Converter station GIS indoor unmanned aerial vehicle inspection system and working method thereof

Abstract

The invention discloses a converter station GIS indoor unmanned aerial vehicle inspection system and a working method thereof. The system is composed of an unmanned aerial vehicle body subsystem, a positioning navigation subsystem, a multi-dimensional detection subsystem, an anti-interference communication subsystem, a ground control subsystem and an energy management subsystem, aiming at the characteristics that a GIS indoor space is closed, equipment is dense and an electromagnetic environment is complex, the system adopts a multi-source fusion positioning technology to ensure accurate navigation, and a special detection module is integrated to realize infrared temperature measurement and visual identification, so that the system is high in reliability. Reliable communication is guaranteed through frequency hopping and spread spectrum and a relay network, full-automatic inspection and charging capacity is achieved, and the working method comprises the whole process of environment modeling, autonomous flight, cooperative detection, exception handling and data summarization. According to the method, the defects of manual and existing automatic inspection modes are overcome, efficient, safe, comprehensive and accurate nondestructive inspection of the GIS equipment of the converter station is realized, and the intelligent level of operation and maintenance of a power system is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment inspection, in particular to a GIS indoor unmanned aerial vehicle inspection system for a converter station and a working method thereof. BACKGROUND

[0002] As the core hub of the UHVDC power transmission system, the converter station undertakes key tasks such as AC / DC power conversion, power transmission and distribution, and its operating state is directly related to the safe and stable operation of the entire power system. As a key device in the converter station, the gas insulated switchgear (GIS) has significant advantages such as small footprint, excellent insulation performance, high reliability, and less maintenance workload. However, once internal insulation aging, partial discharge, or other faults occur, it may cause serious power outages and cause significant economic losses and social impact. Therefore, it is crucial to regularly, efficiently, and accurately inspect the GIS equipment in the converter station to promptly identify potential fault risks. In the inspection of GIS equipment in the converter station, early inspections were mostly dependent on manual inspection, with inspectors observing the appearance of the equipment, listening to the sound of the equipment in operation, and using simple tools to measure relevant parameters. With the development of technology, some automated inspection technologies have emerged, such as using track robots for inspection, laying tracks around the GIS equipment to allow the robot to move along the track and collect equipment information. Some converter stations have also begun to use fixed sensors to monitor parameters such as partial discharge of GIS equipment. At the same time, unmanned aerial vehicle inspection technology has gradually emerged in the power industry, and has been widely used in transmission line inspection and outdoor equipment inspection in substations. However, due to factors such as closed space, dense equipment layout, complex electromagnetic interference, and narrow space, the application of unmanned aerial vehicle inspection technology in the indoor environment of the GIS room in the converter station is not mature enough, and there are few related special systems. Defects and deficiencies of prior art Manual inspection method: The GIS indoor equipment in the converter station is numerous and densely distributed, and manual inspection requires checking each device, resulting in extremely low inspection efficiency and difficulty in meeting rapid inspection requirements. Moreover, the inspection personnel work in a closed environment for a long time, which is labor-intensive and prone to fatigue, leading to missed or incorrect inspections and affecting the accuracy of the inspection. In addition, some equipment is installed in special locations, making it difficult to observe the details of the equipment and posing safety risks. Track robot inspection system: Its operation relies on pre-laid tracks, which requires modification of the GIS room in the converter station, increasing construction costs and difficulty. Moreover, once the track is laid, the inspection range of the robot is fixed, and the inspection path cannot be adjusted flexibly, making it difficult to inspect areas not covered by the track and reducing adaptability. Fixed sensor monitoring technology: the monitoring range of the sensor is limited, only specific location parameters can be monitored, the overall state of the GIS device cannot be comprehensively evaluated, blind spots are prone to occur, and the installation and maintenance of the sensor need to interrupt the device operation, affecting the normal work of the converter station. Disadvantages of existing UAV inspection systems in indoor applications: when the existing UAV inspection system for outdoor equipment of a substation is applied in the indoor environment of a GIS of a converter station, the positioning accuracy of the UAV is affected due to the closed indoor space, and collisions are prone to occur; at the same time, the complex electromagnetic environment in the indoor environment can interfere with the communication and control system of the UAV, causing unstable flight of the UAV, inaccurate collection of device information, and the existing UAV inspection system lacks a special detection module for specific faults of GIS devices, and the inspection function is relatively single. SUMMARY

[0003] To solve the above problems, the present application provides a converter station GIS indoor UAV inspection system and its working method, which is realized by the following technical scheme.

