A distribution network unmanned aerial vehicle inspection result automatic back transmission and defect identification method

Abstract

The application discloses a kind of network unmanned aerial vehicle inspection achievement automatic back transmission and defect identification method, it is related to power distribution network inspection technical field.The purpose is to solve the problems of low efficiency, fault response lag, data transmission without security and defect identification relying on artificial in traditional manual inspection.The method comprises: unmanned vehicle carries unmanned aerial vehicle and hangar drives to parking point, unmanned aerial vehicle takes off and carries out inspection and collects achievement data;After unmanned aerial vehicle returns, unmanned aerial vehicle platform processes and transmits achievement data to unmanned aerial vehicle management platform;Management platform uses AI image recognition technology to identify defects in data, generates identification results;Finally, management platform synchronizes identification results to power grid digital brain platform, and is associated with equipment operating state storage.The application realizes the full-process automation and intelligentization of distribution network inspection, greatly improves the inspection efficiency and the accuracy of data processing, optimizes the fault response mechanism, and provides strong support for the intelligent operation and maintenance of distribution network line.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network inspection technology, specifically, it relates to a method for automatic transmission of inspection results and defect identification by unmanned aerial vehicle (UAV) in power distribution networks. Background Technology

[0002] Currently, the State Grid Corporation of China is making every effort to promote the construction of highly reliable digital urban power grids. As an important carrier of power supply, the safe and stable operation of distribution network overhead lines is directly related to the reliability of power supply and the quality of power grid operation and maintenance. The inspection of distribution network overhead lines is a core link in ensuring the safe operation of the power grid. With the continuous expansion of the scale of distribution network construction, the coverage area and number of towers of distribution network overhead lines have increased significantly, which puts forward higher requirements for the efficiency, timeliness and accuracy of inspection operations. At present, the inspection of distribution network overhead lines in China is still mainly based on the traditional manual inspection mode. Although some areas have tried to introduce drone inspection, a collaborative operation system of unmanned vehicles and drones has not been formed. The collection, transmission and defect identification of inspection results still rely on manual intervention, which is difficult to adapt to the development needs of distribution network digital transformation and large-scale application of drones.

[0003] In actual inspection operations, the core technical problems exposed by traditional inspection methods and simple drone inspection methods create a prominent contradiction with the development needs of power distribution network inspection, specifically manifested in the following ways:

[0004] 1. Low efficiency of inspection operations and significant delays in fault response: Manual inspection is limited by factors such as location, physical strength, and weather. The inspection range is limited and the inspection cycle is long. It is impossible to complete a full coverage inspection of a large area of ​​distribution network lines in a short time, and potential equipment defects and safety hazards are easily overlooked. At the same time, manual inspection cannot achieve rapid fault location. Fault discovery requires manual investigation. The time interval from fault occurrence to inspection response is long. Untimely fault handling can easily expand the power outage area and increase the power outage duration, seriously affecting the reliability of power supply. Even if simple drone inspection is introduced, it is difficult to achieve rapid and specialized inspection of fault points due to the lack of a coordinated scheduling mechanism with unmanned vehicles.

[0005] 2. Lack of standardized processing and secure transmission mechanisms for inspection results, resulting in chaotic data management: Inspection results are mainly collected through manual recording and scattered drone photography, lacking unified collection standards and processing procedures. Data formats are inconsistent and highly redundant. Furthermore, there is no dedicated encryption and secure exchange mechanism when transmitting inspection results between the power intranet and the extranet, which can easily lead to data leakage and tampering. At the same time, the return of results relies heavily on manual summarization, making it impossible to achieve automatic return and multi-platform synchronization. This results in chaotic inspection data management and makes it difficult to form a complete inspection data system.

[0006] 3. Equipment defect identification relies on manual labor, making it difficult to guarantee accuracy and efficiency: In the existing inspection mode, the identification of defects and safety hazards in distribution network equipment relies entirely on the experience and judgment of the inspectors. This requires high professional skills from the inspectors and is easily affected by subjective factors, resulting in missed or incorrect judgments. Even with video data taken by drones, it is still necessary to manually review and analyze each frame, resulting in extremely low defect identification efficiency. It is impossible to quickly complete the defect analysis of a large number of inspection results, making it difficult to meet the defect identification needs of large-scale distribution network inspections.

[0007] No effective solutions have yet been proposed to address the problems in the relevant technologies.

[0008] Therefore, in order to solve the above problems, the present invention provides a method for automatic transmission of distribution network inspection results and defect identification by unmanned aerial vehicle (UAV). Summary of the Invention

[0009] In order to overcome the above-mentioned technical problems, the purpose of this invention is to provide a method for automatic transmission of distribution network inspection results and defect identification by unmanned aerial vehicle (UAV).

