CN118310481B - Inclination measurement method based on inertial measurement unit and non-integrity constraints of distance - Google Patents
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Abstract
The application discloses an incomplete constraint inclinometry method based on an inertial measurement unit and a distance, which comprises the steps of adopting a traction device meeting an incomplete constraint condition to traction an inclinometer in the process that the inclinometer is positioned in an inclinometer pipe, enabling the lateral speed and the vertical speed of the inclinometer when the incomplete constraint condition meets the movement of the inclinometer to be zero, calculating the traction speed of the inclinometer, collecting inertial measurement data of the inclinometer to obtain an initial arrangement data set of the inertial measurement data, inputting the traction speed of the inclinometer and the initial arrangement data set into a Kalman filtering model to perform data filtering processing and data error calculation to obtain data error feedback information, carrying out data correction optimization on the initial arrangement data set to obtain optimized three-dimensional coordinate measurement data of the inclinometer, obtaining the initial three-dimensional coordinate measurement data of a target measurement body, and calculating and determining the deep displacement of the target measurement body. The application has the effect of improving the accuracy of the measurement result of the inclinometer.
Description
Technical Field
The application relates to the technical field of measurement, in particular to an inclination measuring method based on non-integrity constraint of an inertial measurement unit and a distance.
Background
Inclinometer is an instrument for measuring deep displacement monitoring of drilling holes, foundation pits, foundation foundations, coal mine exploration and the like, and the configuration of the inclinometer generally comprises an inclinometer pipe, an inclinometer probe, a control cable and an inclinometer.
In the related art, for example, when measuring deep horizontal displacement of a foundation pit, an inclinometer is usually installed in a vertical borehole penetrating an unstable soil layer to a lower stable stratum, deformation of the inclinometer is observed by using a digital vertical movable inclinometer probe, a control cable and a reader, an initial section of displacement of the inclinometer is established by a surveyor at the time of first observation, a subsequent observation shows a change of section displacement when ground moves, and the probe moves from the bottom to the top of the inclinometer at a set interval during observation, and measurement tilting is sequentially suspended and performed.
At present, when actually mapping, the measured value of the inclinometer is obtained under the condition that the measuring axis is consistent with the displacement direction, but in the actual operation process, the inclinometer can be in a torsion state, and the measured value of the inclination sensor of the inclinometer is easy to have torsion components to cause the reduction of the measurement precision, so the defect of lower precision of the measurement result of the inclinometer exists, and the improvement is provided.
Disclosure of Invention
In order to improve the accuracy of the measurement result of the inclinometer, the application provides an inclination measuring method based on the non-integrity constraint of an inertial measurement unit and a distance.
In a first aspect, the object of the application is achieved by the following technical scheme:
An inclinometry method based on an inertial measurement unit and a non-integrity constraint of distance, comprising:
During the operation of the inclinometer in the inclinometer pipe of the target measuring body, a traction device meeting the non-integrity constraint condition is adopted to traction the inclinometer, and the non-integrity constraint condition meets that the lateral speed and the vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer pipe;
The inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated; acquiring inertial measurement data of an inclinometer by adopting an inertial measurement unit to obtain an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
Inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model for data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the incompleteness constraint condition;
Performing data correction optimization on the initial arrangement data set according to the data error feedback information to obtain optimized three-dimensional coordinate measurement data of the inclinometer;
And acquiring initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.
By adopting the technical scheme, the target measuring body comprises structures such as a foundation pit and a dam, and the like, when the foundation pit is adopted for foundation pit inclinometry by using the inclinometer, the application combines the environmental specificity and the incompleteness constraint condition of the inclinometer measured in the inclinometer on the basis of the combined navigation of the traction distance (namely the moving distance of the inclinometer in the inclinometer and also called mileage data) and inertial navigation measurement data, and restricts the inclinometer to move forwards only along the inclinometer in the operation process in the inclinometer, namely, the speed of the inclinometer perpendicular to the side line of the foundation pit and parallel to the side line direction of the foundation pit is zero in the operation process, namely, the lateral speed and the vertical speed of the inclinometer in the operation process are zero.
During observation, the probe of the inclinometer moves from the bottom to the top of the inclinometer pipe according to a fixed traction distance and a fixed traction speed, and pauses and performs inclination measurement work at a set interval in sequence; the initial arrangement data set is a mechanical arrangement result of the inertial navigation system, at the moment, the initial arrangement data set comprises attitude data, speed data and position data of the inclinometer to be optimized and corrected, the Kalman filtering model is a set Kalman filtering algorithm, the optimized estimation measured value is an optimized estimation value of the attitude data, the speed data and the position data of the inclinometer, and the accuracy of the measurement result of the inclinometer is improved.
