Unmanned aerial vehicle inspection system based on laser point cloud

Abstract

The invention discloses an unmanned aerial vehicle inspection system based on laser point cloud, which is used for inspection management of power transmission lines. The system comprises a data storage and management subsystem which is responsible for storing and managing data obtained from an unmanned aerial vehicle; the route definition and safety control subsystem is used for defining a route based on the laser point cloud, defining the pre-drawn route as a template, applying the template to the same type of power transmission line equipment in batches, providing a flight simulation function of unmanned aerial vehicle patrol, and calculating and displaying the safety distance of the unmanned aerial vehicle in the current flight process in real time through an algorithm; the user interface and decision support subsystem relates to the design and implementation of a user interaction interface, so that an operator can intuitively monitor the inspection process of the unmanned aerial vehicle and make a corresponding decision; and the integration and communication subsystem is used for integrating the subsystems and ensuring that the subsystems can communicate with one another and work cooperatively. The inspection efficiency and safety of the power transmission line can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power inspection, and in particular to an unmanned aerial vehicle inspection system based on laser point cloud. Background Art

[0002] With the development of civil unmanned aerial vehicle technology, it has gradually become possible to use drones for autonomous power line inspection. The integration of drone inspection with basic operation applications will help promote three-dimensional inspection of power transmission, transformation and distribution, fully apply remote intelligent inspection, and promote a significant improvement in production efficiency, benefits and effectiveness.

[0003] As one of the core tasks of smart power grid inspection, UAV intelligent inspection covers all aspects such as transmission and distribution. The operation scenarios are complex, such as the changing regional terrain and diverse environmental conditions, and the types of operation tasks are diverse. At present, there are deficiencies in the research of autonomous inspection, such as route planning, autonomous flight, and data closed loop. It is urgent to carry out in-depth research on UAV route control and analysis, so as to realize autonomous inspection by UAV.

[0004] In the prior art, the invention patent "A power line operation and maintenance system and method based on drones" (publication number CN117673953A) provides a technical solution for a power line maintenance system and method based on drones. The invention uses drones to obtain multi-dimensional data of power transmission lines to achieve comprehensive and efficient collection of power transmission line inspection data, and through data judgment and processing, it determines whether each device has a fault and generates a fault work order, so that the operation and maintenance personnel can perform operation and maintenance on the power line fault. However, the equipment parts that need to be inspected in each inspection task are uncertain, and manual hovering is still required, which does not have autonomy.

[0005] In addition, most existing drone inspection systems are developed based on the client, and directly analyze the point cloud data sent back by the drone, which places a heavy burden on the system configuration used by the customer. Summary of the invention

[0006] Purpose of the invention: In view of the shortcomings of the prior art, the present invention proposes a drone inspection system based on laser point cloud, which realizes drone transmission line inspection based on the web and intelligently generates inspection routes to improve inspection efficiency and safety.

[0007] Technical solution: A drone inspection system based on laser point cloud, including:

[0008] Data storage and management subsystem: responsible for storing and managing data obtained from drones;

[0009] Route definition and safety control subsystem: used to define routes based on laser point clouds, define drawn routes as templates, apply templates in batches to the same type of power transmission line equipment, provide simulated flight functions for drone inspections, and use algorithms to calculate and display the safety distance of the drone during its current flight in real time;

[0010] User interface and decision support subsystem: used to provide a user interaction interface, enabling operators to intuitively monitor the drone inspection process and make corresponding decisions;

[0011] Integration and communication subsystem: used to integrate various subsystems and ensure that they can communicate and work together.

[0012] Furthermore, the route definition and safety control subsystem includes a route definition module, a flight simulation module, and a safety detection module. The route definition module automatically analyzes the equipment point cloud data according to the equipment ledger information and generates waypoints and flight routes according to parameters; the flight simulation module generates a drone simulation model according to the drone model and performs the entire process of autonomous flight operations according to the planned route; the safety detection module calculates the distance between the drone and the equipment body based on the point cloud data, compares it with the pre-set safety distance, identifies unsafe waypoints and moves them outside the safety distance.

