CN113639822B - Auxiliary water level measuring method for monitoring deformation of dam by measuring robot - Google Patents
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Abstract
The invention discloses an auxiliary water level measuring method for monitoring automatic deformation of a dam by using a measuring robot. The invention provides a method for predicting a new dam water level by using a measuring robot and combining Kalman filtering and an image processing algorithm when dam automatic deformation monitoring is carried out. According to the method, a target prism is not required to be arranged, the measuring robot is utilized to monitor the deformation of the dam, meanwhile, the auxiliary measurement of the water level of the dam can be performed, and other additional equipment is not required. Not only can uninterrupted real-time observation be performed under the unattended condition, but also the working personnel can comprehensively know the running condition of the monitoring system and the real-time information of the dam water level in the control machine room, so that the labor and financial resources are greatly saved, and the method has important significance in promoting the safety of the dam.
Description
Technical Field
The invention relates to the field of engineering measurement, in particular to an auxiliary water level measurement method for a measuring robot in dam deformation monitoring.
Background
The measuring robot is the main instrument for engineering measurement of each engineering unit at present, and the application of the measuring robot releases measuring technicians from heavy measurement work. Since the country is built, tens of thousands of dams are built on land, and dam water level deformation monitoring has great influence on long-term stable action of the dams, and has important significance on auxiliary water level measurement research during dam water level monitoring. At present, dam deformation monitoring technology by using a measuring robot is improved, but when the measuring robot is used for measuring, a target prism needs to be arranged, and the position of the prism cannot be changed along with the change of the water level, so that continuous real-time monitoring cannot be performed. To solve this problem, we propose to take a picture with the measurement robot coaxial telescope and then analyze the water level with the help of an image processing algorithm to obtain the water level height. The method not only does not need to arrange a target prism and other equipment, but also can realize uninterrupted real-time observation under the unattended condition, and has important significance for promoting the safety of the dam.
Disclosure of Invention
In order to solve the problems, the invention provides an auxiliary water level measuring method for a measuring robot in dam deformation monitoring. Searching a proper measuring station for erecting a measuring robot, centering and leveling the measuring robot, aiming a telescope cross wire of the measuring robot at a dam water level line after the measuring robot is ready, acquiring data of an inclined distance and a vertical angle, and storing the measured data; and calculating the dam water level according to the data of the inclined distance and the vertical angle measured at the critical line of the water surface by using the known elevation and triangular elevation measuring method of the measuring station. And then, predicting the dam water level through Kalman filtering by combining the historical water level information, controlling the measuring robot telescope to perform rough angle adjustment and photographing according to the prediction result, judging the water level line position through an image processing algorithm, accurately adjusting the overlapping of the cross wire and the water level line, and further obtaining the slant distance and vertical angle data to calculate the new dam water level.
In order to achieve the above object, the present invention provides an auxiliary water level measuring method for monitoring deformation of a dam by a measuring robot, which is characterized in that: the method comprises the following steps:
(S1) a primary artificial learning stage:
(S1.1) aiming the telescope cross hair of the measuring robot at the dam water line and acquiring slant distance and vertical angle data:
placing the measuring robot at a measuring station, aiming a measuring robot telescope cross wire at a dam water level line, acquiring slant distance and vertical angle data, and storing the measured data;
(S1.2) dam water level calculation, comprising:
the known elevation and triangular elevation measuring method of the measuring station is utilized to calculate the water level of the dam according to the data of the inclined distance and the vertical angle measured at the water level line of the dam, and the specific calculation steps are as follows:
H 0 ＝s×sinα；
H b ＝H a +H instrument for measuring and controlling -H 0 ；
Wherein H is a The station elevation is measured; h b Is the dam water level elevation; h 0 Height difference from instrument to measuring station; h Instrument for measuring and controlling Is high in instrument; s is an inclined distance; alpha is a vertical angle.
