CN106767613B - A method of based on information fusion monitoring analysis of landslide motion morphology - Google Patents
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Abstract
A method of based on information fusion monitoring analysis of landslide motion morphology, comprising the following steps: (1) monitoring point is arranged, the continuous several months collects the daily displacement data in each monitoring point；(2) preliminary piecemeal is carried out to each monitoring point, the monitoring point point of same motion trajectory is in same monitoring block；(3) the piecemeal result of each monitoring point is modified；(4) the landslide motion morphology in each monitoring block is indicated by translational motion and rotary motion, determines landslide motion morphology；(5) rapid evaluation is made to landslide motion process according to determining landslide motion morphology.The present invention comprehensively describes the motion morphology between each piece of landslide, block and block, has achieved the purpose that accurate evaluation comes down motion process；Assessment intuitively can be made to landslide motion process, and improve the understanding to landslide mass motion form, effectively avoid the huge hidden danger of landslide bring safety of life and property.
Description
Technical field
The present invention relates to a kind of landslide motion morphology representation methods, and in particular to one kind is slided based on the analysis of information fusion monitoring
The method of slope motion morphology.
Background technique
The generation on landslide can all cause very big threat to the communal facility in the region, people life property safety, therefore,
The motion morphology one on landslide to be domestic and foreign scholars study hot spot.
Currently, the research for the motion morphology that comes down both at home and abroad, is concentrated mainly on and represents entire landslide with single monitoring point
Motion morphology either multiple monitoring points be fused into a monitoring point and studied, can not completely remain landslide movement shape
Become information, so that it is not accurate enough to the description of landslide motion morphology, assessment quickly can not be made to landslide motion process.
Summary of the invention
In view of this, can completely and accurately describe landslide movement deformation the embodiment provides a kind of, it is convenient for
The method based on information fusion monitoring analysis of landslide motion morphology of rapid evaluation is made to landslide motion process.
The embodiment of the present invention provides a kind of method based on information fusion monitoring analysis of landslide motion morphology, including following
Step:
(1) monitoring point is arranged in Study of Landslides overview on landslide, and the continuous several months collects the daily displacement number in each monitoring point
According to；
(2) displacement data for each monitoring point collected daily is taken absolute value respectively, is obtained by accumulative summation each
The accumulative displacement of monitoring point every month judges the movement of each monitoring point according to the variation of the accumulative displacement of each monitoring point every month
State change, and preliminary piecemeal, same movement state are carried out to each monitoring point according to the variation of the motion state of each monitoring point
The monitoring point of variation point is in same monitoring block；
(3) two-dimension analysis is carried out to the plane, component of each displacement data monitoring point every month, and is sentenced according to two-dimension analysis
The break plane motion track of each monitoring point is accordingly modified the preliminary piecemeal result of each monitoring point；
(4) monitoring point in each monitoring block is merged to obtain and merges comprehensive point, it is right respectively by the comprehensive point of fusion
Landslide motion morphology in each monitoring block is indicated, and the translational motion for first passing through monitoring block carries out the motion morphology on landslide
It indicates, then by establishing rigid model, the motion morphology on landslide is indicated by the rotation and translation motion of rigid model,
Then the resulting landslide motion morphology of two kinds of representations is integrated, determines landslide motion morphology；
(5) the landslide motion morphology determined according to step (4) makes rapid evaluation to landslide motion process.
Further, in the step (2), the accumulative displacement of each monitoring point is drawn with the change curve in month, according to change
Change situations such as curve judges the variation in movement deformation period of each monitoring point, the motion state variation includes leveling style shape
Become, exponential type deformation, the similar monitoring point of change curve is classified as same class by stepped deformation and convergence type deformation, will be same
The monitoring point of class is divided to same monitoring block.
Further, in the step (3), according to each monitoring point every month displacement data plane, component draw it is each
Monitoring point is in the motion profile figure of plane, the plane motion track of the preliminary analysis monitoring point accordingly, by analyzing to preliminary
Piecemeal result is verified.
