Unmanned aerial vehicle control method, device and equipment applied to distribution network inspection and medium
The UAV control method solves the problem of low efficiency in manual inspection by generating and optimizing inspection paths and adjusting attitude in real time, thus achieving efficient and safe inspection of power distribution network equipment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANWEI POWER SUPPLY BUREAU OF GUANGDONG POWER GRID CORP
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-16
AI Technical Summary
In existing technologies, manual inspection of power distribution network equipment in complex environments is inefficient and costly, making it difficult to achieve efficient inspection.
By using UAV control methods, an initial inspection path is generated and locally optimized to generate a waypoint sequence. During flight, the UAV attitude is adjusted in real time to ensure that the UAV completes the inspection task along the optimal path.
It improved inspection efficiency, ensured photo quality, reduced labor costs, and enhanced the safety and economy of inspections.
Smart Images
Figure CN122219490A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power grid technology, and in particular to a method, device, equipment and medium for controlling unmanned aerial vehicles (UAVs) used in power distribution network inspection. Background Technology
[0002] A power distribution network (i.e., a distribution point network) includes multiple distribution network devices. These devices need to be inspected regularly to ensure their proper functioning. Some power distribution networks and their devices are deployed in complex environments, such as valleys or areas with construction obstacles, which can complicate inspections.
[0003] In existing technologies, power distribution network equipment located in complex environments is inspected manually. Consequently, the inspection efficiency is low and the labor cost is high.
[0004] Therefore, there is an urgent need for a solution that can inspect power distribution network equipment located in complex environments in order to improve the efficiency of inspection. Summary of the Invention
[0005] The UAV control method, device, equipment, and medium provided in this application embodiment for power distribution network inspection are intended to improve the efficiency of inspecting power distribution network equipment located in complex environments.
[0006] In a first aspect, embodiments of this application provide a drone control method for power distribution network inspection, comprising: generating an initial inspection path based on a preset starting point located in a preset inspection area and the device location of a target power distribution network device in the preset inspection area; and performing local optimization processing on the initial inspection path to obtain an optimized inspection path;
[0007] Based on the optimized inspection path, a waypoint sequence for the UAV is generated; wherein, the waypoint sequence includes flight information for each waypoint;
[0008] During the flight control of the UAV based on the flight information in the waypoint sequence, the flight attitude of the UAV is adjusted according to the UAV parameters and environmental parameters so that the UAV can complete the optimized inspection path.
[0009] In one possible implementation, an initial inspection path is generated based on a preset starting point located in a preset inspection area and the device location of the target power distribution network equipment in the preset inspection area, including:
[0010] Based on the preset starting point, the location of the target power distribution network equipment, and the obstacles located between the preset starting point and the equipment location, N initial paths are generated; where N is a positive integer greater than 1.
[0011] Based on the dangerous locations in the preset inspection area, the N initial paths are eliminated to obtain M initial paths; where M is a positive integer greater than or equal to 1 and less than N; the dangerous locations represent high-risk construction areas and / or unsafe areas with live equipment as the center line;
[0012] The initial inspection path is determined as the initial path with the lowest flight cost among the M initial paths.
[0013] In one possible implementation, the initial inspection path is locally optimized to obtain an optimized inspection path, including:
[0014] Determine each inflection point in the initial inspection path;
[0015] The inflection points whose angles are greater than a preset angle are smoothed to obtain the optimized inspection path.
[0016] In one possible implementation, adjusting the flight attitude of the UAV based on its UAV parameters and environmental parameters includes:
[0017] The drone parameters are acquired in real time, and the environmental parameters of the environment in which the drone is located are acquired in real time.
[0018] The adjustment factor of the drone is determined based on the drone parameters and the environmental parameters;
[0019] The flight attitude of the UAV is adjusted according to the adjustment factor.
[0020] In one possible implementation, determining the adjustment factor of the drone based on the drone parameters and the environmental parameters includes:
[0021] Based on a preset mapping relationship, an adjustment factor corresponding to both the UAV parameters and the environmental parameters is determined, which is the adjustment factor for the UAV.
[0022] The preset mapping relationship represents the correspondence between UAV parameters, environmental parameters, and adjustment factors.
[0023] In one possible implementation, the UAV parameters include UAV operating condition parameters and UAV flight parameters;
[0024] The operating parameters of the UAV include one or more of the following: airflow stability operating parameters, airflow disturbance operating parameters, and electromagnetic interference operating parameters; the flight parameters of the UAV include one or more of the following: position deviation, attitude deviation, and positioning signal.