(S2) periodic automated measurement phase:
(S2.1) predicting the dam water level by kalman filtering in combination with the history water level information:
and predicting the water level information of the new dam by using Kalman filtering and combining the acquired historical water level information.
The state of the water level at time t can be represented by a vector, where p t Indicating the current position of the water level, v t Indicating the rising speed of water level, the acceleration is a t ；
Knowing the state of the water level at time t-1, the state of the water level at time t is now analyzed. Obviously, there are:
v t ＝v t-1 +a t ×Δt；
in both formulas, the output variable is a linear combination of the input variables, i.e. the formulas characterize a linear relationship. We can represent it with a matrix,
order theThen there are:
order the
In the Kalman filtering estimation process, the system state and the observation process are inevitably affected by noise, so that excitation noise mu of the system process is added t ，μ t When the mean value is 0 and the covariance matrix is Q, a first formula in the Kalman filtering equation set, namely a state prediction formula, is obtained, and A is a state transition matrix. It shows how we predict the current state from the last state. And B is the input gain matrix.
X t ＝AX t-1 +Ba t +μ t ；
Wherein X is t Representing the predicted value from the previous state prediction, which is further modified to obtain the optimal prediction;
let the observed value be Y (t) and the corresponding observed noise be sigma t ，σ t When the mean value is 0 and the covariance matrix is R, the observed value Y (t) is related to X (t):
Y t ＝[1 0]X t +σ t ；
this is the system state observation equation for kalman filtering,
let H= [1 0], then
Y t ＝HX t +σ t ；
Wherein H is an observation matrix.
The kalman filter estimation actually consists of two processes: prediction and correction. In the prediction stage, the filter uses the estimation of the last state to make a prediction of the current state; in the correction phase, the filter corrects the predicted value obtained in the prediction phase with the observed value for the current state to obtain a new estimated value that is closer to the true value.
The calculation process of the Kalman filter comprises the following steps:
first, predicting water level:
P′ t ＝AP t-1 A T +Q；
second, correcting the data:
K t ＝P′ t H T (HP′ t H T +R) -1 ；
thirdly, for the iteration of estimating the optimal water level value at the time of k+1 in the next step, updating covariance estimation is needed:
P t ＝(I-K t H)P′ t ；
in the method, in the process of the invention,the predicted value is the t moment; />The estimated value is the Kalman at the moment t; p'. t Estimating an error covariance matrix for Kalman at the moment t; k (K) t The Kalman gain is t time; />Measuring the margin for time t; p (P) t And (5) estimating an error covariance matrix for the time t Kalman.
(S2.2) controlling the measuring robot telescope to perform rough angle adjustment according to the prediction result and photographing:
after predicting the water level by using Kalman filtering, the measuring robot is guided to measure the water level of the dam later, so that the vertical angle of the measuring robot aiming at the water surface needs to be calculated according to the predicted water level data, and the specific operation is as follows:
the position of the measuring robot is unchanged, the horizontal direction of the telescope is unchanged, the horizontal distance from the measuring robot to the standard position is unchanged, the predicted water level value is used for reversely pushing the vertical angle by means of the inverse trigonometric function, and then the measuring robot aims at the standard position according to the vertical angle to take a picture, and the formula is as follows:
H′ 0 ＝H a +H instrument for measuring and controlling -H′ b ；
Wherein y is the horizontal distance from the station to the observation point in the artificial learning stage, H' b To predict dam water level height using Kalman filtering, H' 0 For the observation of the height difference between the site and the predicted dam water level, α' is the vertical angle between the site and the predicted site.
