Coal mine vertical shaft deformation detection method based on tank-mounted mobile laser scanning

Abstract

The invention discloses a coal mine vertical shaft deformation detection method based on tank-mounted mobile laser scanning. The method comprises the specific steps that firstly, a system is installed, and a coordinate system is established; step 2, dynamic data synchronous acquisition; 3, pose correction and static wellbore model reconstruction; and step 4, deformation detection and analysis. An existing cage is used as a platform, normal production is not affected, whole shaft scanning can be completed through one-time lifting, the detection period is shortened to the hour level from the day level, and safety and high efficiency are achieved; continuous and high-density shaft full-section three-dimensional point cloud is obtained in the cage operation process, no measurement dead angle exists, and data are comprehensive; through a proposed vibration elimination algorithm, cage vibration interference is effectively stripped, the precision of a reconstruction model can reach the centimeter level, the engineering monitoring requirement is met, and the precision is high; automatic deformation calculation, analysis and early warning are achieved, a visual visualization report is generated, a producer can manage and make decisions conveniently, and the intelligent degree is high.

Description

Technical Field

[0001] This invention relates to the field of mine safety monitoring and three-dimensional measurement technology, and in particular to a method for detecting deformation of coal mine vertical shafts based on tank-mounted mobile laser scanning. Background Technology

[0002] Coal mine shafts are the vital arteries connecting surface and underground production, and their service condition directly affects the safety of the entire mine. Due to geological stress, mining activities, and groundwater erosion, shafts can experience convergence, deformation, and even rupture. Traditional methods for detecting shaft deformation mainly rely on manual measurement using tools such as linear guides and cross-section measuring instruments at fixed cage strata. These methods suffer from drawbacks such as low efficiency, sparse measuring points, high-risk operations, and difficulty in obtaining continuous full-section information.

[0003] In recent years, 3D laser scanning technology has been introduced into wellbore inspection due to its advantages of high precision, non-contact operation, and rich data. Existing technical solutions mostly fix the scanner at the wellhead or a certain place underground for static scanning, which has a limited scanning range and the deployment of equipment affects production. Some studies have also attempted to place the scanner on a hanging platform or special inspection platform for mobile scanning, but this requires an additional hoisting system, which is costly and has poor compatibility with the normal hoisting system of the mine.

[0004] Using the existing hoisting cage in the mine as a scanning platform is the most convenient and economical method. However, the cage experiences complex multidimensional vibrations during operation, including swaying, twisting, and swinging along the guide rails. These vibrations cause the mounted laser scanner to generate additional motions that are not inherent to the geometry of the shaft, resulting in severe distortion of the collected point cloud data, which cannot be directly used for high-precision modeling. Therefore, effectively eliminating cage vibration interference is the core technical challenge in reconstructing an accurate static shaft model from a dynamic, vibrating moving platform. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for detecting deformation of coal mine vertical shafts based on cage-mounted mobile laser scanning. This method enables the rapid construction and deformation analysis of a full-section three-dimensional model of the shaft without interrupting or minimizing production, and eliminates the interference of cage operation vibration on the scanning data.