[0013] Furthermore, the route definition module generates the route according to the following steps:

[0014] (1) Equipment ledger acquisition: Obtain equipment ledger information from the power grid resource business center;

[0015] (2) Auxiliary planning settings: Automatically analyze the device point cloud data and generate an auxiliary drawing surface with the device center as the base point to assist in the accurate drawing of waypoints;

[0016] (3) Waypoint route drawing: Draw the drone’s refined inspection route based on the inspection scene, equipment model, and drone parameters, mark the target points in the point cloud system, and automatically generate aerial photography points and flight routes based on the parameters;

[0017] (4) Route and template generation: Export routes and generate templates based on drone model, equipment model and parameters, and route parameters.

[0018] Furthermore, the flight simulation module sets simulation parameters according to the actual performance and environmental conditions of the UAV, including flight speed, maximum flight altitude, mounted camera parameters, hovering time, and introduces a physical model of the corresponding UAV model.

[0019] Furthermore, the safety detection module calculates the distance between the current position of the drone and the point cloud in real time based on the multi-dimensional segmentation neighborhood retrieval algorithm of massive point cloud data, and searches for the nearest point as the neighboring point of the drone.

[0020] Furthermore, the safety distance is calculated according to the following formula:

[0021] D1=max{l1, l2, l3, l4}+x1+x2+x3+vt+max{a1, a2, a3, a4}

[0022] Where, I1 and I2 are the critical distances at which the magnetic field on both sides of the transmission line does not affect the operation of the UAV; I3 and I4 are the critical distances at which the electric field on both sides of the transmission line does not affect the operation of the UAV; x1 is the position deviation distance caused by wind; x2 is the positioning distance deviation; x3 is the error distance between the pre-planned route and the actual route of the UAV; v is the flight speed of the UAV, t is the maximum delay of wireless communication; a1 is the wing length of the UAV; a2 is the distance between the UAV nose and its main axis; a3 is the distance between the UAV tail and its main axis; a4 is the distance between the UAV onboard gimbal and the main axis of the UAV.

[0023] Furthermore, the laser point cloud-based drone inspection system is a drone inspection system that uses web endpoint cloud visualization implemented based on three.js technology.

[0024] Furthermore, the web endpoint cloud visualization drone inspection system supports the WebGL engine to implement route definition, including the following steps:

[0025] S1. The pilot searches for the transmission line to be planned and its subordinate towers on the map, clicks the icon to upload or update the point cloud file of the tower, and then enters the route planning page;

[0026] S2. After entering the route drawing page, if there is no route data for the current tower, enter the drawing mode; first use the "Fix Tower Function" button to mark the center point of the tower. The system will obtain the longitude and latitude coordinates of the tower from the power grid resource business center, and mark the size of the tower; the user clicks the "Draw Auxiliary Surface" button to select the tower and automatically generate parallel and vertical auxiliary surfaces to facilitate waypoint drawing;

[0027] S3. The pilot clicks the "Draw Waypoint" button to select the target point to be patrolled in the point cloud. The system automatically generates waypoints in the interface according to the default values ​​and adds the point data to the table on the left. The pilot modifies the parameters and sequence of the waypoints in real time through the form on the right. Finally, the front and rear waypoints are connected in pairs to generate a complete route.

[0028] S4. After completing the route drawing, the pilot conducts a safety check on the route. After clicking the "Safety Check" button, the system uses the previously calculated safety distance to search and check the entire route, locates the waypoints with flight risks and gives prompts; after the safety check, the pilot uses the one-key modification function to simply modify the route, and then re-performs the safety check until it passes, thus completing the definition of the route;

[0029] S5. During the route definition process, the pilot clicks the "Save" button to save the route; after completing the route definition, the pilot clicks the "Publish" button to save the route under the tower or clicks the "Save as Template" button and fills in relevant information to save the route as a template for subsequent reuse of the same type of patrol tasks.

[0030] Furthermore, the route definition module supports that when the camera model carried by the drone changes, the system can automatically match the camera parameters to generate new drone flight route waypoint data while ensuring that the patrol quality is not lost.