[0025] In one possible implementation, the flight information includes one or more of the following: the spatial location of the UAV, the flight speed of the UAV, and hovering requirement information.
[0026] Secondly, embodiments of this application provide a drone control device for power distribution network inspection, comprising: a first generation module, configured to generate an initial inspection path based on a preset starting point located in a preset inspection area and the device location of a target power distribution network device in the preset inspection area; and to perform local optimization processing on the initial inspection path to obtain an optimized inspection path;
[0027] The second generation module is used to generate a waypoint sequence for the UAV based on the optimized inspection path; wherein the waypoint sequence includes flight information for each waypoint;
[0028] The adjustment module is used to adjust the flight attitude of the UAV based on the UAV parameters and environmental parameters during the flight control process based on the flight information in the waypoint sequence, so that the UAV can complete the optimized inspection path.
[0029] In one possible implementation, the first generation module is further configured to:
[0030] Based on the preset starting point, the location of the target power distribution network equipment, and the obstacles located between the preset starting point and the equipment location, N initial paths are generated; where N is a positive integer greater than 1.
[0031] Based on the dangerous locations in the preset inspection area, the N initial paths are eliminated to obtain M initial paths; where M is a positive integer greater than or equal to 1 and less than N; the dangerous locations represent high-risk construction areas and / or unsafe areas with live equipment as the center line;
[0032] The initial inspection path is determined as the initial path with the lowest flight cost among the M initial paths.
[0033] In one possible implementation, the first generation module is further configured to:
[0034] Determine each inflection point in the initial inspection path;
[0035] The inflection points whose angles are greater than a preset angle are smoothed to obtain the optimized inspection path.
[0036] In one possible implementation, the adjustment module is further configured to:
[0037] The drone parameters are acquired in real time, and the environmental parameters of the environment in which the drone is located are acquired in real time.
[0038] The adjustment factor of the drone is determined based on the drone parameters and the environmental parameters;
[0039] The flight attitude of the UAV is adjusted according to the adjustment factor.
[0040] In one possible implementation, the adjustment module is further configured to:
[0041] Based on a preset mapping relationship, an adjustment factor corresponding to both the UAV parameters and the environmental parameters is determined, which is the adjustment factor for the UAV.
[0042] The preset mapping relationship represents the correspondence between UAV parameters, environmental parameters, and adjustment factors.
[0043] In one possible implementation, the UAV parameters in the adjustment module include UAV operating condition parameters and UAV flight parameters;
[0044] The operating parameters of the UAV include one or more of the following: airflow stability operating parameters, airflow disturbance operating parameters, and electromagnetic interference operating parameters; the flight parameters of the UAV include one or more of the following: position deviation, attitude deviation, and positioning signal.
[0045] In one possible implementation, the flight information in the second generation module includes one or more of the following: the spatial location of the UAV, the flight speed of the UAV, and hovering requirement information.
[0046] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;
[0047] The memory stores computer-executed instructions;
[0048] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.
[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.
[0050] The method, apparatus, equipment, and medium for controlling unmanned aerial vehicles (UAVs) in power distribution network inspection provided in this application first generate an initial inspection path based on a preset starting point in a preset inspection area and the location of the target power distribution network equipment in the preset inspection area. The initial inspection path is then locally optimized to obtain an optimized inspection path. Next, a waypoint sequence for the UAV is generated based on the optimized inspection path, where the waypoint sequence includes flight information for each waypoint. Finally, during the flight control of the UAV based on the flight information in the waypoint sequence, the UAV's flight attitude is adjusted according to the UAV's parameters and environmental parameters, enabling the UAV to complete the optimized inspection path and ultimately complete the inspection of the target power distribution network equipment. Because it is an optimally optimized inspection path, it is more efficient than manual inspection, provides better photographic conditions, and can perform inspections more effectively. Attached Figure Description
[0051] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0052] Figure 1 A schematic diagram illustrating the application scenario of the UAV control method for power distribution network inspection provided in this application;
[0053] Figure 2 A flowchart illustrating the UAV control method for power distribution network inspection provided in this application;
[0054] Figure 3 A schematic diagram of the structure of the UAV control device for power distribution network inspection provided in this application;
[0055] Figure 4 A schematic diagram of the structure of the electronic device provided in this application.
