CN114279311A - GNSS deformation monitoring method and system based on inertia - Google Patents
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Abstract
The invention relates to a GNSS deformation monitoring method and system based on inertia, comprising the following steps: receiving MEMS signals of the MEMS inertial system in the current GNSS updating period; performing segmented processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period; analyzing the correlation of the multiple sections of MEMS signals, if the correlation meets a preset condition, determining that the monitored object is stable, and if the correlation does not meet the preset condition, determining that the monitored object is unstable; and judging whether the GNSS observation data of the GNSS deformation monitoring system is complete, if so, acquiring the deformation monitoring result of the current GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result. The invention can solve the problem of data interruption in the existing GNSS deformation monitoring method.
Description
Technical Field
The invention relates to the technical field of deformation monitoring, in particular to a GNSS deformation monitoring method and system based on inertia.
Background
At present, deformation monitoring mainly comprises a deformation monitoring method based on image identification, a deformation monitoring method based on a laser ranging principle and a GNSS deformation monitoring real-time processing method based on Kalman filtering; the deformation monitoring method based on image recognition mainly comprises the steps of collecting images through a camera to obtain a plurality of images, and uploading the images; processing and calculating the uploaded image; calculating the actual relative position between target monitoring points on a single image, and calculating the horizontal closure difference and the vertical closure difference of a measured circle; adopting elevation transmission and displacement transmission, combining the closure difference to carry out adjustment, calculating and storing the actual relative position between the target monitoring points; the steps are circulated once; and comparing the calculation results of the previous and subsequent times to obtain the actual deformation of each target monitoring point relative to the initial target monitoring point in the monitoring plane. The deformation monitoring method based on the laser ranging principle mainly comprises the steps that a displacement monitoring device based on the laser ranging principle is installed in the center or at one end of a structure, and a data receiving device is arranged on the periphery or at the other end of the structure; the laser ranging module periodically rotates and resets for 360 degrees according to a set rotation angle theta and a rotation interval time t, a laser beam is emitted to a monitoring point once when the laser ranging module rotates for one angle theta to serve as one-point distance measurement, and the distance between the laser ranging module and the monitoring point and the corresponding angle are recorded and stored; obtaining multiple monitoring data of the structure through repeated monitoring; and obtaining the displacement of the structure by constructing and comparing two-dimensional monitoring information plane diagrams. The GNSS deformation monitoring real-time processing method based on the Kalman filtering is mainly characterized in that an optimal estimated value of an initial state vector from a reference point to a monitoring point is obtained through a GNSS differential positioning technology; based on Kalman filtering, obtaining a one-step predicted value and a one-step predicted variance of the state vector; acquiring real-time monitoring data; obtaining the standard variance and Kalman gain of the time period data; calculating an optimal estimated value of the state vector and a variance matrix of the optimal estimated value; constructing a residual vector of a sliding window, dynamically adjusting the size of the sliding window, and updating an observation noise variance matrix in real time; and calculating and outputting the displacement.
The GNSS deformation monitoring method can provide all-weather real-time differential solution results, so the technology is in the mainstream position in domestic deformation monitoring work at present, however, the following problems are often caused during monitoring of the method to cause data interruption:
(1) insufficient power supply due to continuous rainy weather or hardware damage of the solar independent power supply system;
(2) the quality of GNSS independent solution results is poor due to GNSS solution system factors such as poor observation data quality caused by ionosphere or troposphere activity abnormity, ephemeris abnormity or environmental factors;
(3) at present, many monitoring scenes are mostly outdoor and construction maintenance personnel are inconvenient to go to the investigation, so that the monitoring scenes with data loss can not be repaired on site in time, and further a monitoring system is caused to fail.
Disclosure of Invention
The invention aims to provide a GNSS deformation monitoring method and system based on inertia, and aims to solve the problem of data interruption in the existing GNSS deformation monitoring method.