Specifically, the application carries out data filtering processing and data fusion calculation on an initial arrangement data set, traction speed and non-integrity constraint condition of an inertial measurement unit through a Kalman filtering algorithm in a Kalman filtering model to obtain data error feedback information containing error feedback, then corrects a mechanical arrangement result of a coordinate system (b system) of the inertial measurement unit based on the data error feedback information to obtain optimized three-dimensional coordinate measurement data of posture, speed and position information of the inclinometer, finally obtains deep accumulated displacement data of a target measurement body (namely a foundation pit) through comparison calculation of the optimized three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body.
In a preferred example of the application, the inertial navigation measurement data comprises three-axis gyroscope data and three-axis accelerometer data, and the inclinometry method comprises the following steps:
Acquiring error vectors, zero offset vectors and scale factor error vectors on a three-dimensional coordinate system in the operation process of the three-axis gyroscope data and the three-axis accelerometer data based on a time sequence, and constructing a 21-dimensional error state vector based on the time sequence;
Constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time sequence and the time sequence algorithm, wherein the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector;
And inputting the 21-dimensional system state vector into the preset Kalman filtering model to obtain a Kalman filtering predicted value.
According to the technical scheme, in order to improve the calculation accuracy of the inclinometer on the deep displacement of the target measurement body, and to solve the problem that the inclinometer generates nonlinear change in the inclinometer based on continuous time conditions and an inertial measurement unit, the combined navigation positioning calculation is performed by adopting a Kalman filter model, the positioning accuracy of an inertial navigation system is improved by adopting the error of the inertial measurement unit as a state vector, specifically, a 21-dimensional error state vector based on a time sequence is firstly constructed, noise interference of the inclinometer in the linear movement measurement process is considered, the state vector of the 21-dimensional system is calculated by taking Gaussian noise data of a plurality of time points into consideration, and the predicted value of Kalman filtering is obtained by a preset Kalman filter model in the next step, so that the purpose of estimating the change state of the inclinometer in the inertial navigation measurement data with measurement noise obtained by measurement of the inclinometer is achieved.
In a preferred example, the present application comprises:
when the inclinometer meets the non-integrity constraint condition, acquiring a corresponding inclinometer speed observation value and speed observation noise, and calculating to obtain a speed observation vector of an inclinometer coordinate system according to the inclinometer speed observation value and the speed observation noise;
The initial speed data of the initial arrangement data set of the inertial navigation measurement data is obtained, and the initial speed data is projected to a inclinometer coordinate system to obtain a corresponding initial speed value;
Inputting a speed observation vector and an initial speed value which are positioned in a coordinate system of the inclinometer into a preset speed error observation algorithm to obtain a speed error observation value;
And inputting the state vector and the speed error observation value of the 21-dimensional system into the preset Kalman filtering model to obtain optimized three-dimensional coordinate measurement data of the inclinometer.
According to the technical scheme, under the condition of incomplete constraint, forward movement or backward movement only occurs in the operation process of the inclinometer in the inclinometer, namely, the lateral speed and the vertical speed of the inclinometer are close to zero in the inclinometer coordinate system, the speed observation vector of the inclinometer coordinate system is obtained through calculation according to the speed observation value and the speed observation noise of the inclinometer on the basis of zero of the lateral speed and the vertical speed of the inclinometer, then initial speed data which are mechanically arranged are the speed values located in a navigation coordinate system (n system) by utilizing an inertia measurement unit, the initial speed data are projected to the inclinometer coordinate system (v system) and calculated to obtain corresponding initial speed values, then a preset speed error observation algorithm is used for calculating to obtain speed error observation values, the speed error observation values are observation information provided for a Kalman filtering model, and optimal estimation of posture data, speed data and position data of the inclinometer can be calculated through the Kalman filtering model.
In a preferred example, the present application comprises:
the 21-dimensional system state vector is shown in a formula (1):
Wherein, Is an inertial navigation position error vector; is an inertial navigation speed error vector; Is an attitude error vector; Is a triaxial gyro zero offset vector; zero offset vector of the triaxial accelerometer; Is a gyro scale factor error vector; a scale factor error vector for the accelerometer;
the time sequence algorithm is shown in a formula (2):
wherein t is time; is the correlation time of the first order gaussian markov process; white noise is driven for a first order gaussian markov process.