[0056] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0058] Figure 1 This is a schematic diagram illustrating an application scenario of the UAV control method for power distribution network inspection provided in this application, such as... Figure 1 As shown, inspection personnel must start from a remote starting point and trek across the entire valley to reach the foot of the mountain where the tower is located before beginning their climb. Most of their time and energy is spent on the journey, rather than on the actual inspection work.
[0059] The steep mountain slopes slowed down the climbing speed, further reducing the effective inspection time.
[0060] Climbing steep, uneven slopes can easily lead to slips, falls, and other safety accidents, posing a primary threat to personal safety.
[0061] When traversing densely wooded valleys, one may face risks such as snake and insect bites, vegetation scratches, and getting lost. These risks increase exponentially in severe weather conditions such as rain, snow, thunderstorms, and heavy fog, often forcing inspection work to be suspended.
[0062] Inspection personnel need to take photos of the towers, but they can only stand on the slope to take the photos, resulting in almost all the photos being taken from an extreme low angle. When standing or walking on a steep slope, it is very easy to shake the camera or mobile phone, resulting in blurry photos.
[0063] Based on the above scenarios, it can be seen that existing technologies suffer from low inspection efficiency and substandard inspection results.
[0064] To address the problems existing in existing technologies, the inventors, during their research on drone control schemes for power distribution network inspection, discovered that controlling the drone to perform digital inspections of power poles on mountains along an optimized inspection path is highly efficient. Furthermore, the drone's attitude is adjusted during flight, ensuring high-quality image capture. Specifically, an initial inspection path is generated based on a preset starting point within a pre-defined inspection area and the location of the target power distribution network equipment within that area. This initial path is then locally optimized to obtain an optimized inspection path. The drone then flies to the power pole location according to the optimized path to perform the inspection task. During flight and at the pole photography location, the drone's attitude is adjusted to ensure stable flight and high-quality image capture. Because the drone flies along the optimal path, efficiency is high; and the continuous attitude adjustment at the hovering photography location ensures high-quality image capture.
[0065] Based on the above-mentioned inventive concept, the UAV control scheme for power distribution network inspection in this application was designed.
[0066] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0067] Figure 2 This is a flowchart illustrating the UAV control method for power distribution network inspection provided in this application, as shown below. Figure 2 As shown, the method includes:
[0068] S201. Generate an initial inspection path based on the preset starting point in the preset inspection area and the equipment location of the target power distribution network equipment in the preset inspection area; and perform local optimization on the initial inspection path to obtain an optimized inspection path.
[0069] For example, the preset starting point in the preset inspection area is Figure 1 The starting point is shown in the elliptical frame.
[0070] For example, the location of the target power distribution network equipment in the preset inspection area is the location of the pole at the top of the mountain, and the pole is the inspection object.
[0071] For example, generating an initial inspection path involves: first, creating a 3D environment model of the inspection area; second, having a drone fly through the area initially, collecting 3D point cloud data from a LiDAR-equipped system, acquiring images from a vision camera, and constructing a 3D environment model, marking elements such as poles, trees, and valleys within the model; third, generating an initial inspection path, and then performing local optimization on this initial path to obtain an optimized inspection path.
[0072] S202. Generate a waypoint sequence for the UAV based on the optimized inspection path; the waypoint sequence includes flight information for each waypoint.
[0073] For example, a waypoint sequence is a route map for a UAV to perform an inspection mission, which consists of a series of waypoints arranged in a specific order.
[0074] For example, the waypoint sequence includes flight information for each waypoint. The flight information includes one or more of the following: the UAV's spatial location, the UAV's flight speed, and hovering requirement information.
[0075] The spatial position of a drone describes its absolute and relative coordinates in three-dimensional space.
[0076] The flight speed of a drone describes its speed relative to the air and the ground.
[0077] Hovering requirements describe the ability to remain stationary at a specific height and position.
[0078] This step is to convert the path into a script that the drone can execute.
[0079] S203. During the flight control of the UAV based on the flight information in the waypoint sequence, the flight attitude of the UAV is adjusted according to the UAV parameters and environmental parameters so that the UAV can complete the optimized inspection path.
[0080] For example, drone parameters include drone operating parameters and drone flight parameters.