In order to achieve the above object, an embodiment of the present invention provides an inertia-based GNSS deformation monitoring method, including the following steps:
s100, receiving an MEMS signal of an MEMS inertial system in a current GNSS updating period;
s200, carrying out segmentation processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period;
step S300, analyzing the correlation of the multiple sections of MEMS signals, if the correlation meets a preset condition, determining that the monitored object is stable, and if the correlation does not meet the preset condition, determining that the monitored object is unstable;
step S400, judging whether GNSS observation data of the GNSS deformation monitoring system are complete or not, if so, acquiring a deformation monitoring result of the GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result if the GNSS observation data are complete and the monitored object is stable.
Preferably, the step S400 further includes:
and if the GNSS deformation monitoring system is complete and the monitored object is unstable, acquiring a deformation monitoring result of the last GNSS updating period output by the GNSS deformation monitoring system, and outputting the deformation monitoring result of the last GNSS updating period after compensating.
Preferably, the step S400 further includes:
and if the monitoring object is not complete, outputting whether the monitoring object is stable or not.
Preferably, the step S200 further includes:
before the MEMS signals are subjected to segmentation processing according to a time sequence, wavelet transformation is carried out on the MEMS signals to obtain wavelet coefficients, and noise signals in the MEMS signals are removed according to a comparison result of a threshold lambda and the wavelet coefficients.
Preferably, the threshold λ is selected by using a surreshrink threshold selection rule.
Preferably, the step S200 further includes:
and before the MEMS signals are subjected to segmented processing according to a time sequence, performing Kalman filtering on the MEMS signals after the noise signals are removed.
Preferably, the step S300 includes:
and measuring the correlation of the multiple MEMS signals according to the LP distance by calculating the LP distance of the multiple MEMS signals, wherein when the LP distance of the two MEMS signals is smaller than a preset threshold, the correlation of the two MEMS signals meets the preset condition, and when the LP distance of the two MEMS signals is larger than or equal to the preset threshold, the correlation of the two MEMS signals does not meet the preset condition.
The embodiment of the invention also provides an inertia-based GNSS deformation monitoring system, which is used for realizing the inertia-based GNSS deformation monitoring method and comprises the following steps:
the signal receiving unit is used for receiving MEMS signals of the MEMS inertial system in the current GNSS updating period;
the signal segmentation unit is used for performing segmentation processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period;
the stability judging unit is used for analyzing the correlation of the multi-section MEMS signals, if the correlation meets a preset condition, the monitored object is determined to be stable, and if the correlation does not meet the preset condition, the monitored object is determined to be unstable;
and the deformation monitoring output unit is used for judging whether the GNSS observation data of the GNSS deformation monitoring system is complete or not, and if the GNSS observation data is complete and the monitored object is relatively stable, acquiring the deformation monitoring result of the current GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result.
Preferably, the deformation monitoring output unit is further configured to:
and if the GNSS deformation monitoring system is complete and the monitored object is unstable, acquiring a deformation monitoring result of the last GNSS updating period output by the GNSS deformation monitoring system, and outputting the deformation monitoring result of the last GNSS updating period after compensating.
Preferably, the deformation monitoring output unit is further configured to:
and if the monitoring object is not complete, outputting whether the monitoring object is stable or not.
According to the GNSS deformation monitoring method and system based on inertia, an MEMS inertial sensor has the advantages of being light, high in precision, low in cost and the like, and is suitable for deformation monitoring engineering application of a structure.
Additional features and advantages of embodiments of the invention will be set forth in the description which follows.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a GNSS deformation monitoring method based on inertia according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an inertia-based GNSS deformation monitoring system according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In addition, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means have not been described in detail so as not to obscure the present invention.
