CN112444187A - Deformation monitoring method and device - Google Patents
Deformation monitoring method and device Download PDFInfo
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Abstract
The invention is suitable for the technical field of public safety, and provides a deformation monitoring method and a deformation monitoring device, wherein the monitoring method comprises the following steps: acquiring a real-time observation data stream and storing the observation data stream; calculating a baseline vector from a reference station to each monitoring station in a period based on the observation data stream stored in the period, and obtaining the corresponding coordinates of the monitoring stations in the period; comparing based on a plurality of the periodic monitoring station coordinates to obtain a plurality of observed deformation quantities, and fitting an observed noise curve based on the plurality of observed deformation quantities; predicting the observed noise of the next period based on the observed noise curve; and calculating the denoised deformation of the next period based on the obtained observed deformation of the next period and the predicted observed noise of the next period. In the invention, the observation noise of the next period is predicted by observing the noise curve, and the deformation quantity is calculated according to the observation noise and the observation deformation quantity of the next period, so that the accuracy of deformation monitoring can be improved.
Description
Technical Field
The invention belongs to the technical field of public safety, and particularly relates to a deformation monitoring method and device.
Background
With the rapid development of economy, more and more large buildings such as high buildings, bridges, reservoirs and the like are built. In the use process of large buildings, due to the influence of long-term external factors (such as load, temperature, strong wind, earthquake and the like), a point building is easy to vibrate or deform and even locally destroy, which is related to the vital interests of people. Therefore, real-time monitoring of the condition of a large building is the best means for preventing the situation in advance. So as to find out in time before the building is deformed greatly and take proper protection measures, thereby reducing loss.
The deformation monitoring means that: the deformation phenomena of the structure are monitored by means of specific measuring instruments, and conventional deformation monitoring techniques may include: carrying out elevation between the side angle measuring edges and the stations by using a total station and a level gauge; processing and monitoring the image data of the large-area deformable body by using technologies such as photogrammetry, remote sensing and the like;
with the advent of GPS (global Positioning system), GPS is used to monitor the deformation of buildings, but the monitoring accuracy is not high due to the influence of multipath errors, cycle slip, random noise, and other factors.
Disclosure of Invention
In view of this, embodiments of the present invention provide a deformation monitoring method and apparatus, so as to solve the problem in the prior art that the monitoring accuracy is not high.
A first aspect of an embodiment of the present invention provides a deformation monitoring method, including:
acquiring a real-time observation data stream and storing the observation data stream;
calculating a baseline vector from a reference station to each monitoring station in a period based on the observation data stream stored in the period, and obtaining the corresponding coordinates of the monitoring stations in the period;
comparing based on a plurality of the periodic monitoring station coordinates to obtain a plurality of observed deformation quantities, and fitting an observed noise curve based on the plurality of observed deformation quantities;
predicting the observed noise of the next period based on the observed noise curve;
and calculating the denoised deformation of the next period based on the obtained observed deformation of the next period and the predicted observed noise of the next period.
A second aspect of the embodiments of the present invention provides a deformation monitoring apparatus, which is configured to perform the steps of the monitoring method described above;
compared with the prior art, the embodiment of the invention has the following beneficial effects: the observation noise of the next period is predicted by observing the noise curve, and the deformation quantity after denoising is calculated according to the observation noise and the observation deformation quantity of the next period, so that the accuracy of deformation monitoring can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a deformation monitoring method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a deformation monitoring apparatus according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The first embodiment is as follows:
fig. 1 shows a schematic flow chart of a deformation monitoring method provided in an embodiment of the present invention, which is detailed as follows:
step S1, acquiring a real-time observation data stream and storing the observation data stream;
specifically, an observation data stream (preferably real-time BDS/GPS data) is acquired and implemented, and the acquired real-time BDS/GPS data stream is decoded to obtain a corresponding processing file, where the processing file includes an observation file and a navigation message file, and the observation file carries corresponding observation data. The real-time observation data is obtained from a GNSS antenna to obtain a real-time BDS/GPS data stream, and the observation data stream is from a GPS system, or a Beidou system, or from both the GPS system and the Beidou system.
Step S2, calculating a baseline vector from the reference station to each monitoring station in a period based on the observation data stream stored in the period, and obtaining the corresponding coordinates of the monitoring stations in the period;
specifically, the currently acquired observation data stream may be stored once in a period, a baseline vector from the reference station to each monitoring station in the period is calculated based on the stored observation data stream in the period, and coordinates of the monitoring station corresponding to the period are acquired; thus, over time, multiple cycles of monitoring station coordinates may be obtained.
Firstly, a GNSS (global navigation satellite system) receiver acquires a real-time BDS/GPS data stream acquired by a GNSS antenna through a set IP address and port, and then sends the data stream to a network, and a PC accesses the set IP address and port of the control file through a socket to acquire the real-time BDS/GPS data stream, converts the data stream into a binary format, stores the binary format as a binary file, stores the binary file once every preset time period, and edits a new file, for example, the file name is: yyr, where ssss denotes a 4-character monitoring station name, ddd is the product day of the year, t denotes a time period number, yy denotes the last two digits of the year, and r denotes an undecoded binary raw (raw) file. Further, the real-time BDS/GPS data stream carries corresponding observation data, e.g. an undecoded binary raw file may be considered as raw observation data. The preset time period may be 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, etc., which is not limited herein.