[0031] Furthermore, the camera parameters are automatically matched to generate new UAV flight route waypoint data, including:

[0032] To ensure that the converted waypoints, the waypoints before conversion, and the target point are on the same straight line, the distance between the waypoint and the target point and the relationship between the focal length and the field of view angle are calculated in the following way:

[0033]

[0034] Where d is the distance between the waypoint and the target point, FOV is the vertical field of view of the camera, H is the tower height, U represents the length of the film, and f is the focal length of the camera;

[0035] Calculate the waypoint parameters, including latitude, longitude and elevation, according to the formula and generate a new route.

[0036] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects:

[0037] (1) The present invention can greatly simplify the existing route planning process and improve the reliability and efficiency of autonomous flight routes of drones by effectively utilizing relevant resources such as drone operations in the field of power inspection. Compared with the traditional method of generating routes by hovering drones to mark waypoints, the present invention can greatly improve the autonomy of drones, reduce the risk of accidents such as collisions caused by human operation, and greatly improve the efficiency of pole tower route planning, achieving 3-6 minutes / base for single straight towers and 8-15 minutes / base for single tension towers. Compared with manual planning, the efficiency of single tower planning is increased by 4 times.

[0038] (2) The present invention comprehensively considers the characteristics of the aircraft and the complexity of the flight environment, generates a safer flight route based on an algorithm, improves the reliability of refined autonomous flight, and ensures the flight safety of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a block diagram of the drone inspection route definition system of the present invention;

[0040] Figure 2 The route definer display interface of the present invention;

[0041] Figure 3 This is a schematic diagram of real-time safety detection of simulated flight of an electric UAV route according to the present invention;

[0042] Figure 4 It is a schematic diagram of real-time alarm for simulated flight of electric UAV route of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings.

[0044] Since the operating environment of power transmission lines is relatively complex and they need to run for a long time, they are prone to equipment aging, line damage, overheating of parts, and other problems, which affect the safe operation of power transmission lines. Applying drone automatic inspection technology in this field can not only improve inspection efficiency and safety, but also realize real-time image and data transmission, facilitate data analysis and processing, and better support the operation and maintenance of power lines.

[0045] When designing a transmission line drone inspection intelligent management system, it is necessary to consider the following major subsystems and combine them in an overall framework to form the entire transmission line inspection intelligent management system.

[0046] Data storage and management subsystem: responsible for effectively storing and managing the large amount of data obtained from drones. This includes functions such as storage, indexing, and querying of images, sensors, and other related information. The use of appropriate database technology can achieve efficient data management and facilitate further analysis and backtracking.

[0047] Route definition and safety control subsystem: The drone inspection system has the ability to define routes and control safety, ensuring that drones can complete inspection tasks safely and efficiently. This subsystem uses a route batch replication mode to quickly define routes, defines pre-drawn routes as templates, and applies them in batches to the same type of power transmission line equipment to improve route drawing efficiency. At the same time, it provides a simulated flight function for drone inspections, and uses algorithms to calculate and display the safety distance of the drone during the current flight in real time.

[0048] User interface and user interface and decision support subsystem: This subsystem involves the design and implementation of the user interaction interface, which enables the operator to intuitively monitor the drone inspection process and make corresponding decisions. For example, the status of the transmission line, fault reports or abnormal situation warnings are displayed on the interface, and intelligent analysis results and suggestions based on the collected data are provided.

[0049] Integration and communication subsystem: This subsystem integrates the various subsystems and ensures that they can communicate and work together. This includes real-time data transmission, command issuance, and status feedback between the UAV and the ground control station.

[0050] In the process of establishing the system, it is usually necessary to establish relevant data models in advance, use the data models to complete the sharing of internal resources, and use other related software to realize the effective application of multi-layer technology under the support of information technology. In addition, in the process of forming the system framework, it is necessary to integrate various professional software together to achieve unified and standardized management of the overall inspection business and realize the system architecture composition. The focus of the present invention is to solve the problem of low efficiency in existing UAV cruise route planning. Therefore, the following description will focus on the implementation of route definition and safety control subsystems. Data storage and management, integration and communication subsystems can be implemented by existing technologies and will not be described in detail in this article.