By adopting the technical scheme, the error state vector of nonlinear change generated by the inertial measurement unit in the measurement process under the continuous time condition is calculated through the formula (1) and the formula (2), and the zero offset vector and the scale factor error vector of the triaxial gyroscope and the triaxial accelerometer in the inertial measurement unit are calculated through the formula (2), so that the inertial measurement unit is conveniently evaluated and calculated in the measurement process due to interference and measurement noise, and the positioning precision of the inertial measurement unit is conveniently improved.
In a preferred example, the present application comprises:
the Kalman state algorithm is shown in a formula (3):
Wherein x k+1 is a state vector of a 21-dimensional system to be estimated, F k+1,k is a dynamic matrix of an inertial navigation system, G k+1,k is a continuous-time noise coefficient matrix of the inertial navigation system, w k is noise of the inertial navigation system, z k+1 is an observation vector of the inertial navigation system, H k+1,k is a measurement matrix, and v k+1,k is measurement noise.
By adopting the technical scheme, the formula (3) can be deduced through the formula (1) and the formula (2), the Kalman state algorithm is a continuous time error state equation of the inclinometer based on inertial navigation measurement data under the continuous time condition, the 21-dimensional system state vector x k+1 to be estimated and the inertial navigation system observation vector z k+1 which are calculated by the formula (3) are error prediction values of the inertial navigation measurement unit provided by the Kalman filtering model.
In a preferred example, the present application comprises:
Speed observation vector of inclinometer coordinate system As shown in formula (4):
Wherein v w is the inclinometer speed observation value, e v is the speed observation noise, and when the non-integrity constraint condition is satisfied in the inclinometer coordinate system, the lateral speed and the vertical speed of the inclinometer are zero;
the corresponding initial velocity value projected to the inclinometer coordinate system As shown in formula (5):
wherein b, n and v respectively represent an inertial measurement unit coordinate system, a navigation coordinate system and an inclinometer coordinate system; For initial velocity data of the initial arrangement dataset, Is the error angle of the mounting angle between the two coordinate systems of the b system and the v system,Representing the direction cosine matrix of n-series conversion to b-series; Is an observed value of the gyroscope, and the method comprises the following steps of, Is a mileage lever arm, an inertia measuring unit is arranged at the center of the inclinometer,Zero;
The preset speed error observation algorithm is shown as a formula (6) by the formula (4) and the formula (5):
Wherein, Is the installation parameter calibrated by the inclinometer, and is a known quantity.
By adopting the technical scheme, the formula (4) and the formula (5) are observation information calculation processes based on the non-integrity constraint condition, under the non-integrity constraint condition, the lateral speed and the vertical speed in the inclinometer coordinate system in the formula (4) are zero, and the inertia measurement unit in the formula (5) is arranged at the central position of the inclinometer, so that the mileage lever arm is zero, namely the lever arm effect is ignored, and the speed observation value of the inclinometer and the initial speed value under the non-integrity constraint condition can be obtained through the formula (4) and the formula (5)The speed error observation equation (6) of the inclinometer is convenient to calculate and obtain the optimal estimated values of the posture data, the speed data and the position data of the inclinometer in a preset Kalman filtering model through the observation information of the equation (6), and the subsequent calculation of the deep displacement of the target measuring body is convenient, so that the accuracy of the measuring result of the inclinometer is improved.
In a second aspect, the object of the present application is achieved by the following technical solutions:
The inclination measuring system based on the non-integrity constraint of the inertia measuring unit and the distance comprises an inclination measuring instrument, a traction device, the inertia measuring unit and a Kalman filtering data optimizing module;
The traction device is used for traction of the inclinometer by adopting a traction device meeting the non-integrity constraint condition in the process that the inclinometer is positioned in an inclinometer pipe of a target measuring body, and the non-integrity constraint condition meets the condition that the lateral speed and the vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer pipe;
The inertial measurement unit is used for acquiring inertial measurement data of the inclinometer and obtaining an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
The Kalman filtering data optimization module is used for inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model to perform data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the non-integrity constraint condition;
And acquiring initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.
By adopting the technical scheme, during observation, the probe of the inclinometer moves from the bottom to the top of the inclinometer pipe according to the fixed traction distance and the fixed traction speed, and pauses and performs the inclination measurement work at the set interval in sequence; the initial arrangement data set is a mechanical arrangement result of the inertial navigation system, at the moment, the initial arrangement data set comprises attitude data, speed data and position data of the inclinometer to be optimized and corrected, the Kalman filtering model is a set Kalman filtering algorithm, the optimized estimation measured value is an optimized estimation value of the attitude data, the speed data and the position data of the inclinometer, and the accuracy of the measurement result of the inclinometer is improved.