Then, if the preset time period is 1 hour, since the processing file is stored once every one hour (at this time, one cycle is one hour), preferably, after each hour elapses, the processing files of a time length before the current time period are merged, and then the merged files are decoded to obtain the processing files, which include the observation file and the navigation message file. The total length of time for the combination of the segments may be 3 hours, 4 hours, 6 hours, 8 hours, etc., or may be one or more days, preferably, 6 hours is selected as the preferred length of time.
Step S3, comparing the coordinates of the monitoring station based on a plurality of periods to obtain a plurality of observation deformation quantities, and fitting an observation noise curve based on the plurality of observation deformation quantities;
specifically, the coordinates of the monitoring station in a plurality of periods which are obtained currently are compared to obtain a plurality of observed deformation quantities, and fitting is performed based on the plurality of observed deformation quantities to obtain an observed noise curve. Preferably, the plurality of periods may be consecutive periods, which may be 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, etc.; if the period is greater than 1 hour, the period is preferably set to a multiple of 2 hours, and more preferably the period is selected to be a divisor of 24, which is convenient to calculate on a daily basis.
Preferably, comparing the coordinates of the monitoring station based on the plurality of periods to obtain a plurality of observed deformation amounts may include: any subsequent periodic coordinate in the monitoring station coordinates of the multiple periods is subtracted from the previous periodic coordinate of the adjacent N periods to obtain the observed deformation of the subsequent period. For example: the observed deformation can be calculated through subtraction, and the observed deformation can be sequentially subtracted, for example, 24 hours is taken as a period, one period is taken as one day, the first day is taken as the first period, the second day is taken as the second period, and the like; comparing the coordinates of the second period and the first period to obtain the observed deformation of the second day relative to the first day, comparing the coordinates of the third period and the second period to obtain the observed deformation of the third day relative to the second day, and the like, and sequentially subtracting; at this time, the latter period (second period) is adjacent to the former other period (first period) by 1 period.
Also for example, the subtraction is performed at intervals, for example, in a period of 12 hours, the first day includes a first period and a second period, the second day includes a third period and a fourth later period, and so on, the odd period refers to 0 to 12 points, and the even period refers to 12 to 24 points, for example; alternate subtraction, such as subtraction of the third period from the first period to obtain the deformation amount of the third period (relative to the first period), subtraction of the fourth period from the second period to obtain the deformation amount of the fourth period (relative to the second period), etc., so that a plurality of deformation amounts can be obtained on a day; where the next cycle is 2 cycles adjacent to the previous cycle. Other cycles may also compare at similar intervals; preferably, the period of the corresponding time of the day is compared with the period of the corresponding time of the previous day, for example, when the period is 1 hour, the period of 0-1 point on the second day is compared with the period of 0-1 point on the first day; in addition, the deformation amount may be obtained in other manners, which is not limited herein. Preferably, N of adjacent N periods may be one of 1, 2, 3, 4, 6, 8, 12, 24, which is also convenient for daily calculation. Each period may be 1 hour, or 2 hours, or 4 hours, or 6 hours, or 12 hours, etc. The invention is not limited thereto.
Step S4, predicting the observation noise of the next period based on the observation noise curve;
specifically, the observation noise of the next cycle is predicted according to the observation noise curve; for example, a value corresponding to the next cycle is obtained from the observed noise curve as the predicted observed noise.
Step S5, calculating the denoised deformation of the next period based on the obtained observed deformation of the next period and the predicted observed noise of the next period;
specifically, the denoised deformation of the next period is calculated according to the obtained observed deformation of the next period and the predicted observed noise of the next period. The obtained observation deformation of the next period is obtained by calculation according to the real-time observation data flow through the steps, the actual deformation and the observation noise are contained in the observation deformation, the deformation after denoising can be obtained by removing the predicted observation noise, and the deformation after denoising can be generally closer to the actual deformation.
In this embodiment, the observation noise of the next cycle is predicted by observing the noise curve, and the deformation amount is calculated according to the observation noise and the observation deformation amount of the next cycle, so that the accuracy of deformation monitoring can be improved.
In a preferable embodiment of this embodiment, the step S1 further includes:
establishing a global control file;
specifically, a global control file (global. config) is first created, which includes: selection of the satellite system, the interval of data processing (e.g., one hour), the elevation of the observation satellite's cutoff, the type of observation used for data processing, the IP address and port from which the processing results are sent, the approximate coordinates of the reference and monitoring stations, and the IP address and port from which each monitoring station sends the data. Further, a control file corresponding to the base line of the reference station and the base line of each monitoring station can be generated respectively based on the global control file; for example: generating a corresponding control file for each monitoring station according to the global control file, wherein the control file is a control file corresponding to the base line of the reference station and the monitoring station, namely generating a control file of a single base line; for example, the global file includes all information between the reference station and each monitoring station, all information is classified according to the monitoring station, preferably, the baseline is classified, and a control file of the baseline between the reference station and each monitoring station is obtained, that is, the number of the control files is consistent with the number of the monitoring stations.
In a preferable aspect of this embodiment, the monitoring method further includes:
updating an observation noise curve according to the observation deformation of the next period;
specifically, after an original observation noise curve is formed, the observation noise curve can be updated according to the observation deformation of the next period; for example: the original observation noise curve is obtained by fitting according to the observation deformation amounts from day 1 to day 10, after the observation deformation amount from day 11 is obtained, the original observation noise curve may be fit according to the observation deformation amounts from day 1 to day 11 to update the original observation noise curve, or may be updated in a window sliding manner, for example, fitting according to the observation deformation amounts from day 2 to day 11, and then updating according to the observation deformation amounts from day 3 to day 12 may be continued, which is not limited herein;
in a preferred embodiment of the present invention, in step S2, calculating the baseline vector from the reference station to each monitoring station in a period based on the stored observation data stream in the period includes:
establishing a three-difference observation equation based on the observation data stream;
specifically, the three-difference observation equation is:
wherein: delta is an interstellar primary difference operator, Delta3The three-difference operator is j and k, the satellite is, r and m are monitoring stations, s is the epoch number of intervals, i is an epoch, and rho represents a pseudo-range observation value epsilon to represent observation noise.
