Converter station double-nest unmanned aerial vehicle cooperative control method

Abstract

The invention discloses a converter station double-nest unmanned aerial vehicle cooperative control method, and the method comprises a system which is composed of a communication network, a control center, two unmanned aerial vehicles and two nests, and the method has the beneficial effects that region division and task allocation can ensure that the routing inspection resources of each region are allocated more uniformly, and the routing inspection efficiency is improved. The route collaboration and the dynamic inspection can better cover all regional equipment of the converter station, can effectively eliminate inspection blind areas, improve the overall inspection coverage rate of the equipment of the converter station, improve the accuracy of equipment problem judgment, avoid misjudgment or missed judgment caused by a single view angle, guarantee the endurance to ensure that the inspection task is efficiently propelled according to a plan, and improve the efficiency of the inspection. Signal relay enables data transmission and analysis to be more efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle inspection, and particularly relates to a method for cooperative control of double-machine-nest unmanned aerial vehicles in a converter station. BACKGROUND

[0002] As the core hub of a direct current power transmission system, a converter station undertakes the key tasks of AC-DC power conversion, voltage conversion and power transmission, and its safe and stable operation is directly related to the reliable power supply of the entire power system. There are various types of equipment in a converter station, and some of the equipment is located at high altitudes or in special geographical locations, so the inspection environment is extremely complex.

[0003] With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles have been widely used in the inspection of converter stations due to their high flexibility, strong endurance, and ability to reach areas that are difficult for humans to reach. Unmanned aerial vehicles can carry high-definition cameras, infrared thermal imagers and other detection equipment to conduct comprehensive and high-precision inspections of converter station equipment, timely detect thermal defects, appearance damage and other problems of the equipment, and greatly improve the efficiency and accuracy of the inspection, thereby providing a strong guarantee for the safe and stable operation of the converter station.

[0004] Although the unmanned aerial vehicle inspection technology has achieved certain application results in the inspection of converter stations, the existing technology still has many defects and deficiencies in the cooperative work of double-machine-nest unmanned aerial vehicles, and it is difficult to fully exert the advantages of double-machine-nest cooperation, which are as follows: 1. In terms of task allocation, the task allocation of double-machine-nest unmanned aerial vehicles in the existing technology lacks scientificity and flexibility; 2. In terms of flight path planning, the flight path planning of two unmanned aerial vehicles in the existing technology is often independent of each other, without effective cooperation and complementation, and there may be a large number of overlapping areas between the two flight paths, resulting in repeated inspection and waste of resources, or there may be blind areas in the inspection, and some key equipment or areas are not covered, affecting the comprehensiveness of the inspection; 3. In terms of cooperative inspection operation, the existing technology is difficult to achieve efficient cooperation between two unmanned aerial vehicles; 4. In terms of endurance guarantee, the existing technology is not flexible enough in terms of power management and task switching of unmanned aerial vehicles; 5. In terms of signal relay, the existing technology has not effectively solved the signal transmission bottleneck in long-distance inspection scenarios. SUMMARY

[0005] To solve the above problems, the present application provides a method for cooperative control of double-machine-nest unmanned aerial vehicles in a converter station, which is realized by the following technical scheme.

[0006] A method for coordinated control of dual-nest UAVs in a converter station includes a system comprising a communication network, a control center, UAVs, and UAV nests, wherein two UAVs and two UAV nests are respectively provided, and further includes the following steps: S1, Regional Division; S2, Task Allocation; S3, route coordination; S4. Dynamic inspection; S5, guaranteed battery life; S6, Signal Relay.

[0007] Furthermore, the control center is equipped with an industrial control server, a collaborative scheduling system, and a data processing platform. The drone nest integrates an autonomous take-off and landing platform, a fully automatic battery swapping module, and a 5G communication base station. The drone is a quadcopter with a maximum take-off weight of 5kg and a flight time of 60 minutes. It is equipped with a high-definition camera, an infrared thermal imager, a lidar, and an edge computing module. All components are connected to a 5G hybrid network via fiber optics to ensure that the data transmission latency does not exceed 50ms.