[0006] The technical solution adopted in this invention is: A method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning, comprising the following steps: Step 1: System installation and coordinate system establishment; An intrinsically safe and explosion-proof enclosure for mining is rigidly installed on the top of the cage. A high-performance edge processing unit, an explosion-proof power supply, and a switch are deployed inside the enclosure. The edge processing unit communicates with the wellhead through a wireless communication module and a wireless signal transmitter installed in the shaft, thereby uploading information to the ground control center. A 3D laser scanner and an inertial measurement unit (IMU) are rigidly installed at the center of the top of the explosion-proof enclosure. The two communicate with the edge processing unit through a network cable and a switch inside the enclosure to achieve real-time transmission of measurement data. With the center of the wellhead as the origin, The axis is vertically downward to determine the fixed coordinate system of the wellbore. Through precise measurements, a coordinate system for the cage carrier is established with the origin at the geometric center of the cage carrier. and laser scanner coordinate system ; Step 2: Dynamic data synchronization collection; The cage moves at a constant speed During a lifting cycle, the laser scanner acquires raw dynamic point cloud data of the wellbore. The gyroscope and accelerometer inside the inertial measurement unit (IMU) collect the angular velocity of the cage, respectively. and acceleration All data is timestamped as raw IMU data; Step 3: Pose correction and static wellbore model reconstruction; Data cleaning and zero-bias correction are performed on the raw IMU data to solve for the cage in the fixed coordinate system of the wellbore. The pose changes under these conditions are analyzed using a vibration filtering algorithm based on unit quaternions and dynamic coordinate transformation to transform the original dynamic point cloud data. Corrected to the fixed coordinate system of the wellbore The static wellbore point cloud after vibration interference was eliminated was obtained. The static wellbore model was reconstructed using a quadratic surface fitting algorithm based on the least squares method. ; Step 4: Deformation detection and analysis; The currently reconstructed wellbore model Compared with historical benchmark models To perform precise registration and comparison, multiple point cloud slices are extracted at the same elevation, and the data from each point cloud slice is collected. and The difference between the radial coordinates and the difference between the lateral coordinates constitutes the radial displacement of the wellbore. and wellbore cross-section convergence Take each point cloud slice and The difference in principal curvature constitutes the change in wellbore curvature. Simultaneous calculation Each point in the middle The closest distance to the surface is used to generate a wellbore deformation chromatogram, which visually displays the location and extent of deformation and automatically marks areas where the deformation exceeds the limit, thus identifying areas where the deformation exceeds the limit. The calculated radial displacement, cross-sectional convergence, and wellbore curvature change are intelligently evaluated to generate an intelligent evaluation report. The location information of the deformation, i.e., the vertical distance of the deformation location from the wellhead, is measured by a 3D laser scanner. The edge processing unit transmits the intelligent evaluation report and deformation location information back to the ground control center in real time for display and storage. The dispatch center staff observe the wellbore deformation in real time and make planned maintenance or immediate maintenance decisions based on the evaluation report.

[0007] Specifically, in step three, the pose change is calculated as follows: Location: ; attitude: ; In the formula, and for Translation and rotation changes over time, and for The translational and rotational changes over time represent the real-time position and attitude changes of the cage.

[0008] Specifically, the vibration filtering algorithm based on unit quaternions in step three is as follows: assuming the well shaft is vertical and continuous, and the ideal motion posture of the cage should change smoothly, a unit quaternion is initialized under this premise, an adaptive Kalman filter is constructed, and by predicting state variables and errors and calculating the Kalman gain, the actual state is corrected and the error is updated, thereby achieving noise reduction filtering during the process of solving the rotation matrix; specifically including: a. from the IMU angular velocity... Initialize the identity quaternion The initialization method is: ; Right now , , , In the formula, Let this be the dynamic unit vector of a measurement point in the IMU. These are respectively the unit vectors Axis components; and attitude update is performed using the first-order Runge-Kutta method: ; b. Construct an adaptive Kalman filter with the following state variables: ,in Zero bias of the gyroscope; measured by accelerometer. The projection onto the horizontal plane is used as an observation to correct attitude errors caused by vibration and integral drift; Based on the previous moment The optimal state, predicting the current moment. Status: In the formula, for time The predicted state, This is the state transition matrix, describing the change of the IMU's angular velocity over time. for time The optimal state, The control matrix describes the degree of influence of external control on angular velocity. for The gyroscope is at zero bias at any given moment; The error range in predicting the current state reflects the reliability of the prediction result: ; express time The larger the value of the prediction error covariance matrix, the less reliable the prediction. express time The optimal error covariance matrix, Represents the noise covariance of the system process; calculate time The Kalman gain is used to balance the weights of the predicted and measured values: In the formula, express time Kalman gain, The observation matrix describes The relationship between measured values ​​and state. To measure the covariance of noise; By combining the predicted and measured values, the current state is obtained. Optimal estimate: In the formula, for time The optimal state, for time The measured value; After the update and correction The uncertainty of the state provides a basis for error in predicting the next moment: In the formula, for time The optimal error covariance matrix has a value less than ; c. After Kalman filtering, the unit quaternion is obtained as follows: Then, the rotation matrix is ​​obtained according to the standardized formula for converting a quaternion to a rotation matrix. : Assumption Then the rotation matrix from the laser scanner coordinate system to the cage carrier coordinate system, expressed in unit quaternions, is: .

