Inclination measurement method based on inertial measurement unit and non-integrity constraints of distance

Abstract

The present application discloses an inclinometer method based on the non-integrity constraint of an inertial measurement unit and a distance, comprising: when the inclinometer is located in the inclinometer tube, the inclinometer is towed by a towing device satisfying the non-integrity constraint condition, and the non-integrity constraint condition satisfies that the lateral speed and vertical speed of the inclinometer when moving are zero; calculating the towing speed of the inclinometer; collecting the inertial navigation measurement data of the inclinometer to obtain the initial arrangement data set of the inertial navigation measurement data; inputting the towing speed of the inclinometer and the initial arrangement data set into the Kalman filter model for data filtering processing and data error calculation to obtain data error feedback information; performing data correction and optimization on the initial arrangement data set to obtain the optimized three-dimensional coordinate measurement data of the inclinometer; obtaining the initial three-dimensional coordinate measurement data of the target measurement body, and calculating and determining the deep displacement of the target measurement body. The present application has the effect of improving the accuracy of the measurement result of the inclinometer.

Description

Technical Field

[0001] The present application relates to the field of measurement technology, and in particular to an inclination measurement method based on non-integrity constraints of an inertial measurement unit and distance. Background Art

[0002] Inclinometer is an instrument used to measure deep displacement monitoring in drilling, foundation pits, foundations, coal mine exploration, etc. The configuration of the inclinometer generally includes an inclinometer tube, an inclinometer probe, a control cable and an inclinometer readout.

[0003] In the related technology, for example, when measuring the deep horizontal displacement of a foundation pit, the inclinometer tube is usually installed in a vertical borehole that passes through the unstable soil layer to the lower stable stratum; the deformation of the inclinometer tube is observed by using a digital vertical active inclinometer probe, a control cable and a readout instrument; the surveying personnel can establish an initial section of the inclinometer tube displacement during the first observation; subsequent observations will show changes in the section displacement when the ground moves; during the observation, the probe moves from the bottom to the top of the inclinometer tube, and stops at the set intervals in turn to measure the inclination.

[0004] At present, in actual surveying and mapping, the measurement value of the inclinometer is obtained under the assumption that the measuring axis is consistent with the displacement direction; however, in the actual operation process, the inclinometer will be in a torsion state, and the measurement value of the inclination sensor of the inclinometer is prone to distortion components, resulting in reduced measurement accuracy. Therefore, there is a defect that the measurement result of the inclinometer is of low accuracy, and there is room for improvement. Summary of the invention

[0005] In order to improve the accuracy of the measurement results of the inclinometer, the present application provides an inclinometer method based on an inertial measurement unit and non-integrity constraints of distance.

[0006] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0007] The inclination measurement method based on the non-integrity constraint of inertial measurement unit and distance includes:

[0008] When the inclinometer is located in the inclinometer casing of the target measurement body, the inclinometer is towed by a towing device satisfying a non-integral constraint condition, wherein the non-integral constraint condition satisfies that when the inclinometer moves along the inclinometer casing, the lateral velocity and the vertical velocity of the inclinometer are zero;

[0009] The inclinometer moves according to a preset fixed traction distance and a fixed traction time, and the traction speed of the inclinometer is calculated; an inertial measurement unit is used to collect inertial navigation measurement data of the inclinometer to obtain an initial arrangement data set of the inertial navigation measurement data, wherein the initial arrangement data set includes attitude data, speed data and position data;

[0010] Inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filter model for data filtering processing and data error calculation to obtain data error feedback information; the preset Kalman filter model satisfies the non-integrity constraint condition;

[0011] Performing data correction and 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;

[0012] Initial three-dimensional coordinate measurement data of the target measurement volume is acquired, and a deep displacement amount of the target measurement volume is calculated and determined according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.

[0013] By adopting the above technical scheme, the target measurement body includes foundation pits, dams and other structures; the present application takes the foundation pit as an example: when the inclinometer is used to measure the inclinometer of the foundation pit, the present application combines the traction distance (that is, the distance the inclinometer moves in the inclinometer tube, also known as mileage data) and the inertial navigation measurement data for combined navigation, and combines the environmental particularity and non-completeness constraints of the inclinometer in the inclinometer tube to constrain the inclinometer to only move forward along the inclinometer tube during operation in the inclinometer tube. The speed of the inclinometer during operation is zero, that is, the lateral speed and vertical speed of the inclinometer during operation are zero.

[0014] During observation, the probe of the inclinometer moves from the bottom to the top of the inclinometer tube at a fixed traction distance and a fixed traction speed, and stops at the set intervals in turn to measure the inclination; the initial arrangement data set of the present application is the mechanical arrangement result of the inertial navigation system, and at this time the initial arrangement data set contains the attitude data, velocity data and position data of the inclinometer to be optimized and corrected; the Kalman filter model is the set Kalman filter algorithm, and the optimized estimated measurement value is the optimized estimated value of the inclinometer attitude data, velocity data and position data, which is conducive to improving the accuracy of the measurement results of the inclinometer.

