Slope instability device installation method, device and medium based on unmanned aerial vehicle deployment

Abstract

This invention relates to the field of monitoring equipment installation, and discloses a method, equipment, and medium for installing a slope instability device based on UAV deployment. The method includes: acquiring tower image data information using a UAV and performing structural feature identification and marking processing on the tower components to determine the installation position of the tower monitoring equipment and the UAV-assisted positioning position; generating a pre-installed robotic arm trajectory scheme based on the robotic arm kinematic model, the monitoring equipment installation position, and the UAV-assisted positioning position; acquiring robotic arm images and performing feature recognition and coordinate calibration processing to determine the image angles of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance; constraining the joint angles of the robotic arm and determining the unconstrained target joint angles of the robotic arm; comparing the joint angle changes with the current joint angle pose of the robotic arm to achieve control of the robotic arm to install the tower monitoring equipment at the monitoring equipment installation position using the angle change values ​​of each joint of the robotic arm.

Description

Technical Field

[0001] This invention relates to the field of monitoring equipment installation, and in particular to a method, equipment, and medium for installing a slope instability device based on UAV deployment. Background Technology

[0002] With the rapid development of drone technology, its application in the field of monitoring has gradually become a research hotspot. Deploying monitoring devices using drones can effectively improve monitoring efficiency and accuracy, and avoid the safety hazards and inefficiencies of traditional manual installation methods.

[0003] However, existing drone deployment technologies still have shortcomings in terms of adaptability to specific scenarios, positioning accuracy, and equipment installation efficiency, affecting the overall performance and reliability of the monitoring system. Existing technology, disclosed in CN114442646B, describes a drone device and a method for deploying a drone work area, published on May 6, 2022. This patent sets boundary rules for work area units through a flight controller, generates work area units based on the location of points of interest, and finally merges multiple work area units to complete the drone's work area deployment. However, this technical solution mainly targets work area deployment around bridges and does not address the specific needs of slope instability monitoring scenarios, such as the precise positioning and installation of tower monitoring equipment. Moreover, this solution lacks a real-time correction mechanism for changes in the drone's hovering position, which may lead to deviations in the installation position of the monitoring equipment and affect the accuracy of data acquisition.

[0004] Existing drone deployment technologies still have certain shortcomings in terms of adaptability to slope instability monitoring scenarios, positioning accuracy, equipment installation efficiency, and stability under complex terrain conditions. Summary of the Invention

[0005] The purpose of this invention is to propose a method, equipment, and medium for installing a slope instability device based on UAV deployment, thereby addressing the technical shortcomings of existing technologies in terms of adaptability, positioning accuracy, equipment installation efficiency, and stability under complex terrain conditions in slope instability monitoring scenarios.

[0006] This invention aims to achieve real-time correction of the hovering position of drones, optimized planning of robotic arm trajectories, and efficient installation of tower monitoring equipment, thereby improving the accuracy and reliability of the monitoring system and meeting the needs of the field of slope instability monitoring for efficient and accurate monitoring.

[0007] Specifically, the present invention provides a method for installing a slope instability device based on UAV deployment, comprising the following steps: S1. Based on the image data information of the power pole tower acquired by the UAV, the structural features of the pole tower components are identified and marked, and the installation position of the pole tower monitoring equipment and the UAV-assisted positioning position are determined; S2. Based on the kinematic model of the robotic arm, the installation position of the monitoring equipment, and the drone-assisted positioning position, generate a pre-installed robotic arm trajectory scheme; S3. Based on the pre-installed robotic arm trajectory scheme, control the rotation of the robotic arm so that the distance between the tower monitoring device and the installation position of the monitoring device reaches the set distance, and acquire the robotic arm image for feature recognition and coordinate calibration processing to determine the image angle of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance of the image; S4. Based on the image angles of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance of the image, constrain the joint angles of the robotic arm and determine the unconstrained target joint angles of the robotic arm. Compare the joint angle changes with the current joint angle pose of the robotic arm to obtain the angle change values ​​of each joint of the robotic arm so as to control the robotic arm to install the tower monitoring equipment at the monitoring equipment installation position.

[0008] A storage device that stores instructions and data for implementing a method for installing a slope instability device based on UAV deployment.

[0009] An installation device for a slope instability device based on UAV deployment includes: a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement a method for installing a slope instability device based on UAV deployment.