[0009] Specifically, the dynamic coordinate transformation in step three is as follows: For Every raw dynamic point cloud collected in real time A dynamic coordinate transformation model is used to transform it to the fixed coordinate system of the wellbore. : In the formula, This represents the corrected static wellbore point cloud. , This represents the rotation matrix and translation vector between the lidar and the wellbore; this step pulls the scanner's viewpoint, which oscillates with the vibration of the cage, back to a fixed spatial reference frame, namely the wellbore's fixed coordinate system; Translation vector , These represent the coordinates of the laser scanner relative to the coordinates of the cage carrier. The amount of translation of the axis.

[0010] Specifically, in step three, the static wellbore model is reconstructed using a quadratic surface fitting algorithm based on the least squares method. The specific steps are as follows: Let the corrected static wellbore point cloud set be... a certain point in the middle of The neighborhood point set is , Laser scanner coordinate system Coordinates; establish the equation of the quadratic surface through neighborhood points: In the formula, These represent the horizontal x-coordinate and vertical y-coordinate in the image coordinate system provided by the RGB image of the laser scanner when generating point cloud data. express Neighborhood A point cloud, Indicates the neighborhood The horizontal and vertical coordinates of a point cloud RGB image; Further study of the curved surface Taking the partial derivative, we get Then the unit normal vector of the surface The calculation is as follows: ; according to The first and second fundamental vectors of the surface are calculated as follows: First fundamental vector: Second fundamental vector: ; Calculate the mean curvature: ; Gaussian curvature: ; Based on the above results, the principal curvature is calculated as follows: ; Finally, the set of all the above surface fitting calculation results and the spatial measurement data from the laser scanner is used as the reconstructed static wellbore model. , ,in, This represents the lateral coordinates of a point cloud slice measured by a laser scanner. This represents the radial coordinates of a point cloud slice measured by a laser scanner.

[0011] Specifically, in step four, the intelligent evaluation of the calculated radial displacement, cross-sectional convergence, and wellbore curvature change involves the following steps: In the formula, For wellbore deformation index, This is the proportionality constant for radial displacement, typically taken as 0.3. This is the proportionality constant for the cross-sectional convergence, typically taken as 0.4. This is the proportionality constant for the change in wellbore curvature, typically taken as 0.3; Based on the severity of deformation The following assessment levels and decision recommendations are provided: Level 1: The wellbore deformation is 0, maintaining normal production status; Level 2: The overhaul is scheduled to be completed within the month; Level 3: The maintenance is scheduled to be completed within the week; Level 4: Repair immediately.

[0012] Due to the adoption of the technical solution described above, the present invention has the following advantages: This invention utilizes existing cages as a platform, without affecting normal production. A single lift can complete a full shaft scan, reducing the detection cycle from days to hours, ensuring safety and efficiency. During cage operation, it obtains continuous, high-density 3D point clouds of the entire shaft cross-section, eliminating measurement blind spots and providing comprehensive data. Through the proposed vibration elimination algorithm, it effectively eliminates cage vibration interference, achieving centimeter-level accuracy in the reconstructed model, meeting engineering monitoring requirements with high precision. It also enables automated deformation calculation, analysis, and early warning, generating intuitive visual reports that facilitate producer management decisions, demonstrating a high degree of intelligence. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the system installation of the present invention.

[0014] Figure 2 This is a schematic diagram of the dynamic coordinate transformation in step three of the present invention.

[0015] In the diagram: 1-cage, 2-3D laser scanner, 3-inertial measurement unit, 4-explosion-proof box, 5-shaft, 6-hoisting wire rope; Wellbore Fixed Coordinate System ( ), cage carrier coordinate system ( ), laser scanner coordinate system ( ); Assume a point in the wellbore space The dashed arrows in the diagram represent coordinate transformation relationships: . Detailed Implementation

[0016] The present invention will be further explained and described below with reference to the accompanying drawings and embodiments. However, this should not be construed as limiting the scope of protection of the present invention. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention.