[0015] Specifically, the present application performs data filtering and data fusion calculation on the initial arrangement data set, traction speed and non-integrity constraint conditions of the inertial measurement unit through the Kalman filter algorithm in the Kalman filter model to obtain data error feedback information containing error feedback, and then corrects the mechanical arrangement result of the inertial measurement unit coordinate system (b system) based on the data error feedback information, that is, obtains the optimized three-dimensional coordinate measurement data of the inclinometer attitude, speed and position information, and finally obtains the deep cumulative displacement data of the target measurement body (i.e., the foundation pit) by comparing and calculating the optimized three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body; the present application improves the three-dimensional coordinate measurement accuracy of the inclinometer combined navigation by introducing non-integrity constraints as auxiliary information, and then obtains the internal cumulative displacement of the high-precision target measurement body through the initial three-dimensional coordinate measurement data and the high-precision optimized three-dimensional coordinate measurement data of this measurement, thereby achieving the purpose of improving the accuracy of the measurement result of the inclinometer.

[0016] In a preferred example of the present application: the inertial navigation measurement data includes three-axis gyroscope data and three-axis accelerometer data, and the inclination measurement method includes:

[0017] Based on the time series, during the operation of the three-axis gyroscope data and the three-axis accelerometer data, the error vector, the zero bias vector and the proportional factor error vector in the three-dimensional coordinate system are collected to construct a 21-dimensional error state vector based on the time series; the time points corresponding to the multiple measurement points of the inclinometer are determined; the Gaussian noise data at each time point is obtained, and the time series algorithm is constructed according to the zero bias vector and the proportional factor error vector corresponding to the three-axis gyroscope data and the three-axis accelerometer data at each time point;

[0018] Constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time series and the time series algorithm; the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector;

[0019] The 21-dimensional system state vector is input into the preset Kalman filter model to obtain a Kalman filter prediction value.

[0020] By adopting the above technical scheme, in order to improve the calculation accuracy of the inclinometer for the deep displacement of the target measurement body, taking into account the problem that the inertial measurement unit of the inclinometer produces nonlinear changes under continuous time conditions in the inclinometer in the inclinometer tube, the present application adopts a Kalman filter model for combined navigation positioning solution, and improves the positioning accuracy of the inertial navigation system by adopting the error of the inertial measurement unit as the state vector; specifically, a 21-dimensional error state vector based on a time series is first constructed, and the noise interference of the inclinometer in the process of linear movement measurement is considered. The 21-dimensional system state vector is calculated through Gaussian noise data at multiple time points; in the next step, the Kalman filter model is used to obtain the predicted value of the Kalman filter, thereby achieving the purpose of estimating the change state of the inclinometer in the inertial navigation measurement data with measurement noise obtained based on the inclinometer measurement.

[0021] In a preferred example, the present application includes:

[0022] When the inclinometer satisfies the non-holonomic constraint condition, a corresponding inclinometer velocity observation value and velocity observation noise are obtained, and a velocity observation vector of the inclinometer coordinate system is calculated according to the inclinometer velocity observation value and velocity observation noise;

[0023] Acquire initial velocity data of an initial arrangement data set of the inertial navigation measurement data; project the initial velocity data to an inclinometer coordinate system to obtain a corresponding initial velocity value;

[0024] Inputting the velocity observation vector and the initial velocity value in the inclinometer coordinate system into a preset velocity error observation algorithm to obtain a velocity error observation value;

[0025] The 21-dimensional system state vector and the velocity error observation value are input into the preset Kalman filter model to obtain the optimized three-dimensional coordinate measurement data of the inclinometer.

[0026] By adopting the above technical scheme, under the condition of non-holonomic constraints, the inclinometer will only move forward or backward during operation in the inclinometer tube, that is, in the inclinometer coordinate system, the lateral speed and vertical speed of the inclinometer are close to zero; based on the fact that the lateral speed and vertical speed of the inclinometer are zero, the present application calculates the speed observation vector of the inclinometer coordinate system according to the inclinometer speed observation value and the speed observation noise; then, the initial speed data mechanically arranged by the inertial measurement unit is used, and the initial speed data at this time is the speed value located in the navigation coordinate system (n system); it is projected to the inclinometer coordinate system (v system), and the corresponding initial speed value is calculated; then, the preset speed error observation algorithm calculates the speed error observation value, and the speed error observation value provides observation information for the Kalman filter model, and the optimal estimate of the attitude data, speed data and position data of the inclinometer can be calculated through the Kalman filter model.

[0027] In a preferred example, the present application includes:

[0028] The 21-dimensional system state vector is shown in formula (1):

[0029]

[0030] in, is the inertial navigation position error vector; is the inertial navigation velocity error vector; is the attitude error vector; is the three-axis gyro zero bias vector; is the zero bias vector of the three-axis accelerometer; is the gyro scale factor error vector; is the accelerometer scale factor error vector;

[0031] The time series algorithm is shown in formula (2):

[0032]

[0033] Where, t is time; is the correlation time of the first-order Gauss-Markov process; is the driving white noise of the first-order Gauss-Markov process.

[0034] By adopting the above technical solution, the error state vector of the inertial measurement unit that produces nonlinear changes under continuous time conditions during the measurement process is calculated using formulas (1) and (2), and the zero bias vector and proportional factor error vector of the three-axis gyroscope and three-axis accelerometer in the inertial measurement unit are solved using formula (2), so as to facilitate the evaluation and calculation of the interference and measurement noise of the inertial measurement unit during the measurement process, thereby facilitating the improvement of the positioning accuracy of the inertial measurement unit.