Specifically, the application carries out data filtering processing and data fusion calculation on an initial arrangement data set, traction speed and non-integrity constraint condition of an inertial measurement unit through a Kalman filtering algorithm in a Kalman filtering model to obtain data error feedback information containing error feedback, then corrects a mechanical arrangement result of a coordinate system (b system) of the inertial measurement unit based on the data error feedback information to obtain optimized three-dimensional coordinate measurement data of posture, speed and position information of the inclinometer, finally obtains deep accumulated displacement data of a target measurement body (namely a foundation pit) through comparison calculation of the optimized three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body.
In a third aspect, the object of the present application is achieved by the following technical solutions:
An intelligent device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above described inclinometry method based on an inertial measurement unit and a non-integrity constraint of distance when the computer program is executed.
In a fourth aspect, the object of the present application is achieved by the following technical solutions:
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described method of inclinometry based on an inertial measurement unit and a non-integrity constraint of distance.
In summary, the present application includes at least one of the following beneficial technical effects:
1. During observation, the probe of the inclinometer moves from the bottom to the top of the inclinometer pipe according to a fixed traction distance and a fixed traction speed, and pauses and performs inclination measurement work at a set interval in sequence; the application takes the initial arrangement data set, the traction speed and the non-integrity constraint condition of the inertial measurement unit as auxiliary information, carries out data filtering processing and data fusion calculation by the Kalman filtering algorithm in the Kalman filtering model to obtain data error feedback information containing error feedback, then corrects the mechanical arrangement result of an inertial measurement unit coordinate system (b system) based on the data error feedback information, namely, obtains optimized three-dimensional coordinate measurement data of the inclinometer attitude, speed and position information, finally obtains deep layer accumulated displacement data of a target measurement body (namely, the three-dimensional coordinate measurement data of a foundation pit through comparison calculation by optimizing the three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body, thereby achieving the purpose of improving the accuracy of the measurement result of the inclinometer;
2. In order to improve the calculation precision of the inclinometer on the deep displacement of a target measuring body, and to solve the problem that the inclinometer generates nonlinear change in an inclinometer under the continuous time condition and based on an inertial measurement unit, the application adopts a Kalman filtering model to carry out combined navigation positioning calculation, and improves the positioning precision of the inertial navigation system by adopting the error of the inertial measurement unit as a state vector;
3. equation (4) and equation (5) are based on the observation information calculation process under the non-integrity constraint condition, the values of the lateral speed and the vertical speed in the inclinometer coordinate system in equation (4) are zero, and the inertial measurement unit in equation (5) is arranged at the central position of the inclinometer so that the mileage lever arm is zero, namely the lever arm effect is ignored, and the inclinometer speed observation value and the initial speed value under the non-integrity constraint condition can be obtained through equation (4) and equation (5) The speed error observation equation (6) of the inclinometer is convenient to calculate and obtain the optimal estimated values of the posture data, the speed data and the position data of the inclinometer in a preset Kalman filtering model through the observation information of the equation (6), and the subsequent calculation of the deep displacement of the target measuring body is convenient, so that the accuracy of the measuring result of the inclinometer is improved.
Drawings
FIG. 1 is a flow chart of a method of inclinometry based on the non-integrity constraints of inertial measurement units and distance in an embodiment of the present application;
FIG. 2 is a flow chart of one of the methods of inclinometry based on the non-integrity constraints of inertial measurement units and distance in an embodiment of the present application;
FIG. 3 is another flow chart of an exemplary method for inclinometry based on the non-integrity constraints of inertial measurement units and distance in accordance with an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
In an embodiment, as shown in fig. 1, the application discloses an inclination measuring method based on the non-integrity constraint of an inertial measurement unit and a distance, which specifically comprises the following steps:
S1, during the operation process that the inclinometer is positioned in an inclinometer pipe of a target measuring body, a traction device meeting the non-integrity constraint condition is adopted to traction the inclinometer, and when the non-integrity constraint condition meets the condition that the inclinometer moves along the inclinometer pipe, the lateral speed and the vertical speed of the inclinometer are zero.
In this embodiment, the target measuring body includes large-scale structures such as a foundation pit, a dam, and a slope, and the deep displacement measuring slope of the foundation pit is taken as an example.