Further, in the step (4), the displacement data of all monitoring points in each monitoring block is led in blocks
It crosses Kalman filtering blending algorithm to be merged, it is comprehensive in the fusion of fusion center to obtain all monitoring points in each monitoring block
Point；
One Dimension Analysis is carried out to the comprehensive point of the fusion of each monitoring block, obtains the movement shape of the comprehensive point of each monitoring block fusion
State；
Two-dimension analysis is carried out to the comprehensive point of the fusion of each monitoring block, obtains the motion profile of each monitoring block；
The motion profile of comprehensive each monitoring block obtains landslide motion morphology.
Further, the Kalman filtering blending algorithm is that the data generated to the sensor of each monitoring point are melted
It closes, each sensor is a module of fusion, is not interfere with each other between sensor and sensor；
Fusion steps are as follows:
The moving displacement and movement velocity of each monitoring point of coming down are calculated, formula is as follows:
In formula: s (x) indicates monitoring point in the moving displacement of x state, and s (x+1) indicates monitoring point in the movement of x+1 state
Displacement, v (x) indicate the movement velocity in x state, and v (x+1) indicates the movement velocity in x+1 state, and a (x) indicates that monitoring point exists
X state is to the acceleration of x+1 state, and T expression monitoring point is in x state to the time of x+1 state；
Become using the moving displacement of each monitoring point in above-mentioned landslide and movement velocity as the state of Kalman's system on landslide
It measures, then available Dynamic monitoring pattern are as follows:
H (k)={ 10 }；
W (k)=a (k)；
In formula: X (k+1) indicate the k+1 moment system mode, X (k) indicate the k moment system mode, Φ (k), H (k),
Γ (k) indicates measuring system parameter, and Z (k) indicates the actual displacement monitoring value of landslide monitoring point, and W (k) and V (k) are respectively indicated
The white Gaussian noise of process and measurement, and the two is irrelevant；
Using Kalman filtering blending algorithm, in blocks by the data of monitoring point after piecemeal, centralization is taken to pass more
Sensor merges each sensor and obtains the optimal estimation value at k+1 moment, i.e., filtered value Zi(k+1), then by each sensing
The filter value that device obtains is merged in fusion center, obtains the comprehensive point value of fusion of each monitoring block.
Further, in the step (4), rigid model is initially set up, regards each monitoring block as a rigid body, and establish
Coordinate, using the position of the comprehensive point of the fusion before each rigid motion as the starting point of rigid motion, then with each rigid motion
Terminal of the position of the comprehensive point of fusion afterwards as rigid motion, finds each rigid body every month by particle swarm optimization algorithm and transports
Rotation angle of the dynamic front and back relative to reference axis, and by constantly updating local optimum and global optimum, find error of coordinate
Then it is public to bring this rotation angle into calculating for rotation angle before and after the corresponding each rigid motion of minimum value relative to reference axis
Formula is indicated the motion profile of each rigid body, and the calculation formula is as follows:
In formula: X1For the position of the comprehensive point of the fusion before rigid motion, X2For the comprehensive point of fusion after rigid motion one month
Position, Δ X be rigid body the moon accumulate displacement,It is rotated for rigid body around X-axisAngle, Rθ(y) it is revolved for rigid body around Y-axis
Turn θ angle,It is rotated about the z axis for rigid bodyAngle；
The motion profile of comprehensive each rigid body obtains landslide motion morphology.
Compared with prior art, the invention has the following advantages: the landslide motion morphology based on piecemeal principle is commented
Estimate method, modality curves are displaced by analysis, piecemeal is carried out to the landslide with different motion form, is melted in conjunction with Kalman filtering
Hop algorithm and particle swarm optimization algorithm study the motion morphology between the motion morphology and block and block on each piece of landslide, will
" rigid model " is introduced into each piece that landslide is decomposed, on the basis of analyzing each piece of translational motion, using particle group optimizing
Algorithm finds rotation angle, obtains rotary motion, comprehensively describes the motion morphology between each piece of landslide, block and block, reaches
The purpose of accurate evaluation landslide motion process；And the process of landslide movement deformation is shown by mathematical formulae, dynamic
Ground presents translation and the rotary course of different parts landslide motion morphology variation, by translating and rotating comprehensive descision landslide fortune
Dynamic form intuitively can make assessment to landslide motion process, and improve the understanding to landslide mass motion form, effectively keep away
Exempt from the huge hidden danger of landslide bring safety of life and property.
Detailed description of the invention
Fig. 1 is the flow chart of one embodiment of the invention.
Fig. 2 is the motion profile figure of one embodiment of the invention.