[0017] Combined with appendix Figure 1-2 The method for detecting deformation of coal mine vertical shaft 5 based on onboard mobile laser scanning, as shown, includes the following specific steps: Step 1: System installation and coordinate system establishment; A mine-use intrinsically safe and explosion-proof enclosure 4 with a protection level ≥ IP67 is rigidly installed on the top of cage 1. A high-performance edge processing unit, an explosion-proof power supply, and a switch are deployed inside the enclosure. The edge processing unit communicates with the wellhead through a 5G CPE wireless communication module and a Wi-Fi 6 AP wireless signal transmitter installed in the shaft 5, thereby uploading information to the ground control center. A 3D laser scanner 2 and an inertial measurement unit 3 (IMU) are rigidly installed at the center of the top of the explosion-proof enclosure 4. The two communicate with the edge processing unit through a network cable and the switch inside the enclosure to realize real-time transmission of measurement data. With the center of the wellhead as the origin, The axis is vertically downward to determine the fixed coordinate system of the wellbore. Through precise measurements, a coordinate system for the cage carrier is established with the origin at the geometric center of the cage carrier 1. and laser scanner coordinate system .

[0018] Step 2: Dynamic data synchronization collection; At a constant speed of 2-4 m / s in cage 1 During a lifting cycle, the laser scanner acquires raw dynamic point cloud data of the wellbore at a high frequency, such as 10Hz. The inertial measurement unit (IMU) 3, comprising a gyroscope and an accelerometer, acquires the angular velocity of cage 1 at a frequency of 100 Hz. and acceleration All data is timestamped as raw IMU data.

[0019] Step 3: Pose correction and static wellbore model reconstruction; Data cleaning and zero-bias correction were performed on the raw IMU data to solve the problem of cage 1 in the fixed coordinate system of the wellbore. The pose changes under these conditions are analyzed using a vibration filtering algorithm based on unit quaternions and dynamic coordinate transformation to transform the original dynamic point cloud data. Corrected to the fixed coordinate system of the wellbore The static wellbore point cloud after vibration interference was eliminated was obtained. The static wellbore model was reconstructed using a quadratic surface fitting algorithm based on the least squares method. .

[0020] The pose change is calculated as follows: Location: ; attitude: ; In the formula, and for Translation and rotation changes over time, and for The translational and rotational changes at any given time represent the real-time position and attitude changes of cage 1.

[0021] The vibration filtering algorithm based on unit quaternions is as follows: Assuming the shaft 5 is vertical and continuous, and the ideal motion posture of the cage 1 should change smoothly, a unit quaternion is initialized under this premise. An adaptive Kalman filter is constructed. By predicting the state variables and errors, and calculating the Kalman gain, the actual state is corrected and the error is updated, thereby achieving noise reduction filtering during the process of solving the rotation matrix. Specifically, this includes: a. From IMU angular velocity Initialize the identity quaternion The initialization method is: ; Right now , , , In the formula, Let this be the dynamic unit vector of a measurement point in the IMU. These are respectively the unit vectors Axial components; And the attitude is updated using the first-order Rungekuta method: ; b. Construct an adaptive Kalman filter with the following state variables: ,in Zero bias of the gyroscope; measured by accelerometer. The projection onto the horizontal plane is used as an observation to correct attitude errors caused by vibration and integral drift; Based on the previous moment The optimal state, predicting the current moment. Status: In the formula, for time The predicted state, This is the state transition matrix, describing the change of the IMU's angular velocity over time. for time The optimal state, The control matrix describes the degree of influence of external control on angular velocity. for The gyroscope is at zero bias at any given moment; The error range in predicting the current state reflects the reliability of the prediction result: ; express time The larger the value of the prediction error covariance matrix, the less reliable the prediction. express time The optimal error covariance matrix, Represents the noise covariance of the system process; calculate time The Kalman gain is used to balance the weights of the predicted and measured values: In the formula, express time Kalman gain, The observation matrix describes The relationship between measured values ​​and state. To measure the covariance of noise; By combining the predicted and measured values, the current state is obtained. Optimal estimate: In the formula, for time The optimal state, for time The measured value; After the update and correction The uncertainty of the state provides a basis for error in predicting the next moment: In the formula, for time The optimal error covariance matrix has a value less than ; c. After Kalman filtering, the unit quaternion is obtained as follows: Then, the rotation matrix is ​​obtained according to the standardized formula for converting a quaternion to a rotation matrix. : Assumption Then the rotation matrix from the laser scanner coordinate system to the cage carrier coordinate system, expressed in unit quaternions, is: .