[0010] The beneficial effects provided by this invention are: It enables real-time correction of the drone's hovering position and controls the robotic arm to precisely install the tower monitoring equipment at the monitoring equipment's installation location by controlling the angle changes of each joint of the robotic arm.

[0011] This technology enables the drone to be repositioned and corrected during the installation of monitoring equipment if its hovering position changes. This precise control ensures the accurate installation of the tower monitoring equipment, guaranteeing the accuracy of the data collected on the tower slope. Consequently, it accurately monitors the tower slope condition, preventing slope instability that could lead to tower collapse and endanger the safe operation of the transmission lines.

[0012] By pre-installing a robotic arm trajectory scheme, the rotation angle of the robotic arm can be controlled to be minimized, ensuring that the distance between the pole monitoring equipment carried by the robotic arm and the installation position of the monitoring equipment reaches the set distance. This is combined with constraint of the robotic arm joint angles and determination of the unconstrained target joint angles of the robotic arm. The joint angle changes are compared with the current joint angle pose of the robotic arm to obtain the angle change values ​​of each joint of the robotic arm. This allows the robotic arm to be controlled to install the pole monitoring equipment at the monitoring equipment installation position, thereby improving the installation speed of the pole monitoring equipment. Attached Figure Description

[0013] Figure 1 This is a simplified schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the structural feature identification and marking process for tower components according to the present invention.

[0014] Attached image labels: 1 - Tower structure connection point E, 2 - Tower structure connection point A, 3 - Tower structure connection point B, 4 - Tower structure connection point C, 5 - Tower structure connection point D; Figure 3 This is a schematic diagram of the hardware device used in this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0016] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0017] Example 1: Please refer to Figures 1-2 , Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the structural feature identification and marking process for tower components according to the present invention. This invention provides a method for installing a slope instability device based on UAV deployment, comprising the following steps: S1. Based on the image data information of the power pole tower acquired by the UAV, the structural features of the pole tower components are identified and marked, and the installation position of the pole tower monitoring equipment and the UAV-assisted positioning position are determined; Control the drone to fly within the range of the robotic arm installing the tower monitoring equipment, ensuring that the distance between the end point of the robotic arm and the installation position of the monitoring equipment is less than or equal to the diameter of the robotic arm, so as to ensure that the tower monitoring equipment is installed at the tower monitoring equipment installation position by controlling the robotic arm.

[0018] Based on tower image data acquired by UAVs, the tower component structural feature identification and marking processing is performed on the tower image data to obtain the tower component structural feature identification and marking processing results. Based on the tower component structural feature identification and marking processing results, the installation position of the tower monitoring equipment and the UAV-assisted positioning position are determined. The tower monitoring equipment installation position is used to install the tower monitoring equipment, and the UAV-assisted positioning position is used to correct the UAV's hovering position if it changes during the installation process. This ensures precise control of the installation of the tower monitoring equipment at the tower monitoring equipment installation position, guarantees the accuracy of the tower slope data information collected by the tower monitoring equipment, and thus accurately monitors the tower slope condition to avoid slope instability that could cause the transmission tower to collapse and endanger the operation safety of the transmission line.

[0019] Step S1 specifically includes the following steps: S11. Control the drone to fly to the area where the robotic arm installs the tower monitoring equipment and acquire tower image data information; S12. Process the tower image data and identify the structural features of tower components. S13. Mark the connection points of the tower structure based on the results of the tower component structural feature identification and processing. S14. Based on the processing results of the pole structure connection point marking, select the pole monitoring equipment installation position and the UAV-assisted positioning position. The UAV-assisted positioning position is used to correct the UAV hovering position if the UAV hovering position changes during the installation of the monitoring equipment, so as to accurately control the installation of the pole monitoring equipment at the pole monitoring equipment installation position.

[0020] In the above process, the feature recognition mentioned in this invention can be performed using the YOLOv5 model based on deep learning for pole and tower structure connection point detection. Since the YOLOv5 model is an existing model, it only needs to be trained using the corresponding dataset, and this invention will not elaborate on it further. Of course, in some other embodiments, other corresponding models can also be used for structural connection point detection. This invention is only used for illustrative purposes and is not intended to limit the scope of the invention.