Fig. 3 is the S-T curve graph of each monitoring point of the stability of the embodiment of the present invention 1.
Fig. 4 is the S-T curve graph of each monitoring point of the step type 1 of the embodiment of the present invention 1.
Fig. 5 is the S-T curve graph of each monitoring point of the step type 2 of the embodiment of the present invention 1.
Fig. 6 is the monitoring point block diagram of the embodiment of the present invention 1.
Fig. 7 is the S-T curve graph of the comprehensive point of A monitoring block fusion of the embodiment of the present invention 1.
Fig. 8 is the S-T curve graph of the comprehensive point of B monitoring block fusion of the embodiment of the present invention 1.
Fig. 9 is the S-T curve graph of the comprehensive point of C monitoring block fusion of the embodiment of the present invention 1.
Figure 10 is the motion profile of the comprehensive point of A monitoring block fusion of the embodiment of the present invention 1.
Figure 11 is the motion profile of the comprehensive point of B monitoring block fusion of the embodiment of the present invention 1.
Figure 12 is the motion profile of the comprehensive point of C monitoring block fusion of the embodiment of the present invention 1.
Figure 13 is the landslide motion morphology figure of the A monitoring block of the embodiment of the present invention 1.
Figure 14 is the landslide motion morphology figure of the B monitoring block of the embodiment of the present invention 1.
Figure 15 is the landslide motion morphology figure of the C monitoring block of the embodiment of the present invention 1.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is further described.
Embodiment 1
Landslide is to be located at reservoir area of Three Gorges plain boiled water river to come down, specific geographical coordinate are as follows: X: 3433805, Y: 455980, longitude
110 ° of 32'09 " E, 31 ° of 01'34 " N of latitude.
Fig. 1 is please referred to, is embodied as follows:
(1) Study of Landslides overview is arranged 11 monitoring points on the landslide of plain boiled water river, continuously collects from 2006.7-2009.1
The daily displacement data in each monitoring point；
(2) each monitoring point is extracted this 31 months monthly from the report of the plain boiled water river landslide monitoring of 2006.7-2009.1
Daily displacement data, and its absolute value is taken, the accumulative position of each monitoring point every month is calculated by way of accumulative summation
It moves；According to accumulative displacement, S (accumulative displacement)-T (month) curve of each monitoring point is drawn, is judged according to change curve each
The motion profile of monitoring point, the motion profile include leveling style motion profile, exponential type motion profile, stepped motion profile
With convergence type motion profile, the similar monitoring point of change curve is classified as same class, of a sort monitoring point is divided to same
Block is monitored, completes the preliminary piecemeal to monitoring point, the S-T curve of each monitoring block is as described in Fig. 2-5:
Leveling style: ZG91, ZG92, ZG94, ZG119, ZG120 (as shown in Figure 3) are set as A block；
Stepped 1:DX02, DX04, ZG118, ZG93 (as shown in Figure 4), are set as B block；
Stepped 2:DX01, DX03 (as shown in Figure 5), are set as C block；
ZG91, ZG92, ZG94, ZG119, ZG120, DX02, DX04, ZG118, ZG93, DX01, DX03 are to 11 respectively
The name of monitoring point, in order to distinguish；
(3) it counts and two-dimension analysis is carried out to the accumulative displacement of each monitoring point every month, and judged each according to two-dimension analysis
The plane motion track of monitoring point is accordingly modified the piecemeal result of each monitoring point, piecemeal result and accumulative displacement point
It is identical to analyse result, analysis result is as shown in Figure 6.