[0022] Dynamic coordinate transformation specifically refers to: for Every raw dynamic point cloud collected in real time A dynamic coordinate transformation model is used to transform it to the fixed coordinate system of the wellbore. : In the formula, This represents the corrected static wellbore point cloud. , This represents the rotation matrix and translation vector between the lidar and the wellbore 5; this step "pulls back" the scanner's viewpoint, which swings with the vibration of the cage 1, to a fixed spatial reference system, namely the fixed coordinate system of the wellbore; Translation vector , These represent the coordinates of the laser scanner relative to the coordinates of the cage carrier. The amount of translation of the axis.

[0023] Static wellbore model reconstructed using a quadratic surface fitting algorithm based on the least squares method. The specific steps are as follows: Let the corrected static wellbore point cloud set be... a certain point in the middle of The neighborhood point set is , Laser scanner coordinate system Coordinates; establish the equation of the quadratic surface through neighborhood points: In the formula, These represent the horizontal x-coordinate and vertical y-coordinate in the image coordinate system provided by the RGB image of the laser scanner when generating point cloud data. express Neighborhood A point cloud, Indicates the neighborhood The horizontal and vertical coordinates of a point cloud RGB image; Further study of the curved surface Taking the partial derivative, we get Then the unit normal vector of the surface The calculation is as follows: ; according to The first and second fundamental vectors of the surface are calculated as follows: First fundamental vector: Second fundamental vector: ; Calculate the mean curvature: ; Gaussian curvature: ; Based on the above results, the principal curvature is calculated as follows: ; Finally, the set of all the above surface fitting calculation results and the spatial measurement data from the laser scanner is used as the reconstructed static wellbore model. , ,in, This represents the lateral coordinates of a point cloud slice measured by a laser scanner. This represents the radial coordinates of a point cloud slice measured by a laser scanner.

[0024] Step 4: Deformation detection and analysis; The currently reconstructed wellbore model Compared with historical benchmark models To perform precise registration and comparison, multiple point cloud slices are extracted at the same elevation, and the data from each point cloud slice is collected. and The difference between the radial coordinates and the difference between the lateral coordinates constitutes the radial displacement of the wellbore. and wellbore cross-section convergence Take each point cloud slice and The difference in principal curvature constitutes the change in wellbore curvature. Simultaneous calculation Each point in the middle The closest distance to the surface is used to generate a wellbore deformation chromatogram, which visually displays the location and extent of deformation and automatically marks areas where the deformation exceeds the limit, thus identifying areas where the deformation exceeds the limit. The calculated radial displacement, cross-sectional convergence, and wellbore curvature change are intelligently evaluated, and an intelligent evaluation report is generated; specifically: In the formula, For wellbore deformation index, This is the proportionality constant for radial displacement, typically taken as 0.3. This is the proportionality constant for the cross-sectional convergence, typically taken as 0.4. This is the proportionality constant for the change in wellbore curvature, typically taken as 0.3.

[0025] The location information of the deformation, i.e. the vertical distance from the deformation location to the wellhead, is measured by the 3D laser scanner 2. The edge processing unit transmits the intelligent assessment report and deformation location information back to the ground control center in real time for display and storage. The dispatch center staff observe the wellbore deformation in real time and make a plan maintenance or immediate maintenance decision based on the assessment report. Based on the severity of deformation The following assessment levels and decision recommendations are provided: Level 1: The wellbore deformation is 0, maintaining normal production status; Level 2: The overhaul is scheduled to be completed within the month; Level 3: The maintenance is scheduled to be completed within the week; Level 4: Repair immediately.

[0026] The parts of this invention not described in detail are prior art.