[0004] A converter station GIS indoor UAV inspection system, comprising: a UAV body, a positioning and navigation subsystem, a multi-dimensional detection subsystem, an anti-interference communication subsystem, a ground control subsystem and an energy management subsystem, the subsystems work cooperatively through hardware interfaces and software protocols, and are used for autonomous inspection of GIS devices in the closed indoor space of the GIS of the converter station.

[0005] As a further scheme of the present application, the UAV body is a four-rotor structure, adopts a carbon fiber composite material body, has a weight of not more than 3 kg and a size of 600 mm x 600 mm x 300 mm, a modular load cabin is arranged in the middle of the body, a quick-release buckle structure is provided to support the installation and replacement of the detection module, a laser obstacle avoidance sensor is arranged at the bottom of the body, and positioning and navigation antennas and communication antennas are integrated at the upper part of the body.

[0006] As a further scheme of the present application, the positioning and navigation subsystem adopts a positioning scheme integrating visual SLAM, a laser radar module and an inertial navigation unit, the visual SLAM module comprises two fisheye cameras symmetrically installed on both sides of the head of the UAV, the laser radar module is a solid-state laser radar installed at the center of the bottom of the body, and the inertial navigation unit is integrated in the on-board main control board; the on-board processor of the positioning and navigation subsystem adopts an extended Kalman filter algorithm to fuse and process the data of multiple sources of sensors, and outputs positioning information.

[0007] As a further scheme of the present application, the multi-dimensional detection subsystem comprises an infrared thermal imaging module and a device state visual recognition module, the infrared thermal imaging module is coaxially installed with a visible light camera on a two-axis stabilizing holder, the device state visual recognition module is based on a deep learning algorithm and is used for analyzing the collected images to identify device state defects.

[0008] As a further scheme of the present application, the anti-interference communication subsystem adopts a master-slave architecture, comprises a 5.8GHz frequency hopping spread spectrum wireless data transmission radio carried by the unmanned aerial vehicle, and a plurality of wired connection emergency repeaters deployed at key nodes in the GIS room, and the subsystem uses an AES-256 algorithm to encrypt the transmission data.

[0009] As a further scheme of the present application, the ground control subsystem comprises an industrial control computer, a touch display screen and a joystick, and its software system provides an A* algorithm for path planning, a monitoring interface for real-time display of unmanned aerial vehicle state and detection data, a data processing module for automatic generation of inspection reports, and a diagnostic module for monitoring system state.

[0010] As a further scheme of the present application, the energy management subsystem comprises a battery management system and an automatic charging base station, the battery management system is used for real-time monitoring of voltage, current and temperature parameters of the battery and alarming in case of abnormality, and the automatic charging base station supports autonomous docking and wireless charging of the unmanned aerial vehicle through visual positioning.

[0011] A method for conducting an unmanned aerial vehicle inspection in a GIS room of a converter station, comprising the following steps: S1, preparation before inspection, constructing an indoor three-dimensional environment model through laser scanning, importing the ground system and performing path planning and device debugging; S2, unmanned aerial vehicle autonomous take-off, responding to ground command to ascend to a safe height and complete initial positioning; S3, autonomous inspection, the unmanned aerial vehicle flies according to the preset path, hovers at key detection points, cooperates with the multi-dimensional detection subsystem to collect data, and returns the data in real time; S4, abnormality processing, for communication interruption, device failure and low power abnormality, corresponding emergency strategies are executed; S5, autonomous return and data summary, the unmanned aerial vehicle returns to the charging base station and lands, and the ground system processes the data and generates an inspection report.

[0012] As a further scheme of the application, in the step S3, the UAV corrects the path deviation in real time through the positioning and navigation subsystem during flight, the deviation threshold is set to 30 cm, a safety distance of 0.8-1.2 m is maintained from the equipment when hovering at the detection point, and the infrared thermal imaging module scans the surface of the equipment to identify overheating defects with a temperature higher than the ambient temperature by 15 DEG C or more.

[0013] The abnormality processing strategy in the step S4 includes: automatically switching to an emergency repeater when the communication interruption exceeds 1 s, executing a lost connection return flight program after multiple attempts fail, entering a degraded inspection mode when a non-critical module fault is detected, and immediately executing a return flight or emergency landing when a critical flight system fault or battery power is lower than 30%.

