Error self-correction control method for power transmission line foundation pit-dividing three-dimensional laser radar equipment

Abstract

The invention discloses a power transmission line foundation pit three-dimensional laser radar equipment error self-correction control method, and relates to the technical field of power transmission line construction, and the method comprises the following steps: firstly, defining the distance from three-dimensional laser radar equipment to a target point, a horizontal included angle and a vertical included angle based on a Cartesian coordinate system; determining an initial coordinate of the target point; secondly, selecting a reference control point, removing noise and gross error points, correcting offset in the vertical direction, and finely processing point cloud data; thirdly, defining errors in the X-axis direction, the Y-axis direction and the Z-axis direction, and establishing an error correction model; and finally, based on the observation value and an error correction model, carrying out self-correction on the target coordinate, and carrying out correction through an environment feature self-adaptive error weight distribution mechanism. According to the method, closed-loop iterative optimization is performed through a multi-level data verification mechanism, the measurement precision is ensured, the operation efficiency is improved, the environmental adaptability is enhanced, and the method is suitable for power transmission line foundation pit division operation under complex terrains.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line construction, and in particular to a method for self-correcting and controlling errors of three-dimensional laser radar equipment for foundation pits of power transmission lines. Background Art

[0002] In modern power system construction, the accuracy and efficiency of transmission line foundation pit positioning are critical factors in ensuring the smooth progress of the entire project. Traditional transmission line foundation pit positioning primarily utilizes traditional equipment such as theodolites and total stations. This is inefficient, requires high technical skills from surveyors, and is susceptible to human influence, leading to deviations in the positioning of control piles, directional piles, high and low leg positions, and anchor bolts. With the rapid construction of major new energy base projects in my country and the construction of ultra-high voltage transmission lines, transmission line installation areas are gradually extending to mountainous areas, plateaus, and other areas with complex terrain. The drawbacks of traditional pit positioning methods are becoming increasingly prominent.

[0003] 3D laser mapping radars, based on 3D simultaneous localization and mapping (SLAM) technology, can clearly display the three-dimensional terrain information of transmission line operation scenarios. Using this type of equipment helps to improve the automation of transmission line foundation pit subdivision operations and reduce the impact of manual operation. However, in complex terrain with high plant cover and fragmented landforms, as well as in scenarios with low reflectivity of the detected target, measurement errors can occur due to the performance limitations of the lidar equipment itself. Reasonable measures must be taken to control these errors and ensure the accuracy of transmission line foundation pit subdivision.

[0004] In view of this, the present invention proposes a self-correction control method for the error of three-dimensional laser radar equipment for transmission line foundation pit division, which changes the traditional theodolite pit division mode of transmission line foundation. While utilizing three-dimensional laser radar equipment, it controls the measurement error and efficiently and accurately performs on-site operations of transmission line tower foundation pit division. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a self-correction control method for the error of three-dimensional laser radar equipment in the foundation pit of the transmission line.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The error self-correction control method of three-dimensional laser radar equipment for transmission line foundation pits includes the following steps:

[0008] S1. Based on the Cartesian coordinate system, define the distance L, horizontal angle θ, and vertical angle ψ between the 3D laser radar device and the target point A, identify the relative position relationship between the 3D laser radar device and the target point, determine the coordinates of the target point in the Cartesian coordinate system, and form the initial observation results;

[0009] S2. Select reference control point B, remove noise and gross error points, correct vertical offset, and refine the 3D laser point cloud data.

[0010] S3. Define the errors in the X, Y, and Z axes in the Cartesian coordinate system as Δε x-A , Δε y-A , Δε z-A ,Based on the equipment position information calibrated by the multi-mode positioning device bound to the 3D laser radar, an error correction model for the 3D laser point cloud based pit classification equipment is established;

[0011] S4. The target coordinates are self-corrected based on the observation values ​​and the error correction model, and the errors are corrected through an adaptive error weight distribution mechanism based on environmental characteristics.