In practical operation standard, the direction of the inclinometer pipe is regulated to be consistent with the vertical direction when the inclinometer pipe is installed and buried in the early stage, so that the subsequent inclinometry work is facilitated.
S2, the inclinometer moves according to a preset fixed traction distance and a fixed traction time, the traction speed of the inclinometer is calculated, inertial measurement unit is adopted to collect inertial measurement data of the inclinometer, and an initial arrangement data set of the inertial measurement data is obtained, wherein the initial arrangement data set comprises attitude data, speed data and position data.
In this embodiment, in the operation process of the inclinometer, the inclinometer sequentially measures according to a plurality of sampling points distributed at equal intervals along the bottom to the top of the inclinometer, for example, each time a distance of 50 cm is detected, and the inclinometer moves according to a set traction speed in the inclinometer process.
Specifically, when the inclinometer is adopted to perform foundation pit inclinometry, the application combines the environmental specificity and non-integrity constraint conditions measured by the inclinometer in the inclinometer on the basis of the combined navigation of the traction distance (namely the moving distance of the inclinometer in the inclinometer, also called mileage data) and inertial navigation measurement data, and constrains the inclinometer to move forwards only along the inclinometer in the process of operation in the inclinometer, namely, the speed of the inclinometer in the process of operation and in the direction vertical to the foundation pit side line and parallel to the foundation pit side line is zero, namely, the lateral speed and the vertical speed of the inclinometer in the process of operation are zero.
And S3, inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model to perform data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the non-integrity constraint condition.
In this embodiment, the kalman filter model is a kalman filter;
The Kalman filtering model is a set Kalman filtering algorithm, and the optimized estimation measured value is the optimized estimation value of the attitude data, the speed data and the position data of the inclinometer, so that the accuracy of the measurement result of the inclinometer is improved.
And S4, carrying out data correction optimization on the initial arrangement data set according to the data error feedback information to obtain optimized three-dimensional coordinate measurement data of the inclinometer.
In this embodiment, the initial arrangement data set, the traction speed and the non-integrity constraint condition of the inertial measurement unit are subjected to data filtering processing and data fusion calculation through a kalman filtering algorithm in a kalman filtering model to obtain data error feedback information containing error feedback, and then the mechanical arrangement result of the coordinate system (b system) of the inertial measurement unit is corrected based on the data error feedback information, so as to obtain optimized three-dimensional coordinate measurement data of the attitude, speed and position information of the inclinometer.
S5, acquiring initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.
The method comprises the steps of obtaining initial three-dimensional total surface data of each measuring point through an inclinometer and optimizing three-dimensional coordinate measuring data, mapping initial trajectory lines of the inclinometer in an inclinometer pipe, taking the initial trajectory lines of the inclinometer as reference displacement lines of accumulated displacement of a target measuring body, taking the initial trajectory lines of the current measurement as input quantity, obtaining one measuring point at intervals of 0.5 meter according to inclinometry requirements, and obtaining the deep displacement of the target measuring body by calculating the displacement of the target measuring body at the point by calculating the distance from each measuring point to the reference displacement lines.
The application improves the measurement precision of the integrated navigation three-dimensional coordinates of the inclinometer by introducing incomplete constraint as auxiliary information, and obtains the internal accumulated displacement of the high-precision target measuring body by optimizing the three-dimensional coordinate measuring data and the high-precision optimized three-dimensional coordinate measuring data of the measurement, thereby achieving the purpose of improving the precision of the measuring result of the inclinometer.
In one embodiment, as shown in FIG. 2, the method of inclinometry based on the non-integrity constraint of inertial measurement unit and distance includes establishing a Kalman filter state equation to provide a predicted value for the Kalman filter, the inertial measurement data including three-axis gyroscope data and three-axis accelerometer data, the method of inclinometry including:
S10, based on a time sequence, acquiring an error vector, a zero offset vector and a scale factor error vector on a three-dimensional coordinate system in the operation process of the three-axis gyroscope data and the three-axis accelerometer data, and constructing a 21-dimensional error state vector based on the time sequence.
In the embodiment, under the condition of incomplete constraint, the inclinometer only moves forwards or retreats in the operation process of the inclinometer in the inclinometer, namely, the lateral speed and the vertical speed of the inclinometer are close to zero in the coordinate system of the inclinometer, and the speed observation vector of the coordinate system of the inclinometer is calculated according to the speed observation value and the speed observation noise of the inclinometer on the basis of zero lateral speed and vertical speed of the inclinometer.