(S2.3) judging the position of the water level line through an image processing algorithm and accurately adjusting the overlapping of the cross hair and the water level line:
and (3) firstly carrying out gray level image processing on a photo shot by using a coaxial camera of the measuring robot through an image processing algorithm, and then carrying out roberst operator segmentation to judge whether the obtained water line position is correct or not according to the finally obtained picture. If the cross wire is overlapped with the water line, photographing by using a measuring robot; if the cross wire is above the water level line, controlling the measuring robot telescope to downwards adjust the corresponding distance; if the cross wire is below the water line, controlling the measuring robot telescope to adjust the corresponding distance upwards, and causing errors until the cross wire is overlapped with the water line;
(S2.4) acquiring the slant distance and vertical angle data to calculate a new dam water level:
and acquiring the slant distance and vertical angle data of the position by using a measuring robot, and calculating the new dam water level.
As an optimal scheme, the measuring robot is arranged at a high position near the dam, and a proper position of the dam crest is selected as a reference, so that a cross wire of a telescope of the measuring robot is aimed at a water line of the dam, and the data of the inclined distance and the vertical angle can be measured; the coaxial camera of the measuring robot is utilized to shoot and store the water surface so as to carry out image processing analysis subsequently;
the Kalman filtering is an algorithm for optimally estimating the system state by utilizing a linear system state equation and inputting and outputting observation data through a system, a state space model of signals and noise is adopted, and the estimation of state variables is updated by utilizing the estimated value of the previous moment and the observed value of the current moment to obtain the estimated value of the appearance moment.
Further, searching a proper measuring station for erecting a measuring robot, centering and leveling the measuring robot, aiming a measuring robot telescope cross wire at a dam water level line after the measuring robot is ready, acquiring slant distance and vertical angle data, and storing the measured data; calculating the dam water level according to the data of the inclined distance and the vertical angle measured at the critical line of the water surface by using the known elevation and triangular elevation measuring method of the measuring station; and then combining the historical water level information, predicting the dam water level through Kalman filtering, controlling the coaxial telescope of the measuring robot, performing rough angle adjustment and photographing according to the predicted result, judging the water level line position by using an image processing algorithm, accurately adjusting the overlapping of the cross wire and the water level line, and further obtaining the slant distance and vertical angle data to calculate the new dam water level. The method comprises the following specific steps:
the elevation H of the point A is known in (S1.2) a Elevation H of desired point B b ：
The measuring robot is arranged at the point A, the observation point B which is found in advance is aimed at, the measured vertical angle is alpha, the inclined distance from A to B is s, and the measured height of the measuring robot is H Instrument for measuring and controlling ；
The height difference is:
H 0 ＝s×sinα；
the elevation of the point B is as follows:
H b ＝H a +H instrument for measuring and controlling -H 0 ；
The Kalman filtering in (S2.1) predicts the dam water level as follows:
first, we are from the optimal water level value at time t-1Predicting the state value of the system at time t>
Second, the error covariance P of last time t-1 And process noise Q predicts new error P' t ，P′ t ＝AP t-1 A T +Q；
Third, calculating Kalman gain, K t ＝P′ t H T (HP′ t H T +R) -1 ；
Fourth, a correction update is performed,this->The optimal water level value at the moment t is obtained.
Fifth step, estimate for next stepThe iteration of calculating the optimal water level value at time t+1 performs an updating operation, namely updating P t Value, P t ＝(I-K t H)P′ t 。
The image processing algorithm in (S2.2) determines whether the predicted water line position is correct:
kalman filtering predicted dam water level is H' b The horizontal distance between the observation point and the station measured in the artificial learning stage is y, and the height difference between the observation point and the predicted dam water level is H' 0 The vertical angle between the measuring station and the predicted point is alpha'; using the inverse trigonometric function, a vertical angle can be obtained:
H′ 0 ＝H a +H instrument for measuring and controlling -H′ b ；
And (S2.3) taking a picture by using the coaxial camera of the measuring robot, and judging whether the predicted water line position is correct or not through an image processing algorithm. If the cross wire is overlapped with the water line, photographing by using a measuring robot; if the cross wire is above the water level line, controlling the measuring robot telescope to downwards adjust the corresponding distance; if the cross wire is below the water line, the measuring robot telescope is controlled to adjust the corresponding distance upwards, and errors exist until the cross wire coincides with the water line.