[0008] Further, in S1, the region division includes the following steps: S11. Use a geographic information system to digitally model the converter station; S12. Import device coordinates, height, and electromagnetic environment; S13. The control center calculates the straight-line distance between the UAV and the nest; S14. Intelligent area division is carried out by combining the electromagnetic interference intensity and equipment height of the drone's location. S15. Conduct task scheduling for drones within the area.

[0009] Furthermore, in step S15, the control center receives UAV status data in real time and performs task scheduling through the following logic: S151. Emergency tasks are prioritized and the response time shall not exceed 5 minutes. S152. Routine tasks are assigned according to the principle of proximity. If the battery level of the drone responsible for the nest is less than 30%, it will automatically switch to another drone in the nest. S153. Overlapping area tasks adopt a load balancing strategy, which dynamically allocates tasks based on the remaining workload of the two drones to avoid overloading a single drone. S154. Scheduling instructions are issued through an encrypted communication protocol to ensure secure instruction transmission.

[0010] Furthermore, in S3, the route coordination includes the following steps: S31. Generate customized waypoints for equipment within the converter station; S32. Use a ground-based 3D laser scanner to perform a full-area scan of the converter station and generate a 3D point cloud model; S33. Automatically identify the position and outline of customized waypoints through point cloud denoising and feature extraction; S34. Based on the path planning algorithm, complementary flight paths are generated for the two UAVs.

[0011] Furthermore, in S4, when a suspected fault is detected in a certain UAV, the system initiates dynamic inspection, which includes the following steps: S41. Immediately upload the fault location, fault type, and raw data of the detected drone; S42. The control center generates a verification command within 10 seconds and sends it to another drone, which includes the target location, shooting angle and sensor parameters. S43. After receiving the instruction, another drone flies along the optimal path to the target area and collects data from an angle perpendicular to the detection drone. S44. The control center compares the two sets of data. If the feature matching degree exceeds 90%, it is determined to be a real fault; otherwise, it is marked as a suspected fault pending manual review.

[0012] Furthermore, in S5, the battery life guarantee includes the following steps: S51. The drone monitors the remaining battery capacity in real time through a high-precision power sensor. S52. The system sets three levels of battery remaining capacity thresholds, including warning threshold, handover threshold and forced return threshold. S53. When the remaining battery capacity reaches the switching threshold, the control center triggers task switching and returns to the home base to replace the battery. S54. When the remaining battery capacity reaches the forced return threshold, automatically return to the home terminal to replace the battery.

[0013] Furthermore, in S52, a high-precision power sensor is installed inside the drone to monitor the remaining battery capacity in real time, and the system sets three power threshold levels: S521, Warning threshold is 30% battery remaining capacity: The drone sends a low battery warning to the control center and automatically plans the return route; S522, Switching threshold is 25% of remaining battery capacity: If the remaining task duration exceeds 5 minutes, the control center will trigger task switching; S523, Forced return threshold is 20% of remaining battery capacity: The drone immediately terminates the mission and returns to home along the shortest path.

[0014] Furthermore, in S522, during the task switching, the two drones complete data synchronization at a preset handover point to ensure task continuity. The error of the handover point is less than 3 meters. The data synchronization adopts 5G D2D direct connection technology with a transmission rate of 50Mbps and a synchronization time of ≤2 seconds.

[0015] Further, in S6, the signal relay includes the following steps: S61. The control center monitors the communication signal strength between the UAV and the nest in real time. S62. When the inspection area is more than 1 kilometer away from the machine nest, the system automatically starts the relay mode; S63. Calculate the optimal relay location based on terrain data; S64, Relay communication link construction.