[0051] According to the functional design of the route definition and safety control subsystem, it can be understood that safety control is actually part of route planning, so the route definition and safety control subsystem is also referred to as the drone inspection route definition system in the following text. As the name implies, its main function or purpose is to define the drone inspection route, including route definition and safety control.

[0052] According to the demand analysis of the UAV transmission line route planning and flight control simulation system, the main functional modules such as route definition, simulated flight, and safety detection are designed for the UAV inspection route definition system, such as Figure 1 shown.

[0053] (I) Route Definition

[0054] The core of the drone inspection route definer is to accurately define the inspection route of the drone and integrate the point data of the inspection equipment in the business, so as to realize the connection between the route and the business application data. Figure 2 As shown, the interface shows a waypoint list of 14 waypoints of a transmission line, including name, nose, gimbal, action, distance, and provides data display of corresponding route parameters and equipment parameters.

[0055] The key steps in route definition are as follows:

[0056] (1) Equipment inventory acquisition: Obtain equipment inventory information from the power grid resource business center, including equipment type, voltage level, inspection point information, etc.

[0057] (2) Auxiliary planning settings: Automatically analyze the device point cloud data and generate an auxiliary drawing surface with the center of the device as the base point to assist in the accurate drawing of waypoints. The point cloud data is a data set containing a large number of three-dimensional coordinate points generated by scanning the ground or objects using a laser radar (LiDAR). Each point in the LiDAR point cloud data contains three-dimensional coordinate information (X, Y, Z), which can be obtained by the airborne LiDAR system.

[0058] (3) Waypoint route drawing: Draw the detailed inspection route of the drone according to the inspection scene and equipment model such as tower type, string type and parameters of the drone. Mark the target points in the point cloud system and automatically generate aerial photography points and flight routes according to the parameters. It also provides flight parameter adjustment functions such as waypoint position and angle to achieve detailed route drawing.

[0059] (4) Route and template generation: Routes that verify flight safety can be exported, and templates can be generated based on the drone model, equipment model and parameters, route parameters, etc. This enables batch replication of route planning for equipment in the same type of scenario.

[0060] The present invention uses a drone inspection route definition system with web endpoint cloud visualization based on three.js technology. Three.js technology is based on javascript and can directly run graphics driver games and graphics driver webGL engines applied to browsers. WebGL (Web Graphics Library) is a 3D drawing protocol that provides hardware 3D accelerated rendering for HTML5 Canvas, so that Web developers can use system graphics cards to display 3D scenes and models more smoothly in browsers, and can also create complex navigation and data visualization.

[0061] Using the virtual drone power transmission line inspection visualization system built based on WebGL, the key steps of route definition are as follows:

[0062] S1. After the pilot searches and selects the transmission line to be planned and its subordinate towers on the map, he can click the icon to upload or update the point cloud file of the tower and then enter the route planning page.

[0063] S2. After entering the route drawing page, if the current tower has no route data, enter the drawing mode. First, use the "Fix Tower Function" button to mark the center point of the tower. The system will obtain the longitude and latitude coordinates of the tower from the power grid resource business center, and mark the size of the tower. In addition, the user needs to click the "Draw Auxiliary Surface" button to select the tower to automatically generate parallel and vertical auxiliary surfaces to facilitate waypoint drawing.

[0064] S3. The pilot clicks the "Draw Waypoint" button to select the target point to be patrolled in the point cloud. The system will automatically generate waypoints in the interface according to the default values ​​and add the point data to the table on the left. The pilot can modify the parameters and order of the waypoints in real time through the form on the right. Finally, the front and rear waypoints are connected in pairs to generate a complete route.

[0065] S4. After completing the route drawing, the pilot needs to conduct a safety check on the route. After clicking the "Safety Check" button, the system will use the previously calculated safety distance to search and check the entire route, locate the waypoints with flight risks and give prompts. After the safety check, the pilot can use the one-click modification function to make simple modifications to the route. After the modification, the safety check needs to be repeated until it passes, and the definition of the route is completed.