[0014] The application has the following beneficial effects: 1. High inspection efficiency, low labor intensity and high accuracy In the prior art, artificial inspection needs to check dense GIS equipment one by one, which is low in efficiency and prone to missed inspection and misinspection due to personnel fatigue. The application adopts an unmanned aerial vehicle automatic inspection mode, and the unmanned aerial vehicle can autonomously complete continuous inspection of multiple devices according to a planned inspection path or remote control, without the need for artificial point-by-point checking, thereby greatly shortening the time consumption of a single round of inspection, significantly improving the inspection efficiency, and fundamentally reducing the labor intensity, since the unmanned aerial vehicle does not need to enter a closed GIS room for long-time operation. In addition, the high-definition imaging device carried by the unmanned aerial vehicle can stably collect device data, avoiding the deviation caused by artificial subjective judgment, and combining data processing algorithms to analyze information, further improving the accuracy of the inspection results.

[0015] Low modification cost, small construction difficulty, and strong flexibility and environmental adaptability of inspection The track-type robot inspection system needs to lay a fixed track, and the modification process has a great impact on the GIS indoor environment and is high in cost, and the track limits the inspection range. The unmanned aerial vehicle inspection system of the application does not need to lay a track, only needs to preset necessary positioning marks indoors or use environmental features for positioning, thereby reducing the modification workload of the existing site, reducing the construction difficulty and modification cost. At the same time, the unmanned aerial vehicle can flexibly adjust the inspection path due to its aerial movement characteristics, can reach narrow spaces, the top of devices and other areas that cannot be covered by the track, has stronger adaptability to complex layout GIS indoor environments, and can realize omnidirectional inspection without dead angles.

[0016] Comprehensive monitoring range, eliminating monitoring blind spots, and not affecting the normal operation of the converter station Fixed sensors can only monitor parameters at specific locations, resulting in numerous blind spots, and installation and maintenance require interruption of equipment operation. The UAV of this invention, equipped with various specialized detection devices (such as infrared thermal imagers), can comprehensively collect data on different parts and parameters of the GIS equipment during flight, covering the entire equipment and key details, effectively eliminating monitoring blind spots. Furthermore, the deployment and recovery of the UAV are simple, requiring no shutdown of the GIS equipment, and inspections can be completed while the equipment is operating normally, avoiding interference with converter station operation caused by inspection work.

[0017] It boasts high positioning accuracy, strong resistance to electromagnetic interference, and rich and targeted inspection functions. Existing drones used in indoor GIS environments suffer from inaccurate positioning, susceptibility to electromagnetic interference, and limited functionality. This invention addresses these issues by employing multi-source fusion positioning technology (such as fusion of visual positioning, LiDAR positioning, and inertial navigation positioning) to improve drone positioning accuracy in enclosed spaces and reduce collision risks. By optimizing the electromagnetic shielding design and anti-interference algorithms of the drone's communication module, the system's stability in complex electromagnetic environments is enhanced, ensuring uninterrupted data transmission and flight control. Furthermore, the system integrates a dedicated detection module for GIS equipment-specific faults (such as overheating), enabling precise detection of critical equipment status parameters. Compared to general-purpose drone inspection systems, this more targeted approach allows for more effective detection of potential GIS equipment faults. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a schematic diagram comparing the defects of the prior art described in this invention with corresponding solutions; Figure 2 This is a schematic diagram showing the system composition and functions described in this invention; Figure 3 This is a schematic diagram of the inspection workflow steps described in this invention; Figure 4 This is a schematic diagram showing the data types and uses of the inspection system described in this invention; Figure 5 This is a schematic diagram illustrating the data processing and transmission technology described in this invention. Detailed Implementation

[0020] 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.

[0021] like Figures 1-5 As shown, the present invention has the following two specific embodiments.

[0022] Example 1, System Composition: A converter station GIS indoor unmanned aerial vehicle (UAV) inspection system includes: a UAV body, a positioning and navigation subsystem, a multi-dimensional detection subsystem, an anti-interference communication subsystem, a ground control subsystem, and an energy management subsystem. The subsystems work together through hardware interfaces and software protocols to perform autonomous inspection of GIS equipment in the enclosed indoor space of the converter station GIS.