[0035] In a preferred example, the present application includes:

[0036] The Kalman state algorithm is shown in formula (3):

[0037]

[0038] Among them, x k+1 is the 21-dimensional system state vector to be estimated; F k+1,k is the dynamic matrix of the inertial navigation system; G k+1,k is the continuous-time inertial navigation system noise coefficient matrix; w k is the inertial navigation system noise, z k+1 Represents the observation vector of the inertial navigation system; H k+1,k The representation is called the measurement matrix; v k+1,k To measure noise.

[0039] By adopting the above technical solution, formula (3) can be derived from formula (1) and formula (2). The Kalman state algorithm is the continuous time error state equation of the inclinometer under continuous time conditions and based on the inertial navigation measurement data. The 21-dimensional system state vector x to be estimated calculated by formula (3) is k+1 , inertial navigation system observation vector z k+1 Provides the Kalman filter model with the error prediction value of the inertial measurement unit.

[0040] In a preferred example, the present application includes:

[0041] The velocity observation vector of the inclinometer coordinate system As shown in formula (4):

[0042]

[0043] Among them, v w is the observed value of inclinometer velocity; e v is the velocity observation noise; in the inclinometer coordinate system, when the non-holonomic constraint condition is met, the lateral velocity and vertical velocity of the inclinometer are zero;

[0044] The corresponding initial velocity value projected to the inclinometer coordinate system is As shown in formula (5):

[0045]

[0046] Wherein, b, n, and v represent the inertial measurement unit coordinate system, the navigation coordinate system, and the inclinometer coordinate system, respectively; is the initial velocity data of the initial arrangement data set, is the installation angle error angle between the two coordinate systems b and v, Represents the direction cosine matrix from the n system to the b system; is the gyroscope observation value, It is the mileage arm, and the inertial measurement unit is installed at the center of the inclinometer. is zero;

[0047] From formula (4) and formula (5), it can be deduced that the preset speed error observation algorithm is shown in formula (6):

[0048]

[0049] in, It is the installation parameter calibrated by the inclinometer and is a known quantity.

[0050] By adopting the above technical solution, formula (4) and formula (5) are based on the observation information calculation process under the non-holonomic constraint condition. Under the non-holonomic constraint condition, the values ​​of the lateral velocity and vertical velocity in the inclinometer coordinate system in formula (4) are zero, and the inertial measurement unit in formula 5 is installed at the center position of the inclinometer, so that the mileage lever arm is zero, that is, the lever arm effect is ignored; the inclinometer velocity observation value and the initial velocity value under the non-holonomic constraint condition can be obtained by formula (4) and formula (5): The velocity error observation equation (6) is used to facilitate the calculation of the optimal estimated values ​​of the attitude data, velocity data and position data of the inclinometer in a preset Kalman filter model through the observation information of formula (6); it is convenient for the subsequent calculation of the deep displacement of the target measurement body, which is beneficial to improve the accuracy of the measurement result of the inclinometer.

[0051] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0052] An inclinometer system based on inertial measurement unit and non-integrity constraints of distance, comprising: an inclinometer, a traction device, an inertial measurement unit and a Kalman filter data optimization module;

[0053] A traction device is used to traction the inclinometer when the inclinometer is located in the inclinometer casing of the target measurement body, using the traction device that satisfies the non-integrity constraint condition, wherein the non-integrity constraint condition is satisfied that when the inclinometer moves along the inclinometer casing, the lateral speed and the vertical speed of the inclinometer are zero; the inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated;

[0054] An inertial measurement unit, used to collect inertial navigation measurement data of the inclinometer, and obtain an initial arrangement data set of the inertial navigation measurement data, wherein the initial arrangement data set includes attitude data, velocity data and position data;

[0055] A Kalman filter data optimization module is used to input the traction speed of the inclinometer and the initial arrangement data set into a preset Kalman filter model to perform data filtering processing and data error calculation to obtain data error feedback information; the preset Kalman filter model satisfies the non-integrity constraint condition;

[0056] The initial arrangement data set is optimized for data correction according to the data error feedback information to obtain optimized three-dimensional coordinate measurement data of the inclinometer; the initial three-dimensional coordinate measurement data of the target measurement body is obtained, and the deep displacement of the target measurement body is calculated and determined according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.

[0057] By adopting the above technical solution, during observation, the probe of the inclinometer moves from the bottom to the top of the inclinometer tube according to a fixed traction distance and a fixed traction speed, and stops at the set intervals in turn to measure the inclination; the initial arrangement data set of the present application is the mechanical arrangement result of the inertial navigation system, and at this time, the initial arrangement data set contains the attitude data, velocity data and position data of the inclinometer to be optimized and corrected; the Kalman filter model is a set Kalman filter algorithm, and the optimized estimated measurement value is the optimized estimated value of the inclinometer attitude data, velocity data and position data, which is conducive to improving the accuracy of the measurement result of the inclinometer.