Obtaining an error equation based on the established three-difference observation equation;
specifically, since the coordinates (x, y, z) of the monitoring point are only three unknowns in the equation, based on the above three-difference observation equation, the corresponding error equation is obtained as follows:
V(i，i+s)＝A(i，i+s)δX+T(i，i+s)；
δX＝-(ATPA)-1ATPT；
and T (i, i + s) ═ Δ3ρ0(i，i+s)-Δ3φ0(i，i+s)；
Where δ X is an estimate of the coordinate correction of the monitoring station, ρ0Representing an approximation of the satellite-to-receiver distance, P being a weighted array of three-difference observations, T (i, i + s) representing a constant term, Δ3φ0(i, i + s) represents a carrier observation value, and A (i, i + s) represents a design matrix.
Performing cycle slip detection based on an error equation;
specifically, when cycle slip does not occur, the values of the constant term T (i, i + s) and the correction amount V (i, i + s) are relatively small, and when T (i, i + s) is greater than a first preset value and/or V (i, i + s) is greater than a second preset value, it can be considered that cycle slip currently exists, and therefore, in performing iterative calculation of least squares, the weight given to the tristimulus observed value is 0, i.e., P (i, i + s) is 0, and as the number of epochs calculated increases, the tristimulus solution finally converges and rounds V (i, i + s), i to i + s, are rounded, i to i + s, and as the coordinates converge, the coordinates obtained by the tristimulus solution are updated. As the three-difference residual error method is adopted for detecting the cycle slip, the three-difference observation value eliminates the clock error of a receiver and the clock error of a satellite, the influence of an ionosphere and a troposphere is greatly weakened in a short base line, and the ambiguity of the whole cycle is also eliminated when the difference is calculated among epochs. The first preset value and the second preset value may be set according to actual conditions, and this is not limited herein.
Preferably, the ambiguity float solution may be calculated based on the results of the cycle slip detection;
specifically, firstly, a double-difference observation equation is established, and the double-difference observation method specifically comprises the following steps:
then calculating a ambiguity floating point solution based on a double-difference observation equation and a normal equation;
for example, in BDS/GPS joint data processing, for each epoch, the corresponding observation error equation is:
Preferably, the weight ratio between the BDS and the GPS observation values may be adjusted to be, for example, 1: 1, 1.5: 1, 2: 1, and L is OMC, when a double-difference observation equation is formed, a satellite with the highest altitude angle is selected for the BDS system and the GPS system respectively as the reference satellite of the system, for example, for the BDS system, which corresponds to a plurality of satellites, the altitude angles of each satellite are not consistent, and at this time, a satellite with the highest altitude angle may be used as the reference satellite of the BDS system. Then adding the result of the observed value error equation into a normal equation
If the residual error corresponding to the baseline result corresponding to the current single epoch is smaller than the set value, adding the residual error result of the epoch into the following equation through a normal equation superposition equation, and further obtaining a float solution (namely coordinate data of the monitoring station) with an estimated value and a ambiguity float solution, wherein the equation is as follows:
wherein n is the total number of epochs,
representing double-difference ambiguities.
Performing ambiguity fixing based on the ambiguity floating solution result to obtain a fixed solution, wherein the fixed solution is used for solving a baseline vector from the reference station to each monitoring station;
specifically, the ambiguity is further fixed according to the ambiguity floating solution to obtain a fixed solution, and the fixed solution includes coordinates of the corresponding monitoring station.
Firstly, calculating the ambiguity of the wide lane based on the ambiguity floating solution result to obtain the ambiguity solution of the wide lane;
in particular, according to the formula
To calculate the ambiguity of the wide lane to obtain the corresponding ambiguity solution of the wide lane, wherein phiWL＝φL1-φL2One week is taken as a time unit. Wherein WL represents the width term, fL1Denotes the carrier frequency, phi, of L1L1Represents the L1 carrier observation, PL1Representing pseudorange observations.
Further, using the double-difference observations of pseudoranges on the BDS satellite signals B1 and B2 or the GPS satellite signals L1 and L2, a corresponding error equation is constructed:
wherein，R1、R2、RWLRespectively correspond to L1、L2And LWLV is a residual vector, B is a design matrix formed by direction cosines from the receiver to the satellite, I is a unit matrix,
representing the parameter to be estimated
Representing the width lane ambiguity,/RAnd lWLAn OMC (Observation Minus Calculation, common vector) that represents a pseudo range and a WL phase Observation value, respectively;
then, carrying out ambiguity fixing based on the widelane ambiguity solution to obtain a fixed solution;
specifically, ambiguity fixing is carried out based on the widelane ambiguity solution to obtain a fixed solution when the widelane ambiguity N isWLAfter fixing, the WL phase observations may be restored to pseudoranges PWLWherein:
raw phase observations in conjunction with satellite L1
The error equation for the observation is again constructed as:
wherein the content of the first and second substances,
and vL1Residual vectors representing WL pseudorange observations and L1 phase observations respectively,
and lL1Respectively represent OMC constant vectors;
further, the air conditioner is provided with a fan,fixing the double-difference ambiguity of L1 by using LAMBDA algorithm search to obtain double-difference ambiguity N1, and obtaining integer ambiguity N fixed on L2 by the following formula2＝N1-NWLThen the double-difference ambiguity N of the two original observations1And N2Are fixed to obtain a ambiguity fix solution that can be considered as the coordinates of the monitoring station.