(4) the landslide motion morphology in each monitoring block is indicated, first passes through the translational motion of monitoring block to landslide
Motion morphology be indicated；
The displacement data of all monitoring points in each monitoring block is passed through into Kalman filtering blending algorithm in blocks
It is merged, obtains in each monitoring block all monitoring points in the comprehensive point of the fusion of fusion center；
Kalman filtering blending algorithm is that the data generated to the sensor of each monitoring point merge, each sensing
Device is a module of fusion, is not interfere with each other between sensor and sensor, and system is added in any one sensor, or exits
System, or any one sensor there is a problem, will not influence other sensors and whole system, and system still can
To work normally, in Landslide Forecast System, the displacement data that each sensor generates be it is incoherent, can be according to no feedback information
Structure takes centralized Multi-sensor Fusion；
Fusion steps are as follows:
The moving displacement and movement velocity of each monitoring point of coming down are calculated, formula is as follows:
In formula: s (x) indicates monitoring point in the moving displacement of x state, and s (x+1) indicates monitoring point in the movement of x+1 state
Displacement, v (x) indicate the movement velocity in x state, and v (x+1) indicates the movement velocity in x+1 state, and a (x) indicates that monitoring point exists
X state is to the acceleration of x+1 state, and T expression monitoring point is in x state to the time of x+1 state；
Become using the moving displacement of each monitoring point in above-mentioned landslide and movement velocity as the state of Kalman's system on landslide
It measures, then available Dynamic monitoring pattern are as follows:
H (k)={ 10 }；
W (k)=a (k)；
In formula: X (k+1) indicate the k+1 moment system mode, X (k) indicate the k moment system mode, Φ (k), H (k),
Γ (k) indicates measuring system parameter, and Z (k) indicates the actual displacement monitoring value of landslide monitoring point, and W (k) and V (k) are respectively indicated
The white Gaussian noise of process and measurement, and the two is irrelevant；
Using Kalman filtering blending algorithm, in blocks by the data of monitoring point after piecemeal, centralization is taken to pass more
Sensor merges each sensor and obtains the optimal estimation value at k+1 moment, i.e., filtered value Zi(k+1) then by each sensor
Obtained filter value is merged in fusion center, obtains the comprehensive point value of fusion of each monitoring block；
The comprehensive point of the fusion of A monitoring block is named as NG01, and the comprehensive point of the fusion of B monitoring block is named as NG02, and C monitors block
The comprehensive point of fusion is named as NG03；
One Dimension Analysis is carried out to the comprehensive point of the fusion of each monitoring block, obtains the movement shape of the comprehensive point of each monitoring block fusion
State variation, i.e. S-T curve graph, such as Fig. 7-9；
Two-dimension analysis is carried out to the comprehensive point of the fusion of each monitoring block, obtains the movement rail of the comprehensive point of each monitoring block fusion
Mark, such as Figure 10-12；
The motion profile of comprehensive each monitoring block obtains landslide motion morphology.
By establishing rigid model, table is carried out to the motion morphology on landslide by the rotation and translation motion of rigid model
Show, initially set up rigid model, regards each monitoring block as a rigid body, and establish coordinate, with melting before each rigid motion
Starting point of the position of comprehensive point as rigid motion is closed, then using the position of the comprehensive point of the fusion after each rigid motion as just
The terminal of body movement finds rotation angle of each rigid motion front and back every month relative to reference axis by particle swarm optimization algorithm
Degree, and by constantly updating local optimum and global optimum, before finding the corresponding each rigid motion of error of coordinate minimum value
Afterwards relative to the rotation angle of reference axis, then brings this rotation angle into calculation formula and the motion profile of each rigid body is carried out
It indicates, the calculation formula is as follows:
In formula: X1For the position of the comprehensive point of the fusion before rigid motion, X2For the comprehensive point of fusion after rigid motion one month
Position, Δ X be rigid body the moon accumulate displacement,It is rotated for rigid body around X-axisAngle, Rθ(y) it is revolved for rigid body around Y-axis
Turn θ angle,It is rotated about the z axis for rigid bodyAngle；
The motion profile of comprehensive each rigid body obtains landslide motion morphology；The comprehensive resulting landslide movement of two kinds of representations
Form determines landslide motion morphology.
The landslide translational motion form of all monitoring points in each monitoring block is obtained by One Dimension Analysis, wherein block A is in
Now stable ascendant trend, and monthly shift value is smaller, within the monitoring phase, is substantially at the stationary phase of creep extruding, B block is wherein supervised
The 11-13 month (2007.6-2007.8) of survey phase belongs to the sliding phase, and the displacement of monitoring point is significantly greater, and C block and B block are transported
Dynamic form is similar；
Monitoring block is obtained by two-dimension analysis and monitors the landslide translational motion form between block.Obtain the plane of monitoring block
Motion diagram, predominantly X/Y plane, X-axis indicate the movement in due east direction, and Y-axis indicates the movement of direct north, at the movement of NG01
In stationary phase, moving displacement is substantially without obvious movement (measurement error is ± 5mm)；The rear 7-8 month (2008.7- of monitoring phase
2009.1), comprehensive monitoring point gradually begins with distorted movement by a small margin, but moving displacement size is still smaller, with smaller
Displacement northeastward direction move.Stationary phase --- sliding phase --- stationary phase is presented in the dynamic rail mark of NG02.Wherein stationary phase, prison
Measuring point displacement size is translated compared with global displacement size very little, but substantially to east by north direction；The sliding phase is inclined eastwards with larger displacement
The north is to translation；After the sliding phase, and it is in stationary phase, moving displacement is unobvious, but northwards direction translates substantially.The fortune of NG03
Motion morphology of the dynamic rail mark with above-mentioned NG02；As illustrated in figs. 13-15.