Referring to fig. 1, an embodiment of the invention provides an inertia-based GNSS deformation monitoring method, including the following steps:
s100, receiving an MEMS signal of an MEMS inertial system in a current GNSS updating period;
s200, carrying out segmentation processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period;
step S300, analyzing the correlation of the multiple sections of MEMS signals, if the correlation meets a preset condition, determining that the monitored object is stable, and if the correlation does not meet the preset condition, determining that the monitored object is unstable;
step S400, judging whether GNSS observation data of a GNSS deformation monitoring system are complete or not, if so, acquiring a deformation monitoring result of the GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result if the GNSS observation data are complete and a monitored object is stable;
if the GNSS deformation monitoring system is complete and the monitored object is unstable, acquiring a deformation monitoring result of the last GNSS updating period output by the GNSS deformation monitoring system, and outputting the deformation monitoring result of the last GNSS updating period after compensating the deformation monitoring result; specifically, in this step, when the monitored object is unstable, the deformation monitoring result of the current GNSS update cycle is not output, because the deformation monitoring result of the current GNSS update cycle is unreliable due to the instability of the monitored object, and therefore, compensation is performed according to the deformation monitoring result of the previous GNSS update cycle, and specific compensation methods may be various, and the deformation trend of the monitored object can be obtained by counting the deformation monitoring results of the historical GNSS update cycles, and the deformation monitoring result is compensated according to the deformation trend;
if the monitoring object is not complete, outputting whether the monitoring object is stable; specifically, in this step, because the GNSS observation data is incomplete, the deformation monitoring result of the GNSS deformation monitoring system is unreliable, and at this time, only whether the monitored object is stable is output, and meanwhile, an alarm notification is sent to the monitoring staff to inform the monitoring staff of maintaining the GNSS deformation monitoring system.
Specifically, the method of the present embodiment has the following data combination ideas for different monitoring environments:
(1) monitoring environment with complete GNSS observation data amount;
in the scene, because the data output frequency of the MEMS device is far higher than the updating frequency of GNSS observation data, the MEMS output data in one GNSS updating period is processed in a segmented mode, correlation analysis is carried out on the data in different time sequence segments, and if the correlation is high, the MEMS monitoring result shows that a monitored object is stable. At the moment, if the three-axis direction output by the GNSS differential positioning system is obviously different from the last moment due to the anomaly of the ionosphere or the troposphere, the three-axis direction positioning result at the last moment is compensated for random white noise to be output, and the actual calculation result of the GNSS differential positioning system is not output.
(2) The GNSS observation data amount is complete, and the GNSS data amount is less or missing;
in the scene, because GNSS monitoring cannot achieve an accurate positioning effect, independent monitoring is required to be performed through an MEMS sensor, segmented data correlation analysis is performed on MEMS six-axis output data in each GNSS data updating period, and the output result is whether the target monitoring object is stable or not; the method aims to perform qualitative analysis rather than quantitative calculation on the deformation of a monitored target after the GNSS monitoring method fails, and avoids the serious loss of people's life and property or social public property caused by dangerous situations of a target structure in the period.
Specifically, in this embodiment, the step S200 further includes:
before the MEMS signals are subjected to segmentation processing according to a time sequence, wavelet transformation is carried out on the MEMS signals to obtain wavelet coefficients, and noise signals in the MEMS signals are removed according to a comparison result of a threshold lambda and the wavelet coefficients.
The time-frequency analysis method is the core of all denoising theories and methods, and due to the limitation of Fourier transform on the non-stationary process, when the data acquired by the MEMS inertial sensor are processed, the measurement noise can be effectively removed from the data by adopting wavelet transform, and effective signal information can be extracted.