[0021] In the above process, the hovering position correction of the UAV adopts a visual servo control method. By comparing the image feature deviation between the current image and the preset auxiliary positioning point, a PID control signal is generated to adjust the UAV's posture and realize real-time fine adjustment of the hovering position.

[0022] For example, the image data of the tower is processed and the structural features of the tower components are identified. The structural features of the tower components are then identified, and the results of this identification are used to mark the connection points of the tower structure, marking connection points A, B, C, D, and E (e.g., ...). Figure 2As shown in the diagram, the installation location of the tower monitoring equipment and the UAV-assisted positioning location are selected based on the marked tower structure connection points A, B, C, D, and E. Tower structure connection point A is selected as the installation location for the tower monitoring equipment. One or more of the tower structure connection points B, C, and D can be selected as the UAV-assisted positioning location. If the UAV's hovering position changes during the installation process, the UAV's hovering position is corrected based on the UAV-assisted positioning location, thereby precisely controlling the installation of the tower monitoring equipment at the designated location and ensuring the accuracy of the tower slope data collected by the tower monitoring equipment. It should be noted that the number and location of the tower structure connection points selected as the UAV-assisted positioning location can be determined according to the actual usage. All selections based on the tower structure connection point marking processing results as UAV-assisted positioning locations fall within the protection scope of this invention.

[0023] S2. Based on the kinematic model of the robotic arm, the installation position of the monitoring equipment, and the drone-assisted positioning position, generate a pre-installed robotic arm trajectory scheme; It should be noted that the process involves acquiring pole image data via drones and performing structural feature identification and marking on pole components. Based on this identification and marking, the installation location of the pole monitoring equipment and the drone-assisted positioning location are determined. A kinematic model of the robotic arm is constructed. Based on this model, the installation location of the monitoring equipment, and the drone-assisted positioning location, a pre-installed robotic arm trajectory scheme is generated to minimize the robotic arm's rotation angle and ensure that the distance between the pole monitoring equipment carried by the robotic arm and its installation location reaches a predetermined distance.

[0024] Specifically, step S2 includes the following steps: S21. Perform coordinate calibration on the installation location of the monitoring equipment and the location assisted by the UAV, and determine the distance between the UAV and the location assisted by the UAV. S22. Determine the hovering position of the drone based on the distance between the drone and the drone's assisted positioning position; S23. Based on the distance between the pole monitoring equipment carried by the robotic arm and the installation position of the monitoring equipment, generate a pre-installed robotic arm trajectory scheme using the kinematic model of the robotic arm.

[0025] For example, coordinate calibration is performed on the installation location of the monitoring equipment and the location assisted by the UAV to obtain the coordinates of the installation location and the location assisted by the UAV. The distance between the UAV and the location assisted by the UAV, and the distance between the monitoring equipment carried by the robotic arm and the installation location of the monitoring equipment, can be determined using a ranging unit (such as a laser ranging module or an ultrasonic ranging module). The measuring equipment and methods used for measuring these distances are not specifically limited in this invention. Determining the UAV's hovering position based on the distance between the UAV and the location assisted by the UAV, and generating a pre-installed robotic arm trajectory scheme based on the distance between the monitoring equipment carried by the robotic arm and the installation location of the monitoring equipment, both fall within the scope of protection of this invention. It should be noted that the pre-installed robotic arm trajectory scheme can control the robotic arm's rotation angle to be minimized, ensuring that the distance between the monitoring equipment carried by the robotic arm and the installation location of the monitoring equipment reaches the set distance, thereby improving the installation rate of the monitoring equipment.

[0026] It should be noted that the steps for constructing the robotic arm motion model in step S2 are as follows: Construct a coordinate system arrive The homogeneous transformation matrix of is expressed as follows:

[0027] in, For joint angle, This is due to joint displacement. For the connecting rod torsion angle, The length of the link; The pose matrix of the robotic arm's end effector relative to the robotic arm base is constructed as follows:

[0028] in, , The robot arm's end effector posture is described by a 3×3 rotation matrix. is a 3×1 translation vector describing the position of the robotic arm's end effector. Indicates from the first From the coordinate system to the first Homogeneous transformation matrix of coordinate systems; Establish the coordinate relationship between the end effector of the robotic arm and the tower monitoring equipment to accurately move the tower monitoring equipment to the installation location.

[0029] In the above process, the coordinate calibration can be performed using Zhang's calibration method to calibrate the camera's intrinsic parameters, and combined with the eye-in-hand calibration method to realize the transformation between the robot arm coordinate system and the camera coordinate system.