[0012] Furthermore, the specific process of S1 is as follows:

[0013] S1-101: Define the measurement parameters of the 3D LiDAR basic pit division equipment:

[0014] The location of the 3D laser radar basic pit division equipment is taken as the origin O(0, 0, 0), and the distance from the 3D laser radar basic pit division equipment to any target point A(X A , Y A , Z A ) is the straight-line distance L A ;

[0015] The horizontal angle θ is defined as the horizontal plane rotating clockwise from the true north direction of the three-dimensional laser radar basic pit equipment to the measurement target point A (X A , Y A , Z A ) directions;

[0016] Define the vertical angle ψ, which is the vertical angle of the three-dimensional laser radar base pit device rotating upward or downward along the horizontal plane to the measurement target point A (X A , Y A , Z A ) directions;

[0017] S1-102: Establish 3D laser radar basic pit equipment and measure target point A(X A , Y A , Z A )'s relative position relationship:

[0018] According to the parameters defined in step S1-101, the relative position relationship between the 3D laser radar basic pit division equipment and the measurement target point A is established, and the coordinate information is converted into the Cartesian coordinate system using the following formula:

[0019] X A =L A ×cos(ψ)×cos(θ), Y A =L A ×cos(ψ)×sin(θ), Z A =L A ×sin(ψ)

[0020] After the conversion is completed, the obtained coordinate information is the initial observation result formed based on the measurement data of the current three-dimensional lidar basic pit division equipment.

[0021] Furthermore, the specific operation steps of S2 are as follows:

[0022] S2-101: Based on the provided pit operation design documents, use real-time dynamic positioning equipment to determine the position of the center pile at the operation site as the reference control point B (X B , Y B , Z B ), providing a benchmark for subsequent data correction;

[0023] S2-102: Determine any target point A(X A , Y A , Z A ) and the reference control point B(X B , Y B , Z B ) values:

[0024] Δx A =|X A -X B |, Δy A =|Y A -Y B |, Δz A =|Z A -Z B |;

[0025] S2-103: When Δx A >2σ dyn or Δy A >2σ dyn or Δz A >2σ dyn , then it is considered that the corresponding target point A(X A , Y A , Z A) are gross error points and are deleted to refine the point cloud data obtained by the 3D laser radar device; where σ dyn is the dynamic threshold;

[0026] S2-104: Use the filtered 3D laser point cloud data to estimate the relative position and use the following formula to determine any measurement target point A(X A , Y A , Z A ) and the reference control point B(X B , Y B , Z B )'s relative position: X rel-A =X A -X B 、Y rel-A =Y A -Y B , Z rel-A =Z A -Z B .

[0027] Furthermore, the dynamic threshold σ dyn The determination process is as follows:

[0028] Integrate temperature and humidity sensors into 3D lidar equipment to collect temperature T and humidity H data of the working environment in real time;

[0029] Through experimental calibration, the influence of temperature and humidity on laser ranging error is calibrated, and a linear relationship model is established: ΔL=k T ×ΔT+k H ×ΔH, where ΔT and ΔH are the deviations between the current temperature and humidity and the calibration reference values, k T 、k H is the correction factor;

[0030] Dynamic threshold calculation Among them L 标 is the calibration distance, σ r is a fixed threshold.

[0031] Furthermore, the specific operation steps of S3 are as follows:

[0032] S3-101: Define any target measurement point A(X A , Y A , Z A ) In the Cartesian coordinate system, the errors in the X, Y, and Z axes are Δε x-A , Δε y-A , Δε z-A :

[0033] Where R is the reference control point B(XB , Y B , Z B ) to the Euler angle rotation matrix of the origin O(0, 0, 0) where the 3D laser radar basic pit equipment is located:

[0034]

[0035] The three angles of pitch angle θ, yaw angle ψ, and roll angle φ are respectively the reference control point B(X B , Y B , Z B ) For the clockwise rotation angles of the Cartesian coordinate system X, Y, and Z axes, no additional roll angle φ is usually required, and the pitch angle θ and yaw angle ψ can be calculated by the following formula:

[0036]

[0037] S3-102: Based on step S3-101, any target measurement point A(X A , Y A , Z A ) to correct the coordinates and obtain the corrected coordinate information:

[0038]

[0039] Furthermore, the specific operation steps of S4 are as follows:

[0040] In the initial observation of step S1, the terrain slope of the target point A calculated by the LiDAR point cloud and the reflectivity value analyzed by the laser echo intensity are recorded simultaneously;

[0041] Define the error correction weight based on the slope α and reflectivity β:

[0042] W x =k1×e -α +k1×β,W y 、W z The correction is carried out in the same way, where k1 and k2 are experimental calibration coefficients, and the obtained W x 、W y 、W z In the coordinate correction process, the corresponding error Δε x-A , Δε y-A , Δε z-A Multiply to achieve dynamic error compensation.