[0027] The embodiments selected herein for the purpose of disclosing the inventive objectives are currently considered suitable; however, it should be understood that the invention is intended to include all variations and modifications of the embodiments that fall within the scope of this concept and invention.

Claims

1. A method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning, characterized in that, The specific steps are as follows: Step 1: System installation and coordinate system establishment; An intrinsically safe and explosion-proof enclosure for mining is rigidly installed on the top of the cage. A high-performance edge processing unit, an explosion-proof power supply, and a switch are deployed inside the enclosure. The edge processing unit communicates with the wellhead through a wireless communication module and a wireless signal transmitter installed in the shaft, thereby uploading information to the ground control center. A 3D laser scanner and an inertial measurement unit (IMU) are rigidly installed at the center of the top of the explosion-proof enclosure. The two communicate with the edge processing unit through a network cable and a switch inside the enclosure to achieve real-time transmission of measurement data. With the center of the wellhead as the origin, The axis is vertically downward to determine the fixed coordinate system of the wellbore. Through precise measurements, a coordinate system for the cage carrier is established with the origin at the geometric center of the cage carrier. and laser scanner coordinate system ; Step 2: Dynamic data synchronization collection; The cage moves at a constant speed During a lifting cycle, the laser scanner acquires raw dynamic point cloud data of the wellbore. The inertial measurement unit (IMU) includes a gyroscope and an accelerometer, which respectively collect the angular velocity of the cage. and acceleration All data is timestamped as raw IMU data; Step 3: Pose correction and static wellbore model reconstruction; Data cleaning and zero-bias correction are performed on the raw IMU data to solve for the cage in the fixed coordinate system of the wellbore. The pose changes under these conditions are analyzed using a vibration filtering algorithm based on unit quaternions and dynamic coordinate transformation to transform the original dynamic point cloud data. Corrected to the fixed coordinate system of the wellbore The static wellbore point cloud after vibration interference was eliminated was obtained. The static wellbore model was reconstructed using a quadratic surface fitting algorithm based on the least squares method. ; Step 4: Deformation detection and analysis; The currently reconstructed static wellbore model Compared with historical benchmark models To perform precise registration and comparison, multiple point cloud slices are extracted at the same elevation, and the data from each point cloud slice is collected. and The difference between the radial coordinates and the difference between the lateral coordinates constitutes the radial displacement of the wellbore. and wellbore cross-section convergence Take each point cloud slice and The difference in principal curvature constitutes the change in wellbore curvature. Simultaneous calculation Each point in the middle The closest distance to the surface is used to generate a wellbore deformation chromatogram, which visually displays the location and extent of deformation and automatically marks areas where the deformation exceeds the limit, thus identifying areas where the deformation exceeds the limit. The calculated radial displacement, cross-sectional convergence, and wellbore curvature change are intelligently evaluated to generate an intelligent evaluation report. The location information of the deformation, i.e., the vertical distance of the deformation location from the wellhead, is measured by a 3D laser scanner. The edge processing unit transmits the intelligent evaluation report and deformation location information back to the ground control center in real time for display and storage. The dispatch center staff observe the wellbore deformation in real time and make planned maintenance or immediate maintenance decisions based on the evaluation report.

2. The method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning according to claim 1, characterized in that: In step three, the pose change is calculated as follows: Location: ; attitude: ; In the formula, and for Translation and rotation changes over time, and for The translational and rotational changes over time represent the real-time position and attitude changes of the cage.