[0016] The beneficial effects of this invention are: 1. More Efficient and Rational Task Allocation: This invention allocates tasks based on the distance between the drone nest and the inspection area, assigning nests closer to the inspection area to those areas, and other nests to other areas. This method reduces drone flight distance, lowers energy consumption, and improves inspection efficiency. Furthermore, compared to existing technologies that simply allocate tasks based on distance, this invention, while not adding complex parameters, focuses on the key factor of distance, avoiding resource waste and ensuring a more balanced allocation of inspection resources across all areas, thus better covering all equipment in the converter station. 2. Enhanced Route Complementarity: This invention emphasizes the complementary design of the two UAV routes, minimizing overlapping inspection sections and expanding the inspection coverage. Compared to existing technologies that suffer from insufficient route planning flexibility and are prone to blind spots or duplication, this invention's route planning is more targeted. Based on the distribution of converter station equipment and regional characteristics, the inspection routes of the two UAVs can coordinate with each other, effectively eliminating blind spots and improving the overall inspection coverage of converter station equipment. 3. More timely and comprehensive collaborative inspection response: When one drone detects a problem, this invention quickly notifies another drone through the control center to inspect from different angles. This collaborative mechanism solves the problem of delayed collaborative response in existing technologies, enabling timely follow-up and multi-angle detection of problems. Inspection data from multiple perspectives provides a more comprehensive basis for fault diagnosis, improving the accuracy of equipment problem judgment and avoiding misjudgments or omissions caused by a single perspective. 4. More Reliable Battery Life: In this invention, when one drone's battery is low, another drone is promptly switched on to continue the inspection task, ensuring the continuity of the inspection work. Compared to the poor battery life coordination in existing technologies, the switching mechanism of this invention is smoother, accurately grasps the switching timing, avoids inspection interruptions or omissions due to battery issues, ensures that the inspection task proceeds efficiently as planned, and reduces time waste and task delays caused by insufficient battery life. 5. More Efficient Data Transmission and Analysis: The drones transmit the collected photos to the control center in a timely manner for data analysis. Although this invention is based on existing transmission methods, the combination of a collaborative working mode makes data collection more coherent and comprehensive. The control center can perform comprehensive analysis based on the inspection data from two drones, improving the accuracy of equipment status assessment. Simultaneously, when the inspection task is long-distance, one drone acts as a signal relay, solving the problem of unstable long-distance data transmission and ensuring that data is transmitted to the control center in a timely and complete manner, providing a reliable data foundation for data analysis. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of the converter station dual-nest UAV cooperative control method described in this invention; Figure 2 This is a flowchart of the region division and task allocation process of the present invention; Figure 3 This is a flowchart illustrating the battery life guarantee process of the present invention. Figure 4 A flowchart illustrating the dynamic inspection process of this invention; Figure 5 The distribution diagram of the three-level power threshold of this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figures 1-5 As shown, the present invention has the following specific embodiments.

[0021] Example 1 A method for coordinated control of dual-nest UAVs in a converter station includes a system comprising a communication network, a control center, UAVs, and UAV nests, wherein two UAVs and two UAV nests are respectively provided, and further includes the following steps: S1, Regional Division; S2, Task Allocation; S3, route coordination; S4. Dynamic inspection; S5, guaranteed battery life; S6, Signal Relay.

[0022] Preferably, the control center is equipped with an industrial control server, a collaborative scheduling system, and a data processing platform. The drone nest integrates an autonomous take-off and landing platform, a fully automatic battery swapping module, and a 5G communication base station. The drone is a quadcopter with a maximum take-off weight of 5kg and a flight time of 60 minutes. It is equipped with a high-definition camera, an infrared thermal imager, a lidar, and an edge computing module. All components are connected to a 5G hybrid network via fiber optics to ensure that the data transmission latency does not exceed 50ms.

[0023] In this embodiment, Step 1: Digital Modeling and Neighborhood Deployment of Converter Station Area A 3D laser scanner was used to perform a full-area scan of the converter station to obtain data such as equipment coordinates, height, outline, and electromagnetic interference intensity, and a high-precision 3D digital model (accuracy ±5mm) was constructed and imported into the control center database.

[0024] Two drone nests (nest 1 and nest 2) are deployed on the east and west edges of the converter station. The spacing between the nests is determined according to the maximum span of the converter station (usually 800-1500m) to ensure that the overlapping area of ​​the coverage of the two nests is not less than 10% of the total area of ​​the converter station (for collaborative inspection).

