High dynamic deformation monitoring data processing method combining MEMS and GNSS observation data

Abstract

The invention discloses a high-dynamic deformation monitoring data processing method combining MEMS (Micro Electro Mechanical System) and GNSS (Global Navigation Satellite System) observation data, and particularly relates to the technical field of high-dynamic deformation monitoring data processing, which specifically comprises the following steps of: 1, acquiring MEMS observation data and GNSS observation data of an observation target; basic data about an observation target is determined, and related data processing is carried out; 2, judging the deformation frequency of the observation target through an MEMS accelerometer; judging the deformation frequency of the observation target through the related deformation basic data of the observation target; 3, performing quality control on the GNSS high-dynamic deformation monitoring data; 4, determining the deformation amplitude of the observation target through the GNSS data; judging the deformation amplitude of the observation target through the related deformation basic data of the observation target; and 5, carrying out data fusion processing on the MEMS observation data and the GNSS observation data.

Description

Technical Field

[0001] The present invention belongs to the technical field of high dynamic deformation monitoring data processing, and in particular relates to a high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data. Background Art

[0002] Global Navigation Satellite Systems (GNSS) are powerful tools used extensively for positioning, navigation, and time synchronization. They calculate the receiver's precise position, velocity, and time information by receiving signals from multiple satellites.

[0003] GNSS signals are interfered with by the atmosphere (such as the ionosphere and troposphere) during propagation, resulting in signal delay and attenuation, which increases positioning errors. Ground reflection and scattering (multipath effect) complicate the received signal, especially in urban canyons, forests or areas with complex terrain, which further reduces positioning accuracy. For objects with high-frequency and low-amplitude deformations, these tiny errors may be magnified in dynamic analysis, affecting the accuracy of observations. Summary of the invention

[0004] The purpose of the present invention is to provide a high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data, so as to solve the technical problem in the prior art that GNSS has poor observation effect on objects in a high frequency and low amplitude deformation state.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Step 1. Collect MEMS observation data and GNSS observation data of the observed target; use MEMS accelerometer to continuously collect acceleration data of the observed target at key positions or directions. Before the MEMS accelerometer collects information, a three-dimensional coordinate system is established with the observed target as the center based on the spatial range of the observed target. According to the three-dimensional coordinate system, the MEMS accelerometer obtains the acceleration of the observed target in different directions. Through the vector decomposition method, the acceleration of the observed target on different three-dimensional coordinate axes is obtained. Based on the angular change between the observed target and different three-dimensional coordinate planes, the acceleration of the angular change of the observed target on different planes is obtained. Use the GNSS receiver to collect the position information of the observed target, including: the longitude, latitude and altitude of the observed target, and determine the coordinates of certain specific points of the observed target on the three-dimensional coordinate axis, and filter and denoise preprocess the collected acceleration data.

[0007] Step 2: Determine the deformation frequency of the observed target through the MEMS accelerometer; use the MEMS accelerometer to continuously collect the acceleration data of the observed target at key positions or directions, and derive the rate of change of acceleration over time based on the preprocessed acceleration data of the observed target at key positions or directions. According to the rate of change of the observed target acceleration, quantify the deformation degree of the observed target, and set the deformation degree value calculation formula. The specific formula is as follows:

[0008]

[0009] Where B represents the deformation degree value, t represents the observation time of each deformation degree calculation cycle of MEMS, and a p It represents the acceleration of the observed target in the horizontal direction, that is, the horizontal acceleration of the observed target obtained by the acceleration components of the two coordinate axes on the horizontal plane in the three-dimensional coordinate system, a c It represents the acceleration of the observed target in the vertical direction, θ represents the acceleration of the observed target inclination angle, and the actual value of time t is determined according to the specific situation and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders.

[0010] Based on the type, structural material and functional characteristics of the observed target, by consulting relevant standards and specifications, analyzing the historical acceleration data related to the observed target, determining the acceleration range of the observed target under the operating state that meets the purpose of deformation monitoring, and determining the deformation degree threshold based on the acceleration range set. When the deformation degree value of the observed target obtained by the MEMS accelerometer is greater than or equal to the deformation degree threshold, it is judged that the observed target is in a high-frequency deformation state.

