Digital tower air-ground cooperative inspection method

Abstract

The invention discloses a digital tower air-ground cooperative inspection method. According to the technical scheme, the method is characterized by comprising the following steps of: 1, constructing a flight patrol strategy of a digital power transmission line by using an airborne three-dimensional laser radar; and a second part: carrying out scanning station distribution of the ground type three-dimensional laser radar. Different selection schemes are made according to geology and working conditions of different regions. In a geological disaster high-incidence area, a vertical take-off and landing fixed-wing aircraft is selected to carry out rapid and large-area patrol operation within a channel range, point cloud is collected and settled, advantage complementation is carried out in combination with all-dimensional, multi-angle and high-precision point cloud data collected by a ground type three-dimensional laser radar, and a large-range power transmission line tower body is formed; and additionally, point cloud fused with high-precision tower footing of a key pole tower is added, so that the anomalies of pole tower settlement, pole tower inclination and the like in a large-area range can be rapidly detected.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and more specifically, particularly relates to a digital pole tower air-ground collaborative inspection method. Background Art

[0002] Line towers are becoming increasingly important for the safe and normal operation of power grids. Since the lines are erected in the air, they need to withstand the threats of self-weight, wind, rainstorms and other factors. The operating conditions are relatively harsh, and they are prone to safety hazards such as pole collapse and tilting. In addition, various construction and renovation projects have been frequent in recent years, and the inspection pressure of operation and maintenance personnel has increased. Timely troubleshooting and improving the reliability of distribution network operation are top priorities.

[0003] With the development of computer graphics and computer-aided design technology, the digitization of transmission lines has become more and more popular, and the depth and content of various professional line design have also increased significantly. However, the current inspection method is still inefficient and needs to be improved. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a digital pole tower air-ground collaborative inspection method to solve the problems raised in the background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a digital tower air-ground collaborative inspection method, comprising the following steps:

[0006] Part 1: Using airborne 3D laser radar to build a patrol strategy for digital power transmission lines: including the first step, selecting the optimal model based on the line conditions; the second step, confirming the constraints: confirming the kinematic constraints of the drone itself; the third step, path planning, including route planning, static path planning and dynamic path planning; the fourth step, confirming the emergency safety strategy during the patrol;

[0007] Part 2: Scanning and station layout of ground-based 3D laser radar: including the first step, control measurement: use stake point coordinates to carry out scanning; the second step, target setting: use total station mapping, free station setting and point cloud registration to set targets; the third step, conduct comparative analysis of airborne and ground-based tower laser point clouds, and determine accurate evaluation of abnormal conditions of channels and towers.

[0008] Preferably, when selecting the aircraft model based on the route conditions, for areas prone to strong winds, choose a medium-sized aircraft with strong wind resistance; for areas where heavy ice is easily moved, choose an aircraft with long endurance; for areas prone to geological disasters such as mudslides, which are large in area, choose a fixed-wing aircraft for large-area and long-flight data collection.

[0009] Preferably, when the constraint conditions are confirmed, the kinematic constraint conditions of the drone itself are confirmed, including minimum step length, maximum and minimum flight altitudes, maximum turning angle, maximum range, and maximum pitch angle.

[0010] In the minimum step length constraint, the distance between the current state and the next state to continue to fly in a straight line is set as the minimum step length, using L min represents the minimum step length, and the control satisfies the following constraints: L min <Lx; where X represents the current state, X1, X2, and X3 represent the candidate nodes for the next node, and the corresponding step sizes are L1, L2, and L3 respectively;

[0011] In the above-mentioned maximum and minimum flight altitude constraints, the minimum flight altitude H is set according to the actual situation while satisfying the physical property constraints of the drone. min , and set the maximum flight altitude H that meets the physical and mechanical properties of the aircraft max , then the flight height of the UAV track is H x The constraints are: H max >H x >H min .

[0012] Preferably, in the maximum range constraint, assuming the average range of the drone per hour is y, the average fuel consumption per hour is z, the maximum amount of fuel that the drone can carry is w, and the longest range that can be flown is Lroad, then the constraint condition for the longest range is:

[0013] Preferably, in the maximum turning angle constraint: the maximum turning angle that can be achieved by the UAV according to its aircraft braking principle is θ, then the turning angle during the flight of the UAV is Satisfy the following formula:

[0014] Preferably, in the maximum pitch angle constraint: the pitch angle of the drone is adjusted by changing the engine speed to control the resultant force on the drone, wherein the maximum pitch angle of the drone is λ xmax , the maximum elevation angle is λ ymax , then the constraints satisfied by the pitch angle λ of the drone are as follows: xmax <λ<λ ymax .