Wherein a is a scale factor and b is a displacement factor. In order to adapt the wavelet transform to the characteristics of the data of the signal to be analyzed, the wavelet transform can be made to have a variable focal length characteristic by changing the values of the scale a and the time b. In order to keep the translation amount in the time domain continuous, binary discretization can be performed on the scale factor, and the specific method is to perform discretization processing on the scale function, and generally take am＝2mAnd b is subjected to uniform discretization value taking, and the change of displacement is still continuous and uninterrupted. The corresponding wavelet function is:
therefore, the raw signal s (k) of the inertial sensor is transformed by a dyadic wavelet as follows:
after wavelet transform, the wavelet coefficient d can be calculatedm,n,dm,nThe method comprises a signal wavelet coefficient and a noise wavelet coefficient, wherein the noise removal is carried out according to the difference of the statistical properties of the signal and the noise after wavelet transformation. The wavelet coefficients of the significant signal have a larger magnitude and the noise wavelet coefficients have a smaller magnitude. According to the set threshold lambda, noise is eliminated, and a useful signal is reserved in the following judgment form:
wherein
The wavelet coefficient after denoising; sign (·) represents a sign function; a is conversion coefficient and has a value range of [0, 1]When a is 0, the threshold value judgment method is a hard threshold value method, and when a is 1, the threshold value judgment method is a soft threshold value, and the value in the scheme is 0.5; λ is a threshold value.
Preferably, the threshold λ is selected by using a surreshrink threshold selection rule.
The selection of the threshold lambda directly determines the quality of the denoised signal. If the threshold value is selected to be too small, part of noise information is still contained in the denoised signal value, and if the threshold value is selected to be too large, part of effective signals in the signals are mistakenly removed. Therefore, in this embodiment, a SUREShrink threshold method is adopted, and the size of the threshold is determined by using a minimization criterion, which is essentially an unbiased estimation in the mean square error sense, and the specific steps are as follows:
a. obtaining the length N of the obtained signal;
the obtained wavelet coefficients are arranged in sequence from small to large respectively to generate a brand new coefficient vector d ═ d1,d2,…,dN]Where d is1≤d2≤…≤dN；
b. Calculating a risk vector R ═ R1,r2,…,rN]Wherein, in the step (A),
minimum value r in risk vector obtained in the previous stepkAs a risk value, from rkCalculating wavelet coefficient d from corresponding k valuekThen the threshold λ takes on the value
To indicate.
Specifically, in this embodiment, the step S200 further includes:
and before the MEMS signals are subjected to segmented processing according to a time sequence, performing Kalman filtering on the MEMS signals after the noise signals are removed.
The data collected by the MEMS has the characteristic of time series, so that the characteristic information of the MEMS data can be described by establishing a corresponding mathematical model.
Statistically, the MEMS time series data partial autocorrelation function
"truncation" occurs at 2 nd order, i.e. autocorrelation function of the sequence
Can converge to 0, so it can be judged that the MEMS time-series data belongs to the AR (2) model, expressed as:
In this scheme, the regression coefficients of the model are found by least squares theoretical estimation, i.e.
The time series modeling method can be used for obtaining good results under general monitoring conditions theoreticallyThe effect is expressed, however, in consideration of the fact that MEMS collected data in practical application has nonlinear characteristics, the prediction accuracy of the time sequence is low and the limitation is large only through the model; therefore, from the perspective of optimal estimation, the method of the embodiment optimizes the denoised time series data by adopting the optimal estimation idea of kalman filtering, so as to make up the inaccurate defect of the wavelet denoised data, thereby achieving the optimal estimation of the acquired signal; the calculation steps are as follows:
(1) time updating
The AR (2) model can know the MEMS time series signal expression:
let tkThe state value at the moment is xtThen, the one-step prediction state equation is as follows:
xk,k-1＝Axk-1,k-1+Buk
in the formula, xkIs the true state that the system is in at the moment,
ukis the control quantity of the dynamic system at time k, uk＝[at,0]T；WkIs the measurement white noise of the dynamic system; coefficient matrix
Coefficient matrix B ═ 10]T
The corresponding predicted mean square error is:
pk,k-1＝Apk-1,k-1A′+Q
(2) measurement update
Filtering gain:
Kg＝pk,k-1H′(Hpk,k-1H′+R)-1
updating the measured value:
xk,k＝xk,k-1+Kg(zk-Hxk,k-1)
updating an error equation:
pk,k-1＝Apk-1,k-1A′+Q
specifically, in this embodiment, the step S300 includes:
and measuring the correlation of the multiple MEMS signals according to the LP distance by calculating the LP distance of the multiple MEMS signals, wherein when the LP distance of the two MEMS signals is smaller than a preset threshold, the correlation of the two MEMS signals meets the preset condition, and when the LP distance of the two MEMS signals is larger than or equal to the preset threshold, the correlation of the two MEMS signals does not meet the preset condition.