[0058] Specifically, the present application performs data filtering and data fusion calculation on the initial arrangement data set, traction speed and non-integrity constraint conditions of the inertial measurement unit through the Kalman filter algorithm in the Kalman filter model to obtain data error feedback information containing error feedback, and then corrects the mechanical arrangement result of the inertial measurement unit coordinate system (b system) based on the data error feedback information, that is, obtains the optimized three-dimensional coordinate measurement data of the inclinometer attitude, speed and position information, and finally obtains the deep cumulative displacement data of the target measurement body (i.e., the foundation pit) by comparing and calculating the optimized three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body; the present application improves the three-dimensional coordinate measurement accuracy of the inclinometer combined navigation by introducing non-integrity constraints as auxiliary information, and then obtains the internal cumulative displacement of the high-precision target measurement body through the initial three-dimensional coordinate measurement data and the high-precision optimized three-dimensional coordinate measurement data of this measurement, thereby achieving the purpose of improving the accuracy of the measurement result of the inclinometer.

[0059] In the third aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0060] An intelligent device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned inclination measurement method based on the non-integrity constraint of an inertial measurement unit and a distance are implemented.

[0061] In a fourth aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0062] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned inclination measurement method based on the non-integrity constraint of an inertial measurement unit and a distance are implemented.

[0063] In summary, the present application includes at least one of the following beneficial technical effects:

[0064] 1. During observation, the probe of the inclinometer moves from the bottom to the top of the inclinometer tube at a fixed pulling distance and a fixed pulling speed, and stops at the set spacing in turn to measure the inclination; the initial arrangement data set of the present application is the mechanical arrangement result of the inertial navigation system, and the initial arrangement data set at this time contains the attitude data, velocity data and position data of the inclinometer to be optimized and corrected; the Kalman filter model is the set Kalman filter algorithm, and the optimized estimated measurement value is the optimized estimated value of the attitude data, velocity data and position data of the inclinometer, which is conducive to improving the accuracy of the measurement result of the inclinometer; specifically, the present application uses the Kalman filter algorithm in the Kalman filter model to filter the initial arrangement data set of the inertial measurement unit, the pulling speed and the non-integrity constraint conditions, and the data The data error feedback information including the error feedback is obtained by fusion calculation, and then the mechanical arrangement result of the inertial measurement unit coordinate system (b system) is corrected based on the data error feedback information, that is, the optimized three-dimensional coordinate measurement data of the inclinometer attitude, speed and position information is obtained, and finally the deep cumulative displacement data of the target measurement body (i.e., the foundation pit) is obtained by comparing and calculating the optimized three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body; the present application improves the three-dimensional coordinate measurement accuracy of the inclinometer combined navigation by introducing non-integrity constraints as auxiliary information, and then obtains the internal cumulative displacement of the high-precision target measurement body through the initial three-dimensional coordinate measurement data and the high-precision optimized three-dimensional coordinate measurement data of this measurement, thereby achieving the purpose of improving the accuracy of the measurement result of the inclinometer;

[0065] 2. In order to improve the calculation accuracy of the inclinometer for the deep displacement of the target measurement body, considering the problem that the inertial measurement unit of the inclinometer produces nonlinear changes under continuous time conditions in the inclinometer in the inclinometer tube, the present application adopts the Kalman filter model for combined navigation positioning solution, and improves the positioning accuracy of the inertial navigation system by using the error of the inertial measurement unit as the state vector; specifically, firstly construct a 21-dimensional error state vector based on the time series, consider the noise interference of the inclinometer in the process of linear movement measurement, and calculate the 21-dimensional system state vector through Gaussian noise data at multiple time points; next, obtain the predicted value of the Kalman filter through the preset Kalman filter model, so as to achieve the purpose of estimating the change state of the inclinometer in the inertial navigation measurement data with measurement noise obtained by the inclinometer;

[0066] 3. Formula (4) and Formula (5) are based on the observation information calculation process under the non-holonomic constraint conditions. Under the non-holonomic constraint conditions, the values ​​of the lateral velocity and vertical velocity in the inclinometer coordinate system in Formula (4) are zero, and the inertial measurement unit in Formula 5 is installed at the center of the inclinometer, so that the mileage lever arm is zero, that is, the lever arm effect is ignored; Formula (4) and Formula (5) can be used to obtain the inclinometer velocity observation value and the initial velocity value under the non-holonomic constraint conditions. The velocity error observation equation (6) is used to facilitate the calculation of the optimal estimated values ​​of the attitude data, velocity data and position data of the inclinometer in a preset Kalman filter model through the observation information of formula (6); it is convenient for the subsequent calculation of the deep displacement of the target measurement body, which is beneficial to improve the accuracy of the measurement result of the inclinometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of an inclination measurement method based on non-integrity constraints of an inertial measurement unit and distance in one embodiment of the present application;

[0068] Figure 2 It is one of the flow charts in the inclination measurement method based on the non-integrity constraint of the inertial measurement unit and the distance in one embodiment of the present application;

[0069] Figure 3 This is another flow chart of an inclination measurement method based on non-integrity constraints of an inertial measurement unit and distance in one embodiment of the present application. DETAILED DESCRIPTION

[0070] The present application is further described in detail below in conjunction with the accompanying drawings.