[0066] S5. During the route definition process, the pilot can click the "Save" button to save the route. After completing the route definition, the pilot needs to click the "Publish" button to save the route under the tower or click the "Save as Template" button and fill in relevant information to save the route as a template for subsequent reuse of the same type of patrol tasks.

[0067] In the above step S4, the safety inspection function is to locate the waypoints with flight risks by calculating the safety distance between the drone and the object (facilities and equipment of the transmission line). Among them, the safety distance is also the risk distance, which is a range threshold for the drone to remain safe even if it is far away from the risk. The patrol risk distance of the drone is not only affected by the stability and control accuracy of the drone itself, but also needs to consider the electric field and magnetic field environment, the discharge risk between the drone and the high-voltage equipment, and the potential impact of the partial discharge of the drone on its measurement and control system. The present invention adopts a multi-factor superposition method for the safety distance of the line drone to conduct a comprehensive and in-depth analysis of multiple key factors affecting the safe flight of the drone. These factors include: the critical distance effect of the electromagnetic field on both sides of the transmission line on the operation of the drone, the position deviation of the drone caused by the wind factor, the distance deviation of the Beidou / GPS positioning system, the error between the pre-planned route and the actual flight trajectory of the drone, the product effect between the drone flight speed and the maximum delay of wireless communication, the geometric distance between the drone wings, nose, tail and airborne gimbal and the main axis of the drone, etc. In the actual application process, it is also necessary to comprehensively consider the mutual exclusion and cumulative effects between the various factors to ensure that various potential risks can be fully and accurately reflected when calculating the safety distance. The specific algorithm of the drone safety distance in the present invention is as follows:

[0068] D1=max{l1, l2, l3, l4}+x1+x2+x3+vt+max{a1, a2, a3, a4}

[0069] Where, I1 and I2 are the critical distances at which the magnetic field on both sides of the transmission line does not affect the operation of the UAV; I3 and I4 are the critical distances at which the electric field on both sides of the transmission line does not affect the operation of the UAV; x1 is the position deviation distance caused by wind; x2 is the Beidou / GPS positioning distance deviation; x3 is the error distance between the pre-planned route and the actual route of the UAV; v is the flight speed of the UAV, t is the maximum delay of wireless communication; a1 is the wing length of the UAV; a2 is the distance between the UAV nose and its main axis; a3 is the distance between the UAV tail and its main axis; a4 is the distance between the UAV onboard gimbal and the UAV main axis.

[0070] (II) Simulation flight

[0071] After completing the route definition, a drone simulation model can be generated based on the drone model parameters, allowing the drone to autonomously fly the entire process according to the planned route, and dynamically calculate the distance between the drone and the device body based on the point cloud data retrieval algorithm (i.e., the multi-dimensional segmentation neighborhood retrieval algorithm below). The specific calculation method is described below. Through simulated flight, real-time alarms for abnormal points or routes are achieved, and parameters such as flight speed and tower circling time are displayed in real time. A one-click modification function is provided for unsafe waypoints. Unsafe waypoints in the route are translated outward along the connecting line to a safe distance to improve the efficiency of route planning.

[0072] (III) Safety Testing

[0073] In order to ensure the authenticity of the simulated flight, the system designed by the present invention sets simulation parameters according to the actual performance and environmental conditions of the UAV, such as flight speed, maximum flight altitude, mounted camera parameters, hovering time, etc., and introduces the physical model of the corresponding UAV model. In the process of simulated flight, a multi-dimensional segmentation neighborhood retrieval algorithm for massive point cloud data is proposed to search for the nearest point as the neighboring point of the UAV. The multi-dimensional neighborhood segmentation retrieval algorithm is a method of obtaining the nearest point by continuously segmenting and compressing the surface according to the xyz axis in space. The specific process is as follows:

[0074] (1) Sort the space by x coordinate to find the median value a, cut the space according to x=a, and determine which subspace the target point should be located in;

[0075] (2) Sort the remaining space by y coordinate to find the median value b, cut the space according to y = b, and determine which subspace the target point should be located in;

[0076] (3) Sort the remaining space by z coordinate to find the median value c, cut the space according to z = c, and determine which subspace the target point should be located in;

[0077] Repeat the process (1)-(3) until there is only one point in the space, which is the nearest point to be retrieved.