[0025] Step 2: Dynamic task allocation based on distance priority Based on the straight-line distance between the equipment and the two generator cells in the 3D model, the electromagnetic interference intensity (areas with interference values ​​>80dBμV / m are preferentially assigned to those closer to the generator cells), and the equipment height (high-altitude equipment >10m is marked as a key inspection target), the control center divided the converter station into 3 sub-areas: Area A: The area closer to Nest 1 (≤500m), mainly under the responsibility of the Nest 1 UAV (UAV A); Area B: The area closer to Nest 2 (≤500m), mainly under the responsibility of the Nest 2 UAV (UAV B); Zone C: The overlapping area covered by the two drone nests (distance difference ≤ 100m), which is inspected by the two drones in a coordinated manner.

[0026] The control center receives real-time data on drone battery level, location, and mission progress. When a mission in a certain area becomes urgent (e.g., an equipment alarm) or the responsible drone runs out of power, the mission is automatically switched to another drone in another drone pod.

[0027] Step 3: Complementary Route Planning and Generation Based on the characteristics of the equipment in areas A, B, and C, the control center generates complementary flight paths for UAVs A and B: Area A route: Covering all equipment in Area A, with a focus on planning vertical waypoints (1.5m away from the equipment) for high-altitude equipment such as the top of converter valves and transformer bushings. Area B route: Covers all equipment in Area B, with a focus on circumferential waypoints (at 90° intervals) for horizontally distributed equipment such as reactors and surge arresters. Flight routes in Zone C: Drone A is responsible for acquiring visible light images of the equipment in Zone C (angle 0°~60°), and Drone B is responsible for infrared thermal imaging of the same equipment (angle 120°~180°). Flight route overlap rate <5%.

[0028] Once the flight path is generated, it is automatically imported into the UAV's onboard navigation module, supporting real-time obstacle avoidance (obstacles are detected by LiDAR, and the path is adjusted within 100ms).

[0029] Step 4: Dual-machine collaborative inspection and fault verification Drones A and B take off autonomously along the planned route. The high-definition camera (20 megapixels) and infrared thermal imager (640×512 resolution) on board collect equipment data in real time and preprocess it through the onboard edge computing module (extracting features such as cracks and temperature anomalies).

[0030] If UAV A detects a suspected fault (such as transformer joint temperature > 80℃), it immediately sends the fault location (RTK-GPS positioning, accuracy ±0.5m) and raw data to the control center via 5G communication.

[0031] The control center triggers a coordination command, and UAV B adjusts its flight path within 10 seconds to collect data a second time from an angle perpendicular to the fault plane (e.g., UAV A takes a picture from the front, and UAV B takes a picture from the side). The fault is determined after the data from the two UAVs are merged (if the matching degree is >90%, it is confirmed as a real fault).

[0032] Step 5: Dynamic Battery Monitoring and Seamless Battery Swapping The drone has a built-in power sensor (sampling frequency 10Hz) that transmits the remaining power (SOC) to the control center in real time. When SOC=30%, the control center plans the return route; When SOC=25% and the remaining task is greater than 5 minutes, the control center instructs another drone to take over the task (e.g., if drone A has insufficient power, drone B flies to the handover point to continue the inspection).

[0033] After returning to its nest, the drone lands precisely using an infrared alignment sensor (positioning accuracy ±1mm). The nest's magnetic battery swapping device (electromagnetic force 50N) automatically removes the old battery and inserts a new one (the whole process takes 90 seconds). Once the battery swap is complete, the drone can take off again immediately.

[0034] Step 6: Long-distance inspection of signal relay When the inspection area is more than 1km away from the nest (signal strength ≤ -85dBm), the control center instructs an idle drone (such as drone A) to take off to a height of 30m as a relay node (avoiding equipment obstruction).

[0035] The relay drone receives data from the inspection drone (such as drone B) via the 2.4GHz band and then forwards it to the drone nest via the 5.8GHz band. The transmission rate is ≥150Mbps and the packet loss rate is <1%, ensuring real-time data transmission over long distances.