[0081] For example, the operating parameters of a UAV include one or more of the following: airflow stability operating parameters, airflow disturbance operating parameters, and electromagnetic interference operating parameters. Airflow stability operating parameters are key indicators describing whether a fluid can maintain a stable and predictable flow state under specific conditions; airflow disturbance operating parameters are a set of key physical quantities used to quantify the unstable state of airflow; electromagnetic interference operating parameters are a set of key indicators used to quantify the interference emission intensity and anti-interference capability of electronic and electrical equipment in a specific electromagnetic environment.
[0082] For example, the UAV flight parameters include one or more of the following: position deviation, attitude deviation, and positioning signal. Position deviation parameter: linear interpolation between the target position and the actual position; attitude deviation parameter: angular interpolation between the target orientation and the actual orientation; positioning signal parameter: raw data parameters sent by the positioning system for calculating its own spatial coordinates and time.
[0083] For example, the position deviation parameter quantifies the linear distance error between the actual point of an object in space and the desired point. Its core function is to drive the actual position to continuously approach the target position by continuously detecting the position deviation and dynamically adjusting the output, thereby achieving precise control. The attitude deviation parameter describes the angular orientation error of the object, which answers the question of whether the object's pointing is accurate. The positioning signal parameter is the raw data from the external system, which is the basis for the UAV to calculate its own position and time.
[0084] This step is to ensure that the drone can fly smoothly and perform inspection tasks better.
[0085] The UAV control method for power distribution network inspection provided in this application first generates an initial inspection path based on a preset starting point in a preset inspection area and the equipment location of the target power distribution network equipment in the preset inspection area. The initial inspection path is then locally optimized to obtain an optimized inspection path. Next, the optimized inspection path is converted into a waypoint sequence for the UAV, which includes flight information for each waypoint. Based on the flight information in the waypoint sequence, the UAV is controlled to perform a photo-taking task at the tower. During flight and at the photo-taking location, the flight attitude is constantly adjusted to ensure stable flight and the capture of high-quality tower photos. Compared to manual tower inspection, this method is more efficient and produces higher-quality photos.
[0086] In one example, an initial inspection path is generated based on a preset starting point located in a preset inspection area and the location of the target power distribution network equipment in the preset inspection area. This includes: generating N initial paths based on the preset starting point, the location of the target power distribution network equipment, and obstacles located between the preset starting point and the equipment location; where N is a positive integer greater than 1; eliminating the N initial paths based on dangerous locations in the preset inspection area to obtain M initial paths; where M is a positive integer greater than or equal to 1 and less than N; dangerous locations represent high-risk construction areas and / or unsafe areas with live equipment as the centerline; and determining the initial path with the lowest flight cost among the M initial paths as the initial inspection path.
[0087] For example, the security and economy of the final path are ensured through three core steps: multi-path generation, security screening, and cost optimization.
[0088] For example, the core objective of the first step is to explore multiple possibilities. The algorithm automatically generates N different initial paths between a preset starting point and the target device's location. Generating only a single shortest path could be extremely dangerous, such as flying close to high-voltage power lines; generating multiple initial paths provides options for subsequent safety and cost optimization. The algorithm identifies the location and extent of obstacles and then uses a search algorithm, such as the A* algorithm (not limited), to find multiple feasible paths that can effectively avoid these obstacles. These paths will differ in length, flight altitude, and detour method.
[0089] The second step is the most critical safety checkpoint in the entire process. Based on preset rules, it reviews the multiple initial paths generated in the first step, eliminating any paths with potential risks. Dangerous locations include high-risk construction areas, such as construction sites and areas near large construction machinery, where there is a risk of collision and signal interference. Dangerous locations also include unsafe areas centered on live equipment. These are unique critical points in power line inspections; to ensure absolute safety, sufficient safety buffer distances need to be established on both sides of live conductors, such as high-voltage transmission lines. Any path requiring drones to cross or get too close to this area will be directly eliminated. After the second step, the N unsafe paths will be eliminated, resulting in M safe paths. If M equals 0, it means there are no safe paths under the current conditions, and the starting point needs to be readjusted or other constraints relaxed.
[0090] The third step is to determine the initial inspection path. After obtaining a set of safe paths, the final step is to select the one with the lowest overall cost as the final initial inspection path. The cost here is a comprehensive concept, mainly including energy cost, time cost, and risk cost.
[0091] Energy cost: The most direct factor is the total path length, because a shorter path usually means less electricity consumption.
[0092] Time cost: The complexity of the path affects flight time. A path may be longer, but if it is straighter and has fewer turns, the drone can cruise at a faster speed, and the total time may be shorter.