Specifically, the 21-dimensional system state vector is shown in formula (1):
Wherein, Is an inertial navigation position error vector; is an inertial navigation speed error vector; Is an attitude error vector; Is a triaxial gyro zero offset vector; zero offset vector of the triaxial accelerometer; Is a gyro scale factor error vector; is an accelerometer scale factor error vector.
S20, determining corresponding time points of the inclinometer at a plurality of measurement points, acquiring Gaussian noise data of each time point, and constructing a time sequence algorithm according to corresponding zero offset vectors and corresponding scale factor error vectors of the triaxial gyroscope data and the triaxial accelerometer data at each time point;
In this embodiment, the time course according to the three-axis gyroscope data and the three-axis accelerometer data is represented by a first-order gaussian markov process, wherein the time series algorithm is a differential equation.
Specifically, the time series algorithm is as shown in formula (2):
wherein t is time; is the correlation time of the first order gaussian markov process; white noise is driven for a first order gaussian markov process.
S30, constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time sequence and the time sequence algorithm, and calculating to obtain the 21-dimensional system state vector according to the 21-dimensional error state vector by the Kalman state algorithm.
Specifically, the kalman state algorithm is shown in formula (3):
Wherein x k+1 is a state vector of a 21-dimensional system to be estimated, F k+1,k is a dynamic matrix of an inertial navigation system, G k+1,k is a continuous-time noise coefficient matrix of the inertial navigation system, w k is noise of the inertial navigation system, z k+1 is an observation vector of the inertial navigation system, H k+1,k is a measurement matrix, and v k+1,k is measurement noise.
S40, inputting the 21-dimensional system state vector into a preset Kalman filtering model to obtain a Kalman filtering predicted value.
Specifically, after a kalman state algorithm is established (i.e. a kalman filtering state equation is established), a 21-dimensional system state vector (i.e. inertial navigation data error of an inertial measurement unit) is input into a kalman filtering model as state information, so as to achieve the purpose of providing a state vector predicted value for the kalman filtering model.
In one embodiment, as shown in FIG. 3, the method for inclinometry based on the non-integrity constraints of inertial measurement unit and distance includes establishing a Kalman filter state equation, the method for inclinometry including:
and S111, when the inclinometer meets the non-integrity constraint condition, acquiring a corresponding inclinometer speed observation value and speed observation noise, and calculating to obtain a speed observation vector of an inclinometer coordinate system according to the inclinometer speed observation value and the speed observation noise.
Specifically, the velocity observation vector of the inclinometer coordinate systemAs shown in formula (4):
Wherein v w is the inclinometer speed observation value, e v is the speed observation noise, and the lateral speed and the vertical speed of the inclinometer are zero when the incompleteness constraint condition is satisfied in the inclinometer coordinate system.
S112, acquiring initial speed data of an initial arrangement data set of inertial navigation measurement data, and projecting the initial speed data to an inclinometer coordinate system to obtain a corresponding initial speed value.
Specifically, the initial velocity value of the inertial measurement unit is used for mechanical arrangement to obtain initial velocity data of the initial arrangement.
Corresponding initial velocity values projected to inclinometer coordinate systemAs shown in formula (5):
wherein b, n and v respectively represent an inertial measurement unit coordinate system, a navigation coordinate system and an inclinometer coordinate system; For initial velocity data of the initial arrangement dataset, Is the error angle of the mounting angle between the two coordinate systems of the b system and the v system,Representing the direction cosine matrix of n-series conversion to b-series; Is an observed value of the gyroscope, and the method comprises the following steps of, Is a mileage lever arm, an inertia measuring unit is arranged at the center of the inclinometer,Zero, i.e. the lever arm effect is ignored.
S113, inputting a speed observation vector and an initial speed value which are positioned in a coordinate system of the inclinometer into a preset speed error observation algorithm to obtain a speed error observation value.
In the present embodiment, the preset speed error observation algorithm is derived from the formula (4) and the formula (5) as shown in the formula (6):
Wherein, Is the installation parameter calibrated by the inclinometer, and is a known quantity.
S114, inputting the state vector of the 21-dimensional system and the speed error observation value into a preset Kalman filtering model to obtain optimized three-dimensional coordinate measurement data of the inclinometer.
In the embodiment, the optimal estimated values of the posture data, the speed data and the position data of the inclinometer are conveniently calculated in a preset Kalman filtering model through the observation information of the formula (6), and the deep displacement of the target measurement body is conveniently calculated subsequently, so that the accuracy of the measurement result of the inclinometer is improved.