(5) the landslide motion morphology determined according to step (4) makes rapid evaluation to landslide motion process.
The present invention is based on the appraisal procedures of the landslide motion morphology of piecemeal principle, modality curves are displaced by analysis, to tool
There is the landslide of different motion form to carry out piecemeal, in conjunction with Kalman filtering blending algorithm and particle swarm optimization algorithm to each piece of landslide
Motion morphology and block and block between motion morphology studied, by " rigid model " be introduced into landslide decompose each piece
In, on the basis of analyzing each piece of translational motion, rotation angle is found using particle swarm optimization algorithm, obtains rotary motion,
Each piece, the motion morphology between block and block for comprehensively describing landslide have achieved the purpose that accurate evaluation comes down motion process；And it will
The process of landslide movement deformation is shown by mathematical formulae, dynamically presents the motion morphology variation of different parts landslide
Translation and rotary course can intuitively do landslide motion process by translating and rotating comprehensive descision landslide motion morphology
It assesses out, and improves the understanding to landslide mass motion form, effectively avoid the huge of landslide bring safety of life and property
Hidden danger.
Claims (5)
1. a kind of method based on information fusion monitoring analysis of landslide motion morphology, which comprises the following steps:
(1) monitoring point is arranged in Study of Landslides overview on landslide, and the continuous several months collects the daily displacement data in each monitoring point；
(2) displacement data for each monitoring point collected daily is taken absolute value respectively, each monitoring is obtained by accumulative summation
The accumulative displacement for putting every month judges the motion state of each monitoring point according to the variation of the accumulative displacement of each monitoring point every month
Variation, and preliminary piecemeal, same movement state change are carried out to each monitoring point according to the variation of the motion state of each monitoring point
Monitoring point point in same monitoring block；
(3) two-dimension analysis is carried out to the plane, component of each displacement data monitoring point every month, and is judged often according to two-dimension analysis
The plane motion track of a monitoring point is accordingly modified the preliminary piecemeal result of each monitoring point；
(4) monitoring point in each monitoring block is merged to obtain and merges comprehensive point, by the comprehensive point of fusion respectively to each
Landslide motion morphology in monitoring block is indicated, and first passes through motion morphology carry out table of the translational motion to landslide of monitoring block
Show, then by establishing rigid model, the motion morphology on landslide is indicated by the rotation and translation motion of rigid model, so
The resulting landslide motion morphology of two kinds of representations is integrated afterwards, determines landslide motion morphology；
(5) the landslide motion morphology determined according to step (4) makes rapid evaluation to landslide motion process；
Using Kalman filtering blending algorithm, in blocks by the data of monitoring point after piecemeal, centralized multisensor is taken
It merges each sensor and obtains the optimal estimation value at k+1 moment, i.e., filtered value Zi(k+1), then each sensor is obtained
To filter value merged in fusion center, obtain the comprehensive point value of fusion of each monitoring block；The Kalman filtering fusion
Algorithm is that the data generated to the sensor of each monitoring point merge, each sensor is a module of fusion,
It is not interfere with each other between sensor and sensor；
Fusion steps are as follows:
The moving displacement and movement velocity of each monitoring point of coming down are calculated, formula is as follows:
In formula: s (x) indicate monitoring point x state moving displacement, s (x+1) indicate monitoring point x+1 state moving displacement,
V (x) indicates the movement velocity in x state, and v (x+1) indicates the movement velocity in x+1 state, and a (x) indicates monitoring point in x state
To the acceleration of x+1 state, T indicates monitoring point in x state to the time of x+1 state；
Using the moving displacement of each monitoring point in above-mentioned landslide and movement velocity as the state variable of Kalman's system on landslide, then
Available Dynamic monitoring pattern are as follows:
H (k)={ 10 }；
W (k)=a (k)；
In formula: X (k+1) indicates the system mode at k+1 moment, and X (k) indicates the system mode at k moment, Φ (k), H (k), Γ (k)
Indicate measuring system parameter, Z (k) indicates the actual displacement monitoring value of landslide monitoring point, and W (k) and V (k) respectively indicate process
With the white Gaussian noise of measurement, and the two is irrelevant.