[0030] S3. Based on the pre-installed robotic arm trajectory scheme, control the rotation of the robotic arm to make the distance between the tower monitoring equipment and the installation position of the monitoring equipment reach the set distance, and acquire robotic arm images for feature recognition and coordinate calibration processing to determine the image angle of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance of the image. Based on the kinematic model of the robotic arm, the installation location of the monitoring equipment, and the UAV-assisted positioning location, a pre-installed robotic arm trajectory scheme is generated. The robotic arm is controlled to rotate according to this pre-installed trajectory scheme, ensuring that the distance between the tower monitoring equipment carried by the robotic arm and its installation location reaches a predetermined distance. The robotic arm image signal is acquired when the distance between the tower monitoring equipment and its installation location reaches the predetermined distance. The robotic arm image signal undergoes feature recognition and coordinate calibration processing, including features of the robotic arm itself, the tower monitoring equipment, and the installation location. The robotic arm features include the angle features of each joint and the features of each section. Based on the feature recognition and coordinate calibration results, the image angles of each joint, the length of each section, and the remaining installation distance are determined. It should be noted that the objects in the robotic arm image signal when the distance between the tower monitoring equipment and its installation location reaches the predetermined distance include at least the robotic arm image, the tower monitoring equipment, and the installation location of the monitoring equipment.

[0031] It should be noted that step S3 includes the following steps: S31. Based on the pre-installed robotic arm trajectory scheme, control the robotic arm to rotate so that the rotation angle of the robotic arm is minimized and the distance between the pole monitoring equipment carried by the robotic arm and the installation position of the monitoring equipment reaches the set distance. S32. Acquire images of the robotic arm and perform identification and coordinate calibration of robotic arm features, tower monitoring equipment features, monitoring equipment installation location features, and coordinate calibration. S33. Determine the image angles of each joint of the robotic arm and the length of each segment of the robotic arm image based on the features of the robotic arm. S34. Determine the remaining distance for image installation based on the characteristics of the tower monitoring equipment and the installation location characteristics of the monitoring equipment.

[0032] For example, after identifying the features of the robotic arm, the tower monitoring equipment, and the installation location of the monitoring equipment in the image, image coordinate calibration is performed to obtain the coordinates of each joint of the robotic arm, the coordinates of each section of the robotic arm, the coordinates of the tower monitoring equipment, and the coordinates of the installation location of the monitoring equipment. After image coordinate calibration, the remaining installation distance can be determined using the coordinates of the tower monitoring equipment and the installation location of the monitoring equipment. The image angles of each joint and the length of each section of the robotic arm can be determined using the coordinates of each joint and each section of the robotic arm (for example, the image angle of the i-th joint can be determined using the coordinates of the i-th section of the robotic arm, the coordinates of the i-th joint, and the coordinates of the (i+1)-th section of the robotic arm; the image length of the i-th section of the robotic arm can be determined using the coordinates of the i-th joint and the (i+1)-th joint).

[0033] S4. Based on the image angles of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance of the image, constrain the joint angles of the robotic arm and determine the unconstrained target joint angles of the robotic arm. Compare the joint angle changes with the current joint angle pose of the robotic arm to obtain the angle change values ​​of each joint of the robotic arm so as to control the robotic arm to install the tower monitoring equipment at the monitoring equipment installation position.

[0034] By controlling the rotation of the robotic arm according to a pre-installed robotic arm trajectory scheme, when the distance between the tower monitoring equipment and its installation position reaches a set distance, images of the robotic arm are acquired and processed for feature recognition and coordinate calibration. Based on the results of feature recognition and coordinate calibration, the image angles of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance are determined. Based on the image angles of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance, joint angle constraints are applied to the robotic arm, and the remaining robotic arm image distance is determined. Based on the remaining robotic arm image distance, the unconstrained target joint angles of the robotic arm are determined, and the changes in joint angles are compared to obtain the angle change values ​​of each joint. The rotation of the robotic arm is controlled by these angle change values, thereby installing the tower monitoring equipment at the monitoring equipment's installation position.