[0023] The drone body features a quadcopter "cross" shaped frame structure. The central frame houses the core processing unit, and each of the four rotor arms has a power unit at its end. Each power unit includes a brushless motor, carbon fiber propellers, and a shock-absorbing bracket connected by rubber dampers to effectively suppress vibration. The drawer-type modular payload compartment measures 200mm × 150mm × 100mm and supports a maximum payload of 3kg. Quick-release clips allow for rapid installation and removal of detection modules such as infrared thermal imagers and visible light cameras. A laser obstacle avoidance sensor is mounted on the bottom of the fuselage, with a detection range of 0.5-5m. The replaceable lithium battery is 24V / 20000mAh, providing at least 30 minutes of flight time.

[0024] Positioning and navigation subsystem: Two fisheye cameras (1280×720 resolution, 190° field of view) of the visual SLAM module are mounted at a certain angle on both sides of the nose and connected to the onboard processor via USB 3.0 interface to collect environmental texture features and build maps in real time. A solid-state LiDAR (scanning frequency 10Hz, ranging range 0.1-10m) is installed at the center of the bottom of the fuselage and connected via Ethernet interface to scan the three-dimensional contour of the device. A six-axis IMU (accelerometer range ±16g, gyroscope range ±2000° / s) is integrated into the main control board and communicates via SPI interface. The onboard processor uses NVIDIA Jetson Xavier NX and runs the extended Kalman filter (EKF) algorithm at a frequency of 50Hz to fuse multi-source data and achieve a positioning accuracy of ±5cm.

[0025] Multi-dimensional detection subsystem: The infrared thermal imaging module uses a 640×512 resolution infrared camera (temperature measurement range -20℃~150℃, accuracy ±2℃), and a 4K resolution visible light camera. Both are coaxially mounted on a two-axis stabilized gimbal (anti-shake range ±0.1°) to ensure synchronized and stable image acquisition. The equipment status visual recognition module, based on deep learning algorithms, automatically analyzes the acquired visible light images to identify meter readings and appearance defects, with an accuracy of no less than 95%. All detection modules communicate with the main control unit via a CAN bus, and the data sampling interval can be set via the ground system within the range of 0.1-1s.

[0026] Anti-jamming communication subsystem: The main communication between the UAV and the ground station uses a 5.8GHz frequency-hopping spread spectrum (FHSS) data radio with a transmission rate of 2Mbps and an indoor communication distance of up to 100m. It supports real-time transmission of H.264 encoded 1080P images and detection data (bandwidth ≤500kbps). 3-5 fixed repeaters are deployed at key nodes such as corners and densely populated equipment areas within the GIS indoor system. The repeaters are connected via wired Ethernet (1Gbps) to form an emergency communication network. All transmitted data is encrypted using the AES-256 algorithm.

[0027] Ground control subsystem: The hardware includes an industrial control computer (CPU i7, 16GB memory, 1TB storage), a 27-inch touch screen, a 6-axis joystick, and an emergency stop button (response time ≤0.1s). The software system has the following functions: it can import indoor 3D models in .obj format, use the A* algorithm for path planning (obstacle avoidance safety distance ≥0.5m), and display the UAV's position (±10cm), battery level, detection data curves, and equipment status (normal / alarm) in real time on the monitoring interface. It can automatically generate inspection reports that include the defect location (associated 3D coordinates), type, severity (classified according to DL / T 544-2010 standard), and handling suggestions. The system diagnostic function monitors the status of each module and triggers alarms when a fault occurs.

[0028] Energy Management Subsystem: The Battery Management System (BMS) monitors battery voltage, current, and temperature in real time. When the voltage difference of a single battery cell is greater than 0.1V or the temperature is higher than 60℃, it automatically cuts off the power supply and triggers the return to home. The automatic charging base station is installed in a safe indoor area and has visual positioning guidance (QR code recognition) with a positioning accuracy of ±5mm. It adopts 60W wireless charging and can fully charge the drone battery in 1.5 hours.

[0029] Example 2, Working Method: The difference from Embodiment 1 is that this embodiment discloses a method for indoor UAV inspection of converter station GIS using the aforementioned system: Step S1: Preparation before inspection (pre-processing stage) The operator first uses LiDAR to scan the GIS indoor environment, constructing a 3D point cloud model with an accuracy of ±2cm. This model is then imported into the ground control system. The location of various equipment (such as circuit breakers and disconnect switches), no-fly zones (within 1m of high-voltage live parts), and key inspection points (such as SF6 gas chamber valves and terminals) are precisely marked in the 3D model. Subsequently, a path is planned according to the inspection requirements (comprehensive inspection or focused inspection): comprehensive inspection requires the path to cover all equipment surfaces with a flight distance of no more than 0.5m; focused inspection only plans paths for equipment with historical defects or key nodes. After the path is generated, it is previewed and confirmed on the ground system. Finally, equipment debugging is performed: ensuring the drone's battery power is ≥80%, performing blackbody calibration on the infrared camera, and checking the communication link signal strength is ≥-70dBm. After debugging, the drone is accurately placed at the takeoff point (aligned with the charging base station).