[0011] Step 3: Quality control of GNSS high dynamic deformation monitoring data; GNSS technology can monitor the dynamic deformation information of the observed target in real time, provide data support for the health monitoring of the observed target, and make timely warnings according to the deformation state of the observed target. The monitoring scene of the observed target has various complexities and uncertainties, such as buildings and vehicles near the GNSS monitoring station that are not related to the observed target. The complexity and uncertainty of the monitoring scene have certain interference to the GNSS signal. The probability of occurrence of a bad observation environment caused by frequent satellite loss of lock or severe occlusion affects the smooth solution of filtering and ambiguity fixation. It is necessary to comprehensively consider three different aspects to achieve quality control of GNSS high dynamic deformation monitoring data. The specific methods are as follows:

[0012] Preprocess the received GNSS data, use digital filtering technology to remove noise interference, use the ionospheric residual method to eliminate the distance between the monitoring station and the satellite, detect cycle slips by making differences between adjacent epochs, and then use MW combination to build a combined model through dual-frequency pseudorange and carrier observation data to eliminate the influence of related errors and determine the epochs with cycle slips. Use Kalman filtering to obtain the posterior residual and standardize it, determine the area where the maximum value of the phase-standardized residual is located and perform variance expansion, set the residual threshold, and when the maximum value of the phase-standardized residual is greater than the threshold, treat it as a cycle slip, and re-determine the maximum value of the phase-standardized residual until the maximum value of the phase-standardized residual is less than or equal to the threshold;

[0013] Step 4: Determine the deformation amplitude of the observed target through GNSS data; install GNSS monitoring stations at key locations around the observed target to ensure that these stations can receive sufficient satellite signals, set a time point every r time units, collect GNSS satellite signal data once every time point, determine the monitoring area of ​​the GNSS monitoring station, determine one or more monitoring points in each monitoring area according to the structure of the observed target, determine the three-dimensional coordinates of each monitoring point in the three-dimensional coordinate axis of the observed target, set one or more reference points near the monitoring area, determine the three-dimensional coordinates of each reference point, ensure that the reference point remains unchanged during the monitoring period, and each reference point corresponds to a monitoring point. Determine the real-time displacement change of the observed target by solving and analyzing the GNSS satellite signal data, and quantify the change amplitude of the observed target between different time points. The specific formula for calculating the change amplitude is as follows:

[0014]

[0015] Where C(m, m+1) represents the change from time point m to m+1, x represents the xth time point, i represents the i-th reference point, n represents a total of n reference points, Δd i It represents the displacement change between the i-th reference point and its corresponding monitoring point. The specific formula is as follows:

[0016]

[0017] Where X(m), Y(m), and Z(m) represent the coordinates of the monitoring point corresponding to the reference point i at time m with respect to different three-dimensional coordinate axes. i (m), Y i (m), Z i (m) represent the coordinates of the reference point i at time m with respect to different three-dimensional coordinate axes.

[0018] A change amplitude threshold H is set. When C(m, m+1) is less than or equal to H, it is judged that the observed target is in a state of small change amplitude in the time period from time point m to m+1.

[0019] Step 5. Perform data fusion processing on the MEMS observation data and the GNSS observation data; perform time normalization processing on the MEMS observation data and the GNSS observation data, determine the MEMS observation data and the GNSS observation data in the same time period, determine the time period with high deformation frequency and small deformation amplitude by judging the deformation frequency and deformation amplitude state of the observation target, convert the MEMS observation data and the GNSS observation data in the time period into a unified format, select a suitable data fusion method according to application requirements and data characteristics, such as the Kalman filtering method, use the selected data fusion method to perform fusion calculation on the MEMS and GNSS observation data, and predetermine the actual data, evaluate the quality and accuracy of the fusion result by comparing with the actual data, continuously adjust the data fusion method until the quality and accuracy of the fusion result meet the actual requirements, improve the fusion result through smoothing, interpolation or filtering processing, and complete the high dynamic deformation monitoring data processing of MEMS and GNSS observation data.