2. the method according to claim 1 based on information fusion monitoring analysis of landslide motion morphology, which is characterized in that institute
It states in step (2), draws the accumulative displacement of each monitoring point with the change curve in month, each monitoring is judged according to change curve
Situations such as variation in the movement deformation period of point, the motion state variation include leveling style deformation, and exponential type deformation is stepped
The similar monitoring point of change curve is classified as same class, of a sort monitoring point is divided to same by deformation and convergence type deformation
Monitor block.
3. the method according to claim 1 based on information fusion monitoring analysis of landslide motion morphology, which is characterized in that institute
State in step (3), according to each monitoring point every month displacement data plane, component draw each monitoring point in the fortune of plane
Dynamic trajectory diagram, the plane motion track of the preliminary analysis monitoring point, verifies preliminary piecemeal result by analysis accordingly.
4. the method according to claim 1 based on information fusion monitoring analysis of landslide motion morphology, which is characterized in that institute
It states in step (4), the displacement data of all monitoring points in each monitoring block is merged by Kalman filtering in blocks
Algorithm is merged, and obtains in each monitoring block all monitoring points in the comprehensive point of the fusion of fusion center；
One Dimension Analysis is carried out to the comprehensive point of the fusion of each monitoring block, obtains the motion state of the comprehensive point of each monitoring block fusion；
Two-dimension analysis is carried out to the comprehensive point of the fusion of each monitoring block, obtains the motion profile of each monitoring block；
The motion profile of comprehensive each monitoring block obtains landslide motion morphology.
5. the method according to claim 4 based on information fusion monitoring analysis of landslide motion morphology, which is characterized in that institute
It states in step (4), initially sets up rigid model, regard each monitoring block as a rigid body, and establish coordinate, with each rigid body fortune
Starting point of the position of the comprehensive point of fusion before dynamic as rigid motion, then with the position of the comprehensive point of the fusion after each rigid motion
The terminal as rigid motion is set, each rigid motion front and back every month is found relative to reference axis by particle swarm optimization algorithm
Rotation angle, and by constantly updating local optimum and global optimum, find error of coordinate minimum value it is corresponding it is each just
Rotation angle of the body movement front and back relative to reference axis, then brings this rotation angle movement of the calculation formula to each rigid body into
Track is indicated, and the calculation formula is as follows:
In formula: X1For the position of the comprehensive point of the fusion before rigid motion, X2It is the position of the comprehensive point of fusion after rigid motion one month
It setting, Δ X is to accumulate displacement by the moon of rigid body,It is rotated for rigid body around X-axisAngle, Rθ(y) angle θ is rotated around Y-axis for rigid body
Degree,It is rotated about the z axis for rigid bodyAngle；
The motion profile of comprehensive each rigid body obtains landslide motion morphology.
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| CN109308542A (en) * | 2018-08-15 | 2019-02-05 | 中国地质大学（武汉） | Landslide earth's surface partition method, equipment and storage equipment based on block chain frame |
| CN109165780A (en) * | 2018-08-15 | 2019-01-08 | 中国地质大学（武汉） | Landslide monitoring point partition method, equipment and storage equipment based on block chain frame |
| CN111580098B (en) * | 2020-04-29 | 2021-07-06 | 深圳大学 | Bridge deformation monitoring method, terminal and storage medium |
| CN112016224B (en) * | 2020-07-28 | 2022-11-18 | 西南大学 | SPH-based soil slope landslide analysis and determination method, system, terminal and medium |
| CN111623722B (en) * | 2020-07-29 | 2020-11-03 | 湖南致力工程科技有限公司 | Multi-sensor-based slope deformation three-dimensional monitoring system and method |
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