[0071] In one embodiment, if Figure 1 As shown, the present application discloses a method for measuring inclination based on the non-integrity constraint of an inertial measurement unit and distance, which specifically includes the following steps:

[0072] S1: When the inclinometer is located in the inclinometer casing of the target measurement body, the inclinometer is towed by a traction device that satisfies the non-integral constraint condition. When the non-integral constraint condition is satisfied and the inclinometer moves along the inclinometer casing, the lateral velocity and vertical velocity of the inclinometer are zero.

[0073] In this embodiment, the target measurement body includes large structures such as foundation pits, dams, slopes, etc.; this embodiment takes the deep displacement measurement of the foundation pit as an example.

[0074] Specifically, the non-integrity constraint condition restricts the inclinometer to move forward or backward only along the inclinometer tube during operation; in actual operation specifications, when the inclinometer tube is installed and buried in the early stage, the direction of the inclinometer tube is adjusted to keep it consistent with the vertical direction to facilitate subsequent inclinometer work.

[0075] S2: The inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated; an inertial measurement unit is used to collect inertial navigation measurement data of the inclinometer to obtain an initial arrangement data set of the inertial navigation measurement data, which includes attitude data, velocity data and position data.

[0076] In this embodiment, during operation, the inclinometer measures inclinometer mode by sequentially measuring at multiple sampling points evenly spaced from the bottom to the top of the inclinometer tube, such as measuring a distance of 50 cm each time, and the inclinometer moves at a set traction speed during the inclinometer measurement process.

[0077] Specifically, when using an inclinometer to measure the inclinometer of a foundation pit, the present application combines the traction distance (i.e., the distance the inclinometer moves in the inclinometer tube, also known as mileage data) and the inertial navigation measurement data for combined navigation, and combines the environmental particularities and non-completeness constraints of the inclinometer measuring in the inclinometer tube to constrain the inclinometer to only move forward along the inclinometer tube during its operation in the inclinometer tube. That is, during the operation of the inclinometer, the speed of the inclinometer perpendicular to and parallel to the foundation pit sideline is zero, so that the lateral speed and vertical speed of the inclinometer during the operation are zero.

[0078] S3: Input the traction speed of the inclinometer and the initial arrangement data set into the preset Kalman filter model to perform data filtering processing and data error calculation to obtain data error feedback information; the preset Kalman filter model satisfies the non-integrity constraint condition.

[0079] In this embodiment, the Kalman filter model is a Kalman filter;

[0080] Specifically, the initial arrangement data set includes the attitude data, velocity data and position data of the inclinometer to be optimized and corrected; the Kalman filter model is a set Kalman filter algorithm, and the optimized estimated measurement value is the optimized estimated value of the inclinometer attitude data, velocity data and position data, which is conducive to improving the accuracy of the measurement results of the inclinometer.

[0081] S4: performing data correction and 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.

[0082] In this embodiment, the initial arrangement data set of the inertial measurement unit, the traction speed and the non-integrity constraint conditions are subjected to data filtering processing and data fusion calculation through the Kalman filter algorithm in the Kalman filter model to obtain data error feedback information containing error feedback, and then the mechanical arrangement result of the inertial measurement unit coordinate system (b system) is corrected based on the data error feedback information, that is, the optimized three-dimensional coordinate measurement data of the inclinometer attitude, speed and position information is obtained.

[0083] S5: Acquire initial three-dimensional coordinate measurement data of the target measurement body, and calculate and determine 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.

[0084] Specifically, the initial three-dimensional total table data and optimized three-dimensional coordinate measurement data of the inclinometer at each measuring point are used to map the initial trajectory line of the inclinometer in the inclinometer tube, and the initial trajectory line of the inclinometer is used as the reference displacement line for the cumulative displacement of the target measurement body. The initial trajectory line of the current measurement is used as the input quantity, and a measurement point is taken at an interval of 0.5 meters according to the inclinometer requirements. The distance from each measuring point to the reference displacement line is calculated as the displacement of the target measurement body at that point, that is, the deep displacement of the target measurement body is obtained.

[0085] Furthermore, by optimizing the three-dimensional coordinate measurement data and the initial three-dimensional coordinate measurement data of the target measurement body, the deep cumulative displacement data of the target measurement body (i.e., the foundation pit) is obtained by comparative calculation; the present application improves the three-dimensional coordinate measurement accuracy of the inclinometer combined navigation by introducing non-complete constraints as auxiliary information, and then obtains the internal cumulative displacement of the high-precision target measurement body through the initial three-dimensional coordinate measurement data and the high-precision optimized three-dimensional coordinate measurement data of this measurement, thereby achieving the purpose of improving the accuracy of the measurement results of the inclinometer.

[0086] In one embodiment, if Figure 2 As shown, the inclination measurement method based on the non-integrity constraint of the inertial measurement unit and the distance includes establishing a Kalman filter state equation, thereby providing a prediction value for the Kalman filter: the inertial navigation measurement data includes three-axis gyroscope data and three-axis accelerometer data, and the inclination measurement method includes:

[0087] S10: Based on the time series, during the operation of the three-axis gyroscope data and the three-axis accelerometer data, the error vector, the zero bias vector and the proportional factor error vector in the three-dimensional coordinate system are collected to construct a 21-dimensional error state vector based on the time series.