It should be noted that, after each point is rectified, a fixed solution is calculated once to obtain coordinates of the monitoring station at different time intervals in one day;
in a further preferred aspect of this embodiment, comparing the coordinates of the monitoring stations based on a plurality of the periods to obtain a plurality of observed deformation amounts specifically includes:
calculating a difference value between the reference station and the monitoring station in a preset direction based on the corresponding baseline vector;
specifically, baseline vectors of the reference station and the monitoring stations are calculated based on the approximate position of the observation station and the approximate position of the monitoring stations and continuous observation data of a preset time period, and baseline vectors consistent with the number of the monitoring stations are obtained.
In a preferred scheme of this embodiment, coordinate conversion is performed on coordinates of a corresponding monitoring station based on a corresponding baseline vector to obtain a conversion result;
and calculating the difference value between the reference station and the monitoring station in a preset direction based on the conversion result.
Specifically, the coordinates (X) of the monitoring station at different time periods will be obtained firstt，Yt，Zt) Reference (X) respectively corresponding to the monitoring points0，Y0，Z0) Comparing and carrying out coordinate conversion, wherein t represents a time period number, and obtaining a conversion result through the following formula;
wherein the sum of the values of the lambda,
respectively representing the geodetic precision and the geodetic latitude of the local ENU coordinate origin. The difference value in the preset direction is calculated based on the conversion result.
In a preferred aspect of this embodiment, fitting the observation noise curve based on the plurality of observation deformation amounts includes:
fitting the plurality of observed deformation quantities according to a fitting function to obtain a fitting function;
specifically, first, a plurality of observed deformation quantities are fitted according to a fitting formula, where the fitting formula specifically includes:
wherein the content of the first and second substances,
yi，tis an observed deformation amount, y 'obtained in a period corresponding to the i period on the t day'i，tFor the fitted observed noise of the corresponding cycle of the i-th day period, AtThe peak of the sine function.
In another preferred aspect of this embodiment, fitting the observation noise curve based on a plurality of observation deformation amounts includes:
fitting the plurality of observed deformation quantities according to a fitting function to obtain a plurality of corresponding peak values;
in a further preferred aspect of this embodiment, predicting the observation noise of the next cycle based on the observation noise curve includes:
performing linear fitting on the obtained multiple peak values to obtain a peak value corresponding to the next period;
obtaining a predicted observation noise curve in the next period according to a peak value corresponding to the next period;
specifically, 24 times y over a plurality of periods within a dayiAnd y'iWhen the sum of squares is minimum, the optimal solution of A can be obtained, and the fitting mode of the peak value is repeated to obtain the peak value A of continuous n days, such as A1、A2、、、An(ii) a The plurality of peaks is A1、A2、、、At(ii) a Performing linear fitting on the obtained multiple peak valuesThe corresponding linear fit function to the peak corresponding to the next cycle is: a. thet+1Ax + b, wherein,
n is the number of observation days;
the observed noise curve in the next period, which is predicted according to the peak value corresponding to the next period, is
In a preferable scheme of this embodiment, step S5 specifically includes:
according to the observed deformation quantity delta of the next periodi，t+1enu and observed noise curve of next cycle
Calculating the deformation after denoising in the next period;
peak value A from the above fittingt+1Obtaining a noise curve of t +1 day
yi，t+1Friend observed noise at time i on day t + 1.
And subtracting the actual observation result of each time interval of the day from the observation noise predicted value of the corresponding time interval of the day to obtain the deformation quantity after denoising.
For example: the corresponding peak A, e.g., y, is obtained from the fitting functioniFor the observed noise at the ith time of the first day, according to the fitting formula
At 24 times y during the dayiAnd y'iWhen the sum of squares is minimum, the optimal solution of A can be obtained, and the fitting mode of the peak value is repeated to obtain the peak value A of continuous n days, such as A1、A2、、、AnTo obtain a fitting function for n days, pair A1、A2、、、AnPerforming linear fitting to obtain a noise curvey is ax + b, and the noise curve y is ax + b and A1、A2、、、AnTo obtain An+1When the continuous observation date t is less than 15 days, the A value of t days is used for predicting the A value of t +1 dayn+1When the continuous observation date is more than 15 days, predicting the A value of the next day by using the A value of the previous 15 days (for example, if the A value of the 20 th day is known, the A value of the previous 15 days of the day is used for prediction, namely, the A value of the 5 th to 19 th days is used for prediction); therefore, prediction is carried out by utilizing the sliding window, and a reliable prediction value is obtained. And then, according to the actual observation result and the corresponding noise fitting result of the current day, the difference value between the actual observation result and the noise fitting result is the real deformation.
In this embodiment, the observation noise of the next cycle is predicted by observing the noise curve, and the deformation amount is calculated according to the observation noise and the observation deformation amount of the next cycle, so that the accuracy of deformation monitoring can be improved.
And secondly, the BDS/GPS data stream is collected in real time, the data stream collected in real time is processed, and the deformation quantity is extracted based on the processing result, so that the deformation quantity monitoring precision can be improved.
Moreover, the observation precision can be improved by adopting a normal equation superposition mode for data processing, and the accuracy of deformation extraction can be improved by carrying out denoising processing on the calculation result.