[0093] Risk cost: Even if the path is within the safe zone, a path that is close to the safety boundary and requires frequent flight path corrections has a higher hidden risk than a path that is above the center of the safe zone and has a smooth flight path.
[0094] This embodiment takes safety as a hard constraint and economic efficiency as an optimization goal, further improving the efficiency of drone inspection of towers.
[0095] In one example, the initial inspection path is locally optimized to obtain the optimized inspection path. This includes: determining each inflection point in the initial inspection path; and smoothing inflection points whose angles are greater than a preset angle to obtain the optimized inspection path.
[0096] For example, the initial inspection path is usually generated by an algorithm, and the path may be a series of straight line segments connected together, forming many sharp turns or zigzag paths. For drones, such paths have several problems: First, the movement is not stable: sharp turns at inflection points will cause the equipment to decelerate and shake, affecting the inspection quality, such as blurry photos, and increasing energy consumption; Second, there are safety hazards: some paths may be too close to obstacles, and there is a risk of collision when making sharp turns; Third, it does not conform to kinematics: real drones have a minimum turning radius limit and cannot achieve theoretically acute-angle turns.
[0097] The purpose of the smoothing process in this embodiment is to make sharp corners smoother without significantly altering the original path, thus making it more suitable for the movement capabilities of the drone.
[0098] In one example, the flight attitude of the drone is adjusted based on the drone's parameters and environmental parameters. This includes: acquiring the drone's parameters in real time and acquiring the environmental parameters of the environment in which the drone is located in real time; determining the adjustment factor of the drone based on the drone parameters and environmental parameters; and adjusting the flight attitude of the drone based on the adjustment factor.
[0099] In one example, the adjustment factor of the drone is determined based on the drone parameters and environmental parameters. This includes: determining the adjustment factor corresponding to both the drone parameters and environmental parameters based on a preset mapping relationship. The preset mapping relationship represents the correspondence between the drone parameters, environmental parameters, and adjustment factor.
[0100] For example, traditional drone control parameters are often fixed, performing well in calm environments but declining in performance when encountering strong winds or low battery power. This embodiment introduces a preset mapping relationship, allowing control parameters to dynamically change according to real-time conditions. For instance, when a low ambient temperature is detected, causing an increase in battery internal resistance, the maximum power limit of the motor (an adjustment factor) can be lowered to prevent a sudden voltage drop in the battery due to excessive current output, thus ensuring flight safety.
[0101] For example, the process of establishing a pre-defined mapping relationship is as follows: through computational fluid dynamics simulation, the aerodynamic effects of the UAV under different wind speeds and attitudes can be simulated, thereby calculating the adjustment factors that need to be compensated in advance; flight tests are conducted under various environmental conditions, and the parameter combinations corresponding to the optimal performance are recorded, thereby constructing a mapping relationship database.
[0102] For example, another preset mapping relationship is established as follows: the quality of the positioning signal is measured by the number of visible GPS satellites. When the number of visible satellites is greater than 8, the quality of the positioning signal is determined to be good. When the number of visible satellites is greater than or equal to 5 and less than or equal to 8, the quality of the positioning signal is determined to be medium. When the number of visible satellites is less than 5, the quality of the positioning signal is determined to be poor.
[0103] The intensity of electromagnetic interference is determined by the electromagnetic interference level, which is divided into level zero, light level, and heavy level.
[0104] The positional deviation is determined by the distance between the drone and the target point. If the distance is less than 1 meter, the positional deviation is small; if the distance is greater than or equal to 1 meter, the positional deviation is large.
[0105] The adjustment factor of the drone is the flight control mode; there are three defined flight control modes: the first flight control mode is the precision mode, which means flying at full speed towards the target point to pursue precision; the second flight control mode is the robust mode, which means prioritizing the stability of the aircraft and flying slowly and cautiously towards the target point; the third flight control mode is the hovering mode, which means stopping forward and hovering steadily in place, waiting for instructions.