[0088] In this embodiment, under the condition of non-holonomic constraints, the inclinometer can only move forward or backward during operation in the inclinometer tube, that is, in the inclinometer coordinate system, the lateral velocity and vertical velocity of the inclinometer are close to zero; this application is based on the fact that the lateral velocity and vertical velocity of the inclinometer are zero, and obtains the velocity observation vector of the inclinometer coordinate system according to the inclinometer velocity observation value and velocity observation noise.

[0089] Specifically, the 21-dimensional system state vector is shown in formula (1):

[0090]

[0091] in, is the inertial navigation position error vector; is the inertial navigation velocity error vector; is the attitude error vector; is the three-axis gyro zero bias vector; is the zero bias vector of the three-axis accelerometer; is the gyro scale factor error vector; is the accelerometer scale factor error vector.

[0092] S20: determining the time points corresponding to the multiple measurement points of the inclinometer; obtaining Gaussian noise data at each time point, and constructing a time series algorithm according to the zero bias vectors and the proportional factor error vectors corresponding to the triaxial gyroscope data and the triaxial accelerometer data at each time point;

[0093] In this embodiment, the change process of the three-axis gyroscope data and the three-axis accelerometer data over time is represented by a first-order Gauss-Markov process, wherein the time series algorithm is a differential equation.

[0094] Specifically, the time series algorithm is shown in formula (2):

[0095]

[0096] Where, t is time; is the correlation time of the first-order Gauss-Markov process; is the driving white noise of the first-order Gauss-Markov process.

[0097] S30: constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time series and the time series algorithm; the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector.

[0098] Specifically, the Kalman state algorithm is shown in formula (3):

[0099]

[0100] Among them, x k+1 is the 21-dimensional system state vector to be estimated; F k+1,k is the dynamic matrix of the inertial navigation system; G k+1,k is the continuous-time inertial navigation system noise coefficient matrix; w k is the inertial navigation system noise, z k+1 Represents the observation vector of the inertial navigation system; H k+1,k The representation is called the measurement matrix; v k+1,k To measure noise.

[0101] S40: Input the 21-dimensional system state vector into a preset Kalman filter model to obtain a Kalman filter prediction value.

[0102] Specifically, after establishing the Kalman state algorithm (i.e., constructing the Kalman filter state equation), the 21-dimensional system state vector (i.e., the inertial navigation data error of the inertial measurement unit) is input into the Kalman filter model as state information to achieve the purpose of providing the Kalman filter model with a state vector prediction value.

[0103] In one embodiment, if Figure 3 As shown, the inclination measurement method based on the non-integrity constraint of the inertial measurement unit and the distance includes establishing a Kalman filter state equation; the inclination measurement method includes:

[0104] S111: When the inclinometer satisfies the non-holonomic constraint condition, the corresponding inclinometer velocity observation value and velocity observation noise are obtained, and the velocity observation vector of the inclinometer coordinate system is calculated according to the inclinometer velocity observation value and the velocity observation noise.

[0105] Specifically, the velocity observation vector of the inclinometer coordinate system is As shown in formula (4):

[0106]

[0107] Among them, v w is the observed value of inclinometer velocity; e v is the velocity observation noise; in the inclinometer coordinate system, when the non-holonomic constraints are met, the lateral velocity and vertical velocity of the inclinometer are zero.

[0108] S112: Acquire initial velocity data of an initial arrangement data set of inertial navigation measurement data; project the initial velocity data to an inclinometer coordinate system to obtain a corresponding initial velocity value.

[0109] Specifically, the initial velocity data of the initial arrangement is obtained by performing mechanical arrangement using the initial velocity value of the inertial measurement unit.

[0110] The corresponding initial velocity value projected to the inclinometer coordinate system As shown in formula (5):

[0111]

[0112] Wherein, b, n, and v represent the inertial measurement unit coordinate system, the navigation coordinate system, and the inclinometer coordinate system, respectively; is the initial velocity data of the initial arrangement data set, is the installation angle error angle between the two coordinate systems b and v, Represents the direction cosine matrix from the n system to the b system; is the gyroscope observation value, It is the mileage arm, and the inertial measurement unit is installed at the center of the inclinometer. is zero, i.e. the lever arm effect is ignored.

[0113] S113: Inputting the velocity observation vector and the initial velocity value in the inclinometer coordinate system into a preset velocity error observation algorithm to obtain a velocity error observation value.

[0114] In this embodiment, it can be deduced from formula (4) and formula (5) that the preset speed error observation algorithm is shown in formula (6):

[0115]

[0116] in, It is the installation parameter calibrated by the inclinometer and is a known quantity.

[0117] S114: Input the 21-dimensional system state vector and the velocity error observation value into a preset Kalman filter model to obtain optimized three-dimensional coordinate measurement data of the inclinometer.

[0118] In this embodiment, it is convenient to calculate the optimal estimated values ​​of the attitude data, velocity data and position data of the inclinometer in a preset Kalman filter model through the observation information of formula (6); it is convenient to subsequently calculate the deep displacement of the target measurement body, thereby facilitating the improvement of the accuracy of the measurement results of the inclinometer.

[0119] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0120] In one embodiment, an inclinometer system based on non-integrity constraints of an inertial measurement unit and a distance is provided. The inclinometer system based on non-integrity constraints of an inertial measurement unit and a distance corresponds to the inclinometer method based on non-integrity constraints of an inertial measurement unit and a distance in the above-mentioned embodiment.