[0015] Preferably, static path planning or dynamic path planning is used in the path planning; the static path planning is: the UAV path planning should include information on the location of the starting point and the target point of the path planning, the environmental representation of obstacles, the planning method and the search method; in static path planning, the map is a two-dimensional array or a three-dimensional array storing data, and the location information of the starting point and the target point on the map is determined by the coordinates of the array, and the equations that satisfy the flight route are listed according to the regional constraints.

[0016] Preferably, during the dynamic path planning, the input information is part of the known information in the map environment, which is part of the obstacle and threat area information obtained by the drone before performing the flight mission. With the help of sensors and cameras installed around the drone body, the environmental information of the unavoidable area to be entered is collected to the autonomous planning system of the drone, and the future path is planned in time; the specific steps of using the particle swarm algorithm for dynamic path planning are as follows: the population size, number of iterations, learning factor, convergence accuracy, particle speed range and other parameters of the particle swarm algorithm are initialized according to the problem to be solved; each particle is given an initial position and speed in the optimization space, and the current position value of the particle is used as the optimal value of the contemporary particle individual; the fitness function of the problem is used to calculate the fitness value of each particle. If the fitness value of the particle at this time is better than the fitness value of the particle in the previous state, the optimal position of the particle is updated, and the optimal position value of the particle in the previous state is replaced by the position value at this time. Otherwise, the position value of the particle is not updated; the requirements of the executable algorithm program are checked. If the upper limit of the number of iterations is reached at this moment, or the result meets the requirements of convergence accuracy, the iteration is stopped and the result is output, otherwise the algorithm continues to perform the optimization task.

[0017] Preferably, during the control measurement: first, a number of cement control piles are evenly arranged around the tower, with the distance D from the piles to the tower body set between 0.7 and 1 times the tower height, and a forced centering hole is reserved at the top of the pile; the coordinates of the control pile points are measured by static GNSS or precision traverse, and scanning is carried out using the coordinates of the pile points.

[0018] Technical effects and advantages of the present invention:

[0019] The present invention makes different selection schemes according to the geological and working conditions in different regions. In areas prone to geological disasters, vertical take-off and landing fixed-wing aircraft are selected to conduct rapid and large-area patrol operations within the channel range, collect and settle point clouds, and combine the ground-based three-dimensional laser radar to collect point cloud data in an all-round, multi-angle and high-precision manner to complement each other's advantages, forming a large-scale transmission line tower body, plus a point cloud fused with a high-precision tower base of a key tower, to quickly detect abnormalities such as tower settlement and tower tilt within a large area. In areas prone to heavy ice, multi-rotor aircraft with high cold resistance are used, and multi-machine collaborative route planning and multi-task automatic allocation are coordinated to quickly complete small-scale and high-density drone patrol operations. Point cloud collection is carried out by reasonably arranging multiple ground laser radars, and high-precision base station data is cooperated to complete the solution of laser point clouds, providing high-precision basic data for tower anomaly detection. In areas with frequent strong winds, aircraft with strong wind resistance are used for patrols to ensure that they can stably fly and work in a strong wind environment. At this time, the reasonable deployment of ground radars is also required to collect high-precision point clouds that are suitable for tower anomaly detection under the condition of air-ground coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of airborne 3D laser scanning;

[0021] Figure 2 This is a flowchart of ground-based 3D laser scanning;

[0022] Figure 3 Sample pictures of different types of drones;

[0023] Figure 4 It is a schematic diagram of the minimum step length;

[0024] Figure 5 It is a flow chart for dynamic path planning;

[0025] Figure 6 Provide a station layout model for total station mapping;

[0026] Figure 7 Provide free station illustrations;

[0027] Figure 8 Provide maps for the overlap and registration of individual stations. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] Example 1

[0030] like Figures 1 to 5 As shown, a digital tower and ground collaborative inspection method includes the following contents:

[0031] The first step is to select the best model for different geographical environments and working conditions. For areas prone to strong winds, choose a medium-sized aircraft with strong wind resistance; for areas prone to heavy ice, choose a model with long endurance; for areas prone to geological disasters such as mudslides, which are large in area, multi-rotor drones cannot quickly carry out operations over a large area, and fixed-wing aircraft must be selected for large-scale, long-flight data collection. Figure 3 Different types of drone styles.