[0043] Furthermore, the S4 also includes

[0044] A multi-level data verification mechanism is used for closed-loop iterative optimization. The process is as follows:

[0045] After eliminating gross error points in S2, the spatial consistency of the point cloud data is verified using the relative position of the reference control point B and the target point A. If the point cloud density in a certain area is lower than the threshold, a local rescan is triggered.

[0046] After the self-calibration in S4, the corrected coordinates are reversed to the original measurement parameters (L, θ, ψ), and the theoretical coordinates are regenerated through step S1-102 and compared with the actual corrected coordinates. If the deviation exceeds the threshold, the S2-S4 process is automatically iterated until convergence.

[0047] The number of iterations and the final deviation value are recorded as pit quality assessment indicators for post-construction acceptance.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] (1) The present invention significantly improves the accuracy and reliability of transmission line foundation pit measurement by introducing a three-dimensional laser radar equipment error self-correction control method. Traditional methods are easily affected by factors such as human operation and equipment performance limitations under complex terrain and environmental conditions, resulting in large measurement errors. The present invention can effectively reduce measurement errors by defining measurement parameters, establishing an error correction model, and an adaptive error weight distribution mechanism, ensuring measurement accuracy in complex environments, greatly improving construction accuracy and reliability, and providing a solid technical guarantee for transmission line construction.

[0050] (2) The present invention changes the traditional mode of using theodolites and other equipment for transmission line foundation pit division. While utilizing three-dimensional laser radar equipment, it also realizes the automation of transmission line tower foundation pit division through error self-correction control method. The method can quickly process three-dimensional laser point cloud data, remove noise and gross error points, and perform closed-loop iterative optimization through a multi-level data verification mechanism, thereby reducing manual intervention and improving operation efficiency. At the same time, the center pile position of the operation site is determined as a reference control point through real-time dynamic positioning equipment, providing a benchmark for subsequent data correction, further improving the degree of automation of the operation, shortening the construction period, reducing labor intensity, and improving construction efficiency.

[0051] (3) The present invention introduces environmental feature analysis during the measurement process. By collecting temperature and humidity data of the working environment in real time and combining it with the linear relationship model calibrated by the experiment, the error threshold is dynamically adjusted, thereby enhancing the adaptability of the measurement system to complex environments. In addition, by finely processing the three-dimensional laser point cloud data, eliminating gross error points and correcting vertical offsets, the data quality is further improved. The closed-loop iterative optimization is performed using a multi-level data verification mechanism, which can automatically detect and correct abnormal points in the data to ensure the consistency and accuracy of the data. This not only improves the reliability of the measurement results, but also provides high-quality data support for subsequent construction acceptance, which helps to improve the quality and safety of the entire transmission line construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 This is a relative position diagram under the Cartesian coordinate system in the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0056] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0057] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0058] like Figure 1-Figure 2 As shown, the error self-correction control method of the three-dimensional laser radar equipment for the transmission line foundation pit includes the following steps:

[0059] S1. Based on the Cartesian coordinate system, define the distance L, horizontal angle θ, and vertical angle ψ between the 3D laser radar device and the target point A, identify the relative position relationship between the 3D laser radar device and the target point, determine the coordinates of the target point in the Cartesian coordinate system, and form the initial observation results;

[0060] S1-101: Define the measurement parameters of the 3D LiDAR basic pit division equipment:

[0061] The location of the 3D laser radar basic pit division equipment is taken as the origin O(0, 0, 0), and the distance from the 3D laser radar basic pit division equipment to any target point A(X A , Y A , Z A ) is the straight-line distance L A ;

[0062] The horizontal angle θ is defined as the horizontal plane rotating clockwise along the true north direction (or other reference direction) of the 3D laser radar basic pit equipment to the measurement target point A (X A , Y A , Z A ) directions;

[0063] Define the vertical angle ψ, which is the angle between the three-dimensional laser radar base pit device and the target point A(X A , Y A , Z A ) directions.