[0121] An inclinometer system based on inertial measurement unit and non-integrity constraints of distance includes an inclinometer, a traction device, an inertial measurement unit and a Kalman filter data optimization module. The detailed description of each functional module is as follows:

[0122] The traction device is used to traction the inclinometer when the inclinometer is located in the inclinometer tube of the target measurement body, using the traction device that satisfies the non-integrity constraint condition, and when the non-integrity constraint condition is satisfied, the lateral speed and vertical speed of the inclinometer are zero when the inclinometer moves along the inclinometer tube; the inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated;

[0123] An inertial measurement unit is used to collect inertial navigation measurement data of the inclinometer to obtain an initial arrangement data set of the inertial navigation measurement data, wherein the initial arrangement data set includes attitude data, velocity data and position data;

[0124] The Kalman filter data optimization module is used to input the traction speed of the inclinometer and the initial arrangement data set into the preset Kalman filter model for data filtering processing and data error calculation to obtain data error feedback information; the preset Kalman filter model satisfies the non-integrity constraint condition;

[0125] The initial arrangement data set is corrected and optimized according to the data error feedback information to obtain the optimized three-dimensional coordinate measurement data of the inclinometer; the initial three-dimensional coordinate measurement data of the target measurement body is obtained, and the deep displacement of the target measurement body is calculated and determined according to the initial three-dimensional coordinate measurement data and the optimized three-dimensional coordinate measurement data.

[0126] For the specific limitations on the inclinometer system based on the non-integrity constraints of the inertial measurement unit and the distance, please refer to the limitations on the inclinometer method based on the non-integrity constraints of the inertial measurement unit and the distance mentioned above, which will not be repeated here; each module in the above-mentioned inclinometer system based on the non-integrity constraints of the inertial measurement unit and the distance can be fully or partially implemented by software, hardware and a combination thereof; each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0127] In one embodiment, an intelligent device is provided, which may be a server. The intelligent device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the intelligent device is used to provide computing and control capabilities. The memory of the intelligent device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the intelligent device is used to store inertial measurement data, mileage data, and a Kalman filter model, etc. The network interface of the intelligent device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for measuring inclination based on the non-integrity constraints of an inertial measurement unit and distance is implemented.

[0128] In one embodiment, a smart device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0129] S1: When the inclinometer is located in the inclinometer casing of the target measurement body, the inclinometer is towed by a towing device that satisfies the non-integrity constraint condition. When the non-integrity constraint condition is satisfied and the inclinometer moves along the inclinometer casing, the lateral velocity and vertical velocity of the inclinometer are zero;

[0130] S2: the inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated; an inertial measurement unit is used to collect inertial navigation measurement data of the inclinometer to obtain an initial arrangement data set of the inertial navigation measurement data, the initial arrangement data set including attitude data, velocity data and position data;

[0131] S3: inputting the traction speed of the inclinometer and the initial arrangement data set into a preset Kalman filter model for data filtering processing and data error calculation to obtain data error feedback information; the preset Kalman filter model satisfies the non-integrity constraint condition; S4: 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;

[0132] S5: Acquire initial three-dimensional coordinate measurement data of the target measurement body, and calculate and determine 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.

[0133] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0134] S1: When the inclinometer is located in the inclinometer casing of the target measurement body, the inclinometer is towed by a towing device that satisfies the non-integrity constraint condition. When the non-integrity constraint condition is satisfied and the inclinometer moves along the inclinometer casing, the lateral velocity and vertical velocity of the inclinometer are zero;

[0135] S2: the inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated; an inertial measurement unit is used to collect inertial navigation measurement data of the inclinometer to obtain an initial arrangement data set of the inertial navigation measurement data, the initial arrangement data set including attitude data, velocity data and position data;

[0136] S3: inputting the traction speed of the inclinometer and the initial arrangement data set into a preset Kalman filter model for data filtering processing and data error calculation to obtain data error feedback information; the preset Kalman filter model satisfies the non-integrity constraint condition; S4: 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;

[0137] S5: Acquire initial three-dimensional coordinate measurement data of the target measurement body, and calculate and determine 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.

[0138] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile 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. As an illustration and not limitation, RAM is available in many 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).

[0139] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0140] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for measuring inclination based on inertial measurement unit and non-integrity constraints of distance, characterized in that: include: When the inclinometer is located in the inclinometer casing of the target measurement body, the inclinometer is towed by a towing device satisfying a non-integral constraint condition, wherein the non-integral constraint condition satisfies that when the inclinometer moves along the inclinometer casing, the lateral velocity and the vertical velocity of the inclinometer are zero; The inclinometer moves according to a preset fixed traction distance and a fixed traction time, and the traction speed of the inclinometer is calculated; an inertial measurement unit is used to collect inertial navigation measurement data of the inclinometer to obtain an initial arrangement data set of the inertial navigation measurement data, wherein the initial arrangement data set includes attitude data, speed data and position data; Inputting the traction speed and the initial arrangement data set of the inclinometer into a preset Kalman filter model to perform data filtering processing and data error calculation to obtain data error feedback information; The preset Kalman filter model satisfies the non-integrity constraint condition; Performing data correction and 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; Acquire initial three-dimensional coordinate measurement data of the target measurement body, and calculate and determine 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 includes three-axis gyroscope data and three-axis accelerometer data, and the inclination measurement method includes: Based on the time series, during the operation of the three-axis gyroscope data and the three-axis accelerometer data, the error vector, the zero bias vector and the proportional factor error vector in the three-dimensional coordinate system are collected to construct a 21-dimensional error state vector based on the time series; the time points corresponding to the multiple measurement points of the inclinometer are determined; the Gaussian noise data at each time point is obtained, and the time series algorithm is constructed according to the zero bias vector and the proportional factor error vector corresponding to the three-axis gyroscope data and the three-axis accelerometer data at each time point; Constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time series and the time series algorithm; the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector; The 21-dimensional system state vector is input into the preset Kalman filter model to obtain a Kalman filter prediction value.