[0106] Under various environmental conditions, such as rainy and sunny days, through multiple flight tests and data analysis, a consistent rule was established: a consistent preset mapping relationship was set up (regardless of whether it's raining or sunny): If the positioning signal strength is good and the electromagnetic interference level is zero, the drone executes the precision mode. The reason for executing the precision mode is that the road conditions are excellent, allowing the drone to fly safely and reach its destination quickly and accurately. If the positioning signal strength is moderate and the electromagnetic interference level is light, the drone executes the robust mode. The reason for executing the robust mode is that road conditions are starting to deteriorate, and the GPS position information may occasionally be incorrect; stable flight is necessary to avoid making abrupt turns due to a single erroneous signal. If the positioning signal strength is poor and the electromagnetic interference level is heavy, the magnitude of the position deviation is further determined. If the position deviation is small, hovering mode is executed because the target point is already very close, and hovering is safer. If the position deviation is large, robust mode is executed because the target point is still far away, requiring slow and careful movement. In other words, the road conditions are already very bad, and the GPS location information may be completely unreliable. The primary task at hand is to ensure the aircraft doesn't crash. If you're already very close to the target point, it's safer to hover in place. If you're still far from the target point, approach cautiously.
[0107] Certain preset mapping relationships are represented in the form of rule tables, as shown in the table below:
[0108]
[0109] It should be noted that the adjustment factor is a set of dynamically adjustable internal parameters or variables in a control system. The adjustment factor does not directly represent physical control commands, such as motor speed, but rather acts as a "behavioral regulator" of the control algorithm. By modifying the key parameters, structure, or strategy of the control law itself in real time, the control system can optimally adapt to the changing environment and its own state, thereby achieving the predetermined control performance target.
[0110] For example, when the wind speed sensor detects a crosswind, such as when passing through a valley area and a sudden crosswind occurs, the system will determine the roll angle compensation amount (adjustment factor) according to a preset mapping relationship, and then automatically tilt the drone appropriately in the direction of the wind to generate a force to counteract the wind force, thereby maintaining a stable position.
[0111] For example, during pole inspection, the drone needs to remain as stable as possible to ensure clear footage. When encountering updrafts, the system can dynamically adjust the derivative parameters in the PID controller to make the control system respond more quickly and suppress the up-and-down swaying of the drone.
[0112] For example, in low-temperature environments, the system will query the mapping database and lower the motor power limit value to prevent accidents caused by battery performance degradation and ensure a safe return.
[0113] Figure 3 This is a schematic diagram of the structure of the UAV control device for power distribution network inspection provided in this application, as shown below. Figure 3 As shown, the UAV control device 30 for power distribution network inspection provided in this embodiment includes:
[0114] The first generation module 301 is used to generate an initial inspection path based on a preset starting point located in a preset inspection area and the equipment location of the target power distribution network equipment in the preset inspection area; and to perform local optimization processing on the initial inspection path to obtain an optimized inspection path.
[0115] The second generation module 302 is used to generate a waypoint sequence for the UAV based on the optimized inspection path; wherein the waypoint sequence includes flight information for each waypoint;
[0116] The adjustment module 303 is used to adjust the flight attitude of the UAV based on the UAV parameters and environmental parameters during the flight control process of the UAV according to the flight information in the waypoint sequence, so that the UAV can complete the optimized inspection path.
[0117] In one possible implementation, the first generation module is further configured to:
[0118] Based on the preset starting point, the location of the target power distribution network equipment, and the obstacles located between the preset starting point and the equipment location, N initial paths are generated; where N is a positive integer greater than 1.
[0119] Based on the dangerous locations in the preset inspection area, N initial paths are eliminated to obtain M initial paths; where M is a positive integer greater than or equal to 1 and less than N; dangerous locations represent high-risk construction areas and / or unsafe areas with live equipment as the center line;
[0120] Determine the initial path with the lowest flight cost among the M initial paths, and use it as the initial inspection path.
[0121] In one possible implementation, the first generation module is further configured to:
[0122] Identify the inflection points in the initial inspection path;
[0123] For inflection points whose angles are greater than a preset angle, smoothing is performed to obtain the optimized inspection path.
[0124] In one possible implementation, the adjustment module is further configured to:
[0125] The drone's parameters are acquired in real time, as are the environmental parameters of the drone's environment.
[0126] Determine the adjustment factors for the drone based on the drone parameters and environmental parameters;
[0127] The flight attitude of the drone is adjusted according to the adjustment factor.
[0128] In one possible implementation, the adjustment module is further configured to:
[0129] Based on the preset mapping relationship, the adjustment factors corresponding to the UAV parameters and environmental parameters are determined according to the UAV parameters and environmental parameters, and these are the adjustment factors of the UAV.