[0064] S1-102: Establish 3D laser radar basic pit equipment and measure target point A(X A , Y A , Z A )'s relative position relationship:

[0065] According to the parameters defined in step S1-101, the relative position relationship between the 3D laser radar basic pit division equipment and the measurement target point A is established, and the coordinate information is converted into the Cartesian coordinate system using the following formula:

[0066] X A =L A ×cos(ψ)×cos(θ), Y A =L A ×cos(ψ)×sin(θ), Z A =L A ×sin(ψ)

[0067] After completing the above conversion, the obtained coordinate information is the initial observation result formed based on the measurement data of the current three-dimensional lidar basic pit division equipment.

[0068] S2. Select reference control point B, remove noise and gross error points, correct vertical offset, and refine the 3D laser point cloud data.

[0069] S2-101: Based on the provided pit operation design documents, use the Real-Time Kinematic (RTK) equipment to determine the location of the center stake at the operation site as the reference control point B (X B , Y B , Z B ), providing a benchmark for subsequent data correction;

[0070] S2-102: Determine any target point A(X A , Y A , Z A ) and the reference control point B(X B , Y B , Z B ) values:

[0071] Δx A =|X A -X B |, Δy A =|Y A -Y B |, Δz A =|Z A -Z B |;

[0072] S2-103: When Δx A >2σ dyn or Δy A >2σ dyn or Δz A >2σ dyn , then it is considered that the corresponding target point A(X A , Y A , Z A ) are gross error points and are deleted to refine the point cloud data obtained by the 3D laser radar device; where σ dyn The determination process is as follows:

[0073] A temperature and humidity sensor is integrated into the 3D lidar device to collect real-time temperature (T) and humidity (H) data of the working environment. The influence of temperature and humidity on laser ranging error is calibrated experimentally, and a linear relationship model is established: ΔL = k T ×ΔT+k H ×ΔH, where ΔT and ΔH are the deviations between the current temperature and humidity and the calibration reference values, k T 、k H is the correction factor;

[0074] Dynamic threshold calculation Among them L 标 is the calibration distance (such as 10m), σ r is a fixed threshold, set to 0.02m.

[0075] S2-104: Use the filtered 3D laser point cloud data to estimate the relative position and use the following formula to determine any measurement target point A(X A , Y A , Z A ) and the reference control point B(X B , Y B , Z B )'s relative position:

[0076] X rel-A =X A -X B 、Y rel-A =Y A -Y B , Z rel-A =Z A -Z B ;

[0077] S3. Define the errors in the X, Y, and Z axes in the Cartesian coordinate system as Δε x-A , Δε y-A , Δε z-A Based on the device position information calibrated by the multi-mode positioning device bound to the 3D laser radar, an error correction model for the 3D laser point cloud-based pit classification device is established; the process is as follows:

[0078] S3-101: Define any target measurement point A(X A , Y A , Z A ) In the Cartesian coordinate system, the errors in the X, Y, and Z axes are Δε x-A , Δε y-A , Δε z-A ,

[0079] Where R is the reference control point B(X B , Y B , Z B ) to the Euler angle rotation matrix of the origin O(0, 0, 0) where the 3D laser radar basic pit equipment is located:

[0080]

[0081] The three angles of pitch angle θ, yaw angle ψ, and roll angle φ are respectively the reference control point B(X B, Y B , Z B ) For the clockwise rotation angles of the Cartesian coordinate system X, Y, and Z axes, no additional roll angle φ is usually required, and the pitch angle θ and yaw angle ψ can be calculated by the following formula:

[0082]

[0083] S3-102: Based on step S3-101, any target measurement point A(X A , Y A , Z A ) to correct the coordinates and obtain the corrected coordinate information:

[0084]

[0085] After the above steps are calibrated, the measurement results of 10 point clouds from P1 to P10 in the working surface are analyzed, and the deviation results are shown in the following table:

[0086]

[0087] It can be seen that after multiple measurements, the average deviations of the measurement results in the X, Y, and Z axes are 0.0176m, 0.0177m, and 0.0181m respectively. On the transmission line foundation pit working surface at a distance of 10 meters, the pit measurement accuracy can be controlled to be no higher than 2‰.