[0035] It should be noted that step S4 further includes the following steps: S41. Construct the current joint angle pose of the robotic arm based on the image angles of each joint of the robotic arm; S42. Based on the remaining distance of the image installation, constrain the number of the end effector and adjacent joint angles of the robotic arm to be 0, and determine the vertical length of the end effector and adjacent robotic arm images; S43. Based on the coordinates of the first joint of the robotic arm and the installation position coordinates of the monitoring equipment, determine the image distance between the first joint of the robotic arm and the installation position of the monitoring equipment, and subtract it from the image diameter of the end and adjacent robotic arms to obtain the remaining robotic arm image distance. S44. Determine the unconstrained target joint angle of the robotic arm based on the remaining robotic arm image distance; Specifically, the unconstrained target joint angle of the robotic arm is calculated using the inverse kinematics (IK) algorithm, specifically by numerical iteration (such as the Jacobian inverse matrix method) or analytical method (such as the DH parameter inverse solution) to ensure that the end effector of the robotic arm accurately reaches the target position.

[0036] S45. Based on the current joint angle pose of the robotic arm, the joint angle of the end effector with a joint angle of 0, the joint angles of the adjacent robotic arms and the target joint angles of the unconstrained robotic arm, determine the angle change values ​​of each joint of the robotic arm. S46. Based on the angle change values ​​of each joint of the robotic arm, the robotic arm is controlled to rotate, thereby installing the tower monitoring equipment at the monitoring equipment installation position.

[0037] For example, the current joint pose of the robotic arm can be constructed based on the image angles of each joint of the robotic arm. Based on the remaining installation distance of the image, the number of robot arm end-effectors and adjacent joints with angles of 0 is constrained, and the vertical length of the end-effectors and adjacent robot arm images is determined. For example, if the robot arm has 7 segments, if the remaining installation distance is less than the length of the 7th robot arm image, the angle of the 7th joint can be constrained to 0; if the remaining installation distance is greater than or equal to the length of the 7th robot arm image, but less than the sum of the lengths of the 7th and 6th robot arm images, the angles of the 7th and 6th joints can be constrained to 0, and so on. The number of robot arm end-effectors and adjacent joints with angles of 0 can be constrained based on the remaining installation distance. Based on the coordinates of the first joint of the robot arm and the installation position coordinates of the monitoring device, the image distance between the first joint of the robot arm and the installation position of the monitoring device is determined. The remaining robot arm image distance is obtained by subtracting the image distance between the first joint of the robot arm and the installation position of the monitoring device from the sum of the lengths of the 7th and 6th robot arm images. The unconstrained target joint angles of the robot arm are determined based on the remaining robot arm image distance. By comparing the current joint angle pose of the robotic arm with the joint angles of the end effector (where the joint angle is 0), neighboring robotic arms, and the target joint angle of the unconstrained robotic arm, the angle change value of each joint of the robotic arm is determined. Based on the angle change value of each joint of the robotic arm, the robotic arm is controlled to rotate, thereby enabling the tower monitoring equipment to be installed at the monitoring equipment installation location.

[0038] Example 2: Please see Figure 3 , Figure 3 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: a slope instability device installation device 401 based on UAV deployment, a processor 402, and a storage device 403.

[0039] A slope instability device installation device 401 based on UAV deployment: The slope instability device installation device 401 based on UAV deployment realizes the slope instability device installation method based on UAV deployment.

[0040] Processor 402: The processor 402 loads and executes the instructions and data in the storage device 403 to implement the method for installing a slope instability device based on UAV deployment.