The method of the embodiment adopts a continuous polynomial segmentation mode to express the time series data, and original characteristics of the data are reserved. And then, carrying out structure deformation analysis by adopting a multidimensional time series LP distance similarity measurement segmentation time series analysis method.
When the uniaxial data collected by the IMU of the MEMS is used for deformation analysis, firstly, the uniaxial data sequence is divided into a plurality of sections, the data length of each section is assumed to be m, then the whole length of the uniaxial data sequence with the length of n is averagely divided into n + m-1 sections, and the length of each section is m. The data matrix is as follows:
IMU single axis data to be collected
Using a column vector V of dimension liThe length of each sequence data segment after segmentation is the same. X ═ V1,…,Vi,…,Vn]Is an IMU uniaxial time series data matrix, and the difference matrix is described as follows:
with IThe way in which the distance between two time series in a single axis of MU acquisition is taken as its similarity measure:
is two time periods ViAnd VjDistance between, time series V when two time periodsiAnd VjThe higher the degree of similarity between them, the closer the calculated value approaches 0; conversely, if the degree of similarity is low, the calculated value approaches 1.
Referring to fig. 2, another embodiment of the present invention further provides an inertia-based GNSS deformation monitoring system, for implementing the above-mentioned inertia-based GNSS deformation monitoring method, including:
the signal receiving unit 1 is used for receiving MEMS signals of the MEMS inertial system in the current GNSS updating period;
the signal segmentation unit 2 is used for performing segmentation processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period;
the stability judging unit 3 is configured to analyze the correlation of the multiple sections of MEMS signals, determine that the monitored object is stable if the correlation satisfies a preset condition, and determine that the monitored object is unstable if the correlation does not satisfy the preset condition;
and the deformation monitoring output unit 4 is used for judging whether the GNSS observation data of the GNSS deformation monitoring system is complete or not, and if the GNSS observation data is complete and the monitored object is stable, acquiring the deformation monitoring result of the current GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result.
Specifically, in this embodiment, the deformation monitoring output unit 4 is further configured to:
and if the GNSS deformation monitoring system is complete and the monitored object is unstable, acquiring a deformation monitoring result of the last GNSS updating period output by the GNSS deformation monitoring system, and outputting the deformation monitoring result of the last GNSS updating period after compensating.
Specifically, in this embodiment, the deformation monitoring output unit 4 is further configured to:
and if the monitoring object is not complete, outputting whether the monitoring object is stable or not.
Specifically, in this embodiment, the signal segmenting unit 2 is configured to:
before the MEMS signals are subjected to segmentation processing according to a time sequence, wavelet transformation is carried out on the MEMS signals to obtain wavelet coefficients, and noise signals in the MEMS signals are removed according to a comparison result of a threshold lambda and the wavelet coefficients.
Specifically, in this embodiment, the threshold λ is selected by using a SUREShrink threshold selection rule.
Specifically, in this embodiment, the signal segmenting unit 2 is configured to:
and before the MEMS signals are subjected to segmented processing according to a time sequence, performing Kalman filtering on the MEMS signals after the noise signals are removed.
Specifically, in this embodiment, the stability determining unit 3 is configured to:
and measuring the correlation of the multiple MEMS signals according to the LP distance by calculating the LP distance of the multiple MEMS signals, wherein when the LP distance of the two MEMS signals is smaller than a preset threshold, the correlation of the two MEMS signals meets the preset condition, and when the LP distance of the two MEMS signals is larger than or equal to the preset threshold, the correlation of the two MEMS signals does not meet the preset condition.