Example two:
based on the first embodiment, fig. 2 shows a schematic structural diagram of a deformation monitoring device provided in an embodiment of the present invention, where the device is configured to perform the steps of the method of the first embodiment, and for convenience of description, only the parts related to the embodiment of the present application are shown:
the monitoring device comprises an acquisition unit 1, a vector calculation unit 2 connected with the acquisition unit 1, a fitting unit 3 connected with the vector calculation unit 2, a prediction unit 4 connected with the fitting unit 3, and a deformation calculation unit 5 connected with the prediction unit 4, wherein:
the device comprises an acquisition unit 1, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a real-time observation data stream and storing the observation data stream;
specifically, an observation data stream (preferably real-time BDS/GPS data) is acquired and implemented, and the acquired real-time BDS/GPS data stream is decoded to obtain a corresponding processing file, where the processing file includes an observation file and a navigation message file, and the observation file carries corresponding observation data. The real-time observation data is obtained from a GNSS antenna to obtain a real-time BDS/GPS data stream, and the observation data stream is from a GPS system, or a Beidou system, or from both the GPS system and the Beidou system.
Firstly, a GNSS (global navigation satellite system) receiver acquires a real-time BDS/GPS data stream acquired by a GNSS antenna through a set IP address and a set port, then sends the data stream to a network, and a PC (personal computer) accesses the set IP address and the set port of the control file through a socket to acquire the real-time BDS/GPS data stream, converts the data stream into a binary format, stores the data stream into a binary file, stores the binary file once every preset time period, and edits a new file. Further, the real-time BDS/GPS data stream carries corresponding observation data, e.g. an undecoded binary raw file may be considered as raw observation data. The preset time period may be 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, etc., which is not limited herein.
Then, if the preset time period is 1 hour, since the processing file is stored once every one hour (at this time, one cycle is one hour), preferably, after each hour elapses, the processing files of a time length before the current time period are merged, and then the merged files are decoded to obtain the processing files, which include the observation file and the navigation message file. The total length of time for the combination of the segments may be 3 hours, 4 hours, 6 hours, 8 hours, etc., or may be one or more days, preferably, 6 hours is selected as the preferred length of time.
The vector calculation unit 2 is used for calculating a baseline vector from the reference station to each monitoring station in a period based on the observation data stream stored in the period, and obtaining the corresponding monitoring station coordinate in the period;
specifically, storing a currently acquired observation data stream once every other period, calculating a baseline vector from a reference station to each monitoring station in the period based on the stored observation data stream in the period, and acquiring coordinates of the monitoring station corresponding to the period; then, obtaining the coordinates of the monitoring station in a plurality of periods after a period of time;
the fitting unit 3 is used for comparing the coordinates of the monitoring station based on a plurality of periods to obtain a plurality of observation deformation quantities and fitting an observation noise curve based on the plurality of observation deformation quantities;
specifically, the coordinates of the monitoring station in a plurality of periods which are obtained currently are compared to obtain a plurality of observed deformation quantities, and fitting is performed based on the plurality of observed deformation quantities to obtain an observed noise curve.
Preferably, the plurality of periods may be consecutive periods, which may be 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, etc., so that if the period is greater than 1 hour, the period is preferably set to a multiple of 2, more preferably, the period is selected to be a divisor of 24, which is convenient on a daily basis.
Preferably, comparing based on the monitoring station coordinates for the plurality of cycles to obtain a plurality of observed deformation amounts may include: and subtracting the former cycle coordinate of the adjacent N cycles from any one of the monitoring station coordinates of the plurality of cycles to obtain the observed deformation of the next cycle.
For example: the observed deformation can be calculated through subtraction, and the observed deformation can be sequentially subtracted, for example, 24 hours is taken as a period, one period is taken as one day, the first day is taken as the first period, the second day is taken as the second period, and the like; comparing the coordinates of the second period and the first period to obtain the observed deformation of the second day relative to the first day, comparing the coordinates of the third period and the second period to obtain the observed deformation of the third day relative to the second day, and the like, and sequentially subtracting; at this time, the latter period (second period) is adjacent to the former other period (first period) by 1 period.
Also for example, the subtraction is performed at intervals, for example, in a period of 12 hours, the first day includes a first period and a second period, the second day includes a third period and a fourth later period, and so on, the odd period refers to 0 to 12 points, and the even period refers to 12 to 24 points, for example; alternate subtraction, such as subtraction of the third period from the first period to obtain the deformation amount of the third period (relative to the first period), subtraction of the fourth period from the second period to obtain the deformation amount of the fourth period (relative to the second period), etc., so that a plurality of deformation amounts can be obtained on a day; where the next cycle is 2 cycles adjacent to the previous cycle. Other cycles may also compare at similar intervals; comparing the time period of the preferred day with the time period of the previous day, for example, when the time period is 1 hour, comparing the time period of 0-1 point of the second day with the time period of 0-1 point of the first day; in addition, the deformation amount may be obtained in other manners, which is not limited herein. Preferably, N of adjacent N periods may be one of 1, 2, 3, 4, 6, 8, 12, 24, which is also convenient for daily calculation. Each period may be 1 hour, or 2 hours, or 4 hours, or 6 hours, or 12 hours, etc. The invention is not limited thereto.
A prediction unit 4 for predicting observation noise of the next cycle based on the observation noise curve;
specifically, the observation noise of the next cycle is predicted according to the observation noise curve; for example, a value corresponding to the next cycle is obtained from the observed noise curve as the predicted observed noise.