[0088] S4, self-correcting the target coordinates based on the observation values ​​and the error correction model, and correcting the errors through an adaptive error weight distribution mechanism based on environmental characteristics, the process is as follows;

[0089] In the initial observation of step S1, the terrain slope (through lidar point cloud computing) and reflectivity value (through laser echo intensity analysis) of target point A are recorded synchronously;

[0090] Define the error correction weight based on slope (α) and reflectivity (β):

[0091] W x =k1×e -α +k1×β,W y 、W z The correction is carried out in the same way, where k1 and k2 are experimental calibration coefficients, and the obtained W x 、W y 、W z In the coordinate correction process, the corresponding error Δε x-A , Δε y-A , Δε z-A Multiplication to achieve dynamic error compensation;

[0092] A multi-level data verification mechanism is used for closed-loop iterative optimization. The process is as follows:

[0093] After the gross error points of S2 are eliminated, the spatial consistency of the point cloud data is verified using the relative position of the reference control point B and the target point A. If the point cloud density of a certain area is lower than the threshold (e.g. <10 points / m 2 ), triggering a local rescan. After self-calibration in S4, the corrected coordinates are back-calculated to the original measurement parameters (L, θ, ψ). The theoretical coordinates are regenerated through step S1-102 and compared with the actual corrected coordinates. If the deviation exceeds a threshold (e.g., 0.02m), the S2-S4 process is automatically iterated until convergence. The number of iterations and the final deviation value are recorded as pit quality assessment indicators for post-construction acceptance.

[0094] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for self-correcting errors in three-dimensional laser radar equipment for transmission line foundation pits, characterized in that: The following steps are involved: S1. Based on the Cartesian coordinate system, define the distance L, horizontal angle θ, and vertical angle ψ between the 3D laser radar device and the target point A, identify the relative position relationship between the 3D laser radar device and the target point, determine the coordinates of the target point in the Cartesian coordinate system, and form the initial observation results; S2. Select reference control point B, remove noise and gross error points, correct vertical offset, and refine the 3D laser point cloud data. S3. Define the errors in the X, Y, and Z axes in the Cartesian coordinate system as Δε x-A , Δε y-A , Δε z-A ,Based on the equipment position information calibrated by the multi-mode positioning device bound to the 3D laser radar, an error correction model for the 3D laser point cloud based pit classification equipment is established; S4. The target coordinates are self-corrected based on the observation values ​​and the error correction model, and the errors are corrected through an adaptive error weight distribution mechanism based on environmental characteristics.

2. The error self-correction control method for three-dimensional laser radar equipment for transmission line foundation pits according to claim 1 is characterized in that: The specific process of S1 is as follows: S1-101: Define the measurement parameters of the 3D LiDAR basic pit division equipment: The location of the 3D laser radar basic pit division equipment is taken as the origin O(0, 0, 0), and the distance from the 3D laser radar basic pit division equipment to any target point A(X A , Y A , Z A ) is the straight-line distance L A ; The horizontal angle θ is defined as the horizontal plane rotating clockwise from the true north direction of the three-dimensional laser radar basic pit equipment to the measurement target point A (X A , Y A , Z A ) directions; Define the vertical angle ψ, which is the vertical angle of the three-dimensional laser radar base pit device rotating upward or downward along the horizontal plane to the measurement target point A (X A , Y A , Z A ) directions; S1-102: Establish 3D laser radar basic pit equipment and measure target point A(X A , Y A , Z A )'s relative position relationship: According to the parameters defined in step S1-101, the relative position relationship between the 3D laser radar basic pit division equipment and the measurement target point A is established, and the coordinate information is converted into the Cartesian coordinate system using the following formula: X A =L A ×cos(ψ)×cos(θ),Y A =L A ×cos(ψ)×sin(θ), Z A =L A ×sin(ψ) After the conversion is completed, the obtained coordinate information is the initial observation result formed based on the measurement data of the current three-dimensional lidar basic pit division equipment.