The system of the present embodiment corresponds to the method of the above embodiment, and the parts of the system of the present embodiment that are not described in detail can be obtained by referring to the method of the above embodiment, and therefore, the details are not described herein again.
According to the GNSS deformation monitoring method and system based on inertia, the MEMS inertial sensor has the advantages of being light, high in precision, low in cost and the like, and is suitable for deformation monitoring engineering application of a structure.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. An inertia-based GNSS deformation monitoring method is characterized by comprising the following steps:
s100, receiving an MEMS signal of an MEMS inertial system in a current GNSS updating period;
s200, carrying out segmentation processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period;
step S300, analyzing the correlation of the multiple sections of MEMS signals, if the correlation meets a preset condition, determining that the monitored object is stable, and if the correlation does not meet the preset condition, determining that the monitored object is unstable;
step S400, judging whether GNSS observation data of the GNSS deformation monitoring system are complete or not, if so, acquiring a deformation monitoring result of the GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result if the GNSS observation data are complete and the monitored object is stable.
2. The method according to claim 1, wherein the step S400 further comprises:
and if the GNSS deformation monitoring system is complete and the monitored object is unstable, acquiring a deformation monitoring result of the last GNSS updating period output by the GNSS deformation monitoring system, and outputting the deformation monitoring result of the last GNSS updating period after compensating.
3. The method according to claim 2, wherein the step S400 further comprises:
and if the monitoring object is not complete, outputting whether the monitoring object is stable or not.
4. The method according to claim 1, wherein the step S200 further comprises:
before the MEMS signals are subjected to segmentation processing according to a time sequence, wavelet transformation is carried out on the MEMS signals to obtain wavelet coefficients, and noise signals in the MEMS signals are removed according to a comparison result of a threshold lambda and the wavelet coefficients.
5. The method according to claim 4, wherein the threshold λ is selected using a SURREShrink threshold selection rule.
6. The method according to claim 4, wherein the step S200 further comprises:
and before the MEMS signals are subjected to segmented processing according to a time sequence, performing Kalman filtering on the MEMS signals after the noise signals are removed.
7. The method according to claim 1, wherein the step S300 comprises:
and measuring the correlation of the multiple MEMS signals according to the LP distance by calculating the LP distance of the multiple MEMS signals, wherein when the LP distance of the two MEMS signals is smaller than a preset threshold, the correlation of the two MEMS signals meets the preset condition, and when the LP distance of the two MEMS signals is larger than or equal to the preset threshold, the correlation of the two MEMS signals does not meet the preset condition.
8. An inertial-based GNSS deformation monitoring system for implementing the inertial-based GNSS deformation monitoring method of any of claims 1-7, comprising:
the signal receiving unit is used for receiving MEMS signals of the MEMS inertial system in the current GNSS updating period;
the signal segmentation unit is used for performing segmentation processing on the MEMS signals according to a time sequence to obtain a plurality of sections of MEMS signals of the current GNSS updating period;
the stability judging unit is used for analyzing the correlation of the multi-section MEMS signals, if the correlation meets a preset condition, the monitored object is determined to be stable, and if the correlation does not meet the preset condition, the monitored object is determined to be unstable;
and the deformation monitoring output unit is used for judging whether the GNSS observation data of the GNSS deformation monitoring system is complete or not, and if the GNSS observation data is complete and the monitored object is relatively stable, acquiring the deformation monitoring result of the current GNSS updating period output by the GNSS deformation monitoring system and outputting the deformation monitoring result.
9. The system of claim 8, wherein the deformation monitoring output unit is further configured to:
and if the GNSS deformation monitoring system is complete and the monitored object is unstable, acquiring a deformation monitoring result of the last GNSS updating period output by the GNSS deformation monitoring system, and outputting the deformation monitoring result of the last GNSS updating period after compensating.
10. The system of claim 9, wherein the deformation monitoring output unit is further configured to:
and if the monitoring object is not complete, outputting whether the monitoring object is stable or not.
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