[0036] Example 2 Intelligent regional division A Geographic Information System (GIS) was used to digitally model the converter station, importing equipment coordinates, height, and electromagnetic environment parameters (measured using an electromagnetic detector with an accuracy of ±2dBμV / m). The system then divided the inspection areas according to the following rules: Using the two racks as centers, calculate the straight-line distance from each device to the rack; The electromagnetic interference intensity at the location of the equipment is taken into account (the stronger the interference, the closer the nest will be prioritized for allocation). Consider the equipment height (high-altitude equipment exceeding 10 meters should be allocated to closer nests to reduce flight energy consumption). Ultimately, two main inspection areas (Area A and Area B) and one overlapping cooperation area (Area C) are formed, ensuring that the average distance from the equipment in Area A to Nest 1 does not exceed 500 meters, and the average distance from the equipment in Area B to Nest 2 does not exceed 500 meters. 2. Dynamic task scheduling The control center receives real-time drone status data (location, battery level, mission progress) and schedules missions using the following logic: Emergency tasks (such as equipment alarms) should be prioritized and the response time should not exceed 5 minutes. Routine tasks are assigned according to the "proximity principle". If the battery level of the drone responsible for the nest is below 30%, it will automatically switch to another drone in another nest. The overlapping area tasks adopt a "load balancing" strategy, which dynamically allocates tasks based on the remaining workload of the two drones to avoid overloading a single drone.

[0037] The scheduling instructions are issued through an encrypted communication protocol (AES-256 encryption) to ensure secure instruction transmission.