[0030] Step S2: Autonomous takeoff of the drone (start-up phase) The ground operator sends a "takeoff command" through the software interface. The UAV responds to the command, starts the power system, and ascends vertically to a safe hovering height of 1.5m above the ground. The positioning and navigation subsystem is then activated, and the visual SLAM and lidar modules begin to work. By matching with the pre-stored 3D environment model, the UAV completes the initial positioning within 3 seconds. After successful positioning, the UAV sends a "ready" signal to the ground system and waits for further inspection commands.

[0031] Step S3: Autonomous Inspection (Core Execution Phase) The UAV flies along a preset path at a speed of 0.5-1 m / s. The positioning and navigation subsystem outputs high-precision position information at a frequency of 50 Hz. When the actual position deviates from the planned path by more than 30 cm, the flight control system adjusts the UAV's attitude (roll / pitch angle ≤ 15°) through a PID algorithm to correct the trajectory. When flying to a key detection point, the UAV automatically hovers (hovering accuracy ±10 cm). The lidar scan confirms that it maintains a safe distance of 0.8-1.2 m from the equipment. Subsequently, multiple detection modules work together: the infrared thermal imaging module scans the equipment surface at a frame rate of 5 fps, generating a temperature field distribution map. If the temperature of a certain area is more than 15°C higher than the ambient temperature, it is marked as an overheating defect; the visual recognition module continuously captures three images of the equipment's appearance, automatically analyzing meter readings and appearance defects. All detection data (raw signals and processing results) are transmitted to the ground system in real time through the anti-interference communication subsystem. The ground system interface updates the equipment status label in real time. If a serious defect is detected, the system triggers an audible and visual alarm, and the UAV pauses its inspection and hovers to await operator instructions.

[0032] Step S4: Exception Handling (Emergency Phase) If the main communication link is interrupted for more than 1 second, the UAV will automatically switch to the nearest repeater for communication. If relay communication also cannot be established, the "return to base" procedure will be initiated. Relying on the IMU and pre-stored environmental maps, the UAV will return to the takeoff point along the original path. During the return, the lidar will continuously perform obstacle avoidance. If a module of the UAV malfunctions (such as camera communication interruption), the ground system will prompt "degraded inspection". The UAV will automatically skip the inspection items that depend on the module and continue to perform other tasks. If the malfunction involves flight safety (such as power abnormality), the emergency landing procedure will be executed immediately. The UAV will autonomously select the nearest open area and land at a descent speed of no more than 0.3 m / s. When the battery level is below 30%, the ground system will prompt "low battery return to base". The UAV will immediately terminate the current mission and return to the charging base station first.

[0033] Step S5: Autonomous Return and Data Summary (Final Stage) After completing the inspection task or receiving a "return command," the drone flies along the planned optimal path to the charging base station. Using its belly-mounted visual sensor, it identifies the QR code on the base station, precisely adjusts its attitude, and finally lands on the charging platform with a deviation of no more than 10mm. The power system is then shut down, and the ground control system automatically collects and processes all inspection data. Combining infrared images with ambient temperature, the system calculates the equipment's temperature rise rate, maps all identified defect locations onto a 3D model for visualization, and automatically generates a detailed inspection report. Simultaneously, the automatic charging base station begins charging the drone, and all inspection data is saved to a database, supporting integration with the superior power operation and maintenance management system. This concludes the entire inspection process.

[0034] 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.