3. The error self-correction control method for three-dimensional laser radar equipment for transmission line foundation pit according to claim 1 is characterized in that: The specific operation steps of S2 are as follows: S2-101: Based on the provided pit operation design documents, use real-time dynamic positioning equipment to determine the center pile position of the operation site as the reference control point B (X B , Y B , Z B ), providing a benchmark for subsequent data correction; S2-102: Determine any target point A(X A , Y A , Z A ) and the reference control point B(X B , Y B , Z B ) values: Δx A =|X A -X B |、Δy A =|Y A -Y B |、Δz A =|Z A -Z B |; S2-103: When Δx A >2σ dyn or Δy A >2σ dyn or Δz A >2σ dyn , then it is considered that the corresponding target point A(X A , Y A , Z A ) are gross error points and are deleted to refine the point cloud data obtained by the 3D laser radar device; where σ dyn is the dynamic threshold; S2-104: Use the filtered 3D laser point cloud data to estimate the relative position and use the following formula to determine any measurement target point A(X A , Y A , Z A ) and the reference control point B(X B , Y B , Z B )'s relative position: X rel-A =X A -X B 、Y rel-A =Y A -Y B , Z rel-A =Z A -Z B .

4. The error self-correction control method for three-dimensional laser radar equipment for transmission line foundation pits according to claim 3 is characterized in that: The dynamic threshold σ dyn The determination process is as follows: Integrate temperature and humidity sensors into 3D lidar equipment to collect temperature T and humidity H data of the working environment in real time; Through experimental calibration, the influence of temperature and humidity on laser ranging error is calibrated, and a linear relationship model is established: ΔL=k T ×ΔT+k H ×ΔH, where ΔT and ΔH are the deviations between the current temperature and humidity and the calibration reference values, k T 、k H is the correction factor; Dynamic threshold calculation Among them L 标 is the calibration distance, σ r is a fixed threshold.

5. The error self-correction control method for three-dimensional laser radar equipment for transmission line foundation pits according to claim 1 is characterized in that: The specific operation steps of S3 are as follows: S3-101: Define any target measurement point A(X A , Y A , Z A ) In the Cartesian coordinate system, the errors in the X, Y, and Z axes are Δε x-A , Δε y-A , Δε z-A : Where R is the reference control point B(X B , Y B , Z B ) to the Euler angle rotation matrix of the origin O(0, 0, 0) where the 3D laser radar basic pit equipment is located: The three angles of pitch angle θ, yaw angle ψ, and roll angle φ are respectively the reference control point B(X B , Y B , Z B ) For the clockwise rotation angles of the Cartesian coordinate system X, Y, and Z axes, no additional roll angle φ is usually required, and the pitch angle θ and yaw angle ψ can be calculated by the following formula: ψ=arctan(Y A ,X B ); S3-102: Based on step S3-101, any target measurement point A(X A , Y A , Z A ) to correct the coordinates and obtain the corrected coordinate information:

6. The error self-correction control method for three-dimensional laser radar equipment for transmission line foundation pits according to claim 1 is characterized in that: The specific operation steps of S4 are as follows: In the initial observation of step S1, the terrain slope of the target point A calculated by the LiDAR point cloud and the reflectivity value analyzed by the laser echo intensity are recorded simultaneously; Define the error correction weight based on the slope α and reflectivity β: W x =k1×e -α +k1×β,W y 、W z The correction is carried out in the same way, where k1 and k2 are experimental calibration coefficients, and the obtained W x 、W y 、W z In the coordinate correction process, the corresponding error Δε x-A , Δε y-A , Δε z-A Multiplication realizes dynamic error compensation.

7. The error self-correction control method for three-dimensional laser radar equipment for transmission line foundation pits according to claim 1 is characterized in that: Said S4 further comprises: A multi-level data verification mechanism is used for closed-loop iterative optimization. The process is as follows: After completing the elimination of gross error points in S2, the spatial consistency of the point cloud data is verified using the relative position of the reference control point B and the target point A. If the point cloud density in a certain area is lower than the threshold, a local rescan is triggered. After the self-calibration in S4, the corrected coordinates are reversed to the original measurement parameters, including L, θ, and ψ. The theoretical coordinates are regenerated through step S1-102 and compared with the actual corrected coordinates. If the deviation exceeds the threshold, the S2-S4 process is automatically iterated until convergence. The number of iterations and the final deviation value are recorded as pit quality assessment indicators for post-construction acceptance.

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