[0078] By calculating the distance between the nearest point found and the drone, the distance between the drone’s current position and the point cloud can be obtained in real time. This distance is compared with the safety distance calculated after comprehensive consideration of safety factors and environmental factors to predict potential collision risks. Figure 3 As shown, the drone route definer will calculate and display the safe distance in real time when performing simulated flight, provide the drone with a camera perspective, and mark the current waypoint and patrol point locations on the interface.

[0079] If the distance between the drone and the point cloud is less than the safe distance during the simulated flight, the drone route definer will flash and issue a warning and display it in a list, such as Figure 4 shown.

[0080] (IV) Adaptive modification of parameterized routes

[0081] When the camera model on the drone changes, the system can automatically match the camera parameters to generate new drone flight route waypoint data while ensuring that the patrol quality is not lost. The quality of drone aerial photography is mainly determined by the camera's field of view and focal length. The field of view determines the width and depth of the ground area that the camera can capture, while the focal length affects the clarity and detail of the image.

[0082] To ensure that the converted waypoints, the waypoints before conversion, and the target point are on the same straight line, the distance between the waypoint and the target point and the relationship between the focal length and the field of view angle can be calculated as follows:

[0083]

[0084] Where d is the distance between the waypoint and the target point, FOV is the vertical field of view of the camera, H is the tower height, U is the length of the film (in mm), and f is the focal length of the camera. Based on the above analysis, when the focal length parameters of the camera change, the new waypoint parameters (latitude, longitude and elevation) can be automatically calculated and a new route can be generated.

[0085] The present invention can greatly simplify the existing route planning process and improve the reliability and efficiency of autonomous flight routes of drones by effectively utilizing related resources such as drone operations in the field of power inspection. Compared with the traditional method of generating routes by hovering drones to mark waypoints, the present invention can greatly improve the autonomy of drones, reduce the risk of accidents such as collisions caused by human operation, and greatly improve the efficiency of pole tower route planning, achieving 3-6 minutes / base for single straight towers and 8-15 minutes / base for single tension towers. Compared with manual planning, the efficiency of single tower planning is increased by 4 times.

Claims

1. A drone inspection system based on laser point cloud, characterized in that: include: Data storage and management subsystem: responsible for storing and managing data obtained from drones; Route definition and safety control subsystem: used to define routes based on laser point clouds, define drawn routes as templates, apply templates in batches to the same type of power transmission line equipment, provide simulated flight functions for drone inspections, and use algorithms to calculate and display the safety distance of the drone during its current flight in real time; User interface and decision support subsystem: used to provide a user interaction interface, enabling operators to intuitively monitor the drone inspection process and make corresponding decisions; Integration and communication subsystem: used to integrate various subsystems and ensure that they can communicate and work together.

2. The system according to claim 1, characterized in that The route definition and safety control subsystem includes a route definition module, a flight simulation module, and a safety detection module. The route definition module automatically analyzes the equipment point cloud data according to the equipment ledger information and generates waypoints and flight routes according to parameters; the flight simulation module generates a drone simulation model according to the drone model and performs the entire process of autonomous flight operations according to the planned route; the safety detection module calculates the distance between the drone and the equipment body based on the point cloud data, compares it with the pre-set safety distance, identifies unsafe waypoints and moves them outside the safety distance.

3. The system according to claim 2, characterized in that The route definition module generates routes according to the following steps: (1) Equipment ledger acquisition: Obtain equipment ledger information from the power grid resource business center; (2) Auxiliary planning settings: Automatically analyze the device point cloud data and generate an auxiliary drawing surface with the device center as the base point to assist in the accurate drawing of waypoints; (3) Waypoint route drawing: Draw the drone’s refined inspection route based on the inspection scene, equipment model, and drone parameters, mark the target points in the point cloud system, and automatically generate aerial photography points and flight routes based on the parameters; (4) Route and template generation: Export routes and generate templates based on drone model, equipment model and parameters, and route parameters.