It should be understood that the sequence number of each step in the above embodiment does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not be construed as limiting the implementation process of the embodiment of the present application.
In one embodiment, an inertial measurement unit and distance based non-integrity constrained inclinometry system is provided, corresponding to the inertial measurement unit and distance based non-integrity constrained inclinometry method of the above embodiment.
An inclination measuring system based on the non-integrity constraint of an inertial measurement unit and a distance comprises an inclination measuring instrument, a traction device, the inertial measurement unit and a Kalman filtering data optimization module. The detailed description of each functional module is as follows:
The traction device is used for traction of the inclinometer by adopting a traction device meeting the incomplete constraint condition in the process that the inclinometer is positioned in an inclinometer pipe of a target measuring body, and when the incomplete constraint condition meets the condition that the inclinometer moves along the inclinometer pipe, the lateral speed and the vertical speed of the inclinometer are zero;
The inertial measurement unit is used for acquiring inertial measurement data of the inclinometer and obtaining an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
The Kalman filtering data optimization module is used for inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model to perform data filtering processing and data error calculation to obtain data error feedback information;
The method comprises the steps of carrying out data correction optimization on an initial arrangement data set according to data error feedback information to obtain optimized three-dimensional coordinate measurement data of a inclinometer, obtaining the initial three-dimensional coordinate measurement data of a target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.
For the specific limitation of the inclination measuring system based on the non-integrity constraint of the inertial measurement unit and the distance, reference may be made to the limitation of the inclination measuring method based on the non-integrity constraint of the inertial measurement unit and the distance, which is not described herein, each module in the inclination measuring system based on the non-integrity constraint of the inertial measurement unit and the distance may be implemented in whole or in part by software, hardware or a combination thereof, and each module may be embedded in hardware or independent of a processor in a computer device, or may be stored in software in a memory in the computer device, so that the processor may call to execute the operations corresponding to each module.
In one embodiment, a smart device is provided, which may be a server. The intelligent device comprises a processor, a memory, a network interface and a database which are connected through a system bus. Wherein the processor of the smart device is configured to provide computing and control capabilities. The memory of the intelligent device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the intelligent device is used for storing inertial measurement data, mileage data, a Kalman filtering model and the like. The network interface of the intelligent device is used for communicating with an external terminal through network connection. The computer program when executed by a processor implements a method of inclinometry based on an inertial measurement unit and a non-integrity constraint of distance.
In one embodiment, a smart device is provided that includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s1, during the operation of the inclinometer in an inclinometer pipe of a target measuring body, adopting a traction device meeting the non-integrity constraint condition to traction the inclinometer, wherein the non-integrity constraint condition meets the condition that the lateral speed and the vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer pipe;
s2, moving the inclinometer according to a preset fixed traction distance and a fixed traction time, and calculating to obtain the traction speed of the inclinometer; acquiring inertial measurement data of an inclinometer by adopting an inertial measurement unit to obtain an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
S3, inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model for data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the non-integrity constraint condition;
S5, acquiring initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
s1, during the operation of the inclinometer in an inclinometer pipe of a target measuring body, adopting a traction device meeting the non-integrity constraint condition to traction the inclinometer, wherein the non-integrity constraint condition meets the condition that the lateral speed and the vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer pipe;
s2, moving the inclinometer according to a preset fixed traction distance and a fixed traction time, and calculating to obtain the traction speed of the inclinometer; acquiring inertial measurement data of an inclinometer by adopting an inertial measurement unit to obtain an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
S3, inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model for data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the non-integrity constraint condition;
S5, acquiring initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The embodiments described above are only for illustrating the technical solution of the present application, but not for limiting the same, and although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solution described in the foregoing embodiments may be modified or some of the features thereof may be replaced by the same, and the modification or replacement should not depart from the spirit and scope of the technical solution of the embodiments of the present application.
