machine according to the observed data of the integrated monitoring station machine, and taking the speed of the integrated monitoring station machine as the combined motion speed; S22, calculating the average value of the MEMS sensor data within an epoch time, subtracting the average value within the current epoch time from the average value within the previous epoch time, and taking the difference as the total acceleration change.

3. The BDS deformation monitoring method in a strong shielding environment according to claim 1 is characterized in that: In S3, the motion state of the monitoring station is determined according to the combined motion velocity and the combined acceleration change. The determination process is as follows: Determine whether the combined velocity and combined acceleration changes of the movement meet the rapid displacement condition. If so, the determination result is a rapid displacement state; determine whether the combined velocity and combined acceleration changes of the movement meet the static condition. If so, the determination result is a static state; The fast displacement conditions are: the combined velocity of movement is greater than n times the combined velocity threshold and the combined acceleration change is greater than n times the combined acceleration change threshold / the combined velocity of movement is greater than the combined velocity threshold for k consecutive epochs / the combined acceleration change is greater than the combined acceleration change threshold for k consecutive epochs. The stationary condition is: when the motion state of the monitoring station is a fast displacement state, the combined velocity of the motion is less than the combined velocity threshold for k consecutive epochs and the combined acceleration change is less than the combined acceleration change threshold for k consecutive epochs; Among them, n≥3, k≥10.

4. The method for monitoring BDS deformation in a strong shielding environment according to claim 3 is characterized in that: The combined motion velocity threshold is calculated by continuously collecting multiple combined motion velocities in a stationary state, and the calculation process is: When the motion state of the monitoring station is a stationary state, multiple motion combined velocities are continuously collected, and the root mean square values ​​of the multiple motion combined velocities are calculated, and n times the root mean square value is used as the motion combined velocity threshold.

5. The BDS deformation monitoring method in a strong shielding environment according to claim 3 is characterized in that: The combined acceleration change threshold is calculated by continuously collecting multiple combined acceleration changes in a stationary state, and the calculation process is: When the motion state of the monitoring station is a stationary state, a plurality of combined acceleration changes are continuously collected, and the root mean square values ​​of the plurality of combined acceleration changes are calculated, and the n-fold root mean square value is used as the combined acceleration change threshold.

6. The method for monitoring BDS deformation in a strong shielding environment according to claim 1, characterized in that: In S4, the process of real-time differential decomposition is: Dual-frequency cycle slip detection is performed on the observation data of the base station receiver and the observation data of the monitoring station integrated machine, and a carrier phase double-difference model is constructed. The carrier phase double-difference model is differentially analyzed according to the reference coordinates of the base station to obtain the first displacement.

7. The method for monitoring BDS deformation in a strong shielding environment according to claim 1, characterized in that: In S5, the process of static multi-epoch large-interval period triple-difference observation differential positioning is: Polynomial cycle slip detection is performed on the observation data of the base station receiver and the observation data of the monitoring station integrated machine, and then polynomial fitting interpolation is performed on the observation data according to the preset interval to construct a linearized model of the three-difference observation equations. The linearized model of the three-difference observation equations is differentially positioned according to the reference coordinates of the base station to obtain the second displacement.

8. The method for monitoring BDS deformation in a strong shielding environment according to claim 1, characterized in that: In S6, the linear regression fitting process is: A linear regression fitting is performed on the first displacement / second displacement, and the linear regression fitting adopts a long-period linear function model and a short-period quadratic function model for fitting respectively to obtain the corresponding first displacement deviation and second displacement deviation. At the same time, the model errors of the current long-period linear function model and the current short-period quadratic function model are judged respectively, and the corresponding first displacement deviation or second displacement deviation is selected and output according to the model error.

9. A BDS deformation monitoring system in a strong occlusion environment, characterized in that: A BDS deformation monitoring method in a strong occlusion environment as described in any one of claims 1 to 8 is applied, comprising: Data acquisition module: obtains the observation data of the base station receiver and the reference coordinates of the base station, and obtains the observation data of the monitoring station integrated machine and the MEMS sensor data of the monitoring station integrated machine; Observation data processing module: calculates the combined velocity and combined acceleration change of the monitoring station based on the observation data of the monitoring station integrated machine and the MEMS sensor data; Monitoring station motion state determination module: determines the motion state of the monitoring station according to the combined velocity and combined acceleration changes; Real-time differential calculation module: performs real-time differential calculation according to the reference coordinates of the base station, the observation data of the base station receiver and the observation data of the monitoring station integrated machine to obtain the first displacement; Static multi-epoch large-interval period triple-difference observation differential positioning module: Static multi-epoch large-interval period triple-difference observation differential positioning is performed according to the reference coordinates of the base station, the observation data of the base station receiver and the observation data of the monitoring station integrated machine to obtain the second displacement; Linear regression fitting displacement deviation module: linear regression fitting is performed according to the first displacement / second displacement to obtain the displacement deviation.

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