4. The system according to claim 2, characterized in that The flight simulation module sets simulation parameters according to the actual performance and environmental conditions of the UAV, including flight speed, maximum flight altitude, mounted camera parameters, hovering time, and introduces the physical model of the corresponding UAV model.

5. The system according to claim 2, characterized in that The safety detection module calculates the distance between the current position of the drone and the point cloud in real time based on the multi-dimensional segmentation neighborhood retrieval algorithm of massive point cloud data, and searches for the nearest point as the neighboring point of the drone.

6. The system according to claim 2, characterized in that The safety distance is calculated according to the following formula: D1=max{l1, l2, l3, l4}+x1+x2+x3+vt+max{a1, a2, a3, a4} Where, I1 and I2 are the critical distances at which the magnetic field on both sides of the transmission line does not affect the operation of the UAV; I3 and I4 are the critical distances at which the electric field on both sides of the transmission line does not affect the operation of the UAV; x1 is the position deviation distance caused by wind; x2 is the positioning distance deviation; x3 is the error distance between the pre-planned route and the actual route of the UAV; v is the flight speed of the UAV, t is the maximum delay of wireless communication; a1 is the wing length of the UAV; a2 is the distance between the UAV nose and its main axis; a3 is the distance between the UAV tail and its main axis; a4 is the distance between the UAV onboard gimbal and the main axis of the UAV.

7. The system according to claim 1, characterized in that The laser point cloud-based drone inspection system is a drone inspection system that uses web endpoint cloud visualization implemented based on three.js technology.

8. The system according to claim 7, characterized in that The web endpoint cloud visualization drone inspection system supports the WebGL engine to implement route definition, including the following steps: S1. The pilot searches for the transmission line to be planned and its subordinate towers on the map, clicks the icon to upload or update the point cloud file of the tower, and then enters the route planning page; S2. After entering the route drawing page, if there is no route data for the current tower, enter the drawing mode; first use the "Fix Tower Function" button to mark the center point of the tower. The system will obtain the longitude and latitude coordinates of the tower from the power grid resource business center, and mark the size of the tower; the user clicks the "Draw Auxiliary Surface" button to select the tower and automatically generate parallel and vertical auxiliary surfaces to facilitate waypoint drawing; S3. The pilot clicks the "Draw Waypoint" button to select the target point to be patrolled in the point cloud. The system automatically generates waypoints in the interface according to the default values ​​and adds the point data to the table on the left. The pilot modifies the parameters and sequence of the waypoints in real time through the form on the right. Finally, the front and rear waypoints are connected in pairs to generate a complete route. S4. After the route is drawn, the pilot conducts a safety check on the route. After clicking the "Safety Check" button, the system uses the previously calculated safety distance to search and check the entire route, locates the waypoints with flight risks and gives prompts; after the safety check, the pilot uses the one-key modification function to simply modify the route, and then re-performs the safety check until it passes, thus completing the definition of the route; S5. During the route definition process, the pilot clicks the "Save" button to save the route; after completing the route definition, the pilot clicks the "Publish" button to save the route under the tower or clicks the "Save as Template" button and fills in relevant information to save the route as a template for subsequent reuse of the same type of patrol tasks.

9. The system according to claim 1, characterized in that The route definition module supports that when the camera model carried by the drone changes, the system can automatically match the camera parameters to generate new drone flight route waypoint data while ensuring that the patrol quality is not lost.

10. The system according to claim 9, characterized in that Automatically match camera parameters to generate new drone flight route waypoint data, including: To ensure that the converted waypoints, the waypoints before conversion, and the target point are on the same straight line, the distance between the waypoint and the target point and the relationship between the focal length and the field of view angle are calculated in the following way: Where d is the distance between the waypoint and the target point, FOV is the vertical field of view of the camera, H is the tower height, U represents the length of the film, and f is the focal length of the camera; Calculate the waypoint parameters, including latitude, longitude and elevation, according to the formula and generate a new route.

Images