2. The inclination measurement method based on the non-integrity constraint of the inertial measurement unit and the distance according to claim 1, characterized in that: include: When the inclinometer satisfies the non-holonomic constraint condition, a corresponding inclinometer velocity observation value and velocity observation noise are obtained, and a velocity observation vector of the inclinometer coordinate system is calculated according to the inclinometer velocity observation value and velocity observation noise; Acquire initial velocity data of an initial arrangement data set of the inertial navigation measurement data; project the initial velocity data to an inclinometer coordinate system to obtain a corresponding initial velocity value; Inputting the velocity observation vector and the initial velocity value in the inclinometer coordinate system into a preset velocity error observation algorithm to obtain a velocity error observation value; The 21-dimensional system state vector and the velocity error observation value are input into the preset Kalman filter model to obtain the optimized three-dimensional coordinate measurement data of the inclinometer.

3. The inclination measurement method based on the non-integrity constraint of the inertial measurement unit and the distance according to claim 1, characterized in that: include: The 21-dimensional system state vector is shown in formula (1): in, is the inertial navigation position error vector; is the inertial navigation velocity error vector; is the attitude error vector; is the three-axis gyro zero bias vector; is the zero bias vector of the three-axis accelerometer; is the gyro scale factor error vector; is the accelerometer scale factor error vector; The time series algorithm is shown in formula (2): Where, t is time; is the correlation time of the first-order Gauss-Markov process; is the driving white noise of the first-order Gauss-Markov process.

4. The inclination measurement method based on the non-integrity constraint of the inertial measurement unit and the distance according to claim 3, characterized in that: include: The Kalman state algorithm is shown in formula (3): Among them, x k+1 is the 21-dimensional system state vector to be estimated; F k+1,k is the dynamic matrix of the inertial navigation system; G k+1,k is the noise coefficient matrix of the continuous-time inertial navigation system; w k is the inertial navigation system noise, z k+1 Represents the observation vector of the inertial navigation system; H k+1,k The representation is called the measurement matrix; v k+1,k To measure noise.

5. Inclinometer system based on inertial measurement unit and non-integrity constraints of distance, characterized in that: include: Inclinometer, traction device, inertial measurement unit and Kalman filter data optimization module; A traction device is used to traction the inclinometer when the inclinometer is located in the inclinometer casing of the target measurement body, using the traction device that satisfies the non-integrity constraint condition, wherein the non-integrity constraint condition is satisfied that when the inclinometer moves along the inclinometer casing, the lateral speed and the vertical speed of the inclinometer are zero; the inclinometer moves according to a preset fixed traction distance and fixed traction time, and the traction speed of the inclinometer is calculated; An inertial measurement unit, used to collect inertial navigation measurement data of the inclinometer, and obtain an initial arrangement data set of the inertial navigation measurement data, wherein the initial arrangement data set includes attitude data, velocity data and position data; A Kalman filter data optimization module is used to input the traction speed of the inclinometer and the initial arrangement data set into a preset Kalman filter model to perform data filtering processing and data error calculation to obtain data error feedback information; The preset Kalman filter model satisfies the non-integrity constraint condition; Performing data correction and 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; Acquire initial three-dimensional coordinate measurement data of the target measurement body, and calculate and determine 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 includes three-axis gyroscope data and three-axis accelerometer data, and the inclination measurement method includes: Based on the time series, during the operation of the three-axis gyroscope data and the three-axis accelerometer data, the error vector, the zero bias vector and the proportional factor error vector in the three-dimensional coordinate system are collected to construct a 21-dimensional error state vector based on the time series; the time points corresponding to the multiple measurement points of the inclinometer are determined; the Gaussian noise data at each time point is obtained, and the time series algorithm is constructed according to the zero bias vector and the proportional factor error vector corresponding to the three-axis gyroscope data and the three-axis accelerometer data at each time point; Constructing a Kalman state algorithm according to the 21-dimensional error state vector of the time series and the time series algorithm; the Kalman state algorithm calculates a 21-dimensional system state vector according to the 21-dimensional error state vector; The 21-dimensional system state vector is input into the preset Kalman filter model to obtain a Kalman filter prediction value.

6. An intelligent device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the inclinometer method based on the non-holonomic constraints of an inertial measurement unit and a distance as claimed in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the inclinometer method based on the non-holonomic constraints of an inertial measurement unit and a distance are implemented as claimed in any one of claims 1 to 4.

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