[0020] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0021] 1. The present invention is based on the high sampling rate of sensors such as MEMS accelerometers and gyroscopes, which can capture high-frequency changes. The MEMS inertial measurement unit can provide continuous navigation and positioning information when the GNSS signal is lost or interrupted. By combining MEMS data with GNSS observation data, it not only improves the accuracy and reliability of observation, but also expands the frequency band range of observation, and enhances the stability and anti-interference ability of the system;

[0022] 2. The present invention uses a MEMS accelerometer to accurately capture the acceleration and angle changes of an object in different time periods, and can directly deduce the deformation speed and direction of the object, thereby more accurately determining the deformation frequency. GNSS is used to provide centimeter-level positioning accuracy, and the deformation amplitude is accurately determined by observing the coordinate changes of the target in the three-dimensional coordinate system, thereby realizing continuous dynamic monitoring of the high-frequency and low-amplitude deformation state of the observed target. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A step diagram of a method for processing high dynamic deformation monitoring data combining MEMS and GNSS observation data is shown;

[0025] Figure 2 A flow chart of data fusion between MEM and GNSS observation data is shown. DETAILED DESCRIPTION

[0026] 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, not all of the embodiments. 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.

[0027] like Figure 1 , Figure 2 As shown, the high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data proposed in this embodiment specifically includes the following steps:

[0028] Step 1. Collect MEMS observation data and GNSS observation data of the observed target; use MEMS accelerometer to continuously collect acceleration data of the observed target at key positions or directions. Before the MEMS accelerometer collects information, a three-dimensional coordinate system is established with the observed target as the center based on the spatial range of the observed target. According to the three-dimensional coordinate system, the MEMS accelerometer obtains the acceleration of the observed target in different directions. Through the vector decomposition method, the acceleration of the observed target on different three-dimensional coordinate axes is obtained. Based on the angular change between the observed target and different three-dimensional coordinate planes, the acceleration of the angular change of the observed target on different planes is obtained. Use the GNSS receiver to collect the position information of the observed target, including: the longitude, latitude and altitude of the observed target, and determine the coordinates of certain specific points of the observed target on the three-dimensional coordinate axis, and filter and denoise preprocess the collected acceleration data.

[0029] Step 2: Determine the deformation frequency of the observed target through the MEMS accelerometer; use the MEMS accelerometer to continuously collect the acceleration data of the observed target at key positions or directions, and derive the rate of change of acceleration over time based on the preprocessed acceleration data of the observed target at key positions or directions. According to the rate of change of the observed target acceleration, quantify the deformation degree of the observed target, and set the deformation degree value calculation formula. The specific formula is as follows:

[0030]

[0031] Where B represents the deformation degree value, t represents the observation time of each deformation degree calculation cycle of MEMS, and ap It represents the acceleration of the observed target in the horizontal direction, that is, the horizontal acceleration of the observed target obtained by the acceleration components of the two coordinate axes on the horizontal plane in the three-dimensional coordinate system, a c It represents the acceleration of the observed target in the vertical direction, θ represents the acceleration of the observed target inclination angle, and the actual value of time t is determined according to the specific situation and needs, and is usually jointly formulated and confirmed by professionals or relevant stakeholders.

[0032] Based on the type, structural material and functional characteristics of the observed target, by consulting relevant standards and specifications, analyzing the historical acceleration data related to the observed target, determining the acceleration range of the observed target under the operating state that meets the purpose of deformation monitoring, and determining the deformation degree threshold based on the acceleration range set. When the deformation degree value of the observed target obtained by the MEMS accelerometer is greater than or equal to the deformation degree threshold, it is judged that the observed target is in a high-frequency deformation state.

[0033] Step 3: Quality control of GNSS high dynamic deformation monitoring data; GNSS technology can monitor the dynamic deformation information of the observed target in real time, provide data support for the health monitoring of the observed target, and make timely warnings according to the deformation state of the observed target. The monitoring scene of the observed target has various complexities and uncertainties, such as buildings and vehicles near the GNSS monitoring station that are not related to the observed target. The complexity and uncertainty of the monitoring scene have certain interference to the GNSS signal. The probability of occurrence of a bad observation environment caused by frequent satellite loss of lock or severe occlusion affects the smooth solution of filtering and ambiguity fixation. It is necessary to comprehensively consider three different aspects to achieve quality control of GNSS high dynamic deformation monitoring data. The specific methods are as follows:

[0034] Preprocess the received GNSS data, use digital filtering technology to remove noise interference, such as wavelet transform or median filtering, use the ionospheric residual method to eliminate the distance between the monitoring station and the satellite, detect cycle slips by making differences between adjacent epochs, and then use MW combination to build a combined model through dual-frequency pseudorange and carrier observation data to eliminate the influence of related errors and determine the epochs with cycle slips. Use Kalman filtering to obtain the posterior residual and standardize it, determine the area where the maximum value of the phase-standardized residual is located and perform variance expansion, set the residual threshold, and when the maximum value of the phase-standardized residual is greater than the threshold, treat it as a cycle slip, and re-determine the maximum value of the phase-standardized residual until the maximum value of the phase-standardized residual is less than or equal to the threshold;

[0035] Step 4: Determine the deformation amplitude of the observed target through GNSS data; install GNSS monitoring stations at key locations around the observed target to ensure that these stations can receive sufficient satellite signals, set a time point every r time units, and in this embodiment, set r to 2 seconds, collect GNSS satellite signal data once every time point, determine the monitoring area of ​​the GNSS monitoring station, determine one or more monitoring points in each monitoring area according to the structure of the observed target, determine the three-dimensional coordinates of each monitoring point in the three-dimensional coordinate axis of the observed target, set one or more reference points near the monitoring area, determine the three-dimensional coordinates of each reference point, ensure that the reference point remains unchanged during the monitoring period, and each reference point corresponds to a monitoring point. Determine the real-time displacement change of the observed target by solving and analyzing the GNSS satellite signal data, and quantify the change amplitude of the observed target between different time points. The specific formula for calculating the change amplitude is as follows:

[0036]

[0037] Where C(m, m+1) represents the change from time point m to m+1, x represents the xth time point, i represents the i-th reference point, n represents a total of n reference points, Δd i It represents the displacement change between the i-th reference point and its corresponding monitoring point. The specific formula is as follows:

[0038]

[0039] Where X(m), Y(m), and Z(m) represent the coordinates of the monitoring point corresponding to the reference point i at time m with respect to different three-dimensional coordinate axes. i (m), Y i (m), Z i (m) represent the coordinates of the reference point i at time m with respect to different three-dimensional coordinate axes.

[0040] A change amplitude threshold H is set. When C(m, m+1) is less than or equal to H, it is determined that the observed target is in a state of small change amplitude in the time period from time point m to m+1. In this embodiment, the change amplitude threshold H is set equal to 7.8.

[0041] Step 5. Perform data fusion processing on the MEMS observation data and the GNSS observation data; perform time normalization processing on the MEMS observation data and the GNSS observation data, determine the MEMS observation data and the GNSS observation data in the same time period, determine the time period with high deformation frequency and small deformation amplitude by judging the deformation frequency and deformation amplitude state of the observation target, convert the MEMS observation data and the GNSS observation data in the time period into a unified format, select a suitable data fusion method according to application requirements and data characteristics, such as the Kalman filtering method, use the selected data fusion method to perform fusion calculation on the MEMS and GNSS observation data, and predetermine the actual data, evaluate the quality and accuracy of the fusion result by comparing with the actual data, continuously adjust the data fusion method until the quality and accuracy of the fusion result meet the actual requirements, improve the fusion result through smoothing, interpolation or filtering processing, and complete the high dynamic deformation monitoring data processing of MEMS and GNSS observation data.

[0042] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0043] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data, characterized in that: include: Step 1: Collect MEMS observation data and GNSS observation data of the observation target; Determine the basic data about the observation target and perform relevant data processing; Step 2: Determine the deformation frequency of the observed target through the MEMS accelerometer; Determine the deformation frequency of the observed target through the relevant deformation basic data of the observed target; Step 3: Perform quality control on GNSS high dynamic deformation monitoring data; Step 4: Determine the deformation amplitude of the observed target through GNSS data; Determine the deformation amplitude of the observed target through the relevant deformation basic data of the observed target; Step 5: Perform data fusion processing on the MEMS observation data and the GNSS observation data; determine the data fusion time period, and perform data fusion processing on the MEMS observation data and the GNSS observation data within the data fusion time period.

2. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 1 is characterized in that: Determine the basic data about the observation target and perform relevant data processing. The specific methods are as follows: Based on the spatial range of the observed target, a three-dimensional coordinate system is established with the observed target as the center. According to the three-dimensional coordinate system, the MEMS accelerometer obtains the acceleration of the observed target in different directions. Through the vector decomposition method, the acceleration of the observed target on different three-dimensional coordinate axes is obtained. Based on the angular change between the observed target and different three-dimensional coordinate planes, the acceleration of the angular change of the observed target on different planes is obtained. The position information of the observed target is collected using a GNSS receiver, and the coordinates of certain specific points of the observed target on the three-dimensional coordinate axis are determined. The collected acceleration data is filtered and pre-processed for noise reduction.

3. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 1 is characterized in that: The deformation frequency of the observed target is determined by the relevant deformation basic data of the observed target. The specific method is as follows: Based on the preprocessed acceleration data of the observed target at the key position or direction, the rate of change of acceleration over time is derived. According to the rate of change of the observed target acceleration, the deformation degree of the observed target is quantified. The formula The deformation degree value B is obtained, where t represents the observation time of each deformation degree calculation cycle of MEMS, and a p Indicates the acceleration of the observed target in the horizontal direction, a c It represents the acceleration of the observed target in the vertical direction, and θ represents the acceleration of the observed target's inclination angle.

4. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 1 is characterized in that: The quality control of GNSS high dynamic deformation monitoring data is carried out by: The received GNSS data is preprocessed, and digital filtering technology is used to remove noise interference. The ionospheric residual method is combined with MW to eliminate the influence of correlation errors and determine the epochs with cycle slips. The Kalman filter is used to determine the area where the maximum value of the phase-normalized residual is located and perform variance expansion. The residual threshold is set. When the maximum value of the phase-normalized residual is greater than the threshold, it is treated as a cycle slip and the maximum value of the phase-normalized residual is re-determined until the maximum value of the phase-normalized residual is less than or equal to the threshold.

5. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 1 is characterized in that: The deformation amplitude of the observed target is determined by the relevant deformation basic data of the observed target. The specific method is as follows: GNSS monitoring stations are installed at key locations around the observation target, and a time point is set every r time units. GNSS satellite signal data is collected once at each time point to determine the monitoring area of ​​the GNSS monitoring station. According to the structure of the observation target, one or more monitoring points are determined in each monitoring area. The three-dimensional coordinates of each monitoring point are determined in the three-dimensional coordinate axis of the observation target. One or more reference points are set near the monitoring area, and the three-dimensional coordinates of each reference point are determined to ensure that the reference point remains unchanged during the monitoring period. Each reference point corresponds to a monitoring point. The real-time displacement change of the observation target is determined by solving and analyzing the GNSS satellite signal data, and the change amplitude of the observation target between different time points is quantified.

6. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 5 is characterized in that: Determine the real-time displacement change of the observed target and quantify the change amplitude of the observed target between different time points. The specific method is as follows: Using the formula Calculate the change range of the observed target at different times, where C(m, m+1) represents the change range from time point m to m+1, x represents the xth time point, i represents the i-th reference point, n represents a total of n reference points, Δd i Represents the calculated displacement change between the i-th reference point and its corresponding monitoring point.

7. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 6 is characterized in that: The displacement change between the i-th reference point and its corresponding monitoring point is calculated by: use Formula, the calculated displacement change between the i-th reference point and its corresponding monitoring point, where X(m), Y(m), and Z(m) represent the coordinates of the monitoring point corresponding to the reference point i at time m about different three-dimensional coordinate axes, respectively. i (m), Y i (m), Z i (m) represent the coordinates of the reference point i at time m with respect to different three-dimensional coordinate axes.

8. The high dynamic deformation monitoring data processing method combining MEMS and GNSS observation data according to claim 1 is characterized in that: Determine the data fusion time period. The specific method is as follows: The MEMS observation data and the GNSS observation data are time-normalized to determine the MEMS observation data and the GNSS observation data in the same time period. By judging the deformation frequency and deformation amplitude of the observation target, the time period with high deformation frequency and small deformation amplitude is screened, and it is determined that the time period belongs to the data fusion time period.

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