A deformation amount calculation unit 5 for calculating a denoised deformation amount of the next cycle based on the obtained observed deformation amount of the next cycle and the predicted observed noise of the next cycle;
specifically, the denoised deformation of the next period is calculated according to the obtained observed deformation of the next period and the predicted observed noise of the next period. The obtained observation deformation of the next period is obtained by calculation according to the real-time observation data flow through the steps, the actual deformation and the observation noise are contained in the observation deformation, the deformation after denoising can be obtained by removing the predicted observation noise, and the deformation after denoising can be generally closer to the actual deformation.
In this embodiment, the observation noise of the next cycle is predicted by observing the noise curve, and the deformation amount is calculated according to the observation noise and the observation deformation amount of the next cycle, so that the accuracy of deformation monitoring can be improved.
In a preferable aspect of this embodiment, the apparatus further includes: a setup unit connected to the acquisition unit 1, wherein:
in a preferred aspect of this embodiment, the prediction unit 4 is further configured to:
updating an observation noise curve according to the observation deformation of the next period;
specifically, after an original observation noise curve is formed, the observation noise curve can be updated according to the observation deformation of the next period; for example: the original observation noise curve is obtained by fitting according to the observation deformation amounts from day 1 to day 10, after the observation deformation amount from day 11 is obtained, the original observation noise curve may be fit according to the observation deformation amounts from day 1 to day 11 to update the original observation noise curve, or may be updated in a window sliding manner, for example, fitting according to the observation deformation amounts from day 2 to day 11, and then updating according to the observation deformation amounts from day 3 to day 12 may be continued, which is not limited herein;
in a preferred embodiment of this embodiment, the vector calculating unit 2 is specifically configured to:
establishing a three-difference observation equation based on the observation data stream;
specifically, firstly, respectively generating control files corresponding to baselines of a reference station and each monitoring station based on a global control file; for example: generating a corresponding control file for each monitoring station according to the global control file, wherein the control file is a control file corresponding to the base line of the reference station and the monitoring station, namely generating a control file of a single base line; for example, the global file includes all information between the reference station and each monitoring station, all information is classified according to the monitoring station, preferably, the baseline is classified, and a control file of the baseline between the reference station and each monitoring station is obtained, that is, the number of the control files is consistent with the number of the monitoring stations.
Specifically, the three-difference observation equation is:
wherein: delta is an interstellar primary difference operator, Delta3The three-difference operator is j and k, the satellite is, r and m are monitoring stations, s is the epoch number of intervals, i is an epoch, and rho represents a pseudo-range observation value epsilon to represent observation noise.
Obtaining an error equation based on the established three-difference observation equation;
specifically, since the coordinates (x, y, z) of the monitoring point are only three unknowns in the equation, based on the above three-difference observation equation, the corresponding error equation is obtained as follows:
V(i，i+S)＝A(i，i+S)δX+T(i，i+S)；
δX＝-(ATPA)-1ATPT；
and T (i, i + s) ═ Δ3ρ0(i，i+s)-Δ3φ0(i，i+s)；
Where δ X is an estimate of the coordinate correction of the monitoring station, ρ0Representing an approximation of the satellite-to-receiver distance, P being a weighted array of three-difference observations, T (i, i + s) representing a constant term, Δ3φ0(i, i + s) represents a carrier observation value, and A (i, i + s) represents a design matrix.
Performing cycle slip detection based on an error equation;
specifically, when cycle slip does not occur, the values of the constant term T (i, i + s) and the correction amount V (i, i + s) are relatively small, and when T (i, i + s) is greater than a first preset value and/or V (i, i + s) is greater than a second preset value, it can be considered that cycle slip currently exists, and therefore, in performing iterative calculation of least squares, the weight given to the tristimulus observed value is 0, i.e., P (i, i + s) is 0, and as the number of epochs calculated increases, the tristimulus solution finally converges and rounds V (i, i + s), i to i + s, are rounded, i to i + s, and as the coordinates converge, the coordinates obtained by the tristimulus solution are updated. As the three-difference residual error method is adopted for detecting the cycle slip, the three-difference observation value eliminates the clock error of a receiver and the clock error of a satellite, the influence of an ionosphere and a troposphere is greatly weakened in a short base line, and the ambiguity of the whole cycle is also eliminated when the difference is calculated among epochs. The first preset value and the second preset value may be set according to actual conditions, and this is not limited herein.
Calculating an ambiguity floating solution based on the cycle slip detection result;
specifically, firstly, a double-difference observation equation is established, and the double-difference observation method specifically comprises the following steps:
then calculating a ambiguity floating point solution based on a double-difference observation equation and a normal equation;
for example, in BDS/GPS joint data processing, for each epoch, the corresponding observation error equation is:
Preferably, the weight ratio between the BDS and the GPS observation values may be adjusted to be, for example, 1: 1, 1.5: 1, 2: 1, etc., and L is OMC, when forming the double-difference observation equation, a satellite with the highest altitude angle is selected for the BDS system and the GPS system respectively as the reference satellite of the system, for example, for the BDS system, which corresponds to a plurality of satellites, the altitude angles of each satellite are not consistent, and at this time, a satellite with the highest altitude angle may be used as the reference satellite of the BDS system. And then adding the result of the observed value error equation into a normal equation, if the residual corresponding to the baseline result corresponding to the current single epoch is smaller than a set value, adding the residual result of the epoch into the following equation through a normal equation superposition equation, and further obtaining a floating solution with an estimated value (namely coordinate data of the monitoring station) and an ambiguity floating solution, wherein the equation is as follows: wherein n is the total number of epochs.