[0041] Storage device 403: The storage device 403 stores instructions and data; the storage device 403 is used to implement the above-mentioned method for installing a slope instability device based on UAV deployment.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for installing a slope instability device based on a deployment of a drone, characterized by: include: The method includes the following steps: S1. Based on the image data information of the power pole tower acquired by the UAV, the structural features of the pole tower components are identified and marked, and the installation position of the pole tower monitoring equipment and the UAV-assisted positioning position are determined; S2. Based on the kinematic model of the robotic arm, the installation position of the monitoring equipment, and the drone-assisted positioning position, generate a pre-installed robotic arm trajectory scheme; S3. Based on the pre-installed robotic arm trajectory scheme, control the rotation of the robotic arm so that the distance between the tower monitoring device and the installation position of the monitoring device reaches the set distance, and acquire the robotic arm image for feature recognition and coordinate calibration processing to determine the image angle of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance of the image; S4. Based on the image angles of each joint of the robotic arm, the image length of each section of the robotic arm, and the remaining installation distance of the image, constrain the joint angles of the robotic arm and determine the unconstrained target joint angles of the robotic arm. Compare the joint angle changes with the current joint angle pose of the robotic arm to obtain the angle change values ​​of each joint of the robotic arm so as to control the robotic arm to install the tower monitoring equipment at the monitoring equipment installation position. Step S1 includes the following steps: S11. Control the drone to fly to the area where the robotic arm installs the tower monitoring equipment and acquire tower image data information; S12. Process the tower image data and identify the structural features of tower components. S13. Mark the connection points of the tower structure based on the results of the tower component structural feature identification and processing. S14. Based on the processing results of the pole structure connection point marking, select the pole monitoring equipment installation position and the UAV-assisted positioning position. The UAV-assisted positioning position is used to correct the UAV hovering position if the UAV hovering position changes during the installation of the monitoring equipment, so as to accurately control the installation of the pole monitoring equipment at the pole monitoring equipment installation position. Step S2 includes the following steps: S21. Perform coordinate calibration on the installation location of the monitoring equipment and the location assisted by the UAV, and determine the distance between the UAV and the location assisted by the UAV. S22. Determine the hovering position of the drone based on the distance between the drone and the drone's assisted positioning position; S23. Based on the distance between the pole monitoring equipment carried by the robotic arm and the installation position of the monitoring equipment, generate a pre-installed robotic arm trajectory scheme using the kinematic model of the robotic arm. Step S3 includes the following steps: S31. Based on the pre-installed robotic arm trajectory scheme, control the robotic arm to rotate so that the rotation angle of the robotic arm is minimized and the distance between the pole monitoring equipment carried by the robotic arm and the installation position of the monitoring equipment reaches the set distance. S32. Acquire images of the robotic arm and perform identification and coordinate calibration of robotic arm features, tower monitoring equipment features, monitoring equipment installation location features, and coordinate calibration. S33. Determine the image angles of each joint of the robotic arm and the length of each segment of the robotic arm image based on the features of the robotic arm. S34. Determine the remaining distance for image installation based on the characteristics of the tower monitoring equipment and the installation location characteristics of the monitoring equipment; Step S4 is as follows: S41. Construct the current joint angle pose of the robotic arm based on the image angles of each joint of the robotic arm; S42. Based on the remaining distance of the image installation, constrain the number of the end effector and adjacent joint angles of the robotic arm to be 0, and determine the vertical length of the end effector and adjacent robotic arm images; S43. Based on the coordinates of the first joint of the robotic arm and the installation position coordinates of the monitoring equipment, determine the image distance between the first joint of the robotic arm and the installation position of the monitoring equipment, and subtract it from the image diameter of the end and adjacent robotic arms to obtain the remaining robotic arm image distance. S44. Determine the unconstrained target joint angle of the robotic arm based on the remaining robotic arm image distance; S45. Based on the current joint angle pose of the robotic arm, the joint angle of the end effector with a joint angle of 0, the joint angles of the adjacent robotic arms and the target joint angles of the unconstrained robotic arm, determine the angle change values ​​of each joint of the robotic arm. S46. Control the rotation of the robotic arm based on the angle change values ​​of each joint of the robotic arm, so as to install the tower monitoring equipment at the monitoring equipment installation position; The steps for constructing the robotic arm motion model in step S2 are as follows: Constructing coordinate systems to The homogeneous transformation matrix of the to the is expressed as follows: in, For joint angle, This is due to joint displacement. For the connecting rod torsion angle, The length of the link; The pose matrix of the robotic arm's end effector relative to the robotic arm base is constructed as follows: wherein, , is a 3 x 3 rotation matrix describing the pose of the robot arm end-effector, is a 3 x 1 translation vector describing the position of the robot arm end-effector, denotes the homogeneous transformation matrix from the coordinate frame of the th joint to the coordinate frame of the th joint. Establish the coordinate relationship between the end effector of the robotic arm and the tower monitoring equipment to accurately move the tower monitoring equipment to the installation location.

2. A storage device, characterized by: The storage device stores instructions and data to implement the slope instability device installation method based on UAV deployment as described in claim 1.

3. A slope instability device installation equipment based on unmanned aerial vehicle deployment, characterized in that: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement the slope instability device installation method based on UAV deployment as described in claim 1.

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