The working principle of the invention is as follows:
according to the method, a measuring robot is erected by searching a proper measuring station, the measuring robot is centered and leveled, after the measuring robot is ready to be used, a telescope cross wire of the measuring robot is aimed at a dam water line, and the data of an inclined distance and a vertical angle are acquired, and the measured data are stored; and calculating the dam water level according to the data of the inclined distance and the vertical angle measured at the critical line of the water surface by using the known elevation and triangular elevation measuring method of the measuring station. And then, predicting the dam water level through Kalman filtering by combining the historical water level information, controlling the measuring robot telescope to perform rough angle adjustment and photographing according to the prediction result, judging the water level line position through an image processing algorithm, accurately adjusting the overlapping of the cross wire and the water level line, and further obtaining the slant distance and vertical angle data to calculate the new dam water level.
The measuring robot is arranged at a high position near the dam, and a proper position of the dam crest is selected as a reference, so that the cross wires of the telescope of the measuring robot are aimed at the water line of the dam, and the data of the inclined distance and the vertical angle can be measured; the coaxial camera of the measuring robot is utilized to shoot and store the water surface so as to carry out image processing analysis subsequently;
the Kalman filtering is an algorithm for optimally estimating the system state by utilizing a linear system state equation and inputting and outputting observation data through a system, adopts a state space model of signals and noise, and utilizes an estimated value at the previous moment and an observed value at the current moment to update the estimation of a state variable and calculate an estimated value at the occurrence moment;
the invention has the advantages and beneficial effects as follows:
the research method has the advantages that on the premise of not arranging the target prism, the coaxial telescope of the measuring robot can be used for shooting, and then the water level is obtained by means of analysis of an image processing algorithm, so that the water level height is obtained. The dam deformation monitoring is carried out by using the measuring robot, and meanwhile, the auxiliary measurement of the dam water level can be carried out without other additional equipment. The method can realize uninterrupted real-time observation under the unattended condition, and the working personnel can comprehensively know the running condition of the monitoring system and the real-time information of the dam water level in the control machine room, so that the manpower and financial resources are greatly saved, and the method has important significance for promoting the safety of the dam.
Drawings
Fig. 1 is a technical scheme of an auxiliary water level measuring method for monitoring automatic deformation of a dam by using a measuring robot.
Fig. 2 is a schematic diagram of the present invention for measuring the dam water level using a measuring robot.
Fig. 3 is a measurement illustration of the inventive coated TS60 total station.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings and specific embodiments.
Example 1:
searching a proper measuring station for erecting a measuring robot, centering and leveling the measuring robot, aiming a telescope cross wire of the measuring robot at a dam water level line after the measuring robot is ready, acquiring data of an inclined distance and a vertical angle, and storing the measured data; calculating the dam water level according to the data of the inclined distance and the vertical angle measured at the critical line of the water surface by using the known elevation and triangular elevation measuring method of the measuring station; and then combining the historical water level information, predicting the dam water level through Kalman filtering, controlling the coaxial telescope of the measuring robot, performing rough angle adjustment and photographing according to the predicted result, judging the water level line position by using an image processing algorithm, accurately adjusting the overlapping of the cross wire and the water level line, and further obtaining the slant distance and vertical angle data to calculate the new dam water level. The method comprises the following specific steps:
the elevation H of the point A is known in (S1.2) a Elevation H of desired point B b ：
The measuring robot is arranged at the point A, the observation point B which is found in advance is aimed at, the measured vertical angle is alpha, the inclined distance from A to B is s, and the measured height of the measuring robot is H Instrument for measuring and controlling ；
The height difference is:
H 0 ＝s×sinα；
the elevation of the point B is as follows:
H b ＝H a +H instrument for measuring and controlling -H 0 ；
The Kalman filtering in (S2.1) predicts the dam water level as follows:
first, we are from the optimal water level value at time t-1Predicting the state value of the system at time t>
Second, the error covariance P of last time t-1 And process noise Q predicts new error P' t ，P′ t ＝AP t-1 A T +Q；
Third, calculating Kalman gain, K t ＝P′ t H T (HP′ t H T +R) -1 ；
Fourth, a correction update is performed,this->The optimal water level value at the moment t is obtained.