[0038] Example 3 3D path modeling A terrestrial 3D laser scanner was used (scanning accuracy ±3mm, point cloud density 80 points / mm). 2 The converter station is scanned across its entire area to generate a 3D point cloud model. Through point cloud denoising (removing noise points more than 5mm off the equipment surface) and feature extraction, the location and outline of key equipment such as converter valves and transformers are automatically identified. 2. Generate customized waypoints for different devices: Converter valve: Set one waypoint at the top, middle and bottom of the equipment, with the waypoints 1.5 meters away from the equipment surface to ensure that all heat sinks of the equipment are covered; Transformer: Set 4 flight points (90° apart) around the circumference of the equipment, 1 meter above the top of the oil tank, covering key parts such as bushings and heat sinks; Reactor: Two diagonal waypoints are provided, covering the coil ends and lead connectors. 3. Coordinated and complementary route generation Based on an improved path planning algorithm (integrating the advantages of AI algorithms), complementary routes are generated for the two aircraft: Nest 1 drone routes cover all waypoints in Area A and the northern half of waypoints in Area C; Nest 2 drone routes cover all waypoints in Area B and the southern half of waypoints in Area C. For large equipment in Zone C, the UAV flight path of Nest 1 focuses on visible light image acquisition (shooting angle 0°~45°), while the UAV flight path of Nest 2 focuses on infrared thermal imaging (shooting angle 135°~180°), achieving multi-angle data complementarity. The flight path automatically avoids obstacles, maintains a safe distance of at least 1 meter between the flight altitude and the top of the equipment, and the flight path between adjacent waypoints is a straight line to reduce turning energy consumption. Example 4 Collaborative inspection technology 1. Multi-sensor data acquisition The drone is equipped with a multi-sensor integrated module: High-definition camera (20 megapixels, 10x optical zoom, 30fps) is used to photograph defects in the appearance of equipment (such as cracks and rust). Infrared thermal imager (resolution 640×512, temperature range -20℃~150℃, accuracy ±2℃) is used to detect abnormal equipment temperatures (such as overheating of connectors). LiDAR (range 50 meters, accuracy ±3 cm) is used to measure the geometry of equipment (such as the spacing between insulator skirts). Sensor data is preprocessed by the onboard edge computing module (compressing the image to 1000×750 pixels and extracting areas with abnormal temperatures) before being transmitted to the control center. 2. Fault Collaborative Verification Process When a suspected malfunction is detected in a drone, the system initiates a collaborative verification mechanism: The drone immediately uploads the fault location (based on RTK-GPS positioning, accuracy ±0.5 meters), fault type, and raw data. The control center generates a verification command within 10 seconds, which includes the target location, shooting angle, and sensor parameters (e.g., the infrared thermal imager needs to be adjusted to high-temperature mode). After receiving the command, another drone flies to the target area along the optimal path and collects data from an angle perpendicular to the detection drone (e.g., the detection angle is 30° and the verification angle is 120°). The control center compares the two sets of data. If the feature matching degree exceeds 90%, it is determined to be a real fault; otherwise, it is marked as a suspected fault pending manual review. Example 5 Battery life protection technology implementation 1. Battery monitoring and task switching The drone has a built-in high-precision battery sensor (sampling frequency 10Hz, measurement error ±1%) to monitor the remaining battery capacity (SOC) in real time. The system has three battery threshold settings: Warning threshold (SOC=30%): The drone sends a low battery warning to the control center and automatically plans its return route. Switching threshold (SOC=25%): If the remaining task duration exceeds 5 minutes, the control center will trigger a task switch. Forced return-to-home threshold (SOC=20%): The drone immediately terminates its mission and returns to home along the shortest path. During task switching, the two machines complete data synchronization at a preset handover point (with an error of no more than 3 meters) (using 5G D2D direct connection technology, transmission rate of 50Mbps, and synchronization time ≤2 seconds) to ensure task continuity. 2. Fully automatic battery swapping device The battery swapping module adopts a magnetic design, and its core components include: Positioning unit: 4 sets of infrared alignment sensors (positioning accuracy ±1mm) to guide the drone to land accurately on the battery swapping platform; Battery swapping mechanism: 2 sets of electromagnetic adsorption electrodes (working voltage 24V, adsorption force 50N), which connect to the drone battery interface through electromagnetic force; Battery compartment: can hold 6 spare batteries (capacity 6000mAh, supports operation in environments from -20℃ to 50℃), and is equipped with a constant temperature control system (temperature control accuracy ±2℃). Battery swapping process: Drone landing → infrared positioning → electrode adsorption → old battery removal → new battery insertion → electrode separation. The entire process is automated, takes ≤90 seconds, and has a single battery swapping success rate of ≥99.5%. Example 6 Signal relay technology implementation 1. Relay Triggering and Node Selection The control center monitors the communication signal strength between the UAV and the nest in real time (via RSSI indicator, in dBm). When the inspection area is more than 1 kilometer away from the nest (RSSI ≤ -85 dBm), relay mode is automatically activated. The system prioritizes drones that have completed their current task and have ≥50% battery power as relay nodes; Calculate the optimal relay location based on terrain data: Select a high point (such as the top of the equipment platform) that can simultaneously cover the inspection drone and the drone nest, ensuring that the communication link is unobstructed and the signal attenuation is ≤60dB. 2. Relay communication link construction The relay drone is equipped with a dual-band communication module: 2.4GHz band (supports 802.11n protocol): used to receive data (images, videos) collected by inspection drones, with a transmission rate ≥150Mbps; 5.8GHz band (supports 802.11ac protocol): used for data backhaul to the data center, with a transmission rate of ≥433Mbps. During relaying, the system dynamically adjusts the transmission power (adjustable from 5 to 20 dBm). When the received signal strength fluctuates by more than 10 dB, it automatically switches channels (a total of 13 available channels) to ensure that the data transmission packet loss rate is ≤1%. For example, when the Nest 2 UAV is inspecting at a distance of 1.5 kilometers, the Nest 1 UAV can ascend to a height of 30 meters to act as a relay, enabling real-time transmission of inspection data with a delay of ≤200 ms.

[0039] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for cooperative control of double-machine nest unmanned aerial vehicles in a converter station, comprising a system composed of a communication network, a control center, unmanned aerial vehicles and nests, wherein two unmanned aerial vehicles and two nests are provided respectively, characterized in that, Further comprising the following steps: S1, area division; S2, task allocation; S3, route coordination; S4, dynamic inspection; S5, endurance guarantee; S6, signal relay.

2. The method according to claim 1, characterized in that: The control center is deployed with an industrial control server, a collaborative scheduling system and a data processing platform. The nest is integrated with an unmanned aerial vehicle autonomous take-off and landing platform, a full-automatic battery replacement module and a 5G communication base station. The unmanned aerial vehicle adopts a quadcopter model, has a maximum take-off weight of 5 kg, a endurance time of 60 minutes, is equipped with a high-definition camera, an infrared thermal imager, a laser radar and an edge computing module, and each component is connected through an optical fiber and a 5G hybrid network, so that the data transmission delay is not more than 50 ms.