[0032] The second step is to confirm the kinematic constraints of the drone itself, including the minimum step length, maximum and minimum flight altitudes, maximum turning angle, maximum range, etc.

[0033] Minimum step length constraint: When the UAV is performing a mission, due to the influence of terrain and environment, when it switches its operating state, it needs to continue to fly in a straight line from the current state to the next state. The distance of this straight line flight is the minimum step length. min represents the minimum step length, the following constraints should be met: L min <Lx, Figure 4 is a schematic diagram of the minimum step length, where X0 represents the current state, X1, X2, and X3 represent the candidate nodes for the next node, and the corresponding step lengths are L1, L2, and L3 respectively. Figure 4 We know that L2 is greater than the minimum step length L min Small, according to the constraint of the minimum step length, the next node can only be generated in X1 and X3.

[0034] Maximum and minimum flight altitude constraints: When the physical property constraints of the drone are met, the minimum flight altitude H needs to be set according to the actual situation. min , and set the maximum flight altitude H that meets the physical and mechanical properties of the aircraft max , then the flight height of the UAV track is H x The constraints are: H max >H x >H min

[0035] Maximum range constraint: The average range of the drone per hour is y, the average fuel consumption per hour is z, the maximum amount of fuel that the drone can carry is w, and the maximum range that can be flown is Lroad. The maximum range constraint is:

[0036]

[0037] Maximum turning angle constraint: According to the braking principle of the aircraft, the maximum turning angle that the UAV can achieve is θ. The turning angle during the flight of the UAV is Satisfy the following formula:

[0038] Maximum pitch angle constraint: The pitch angle of the drone is adjusted by changing the engine speed to control the resultant force on the drone, where the maximum pitch angle of the drone is λ xmax , the maximum elevation angle is λ ymax , then the constraints satisfied by the pitch angle λ of the drone are as follows: xmax <λ<λ ymax .

[0039] The third step is path planning:

[0040] Static path planning: The path planning of the drone should include the information of the starting point and the target point of the path planning, the environmental representation of obstacles, the planning method and the search method. In static path planning, the map is a two-dimensional array or three-dimensional array that stores data. The coordinates of the array can be used to determine the location information of the starting point and the target point on the map. According to the regional constraints, the equations that satisfy the flight path are listed.

[0041] Dynamic path planning: The input information is part of the known information in the map environment, that is, some obstacle and threat area information obtained by the drone before performing the flight mission. Part of the map environment is unknown. With the help of sensors and cameras installed around the drone body, the environmental information of the unavoidable area to be entered is collected into the autonomous planning system of the drone, and the future path is planned in time, so as to achieve the global optimal path planning under the premise of safety. Figure 5 This is a flow chart of dynamic path planning.

[0042] The specific steps of using particle swarm algorithm for dynamic path planning are as follows: Initialize the population size, number of iterations, learning factor, convergence accuracy, particle velocity range and other parameters of the particle swarm algorithm according to the problem being solved; assign each particle an initial position and velocity in the optimization space, and use the current position value of the particle as the optimal value of the contemporary particle individual; use the fitness function of the problem to calculate the fitness value of each particle. If the fitness value of the particle at this time is better than the fitness value of the particle in the previous state, update the optimal position of the particle and replace the optimal position value of the particle in the previous state with the current position value. Otherwise, do not update the position value of the particle; check the executable algorithm program requirements. If the upper limit of the number of iterations is reached at this moment, or the result meets the convergence accuracy requirements, stop the iteration and output the result. Otherwise, the algorithm jumps back to step 3 to continue the optimization task.

[0043] Figure 3The second part of the flow chart is the principle of ground scanning station layout. In order to achieve the accuracy requirement of ±5mm for observation data, it is necessary to adopt the principle of control first and then scanning to carry out the work. After the control measurement is completed, the three main ground station layout modes of total station mapping, free station setting and point cloud registration are used to carry out scanning work.