Fifthly, performing updating operation, namely updating P, for the iteration of estimating the optimal water level value at the time t+1 in the next step t Value, P t ＝(I-K t H)P′ t 。
The image processing algorithm in (S2.2) determines whether the predicted water line position is correct:
kalman filtering predicted dam water level is H' b The horizontal distance between the observation point and the station measured in the artificial learning stage is y, and the height difference between the observation point and the predicted dam water level is H' 0 The vertical angle between the measuring station and the predicted point is alpha'; using the inverse trigonometric function, a vertical angle can be obtained:
H′ 0 ＝H a +H instrument for measuring and controlling -H′ b ；
And (S2.3) taking a picture by using the coaxial camera of the measuring robot, and judging whether the predicted water line position is correct or not through an image processing algorithm. If the cross wire is overlapped with the water line, photographing by using a measuring robot; if the cross wire is above the water level line, controlling the measuring robot telescope to downwards adjust the corresponding distance; if the cross wire is below the water line, the measuring robot telescope is controlled to adjust the corresponding distance upwards, and errors exist until the cross wire coincides with the water line.
Test protocol
The test time is 2021, 5 months and 20 days, and the weather is fine. A measuring station A is determined at a position near a dam, a measuring robot is erected at the position, an observation point B is then determined, a cross wire of a telescope of the measuring robot is aimed at a water level line of the point B, and an inclined distance and a vertical angle at the moment are measured, so that the elevation of the point B is obtained.
1. Required equipment
The measuring robot is used for auxiliary water level measurement when monitoring dam deformation equipment: a Leica TS60 total station, a tripod and a computer.
2. Operation method and data recording
1. Erection of measuring robot
Searching a proper measuring station to erect a measuring robot, and centering and leveling the measuring robot.
2. Operation method of measuring robot for auxiliary water level measurement in dam deformation monitoring and data recording
A, erecting a tripod at the point A, centering and leveling a Leica TS60 total station (0.5', 0.6mm+1ppm) at the measuring station, searching for a proper measuring station to erect a measuring robot, centering and leveling the measuring robot, aiming a measuring robot telescope cross wire at a dam water line after the measuring robot is ready, acquiring slant distance and vertical angle data, and storing the measured data; and calculating the dam water level according to the data of the inclined distance and the vertical angle measured at the critical line of the water surface by using the known elevation and triangular elevation measuring method of the measuring station. And then, predicting the dam water level through Kalman filtering by combining the historical water level information, controlling the measuring robot telescope to perform rough angle adjustment and photographing according to the prediction result, judging the water level line position through an image processing algorithm, accurately adjusting the overlapping of the cross wire and the water level line, and further obtaining the slant distance and vertical angle data to calculate the new dam water level. The method can be proved to be accurate and feasible through comparison of experimental results and actual dam water level.