[0130] Among them, the preset mapping relationship represents the correspondence between UAV parameters, environmental parameters, and adjustment factors.
[0131] In one possible implementation, the UAV parameters in the adjustment module include UAV operating condition parameters and UAV flight parameters;
[0132] The operating parameters of the UAV include one or more of the following: airflow stability operating parameters, airflow disturbance operating parameters, and electromagnetic interference operating parameters; the flight parameters of the UAV include one or more of the following: position deviation, attitude deviation, and positioning signal.
[0133] In one possible implementation, the flight information in the second generation module includes one or more of the following: the spatial location of the UAV, the flight speed of the UAV, and hovering requirement information.
[0134] The UAV control device for power distribution network inspection provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.
[0135] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.
[0136] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.
[0137] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.
[0138] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.
[0139] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.
[0140] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.
[0141] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0142] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.
[0143] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.
[0144] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.
[0145] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.
[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0147] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0148] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0149] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0150] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for controlling unmanned aerial vehicles (UAVs) applied to power distribution network inspection, characterized in that, The method includes: An initial inspection path is generated based on a preset starting point located in a preset inspection area and the equipment location of the target power distribution network equipment in the preset inspection area; and the initial inspection path is locally optimized to obtain an optimized inspection path. Based on the optimized inspection path, a waypoint sequence for the UAV is generated; wherein, the waypoint sequence includes flight information for each waypoint; During the flight control of the UAV based on the flight information in the waypoint sequence, the flight attitude of the UAV is adjusted according to the UAV parameters and environmental parameters so that the UAV can complete the optimized inspection path.
2. The method according to claim 1, characterized in that, An initial inspection path is generated based on a preset starting point located in a preset inspection area and the device location of the target power distribution network equipment in the preset inspection area, including: Based on the preset starting point, the location of the target power distribution network equipment, and the obstacles located between the preset starting point and the equipment location, N initial paths are generated; where N is a positive integer greater than 1. Based on the dangerous locations in the preset inspection area, the N initial paths are eliminated to obtain M initial paths; where M is a positive integer greater than or equal to 1 and less than N; the dangerous locations represent high-risk construction areas and / or unsafe areas with live equipment as the center line; The initial inspection path is determined as the initial path with the lowest flight cost among the M initial paths.
3. The method according to claim 1, characterized in that, The initial inspection path is locally optimized to obtain the optimized inspection path, including: Determine each inflection point in the initial inspection path; The inflection points whose angles are greater than a preset angle are smoothed to obtain the optimized inspection path.
4. The method according to claim 1, characterized in that, Based on the drone's parameters and environmental parameters, the flight attitude of the drone is adjusted, including: The drone parameters are acquired in real time, and the environmental parameters of the environment in which the drone is located are acquired in real time. The adjustment factor of the drone is determined based on the drone parameters and the environmental parameters; The flight attitude of the UAV is adjusted according to the adjustment factor.
5. The method according to claim 4, characterized in that, Based on the UAV parameters and the environmental parameters, the adjustment factor of the UAV is determined, including: Based on a preset mapping relationship, an adjustment factor corresponding to both the UAV parameters and the environmental parameters is determined, which is the adjustment factor for the UAV. The preset mapping relationship represents the correspondence between UAV parameters, environmental parameters, and adjustment factors.
6. The method according to claim 4, characterized in that, The drone parameters include drone operating parameters and drone flight parameters; The operating parameters of the UAV include one or more of the following: airflow stability operating parameters, airflow disturbance operating parameters, and electromagnetic interference operating parameters; the flight parameters of the UAV include one or more of the following: position deviation, attitude deviation, and positioning signal.
7. The method according to any one of claims 1-6, characterized in that, The flight information includes one or more of the following: the spatial location of the UAV, the flight speed of the UAV, and hovering requirement information.
8. A drone control device for power distribution network inspection, characterized in that, include: The first generation module is used to generate an initial inspection path based on a preset starting point located in a preset inspection area and the device location of the target power distribution network equipment in the preset inspection area. The initial inspection path is then locally optimized to obtain the optimized inspection path. The second generation module is used to generate a waypoint sequence for the UAV based on the optimized inspection path; wherein the waypoint sequence includes flight information for each waypoint; The adjustment module is used to adjust the flight attitude of the UAV based on the UAV parameters and environmental parameters during the flight control process of the UAV according to the flight information in the waypoint sequence, so that the UAV can complete the optimized inspection path.
9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.