Performing ambiguity fixing based on the ambiguity floating solution result to obtain a fixed solution, wherein the fixed solution is used for solving a baseline vector from the reference station to each monitoring station;
specifically, the ambiguity is further fixed according to the ambiguity floating solution to obtain a fixed solution, and the fixed solution includes coordinates of the corresponding monitoring station.
Firstly, calculating the ambiguity of the wide lane based on the ambiguity floating solution result to obtain the ambiguity solution of the wide lane;
in particular, according to the formula
To calculate the ambiguity of the wide lane to obtain the corresponding ambiguity solution of the wide lane, wherein phiWL＝φL1-φL2One week is taken as a time unit. Wherein WL represents the width term, fL1Denotes the carrier frequency, phi, of L1L1Represents the L1 carrier observation, PL1Representing pseudorange observations.
Further, using the double-difference observations of pseudoranges on the BDS satellite signals B1 and B2 or the GPS satellite signals L1 and L2, a corresponding error equation is constructed:
wherein R is1、R2、RWLRespectively correspond to L1、L2And LWLV is a residual vector, B is a design matrix formed by direction cosines from the receiver to the satellite, I is a unit matrix,
representing the baseline vector to be estimated,
representing the width lane ambiguity,/RAnd lWLIndividual watchAn OMC (Observation Minus Calculation, common vector) showing pseudo-range and WL phase observed value;
then, carrying out ambiguity fixing based on the widelane ambiguity solution to obtain a fixed solution;
specifically, ambiguity fixing is carried out based on the widelane ambiguity solution to obtain a fixed solution when the widelane ambiguity N isWLAfter fixing, the WL phase observations may be restored to pseudoranges PWLWherein:
raw phase observations in conjunction with satellite L1
The error equation for the observation is again constructed as:
wherein the content of the first and second substances,
and vL1Residual vectors representing WL pseudorange observations and L1 phase observations respectively,
and lL1Respectively represent OMC constant vectors;
further, the L1 double-difference ambiguity is fixed by using LAMBDA algorithm search to obtain a double-difference ambiguity N1, and the integer ambiguity N fixed on L2 is obtained by the following formula2＝N1-NWLThen the double-difference ambiguity N of the two original observations1And N2Are fixed to obtain a ambiguity fix solution that can be considered as the coordinates of the monitoring station.
It should be noted that, after each point is rectified, a fixed solution is calculated once to obtain coordinates of the monitoring station at different time intervals in one day;
in a further preferred embodiment of this embodiment, the fitting unit 3 is specifically configured to:
calculating a difference value between the reference station and the monitoring station in a preset direction based on the corresponding baseline vector;
specifically, baseline vectors of the reference station and the monitoring stations are calculated based on the approximate position of the observation station and the approximate position of the monitoring stations and continuous observation data of a preset time period, and baseline vectors consistent with the number of the monitoring stations are obtained.
In a preferred scheme of this embodiment, coordinate conversion is performed on coordinates of a corresponding monitoring station based on a corresponding baseline vector to obtain a conversion result;
and calculating the difference value between the reference station and the monitoring station in a preset direction based on the conversion result.
Specifically, the coordinates (X) of the monitoring station at different time periods will be obtained firstt，Yt，Zt) Reference (X) respectively corresponding to the monitoring points0，Y0，Z0) The comparison and coordinate conversion are performed, and t represents a time period number, and the conversion result is obtained by the following equation.
Wherein the sum of the values of the lambda,
respectively representing the geodetic precision and the geodetic latitude of the local ENU coordinate origin. The difference value in the preset direction is calculated based on the conversion result.
In a preferred version of this embodiment, the fitting unit 3 is further configured to:
fitting the plurality of observed deformation quantities according to a fitting function to obtain a fitting function;
specifically, first, a plurality of observed deformation quantities are fitted according to a fitting formula, where the fitting formula specifically includes:
wherein the content of the first and second substances,
yi，tis an observed deformation amount, y 'obtained in a period corresponding to the i period on the t day'i，tFor the fitted observed noise of the corresponding cycle of the i-th day period, AtThe peak of the sine function.
In another preferred aspect of this embodiment, the prediction unit 4 is further configured to:
fitting the plurality of observed deformation quantities according to a fitting function to obtain a plurality of corresponding peak values;
in a further preferred aspect of this embodiment, the prediction unit 4 is further configured to:
performing linear fitting on the obtained multiple peak values to obtain a peak value corresponding to the next period;
obtaining a predicted observation noise curve in the next period according to a peak value corresponding to the next period;
in particular, 24 times y within one dayiAnd y'iWhen the sum of squares is minimum, the optimal solution of A can be obtained, and the fitting mode of the peak value is repeated to obtain the peak value A of continuous n days, such as A1、A2、、、An(ii) a The plurality of peaks is A1、A2、、、At(ii) a And performing linear fitting on the obtained multiple peak values to obtain a peak value corresponding to the next period, wherein the corresponding linear fitting function is as follows: a. thet+1Ax + b, wherein,
n is the number of observation days;
the observed noise curve in the next period, which is predicted according to the peak value corresponding to the next period, is
In a preferred embodiment of the present embodiment, the deformation amount calculation unit 5 is specifically configured to:
according to the observed deformation quantity delta of the next periodi，t+1enu and observed noise curve of next cycle
Calculating the deformation after denoising in the next period;
peak value A from the above fittingt+1Obtaining a noise curve of t +1 day
yi，t+1Indicating the i-period observed noise on day t + 1.