Claims (7)
1. An inertial measurement unit and distance based non-integrity constrained inclinometry method, comprising:
During the operation of the inclinometer in the inclinometer pipe of the target measuring body, a traction device meeting the non-integrity constraint condition is adopted to traction the inclinometer, and the non-integrity constraint condition meets that the lateral speed and the vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer pipe;
The inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated; acquiring inertial measurement data of an inclinometer by adopting an inertial measurement unit to obtain an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
Inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model for data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the incompleteness constraint condition;
Performing data correction optimization on the initial arrangement data set according to the data error feedback information to obtain optimized three-dimensional coordinate measurement data of the inclinometer;
Acquiring initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data;
the inertial navigation measurement data comprise triaxial gyroscope data and triaxial accelerometer data, and the inclinometry method comprises the following steps:
Acquiring error vectors, zero offset vectors and scale factor error vectors on a three-dimensional coordinate system in the operation process of the three-axis gyroscope data and the three-axis accelerometer data based on a time sequence, and constructing a 21-dimensional error state vector based on the time sequence;
Constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time sequence and the time sequence algorithm, wherein the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector;
And inputting the 21-dimensional system state vector into the preset Kalman filtering model to obtain a Kalman filtering predicted value.
2. The inertial measurement unit and distance non-integrity constraint based inclinometry method of claim 1, comprising:
when the inclinometer meets the non-integrity constraint condition, acquiring a corresponding inclinometer speed observation value and speed observation noise, and calculating to obtain a speed observation vector of an inclinometer coordinate system according to the inclinometer speed observation value and the speed observation noise;
The initial speed data of the initial arrangement data set of the inertial navigation measurement data is obtained, and the initial speed data is projected to a inclinometer coordinate system to obtain a corresponding initial speed value;
Inputting a speed observation vector and an initial speed value which are positioned in a coordinate system of the inclinometer into a preset speed error observation algorithm to obtain a speed error observation value;
And inputting the state vector and the speed error observation value of the 21-dimensional system into the preset Kalman filtering model to obtain optimized three-dimensional coordinate measurement data of the inclinometer.
3. The inertial measurement unit and distance non-integrity constraint based inclinometry method of claim 1, comprising:
the 21-dimensional system state vector is shown in a formula (1):
Wherein, Is an inertial navigation position error vector; is an inertial navigation speed error vector; Is an attitude error vector; Is a triaxial gyro zero offset vector; zero offset vector of the triaxial accelerometer; Is a gyro scale factor error vector; a scale factor error vector for the accelerometer;
the time sequence algorithm is shown in a formula (2):
wherein t is time; is the correlation time of the first order gaussian markov process; white noise is driven for a first order gaussian markov process.
4. A method of inclinometry based on the non-integrity constraints of inertial measurement unit and distance according to claim 3, comprising:
the Kalman state algorithm is shown in a formula (3):
Wherein x k+1 is a state vector of a 21-dimensional system to be estimated, F k+1,k is a dynamic matrix of an inertial navigation system, G k+1,k is a continuous-time noise coefficient matrix of the inertial navigation system, w k is noise of the inertial navigation system, z k+1 is an observation vector of the inertial navigation system, H k+1,k is a measurement matrix, and v k+1,k is measurement noise.
5. The inclination measuring system based on the non-integrity constraint of the inertia measuring unit and the distance is characterized by comprising an inclinometer, a traction device, the inertia measuring unit and a Kalman filtering data optimization module;
The traction device is used for traction of the inclinometer by adopting a traction device meeting the non-integrity constraint condition in the process that the inclinometer is positioned in an inclinometer pipe of a target measuring body, and the non-integrity constraint condition meets the condition that the lateral speed and the vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer pipe;
The inertial measurement unit is used for acquiring inertial measurement data of the inclinometer and obtaining an initial arrangement data set of the inertial measurement data, wherein the initial arrangement data set comprises attitude data, speed data and position data;
The Kalman filtering data optimization module is used for inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filtering model to perform data filtering processing and data error calculation to obtain data error feedback information, wherein the preset Kalman filtering model meets the non-integrity constraint condition;
Obtaining initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining deep displacement of the target measurement body according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data;
the inertial navigation measurement data comprise triaxial gyroscope data and triaxial accelerometer data, and the inclinometry method comprises the following steps:
Acquiring error vectors, zero offset vectors and scale factor error vectors on a three-dimensional coordinate system in the operation process of the three-axis gyroscope data and the three-axis accelerometer data based on a time sequence, and constructing a 21-dimensional error state vector based on the time sequence;
Constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time sequence and the time sequence algorithm, wherein the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector;
And inputting the 21-dimensional system state vector into the preset Kalman filtering model to obtain a Kalman filtering predicted value.
6. A smart device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the inclinometry method based on the non-integrity constraints of inertial measurement unit and distance as claimed in any one of claims 1 to 4.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the inclinometry method based on the non-integrity constraint of inertial measurement unit and distance according to any one of claims 1 to 4.
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