Claims

1. A converter station GIS room unmanned aerial vehicle inspection system, characterized in that, The unmanned aerial vehicle body, the positioning and navigation subsystem, the multi-dimensional detection subsystem, the anti-interference communication subsystem, the ground control subsystem and the energy management subsystem are cooperatively operated through hardware interfaces and software protocols, and are used for autonomously inspecting GIS equipment in a closed space in a GIS room. The unmanned aerial vehicle body is a four-rotor structure, adopts a carbon fiber composite material body, has a weight of not greater than 3 kg, a size of 600 mm*600 mm*300 mm, and a modularized load cabin arranged in the middle of the body, supports the installation and replacement of detection modules through a quick-release buckle structure, and is provided with a laser obstacle avoidance sensor at the bottom and a positioning and navigation antenna and a communication antenna integrated at the top. 2.The unmanned aerial vehicle inspection system for GIS room in a converter station according to claim 1, characterized in that: The positioning and navigation subsystem adopts a positioning scheme of fusing a visual SLAM module, a laser radar module and an inertial navigation unit, the visual SLAM module comprises two fisheye cameras symmetrically arranged at the two sides of the head of the unmanned aerial vehicle, the laser radar module is a solid-state laser radar arranged at the center of the bottom of the body, and the inertial navigation unit is integrated on the onboard main control board, and the onboard processor of the positioning and navigation subsystem fuses and processes multi-source sensor data through an extended Kalman filtering algorithm and outputs positioning information. 3.The unmanned aerial vehicle inspection system for GIS room in a converter station according to claim 1, characterized in that: The multi-dimensional detection subsystem comprises an infrared thermal imaging module and a device state visual recognition module, the infrared thermal imaging module and a visible light camera are coaxially arranged on a two-axis stabilizing holder, the device state visual recognition module is based on a deep learning algorithm and is used for analyzing collected images to recognize device state defects.

4. The unmanned aerial vehicle inspection system for GIS room in a converter station according to claim 1, characterized in that: The anti-interference communication subsystem adopts a master-slave architecture, comprises a 5.8 GHz frequency hopping spread spectrum wireless data transmission radio carried by the unmanned aerial vehicle and a plurality of wired connection emergency repeaters arranged at key nodes in the GIS room, and the subsystem encrypts transmission data through an AES-256 algorithm.

5. The unmanned aerial vehicle inspection system for converter station GIS room according to claim 1, characterized in that: The ground control subsystem comprises an industrial control computer, a touch display screen and a joystick, a software system thereof provides an A* algorithm for path planning, a monitoring interface for displaying the state of the unmanned aerial vehicle and detection data in real time, a data processing module for automatically generating an inspection report and a diagnosis module for monitoring the state of the system. 6.The unmanned aerial vehicle inspection system for GIS room in a converter station according to claim 1, characterized in that: The energy management subsystem comprises a battery management system and an automatic charging base station, the battery management system is used for monitoring the voltage, current and temperature parameters of the battery in real time and alarming in case of abnormality, and the automatic charging base station supports the unmanned aerial vehicle to autonomously land and wirelessly charge through visual positioning.

7. The unmanned aerial vehicle inspection system for GIS room in a converter station according to claim 1, characterized in that: The method comprises the following steps:

8. A method for unmanned aerial vehicle inspection of a converter station GIS room based on the system of any one of claims 1-7, characterized in that, S1, preparation before inspection, constructing an indoor three-dimensional environment model through laser scanning, importing the model into the ground system and performing path planning and device debugging; S2, the unmanned aerial vehicle autonomously takes off, responds to ground instructions, ascends to a safe height and completes initial positioning; S3, autonomous inspection, the unmanned aerial vehicle flies according to a preset path, hovers at key detection points, cooperatively operates the multi-dimensional detection subsystem to collect data, and returns the data in real time; S4, abnormality processing, corresponding emergency strategies are executed for communication interruption, device failure and low power abnormality. ​ S5, autonomous return and data summary, the unmanned aerial vehicle autonomously returns to the charging base station and lands, and the ground system processes data and generates an inspection report.

9. The method of working according to claim 8, characterized in that: In step S3, the unmanned aerial vehicle corrects the path deviation in real time through the positioning and navigation subsystem during flight, the deviation threshold is set to 30 cm, and when hovering at the detection point, a safety distance of 0.8-1.2 m is maintained from the equipment, and the infrared thermal imaging module scans the surface of the equipment to identify overheating defects with a temperature higher than the ambient temperature by 15℃ or more.

10. The method of claim 8, wherein: The abnormality processing strategy in step S4 includes: automatically switching to an emergency repeater if the communication interruption exceeds 1s, executing a lost connection return program after multiple failed attempts, entering a degraded inspection mode if a non-critical module fault is detected, and immediately executing a return or emergency landing if a critical flight system fault is detected or the battery level is less than 30%.

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