3. The method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning according to claim 2, characterized in that: The vibration filtering algorithm based on unit quaternions in step three is as follows: Assuming the well shaft is vertical and continuous, and the ideal motion posture of the cage should change smoothly, a unit quaternion is initialized under this premise. An adaptive Kalman filter is constructed. By predicting state variables and errors, and calculating the Kalman gain, the actual state is corrected and the error is updated, thereby achieving noise reduction filtering during the solution of the rotation matrix. Specifically, this includes: a. From the IMU angular velocity... Initialize the unit quaternion The initialization method is: ; Right now , , , In the formula, Let this be the dynamic unit vector of a measurement point in the IMU. These are respectively the unit vectors Axis components; and attitude update is performed using the first-order Runge-Kutta method: ; b. Construct an adaptive Kalman filter with the following state variables: ,in Zero bias of the gyroscope; measured by accelerometer. The projection onto the horizontal plane is used as an observation to correct attitude errors caused by vibration and integral drift; based on the previous moment... The optimal state, predicting the current moment. Status: In the formula, for time The predicted state, This is the state transition matrix, describing the change of the IMU's angular velocity over time. for time The optimal state, The control matrix describes the degree of influence of external control on angular velocity. for The gyroscope is at zero bias at any given moment; The error range in predicting the current state reflects the reliability of the prediction result: ; express time The larger the value of the prediction error covariance matrix, the less reliable the prediction. express time The optimal error covariance matrix, Represents the noise covariance of the system process; calculate time The Kalman gain is used to balance the weights of the predicted and measured values: In the formula, express time Kalman gain, The observation matrix describes The relationship between measured values ​​and state. To measure the covariance of noise; By combining the predicted and measured values, the current state is obtained. Optimal estimate: In the formula, for time The optimal state, for time The measured value; After the update and correction The uncertainty of the state provides a basis for error in predicting the next moment: In the formula, for time The optimal error covariance matrix has a value less than ; c. After Kalman filtering, the unit quaternion is obtained as follows: Then, the rotation matrix is ​​obtained according to the standardized formula for converting a quaternion to a rotation matrix. : Assumption Then the rotation matrix from the laser scanner coordinate system to the cage carrier coordinate system, expressed in unit quaternions, is: 。 4. The method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning according to claim 3, characterized in that: The dynamic coordinate transformation in step three specifically refers to: for Every raw dynamic point cloud collected in real time A dynamic coordinate transformation model is used to transform it to the fixed coordinate system of the wellbore. : In the formula, This represents the corrected static wellbore point cloud. , This represents the rotation matrix and translation vector between the lidar and the wellbore. Translation vector , These represent the coordinates of the laser scanner relative to the coordinates of the cage carrier. The amount of translation of the axis.

5. The method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning according to claim 1, characterized in that: In step three, the static wellbore model is reconstructed using a quadratic surface fitting algorithm based on the least squares method. The specific steps are as follows: Let the corrected static wellbore point cloud set be... a certain point in the middle of The neighborhood point set is , Laser scanner coordinate system Coordinates; establish the equation of the quadratic surface through neighborhood points: In the formula, These represent the horizontal x-coordinate and vertical y-coordinate in the image coordinate system provided by the RGB image of the laser scanner when generating point cloud data. express Neighborhood A point cloud, Indicates the neighborhood The horizontal and vertical coordinates of a point cloud RGB image; Further study of the curved surface Taking the partial derivative, we get Then the unit normal vector of the surface The calculation is as follows: ; according to The first and second fundamental vectors of the surface are calculated as follows: First fundamental vector: Second fundamental vector: ; Calculate the mean curvature: ; Gaussian curvature: ; Based on the above results, the principal curvature is calculated as follows: ; Finally, the set of all the above surface fitting calculation results and the spatial measurement data from the laser scanner is used as the reconstructed static wellbore model. , ,in, This represents the lateral coordinates of a point cloud slice measured by a laser scanner. This represents the radial coordinates of a point cloud slice measured by a laser scanner.

6. The method for detecting deformation of coal mine vertical shafts based on onboard mobile laser scanning according to claim 1, characterized in that: In step four, the intelligent evaluation of the calculated radial displacement, cross-sectional convergence, and wellbore curvature change specifically involves: In the formula, For wellbore deformation index, This is the proportionality constant for radial displacement, typically taken as 0.

3. This is the proportionality constant for the cross-sectional convergence, typically taken as 0.

4. This is the proportionality constant for the change in wellbore curvature, typically taken as 0.3; Based on the severity of deformation The following assessment levels and decision recommendations are provided: Level 1: The wellbore deformation is 0, maintaining normal production status; Level 2: The overhaul is scheduled to be completed within the month; Level 3: The maintenance is scheduled to be completed within the week. Level 4: Repair immediately.

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