[0044] The first step is control measurement: First, evenly arrange several cement control piles around the tower. The distance D from the pile to the tower body should be between 0.7 and 1 times the tower height. In order to improve the point accuracy, a forced centering hole can be reserved at the top of the pile. Secondly, the coordinates of the control pile points are measured by static GNSS or precision traverse. Finally, the scanning work is carried out using the coordinates of the pile points.

[0045] The second step is target setting: check the survey area, set and measure the coordinates of topological control points.

[0046] Figure 7 It is a total station mapping method, which requires line of sight between points. Set the control points as 1 to 5. Set up a scanner at point 2 as station A, and set up a target at point 1. Center and level, measure the instrument height and the center height of the target, use the known control point coordinates to complete positioning and orientation, and start scanning. Move forward in sequence, set up a scanner at point 3 as station B, set up a target at point 2... and so on, complete the scanning of station A to D, and calculate the point cloud coordinates. Translate the center point of the instrument (0, 0, 0) to the known coordinates (X0, Y0, Z0). Then rotate the X coordinate system direction of the point cloud to the north direction of the coordinate, so that a point cloud with a geographic coordinate system is obtained.

[0047] Figure 8 It is a free station type, with serial numbers 1 to 14 representing the control point numbers. When the instrument cannot be set up on the control pile, stations are set up freely around the tower along the line, and targets are set up on the control points. At the measuring station A, the three control points 1 to 3 can be observed, and the positioning and orientation are determined by the targets set up on the control points. Similarly, A to F around the tower along the line are observed as measuring stations in turn, and the point cloud coordinates are calculated by observing multiple targets with known center coordinates. The precise scanning coordinates of each target can be obtained by scanning, and the target also has control measurement coordinates. By obtaining the conversion parameters from the scanning coordinates to the control coordinate system and converting the point cloud, the point cloud in the control coordinate system can be obtained. The coordinate conversion formula is as follows:

[0048]

[0049] The above formula contains seven parameters Dx, Dy, Dz, α, β, γ, k, where Dx, Dy, Dz represent the offset α, β, γ in the direction of coordinate axis X, Y, and Z respectively, and k represents the scaling factor.

[0051] The present invention makes different selection schemes according to the geological and working conditions in different regions. In areas prone to geological disasters, vertical take-off and landing fixed-wing aircraft are selected to conduct rapid and large-area patrol operations within the channel range, collect and settle point clouds, and combine the ground-based three-dimensional laser radar to collect point cloud data in an all-round, multi-angle and high-precision manner to complement each other's advantages, forming a large-scale transmission line tower body, plus a point cloud fused with a high-precision tower base of a key tower, to quickly detect abnormalities such as tower settlement and tower tilt within a large area. In areas prone to heavy ice, multi-rotor aircraft with high cold resistance are used, and multi-machine collaborative route planning and multi-task automatic allocation are coordinated to quickly complete small-scale and high-density drone patrol operations. Point cloud collection is carried out by reasonably arranging multiple ground laser radars, and high-precision base station data is cooperated to complete the solution of laser point clouds, providing high-precision basic data for tower anomaly detection. In areas with frequent strong winds, aircraft with strong wind resistance are used for patrols to ensure that they can stably fly and work in a strong wind environment. At this time, the reasonable deployment of ground radars is also required to collect high-precision point clouds that are suitable for tower anomaly detection under the condition of air-ground coordination.

[0052] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital tower and ground collaborative inspection method, characterized in that: The following steps are involved: Part I: Using airborne 3D LiDAR to build a patrol strategy for digital power transmission lines: including the first step, selecting the best model based on the line conditions; The second step is to confirm the constraints: confirm the kinematic constraints of the drone itself; The third step is to carry out path planning, including route planning, static path planning and dynamic path planning; The fourth step is to confirm the emergency safety strategy during the flight patrol; Part 2: Scanning and station layout of ground-based 3D laser radar: including the first step, control measurement: use stake point coordinates to carry out scanning; the second step, target setting: use total station mapping, free station setting and point cloud registration to set targets; the third step, conduct comparative analysis of airborne and ground-based tower laser point clouds, and determine accurate evaluation of abnormal conditions of channels and towers.