3. The method of claim 1, wherein the method further comprises: In the S1, the area division comprises the following steps: S11, using a geographic information system to digitally model the converter station; S12, importing equipment coordinates, height and electromagnetic environment; S13, the control center calculating the straight-line distance of the unmanned aerial vehicle from the nest; S14, intelligently dividing the area in combination with the electromagnetic interference intensity and equipment height at the location of the unmanned aerial vehicle; S15, scheduling tasks for the unmanned aerial vehicles in the area.

4. The method of claim 3, wherein the method further comprises: In the S15, the control center receives real-time unmanned aerial vehicle state data and schedules tasks through the following logic: S151, emergency tasks are preferentially allocated, and the response time is not more than 5 minutes; S152, regular tasks are allocated according to the nearest principle, and if the unmanned aerial vehicle responsible for the nest has a battery level of less than 30%, it is automatically switched to another unmanned aerial vehicle in the nest; S153, in the overlapping area, a load balancing strategy is adopted, and the remaining task amount of the two unmanned aerial vehicles is dynamically allocated to avoid excessive load on a single unmanned aerial vehicle; S154, scheduling instructions are issued through an encrypted communication protocol to ensure the safety of instruction transmission.

5. The method of claim 1, wherein the method further comprises: In the S3, the route coordination comprises the following steps: S31, generating customized waypoints for the equipment in the converter station; S32, using a ground three-dimensional laser scanner to scan the converter station to generate a three-dimensional point cloud model; S33, automatically identifying the location and contour of the customized waypoints through point cloud denoising and feature extraction; S34, generating complementary routes for the two unmanned aerial vehicles based on a path planning algorithm.

6. The method of claim 1, wherein the method further comprises: In the S4, when a suspected fault is detected by a certain unmanned aerial vehicle, the system starts dynamic inspection, which comprises the following steps: S41, the unmanned aerial vehicle immediately uploads the fault location, fault type and original data; S42, the control center generates a verification instruction within 10 seconds and sends it to another unmanned aerial vehicle, including the target location, shooting angle and sensor parameters; S43, after receiving the instruction, the other unmanned aerial vehicle flies to the target area along the optimal path and collects data from a vertical angle with the detecting unmanned aerial vehicle; S44, the control center compares the two sets of data, and if the feature matching degree exceeds 90%, it is determined to be a real fault, otherwise it is marked as a suspected fault for manual review.

7. The method of claim 1, wherein the method further comprises: In the S5, the endurance guarantee comprises the following steps: S51, the unmanned aerial vehicle monitors the remaining capacity of the battery in real time through a high-precision power sensor; S52, the system sets three power thresholds for the remaining capacity of the battery, including a warning threshold, a switching threshold and a forced return threshold; S53, when the battery remaining capacity reaches the switching threshold, the control center triggers task switching and returns to replace the battery; S54, when the battery remaining capacity reaches the forced return threshold, automatically return to replace the battery.

8. The method of claim 7, wherein the method further comprises: In the S52, a high-precision power sensor is arranged in the UAV to monitor the battery remaining capacity in real time, and the system sets three power thresholds: S521, the early warning threshold is battery remaining capacity = 30%: the UAV sends a low power early warning to the control center, and automatically plans a return path; S522, the switching threshold is battery remaining capacity = 25%: if the remaining task duration exceeds 5 minutes, the control center triggers task switching; S523, the forced return threshold is battery remaining capacity = 20%: the UAV immediately terminates the task and returns to the nest by the shortest path.

9. The method of claim 8, wherein the method further comprises: In the S522, when the task is switched, the two UAVs complete data synchronization at the preset handover point to ensure task continuity, the error of the handover point is less than 3 meters, the data synchronization uses 5G D2D direct connection technology, the transmission rate is 50Mbps, and the synchronization time is less than or equal to 2 seconds.

10. The method of claim 1, wherein the method further comprises: In the S6, the signal relay includes the following steps: S61, the control center monitors the communication signal strength between the UAV and the nest in real time S62, when the inspection area is more than 1 km away from the nest, the system automatically starts the relay mode; S63, calculate the optimal position of the relay based on the terrain data; S64, build a relay communication link.

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