And subtracting the actual observation result of each time interval of the day from the observation noise predicted value of the corresponding time interval of the day to obtain the deformation quantity after denoising.
For example: the corresponding peak A, e.g., y, is obtained from the fitting functioniFor the observed noise at the ith time of the first day, according to the fitting formula
At 24 times y during the dayiAnd yiWhen the sum of squares is minimum, the optimal solution of A can be obtained, and the fitting mode of the peak value is repeated to obtain the peak value A of continuous n days, such as A1、A2、、、AnTo obtain a fitting function for n days, pair A1、A2、、、AnLinear fitting is performed to obtain a noise curve y ═ ax + b, and the noise curve y ═ ax + b and a are used to determine1、A2、、、AnTo obtain An+1When the continuous observation date t is less than 15 days, the A value of t days is used for predicting the A value of t +1 dayn+1When the continuous observation date is more than 15 days, predicting the A value of the next day by using the A value of the previous 15 days (for example, if the A value of the 20 th day is known, the A value of the previous 15 days of the day is used for prediction, namely, the A value of the 5 th to 19 th days is used for prediction); therefore, prediction is carried out by utilizing the sliding window, and a reliable prediction value is obtained. And then, according to the actual observation result and the corresponding noise fitting result of the current day, the difference value between the actual observation result and the noise fitting result is the real deformation.
In this embodiment, the observation noise of the next cycle is predicted by observing the noise curve, and the deformation amount is calculated according to the observation noise and the observation deformation amount of the next cycle, so that the accuracy of deformation monitoring can be improved.
And secondly, the BDS/GPS data stream is collected in real time, the data stream collected in real time is processed, and the deformation quantity is extracted based on the processing result, so that the deformation quantity monitoring precision can be improved.
Moreover, the observation precision can be improved by adopting a normal equation superposition mode for data processing, and the accuracy of deformation extraction can be improved by carrying out denoising processing on the calculation result.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
All or part of the flow in the method of the embodiment is realized, and the method can also be finished by instructing relevant hardware through a computer program.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (13)
1. A method of deformation monitoring, comprising:
acquiring a real-time observation data stream and storing the observation data stream;
calculating a baseline vector from a reference station to each monitoring station in a period based on the observation data stream stored in the period, and obtaining the corresponding coordinates of the monitoring stations in the period;
comparing based on a plurality of the periodic monitoring station coordinates to obtain a plurality of observed deformation quantities, and fitting an observed noise curve based on the plurality of observed deformation quantities;
predicting the observed noise of the next period based on the observed noise curve;
and calculating the denoised deformation of the next period based on the obtained observed deformation of the next period and the predicted observed noise of the next period.
2. The deformation monitoring method according to claim 1, wherein the observation data stream is derived from a GPS system, or a beidou system, or both.
3. The deformation monitoring method according to claim 1, wherein the period is 1 hour, or 2 hours, or 4 hours, or 6 hours, or 12 hours.
4. The deformation monitoring method according to claim 1, wherein the observation noise curve is updated according to the observation deformation amount of the next cycle.
5. The deformation monitoring method of claim 1, wherein comparing the plurality of cycles-based monitoring station coordinates to obtain a plurality of observed deformations includes:
and subtracting one period coordinate from another previous period coordinate of the adjacent N periods in the monitoring station coordinates of the plurality of periods to obtain the observation deformation of the period.
6. The deformation monitoring method according to claim 5, wherein N of the adjacent N periods is one of 1, 2, 3, 4, 6, 8, 12, 24.
7. The deformation monitoring method of claim 1, wherein prior to calculating the baseline vector for each of the monitoring stations, further comprising:
establishing a three-difference observation equation based on the observation data stream;
obtaining an error equation based on the established three-difference observation equation;
and performing cycle slip detection based on the error equation.
8. The deformation monitoring method according to claim 7, further comprising, after the cycle slip detection based on the error equation:
calculating an ambiguity floating solution based on the cycle slip detection result;
and fixing the ambiguity based on the ambiguity floating solution result to obtain a fixed solution, wherein the fixed solution is used for solving the baseline vector from the reference station to each monitoring station.
9. The deformation monitoring method according to claim 8, wherein the ambiguity fixing based on the ambiguity floating solution result to obtain a fixed solution comprises:
calculating a widelane ambiguity based on the ambiguity floating solution result;
and carrying out ambiguity fixing based on the widelane ambiguity to obtain a fixed solution.
10. The deformation monitoring method of claim 1, wherein fitting an observed noise curve based on the plurality of observed deformations comprises:
fitting the plurality of observation deformation quantities according to a fitting formula to obtain an observation noise curve;
the fitting formula is:
11. The deformation monitoring method of claim 1, wherein fitting an observed noise curve based on the plurality of observed deformations comprises: fitting the plurality of observed deformation quantities according to a fitting function to obtain a plurality of corresponding peak values; and
predicting observed noise for a next cycle based on the observed noise curve, comprising:
performing linear fitting on the obtained multiple peak values to obtain a peak value corresponding to the next period;
and obtaining a predicted observation noise curve in the next period according to the peak value corresponding to the next period.
12. The deformation monitoring method according to claim 11, wherein the plurality of peaks is a1、A2、、、At(ii) a And performing linear fitting on the obtained multiple peak values to obtain a peak value corresponding to the next period, wherein a linear fitting function is as follows: a. thet+1Ax + b, wherein,
n is the number of observation days.
13. A deformation monitoring device, characterized in that the deformation monitoring device is adapted to perform the method of any of claims 1 to 12.
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