2. A digital tower and ground collaborative inspection method according to claim 1, characterized in that: When selecting the above-mentioned aircraft models based on the route conditions, for areas prone to strong winds, choose medium-sized aircraft with strong wind resistance; for areas where heavy ice is easily moved, choose models with long endurance; for areas prone to geological disasters such as mudslides, which are large in area, choose fixed-wing aircraft for large-area and long-flight data collection.

3. A digital tower and ground collaborative inspection method according to claim 2, characterized in that: When the constraints are confirmed, the kinematic constraints of the drone itself are confirmed, including the minimum step length, the highest and lowest flight altitudes, the maximum turning angle, the longest range, and the maximum pitch angle. In the minimum step length constraint, the distance between the current state and the next state to continue to fly in a straight line is set as the minimum step length, using L min represents the minimum step length, and the control satisfies the following constraints: L min <Lx; where X represents the current state, X1, X2, and X3 represent the candidate nodes for the next node, and the corresponding step sizes are L1, L2, and L3 respectively; In the above-mentioned maximum and minimum flight altitude constraints, the minimum flight altitude H is set according to the actual situation while satisfying the physical property constraints of the drone. min , and set the maximum flight altitude H that meets the physical and mechanical properties of the aircraft max , then the flight height of the UAV track is H x The constraints are: H max >H x >H min .

4. A digital tower and ground collaborative inspection method according to claim 3, characterized in that: In the maximum range constraint, let the average range of the drone per hour be y, the average fuel consumption per hour be z, the maximum amount of fuel that the drone can carry be w, and the maximum range that can be flown be Lroad. Then, the constraint condition for the maximum range is:

5. A digital tower and ground collaborative inspection method according to claim 3, characterized in that: In the maximum turning angle constraint: According to the braking principle of the aircraft, the maximum turning angle that can be achieved by the UAV is θ. The turning angle during the flight of the UAV is Satisfy the following formula:

6. A digital tower and ground collaborative inspection method according to claim 3, characterized in that: In the maximum pitch angle constraint, the pitch angle of the drone is adjusted by changing the engine speed to control the resultant force on the drone, where the maximum pitch angle of the drone is λ xmax , the maximum elevation angle is λ ymax , then the constraints satisfied by the pitch angle λ of the drone are as follows: xmax <λ<λ ymax .

7. A digital tower and ground collaborative inspection method according to claim 6, characterized in that: The path planning adopts static path planning or dynamic path planning; the static path planning is: the UAV path planning should include the information of the starting point and the target point position of the path planning, the environmental representation of the obstacles, the planning method and the search method; in static path planning, the map is a two-dimensional array or a three-dimensional array storing data, and the position information of the starting point and the target point on the map is determined by the coordinates of the array, and the equations that satisfy the flight route are listed according to the regional constraints.

8. A digital tower and ground collaborative inspection method according to claim 7, characterized in that: During the dynamic path planning, the input information is part of the known information in the map environment, which is part of the obstacle and threat area information obtained by the UAV before performing the flight mission. With the help of sensors and cameras installed around the UAV body, the environmental information of the unavoidable area to be entered is collected into the autonomous planning system of the UAV, and the future path is planned in time. The specific steps of using the particle swarm algorithm for dynamic path planning are as follows: the population size, number of iterations, learning factor, convergence accuracy, particle speed range and other parameters of the particle swarm algorithm are initialized according to the problem to be solved; each particle is given an initial position and speed in the optimization space, and the current position value of the particle is used as the optimal value of the contemporary particle individual; the fitness function of the problem is used to calculate the fitness value of each particle. If the fitness value of the particle at this time is better than the fitness value of the particle in the previous state, the optimal position of the particle is updated, and the optimal position value of the particle in the previous state is replaced by the position value at this time. Otherwise, the position value of the particle is not updated; the requirements of the executable algorithm program are checked. If the upper limit of the number of iterations is reached at this moment, or the result meets the convergence accuracy requirement, the iteration is stopped and the result is output, otherwise the algorithm continues to perform the optimization task.

9. A digital tower and ground collaborative inspection method according to claim 8, characterized in that: During the control measurement: firstly, a number of cement control piles are evenly arranged around the iron tower, the distance D from the piles to the tower body is set between 0.7 and 1 times the tower height, and a forced centering hole is reserved at the top of the piles; the coordinates of the control pile points are measured by static GNSS or precision traverse, and the scanning work is carried out using the coordinates of the pile points.

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