Claims (1)
1. An auxiliary water level measuring method for monitoring automatic deformation of a dam by using a measuring robot is characterized in that:
(S1) a primary artificial learning stage:
(S1.1) aiming the telescope cross hair of the measuring robot at the dam water line and acquiring slant distance and vertical angle data:
placing the measuring robot at a measuring station, aiming a measuring robot telescope cross wire at a dam water level line, acquiring slant distance and vertical angle data, and storing the measured data;
(S1.2) dam water level calculation, comprising:
the known elevation and triangular elevation measuring method of the measuring station is utilized to calculate the water level of the dam according to the data of the inclined distance and the vertical angle measured at the water level line of the dam, and the specific calculation steps are as follows:
H 0 ＝s×sinα；
H b ＝H a +H instrument for measuring and controlling -H 0 ；
Wherein H is a The station elevation is measured; h b Is the dam water level elevation; h 0 Height difference from instrument to measuring station; h Instrument for measuring and controlling Is high in instrument; s is an inclined distance; alpha is a vertical angle;
(S2) periodic automated measurement phase:
(S2.1) predicting the dam water level by kalman filtering in combination with the history water level information:
predicting the water level information of the new dam by utilizing Kalman filtering and combining the acquired historical water level information;
the water level being at time tThe state can be represented by a vector, where p t Indicating the current position of the water level, v t Indicating the rising speed of water level, the acceleration is a t ；
Knowing the state of the water level at time t-1, the state of the water level at time t is now analyzed; obviously, there are:
v t ＝v t-1 +a t ×Δt；
in the two formulas, the output variables are linear combinations of the input variables, namely the formulas represent a linear relation; expressed as a matrix:
order theThen there are:
order the
System process excitation noise mu is also added t ，μ t The mean value is 0, the covariance matrix is Q, a first formula in a Kalman filtering equation set, namely a state prediction formula, is obtained, and A is a state transition matrix; it shows how the current state is predicted from the last state; and B is the input gain matrix;
X t ＝AX t-1 +Ba t +μ t ；
wherein X is t Representing the predicted value from the previous state prediction, which is further modified to obtain the optimal prediction;
let the observed value be Y (t) and the corresponding observed noise be sigma t ，σ t Mean value 0, covariance matrix 0R, the observed value Y (t) is related to X (t):
Y t ＝[1 0]X t +σ t ；
i.e. the system state observation equation of kalman filtering,
let H= [1 0], then
Y t ＝HX t +σ t ；
Wherein H is an observation matrix;
the calculation process of the Kalman filter comprises the following steps:
first, predicting water level:
P′ t ＝AP t-1 A T +Q；
second, correcting the data:
K t ＝P′ t H T (HP′ t H T +R) -1 ；
thirdly, for the iteration of estimating the optimal water level value at the time of k+1 in the next step, updating covariance estimation is needed:
P t ＝(I-K t H)P′ t ；
in the method, in the process of the invention,the predicted value is the t moment; />The estimated value is the Kalman at the moment t; p'. t Estimating an error covariance matrix for Kalman at the moment t; k (K) t The Kalman gain is t time; />Measuring the margin for time t; p (P) t Estimating an error covariance matrix for Kalman at the moment t;
(S2.2) controlling the measuring robot telescope to perform rough angle adjustment according to the prediction result and photographing:
after predicting the water level by using Kalman filtering, the measuring robot is guided to measure the water level of the dam later, so that the vertical angle of the measuring robot aiming at the water surface needs to be calculated according to the predicted water level data, and the specific operation is as follows:
the position of the measuring robot is unchanged, the horizontal direction of the telescope is unchanged, the horizontal distance from the measuring robot to the standard position is unchanged, the predicted water level value is used for reversely pushing the vertical angle by means of the inverse trigonometric function, and then the measuring robot aims at the standard position according to the vertical angle to take a picture, and the formula is as follows:
H′ 0 ＝H a +H instrument for measuring and controlling -H′ b ；
Wherein y is the horizontal distance from the station to the observation point in the artificial learning stage, H' b To predict dam water level height using Kalman filtering, H' 0 For the height difference between the observation point and the predicted dam water level, alpha' is the vertical angle between the observation point and the predicted point;
(S2.3) judging the position of the water level line through an image processing algorithm and accurately adjusting the overlapping of the cross hair and the water level line:
the method comprises the steps of firstly, carrying out gray level image processing on a photo shot by a coaxial camera of a measuring robot through an image processing algorithm, then carrying out roberst operator segmentation, and judging whether the position of an obtained water line is correct according to the finally obtained photo; if the cross wire is overlapped with the water line, photographing by using a measuring robot; if the cross wire is above the water level line, controlling the measuring robot telescope to downwards adjust the corresponding distance; if the cross wire is below the water line, controlling the measuring robot telescope to adjust the corresponding distance upwards, and causing errors until the cross wire is overlapped with the water line;
(S2.4) acquiring the slant distance and vertical angle data to calculate a new dam water level:
acquiring the slant distance and vertical angle data of the position by using a measuring robot, and calculating the new dam water level by using the method (S1.2);
in the step (S1.1), the measuring robot is arranged at a high position near the dam, a proper position of the dam top is selected as a reference, and the cross wire of the telescope of the measuring robot is aimed at the water level line of the dam, so that the data of the inclined distance and the vertical angle can be measured; the coaxial camera of the measuring robot is utilized, and the water surface can be photographed and stored for subsequent image processing analysis;
the kalman filtering in the step (S2.1) is an algorithm for optimally estimating the system state by using a linear system state equation and through system input and output observation data, and adopts a state space model of signals and noise, and updates the estimation of state variables by using the estimated value of the previous moment and the observed value of the current moment to obtain the estimated value of the occurrence moment;
searching a proper measuring station for erecting a measuring robot, centering and leveling the measuring robot, aiming a measuring robot telescope cross wire at a dam water line after the measuring robot is ready, acquiring slant distance and vertical angle data, and storing the measured data; calculating the dam water level according to the data of the inclined distance and the vertical angle measured at the critical line of the water surface by using the known elevation and triangular elevation measuring method of the measuring station; then combining the historical water level information, predicting the dam water level through Kalman filtering, controlling the coaxial telescope of the measuring robot, performing rough angle adjustment and photographing according to the predicted result, judging the water level line position by using an image processing algorithm, accurately adjusting the overlapping of the cross wire and the water level line, and further obtaining the inclined distance and vertical angle data to calculate the new dam water level; the method comprises the following specific steps:
the elevation H of the point A is known in (S1.2) a Elevation H of desired point B b ：
The measuring robot is arranged at the point A, the observation point B which is found in advance is aimed at, the measured vertical angle is alpha, the inclined distance from A to B is s, and the measured height of the measuring robot is H Instrument for measuring and controlling ；
The height difference is:
H 0 ＝s×sinα；
the elevation of the point B is as follows:
H b ＝H a +H instrument for measuring and controlling -H 0 ；
The Kalman filtering in (S2.1) predicts the dam water level as follows:
first, from the optimal water level value at time t-1Predicting the state value of the system at time t>
Second, the error covariance P of last time t-1 And process noise Q predicts new error P' t ，P′ t ＝AP t-1 A T +Q；
Third, calculating Kalman gain, K t ＝P′ t H T (HP′ t H T +R) -1 ；
Fourth, a correction update is performed,this->The optimal water level value at the moment t is obtained;
fifthly, performing updating operation, namely updating P, for the iteration of estimating the optimal water level value at the time t+1 in the next step t Value, P t ＝(I-K t H)P′ t ；
The image processing algorithm in (S2.2) determines whether the predicted water line position is correct:
kalman filtering predicted dam water level is H' b The horizontal distance from the observation point to the station measured in the artificial learning stage is y, and the height difference from the observation point to the predicted dam water level is H ′ 0 The vertical angle between the measuring station and the predicted point is alpha'; using the inverse trigonometric function, a vertical angle can be obtained:
H′ 0 ＝H a +H instrument for measuring and controlling -H′ b ；
(S2.3) taking a picture by using a coaxial camera of the measuring robot, and judging whether the predicted water line position is correct or not through an image processing algorithm; if the cross wire is overlapped with the water line, photographing by using a measuring robot; if the cross wire is above the water level line, controlling the measuring robot telescope to downwards adjust the corresponding distance; if the cross wire is below the water line, the measuring robot telescope is controlled to adjust the corresponding distance upwards, and errors exist until the cross wire coincides with the water line.
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