[0010] The objective of this invention can be achieved through the following technical solutions:

[0011] A method for automatically transmitting the inspection results of power distribution network drones and identifying defects, characterized by the following steps:

[0012] S1. The unmanned vehicle, carrying a drone and a drone hangar, travels to the preset pole and line stopping point. The drone takes off from the drone hangar and performs inspections of the overhead power distribution lines according to the preset route. The inspection results data are collected by the gimbal mounted on the drone, including inspection pictures and inspection videos.

[0013] S2. After completing the inspection, the drone returns to the drone hangar, where the drone platform processes the collected inspection data and transmits the processed data back to the drone management platform.

[0014] S3. The drone management platform uses power distribution network defect image recognition technology to identify defects in the returned inspection data, identify equipment defects and safety hazards in the overhead power distribution lines, and generate defect identification results.

[0015] S4. The drone management platform synchronizes the defect identification results to the power grid digital intelligence hub platform and associates and stores the operating status information of unmanned vehicles, drone hangars, and drones with the defect identification results.

[0016] As a preferred technical solution of the present invention, in S1, the unmanned vehicle plans a driving path with a preset stopping point as the turning point, and reports its location and operating status to the vehicle control platform in real time via wireless communication during the driving process. The operating status includes the vehicle's remaining battery power, driving speed and driving trajectory.

[0017] As a preferred technical solution of the present invention, in S1, the preset route of the UAV is entered, modified and managed by the UAV platform. The preset route is bound to the unmanned vehicle parking point and the UAV alternate landing point. During the UAV inspection, the UAV platform obtains the UAV flight status in real time. The flight status includes the UAV position, remaining battery power, flight speed and waypoint execution progress.

[0018] As a preferred technical solution of the present invention, in S2, the processing of inspection results data includes format conversion, data compression and redundant data removal; the UAV platform establishes a communication connection with the UAV hangar through a dedicated power wireless communication network to realize the real-time acquisition of inspection results data.

[0019] As a preferred technical solution of the present invention, in S2, when the UAV platform and the UAV management platform interact with each other, the inspection results data are encrypted; the encrypted inspection results data are then transmitted securely between the power intranet and the extranet via a network security exchange device.

[0020] As a preferred technical solution of the present invention, in S3, the power distribution network defect image recognition technology includes feature extraction, target detection and defect classification of inspection images; the identified equipment defects and safety hazards include tower defects, conductor defects, hardware defects and insulator defects.

[0021] As a preferred embodiment of the present invention, a fault inspection triggering step is also included:

[0022] The power grid digital intelligence central platform obtains power outage analysis information from the distribution network, locates the fault location of the distribution network line, and then pushes the fault information to the drone management platform through the power intranet.

[0023] The drone management platform generates inspection task work orders based on fault information and sends them to the drone platform;

[0024] The drone platform sends unmanned vehicle dispatch instructions to the vehicle control platform via wireless communication, and simultaneously sends drone inspection instructions to the drone hangar, dispatching unmanned vehicles and drones to the fault location to carry out special inspections.

[0025] As a preferred technical solution of the present invention, the UAV platform encrypts and encodes the unmanned vehicle scheduling instructions and then broadcasts them; the vehicle receives the encoded instructions and sequentially completes decoding and decryption, and sends the decrypted scheduling instructions to the vehicle control platform, which then executes the unmanned vehicle scheduling.

[0026] As a preferred embodiment of the present invention, it also includes a step of synchronizing inspection results data across multiple terminals:

[0027] The drone management platform synchronizes inspection data and defect identification results to the drone management platform of the power management information zone, and then pushes them to the power grid digital intelligence hub platform through the power intranet, realizing centralized monitoring and display of distribution network line inspection data.

[0028] As a preferred technical solution of the present invention, during non-inspection periods, the drone hangar collects equipment status information through on-site monitoring equipment. The equipment status information includes hangar temperature and humidity, ambient wind speed and rainfall, drone on-site status, and cabin abnormality information. The hangar actively uploads the equipment status information to the drone platform and pushes the drone video and on-site monitoring video to the drone platform for preview and storage through national standard transmission methods.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. Significantly improve inspection efficiency and achieve rapid fault response: By using a collaborative inspection mode with unmanned vehicles (UAVs) equipped with drone hangars, traditional manual inspections are replaced. UAVs can perform continuous multi-point inspections according to planned paths, breaking through the geographical and physical limitations of manual inspections. They can complete inspection tasks over a wider area within a specified time and shorten the inspection cycle. At the same time, relying on the power outage analysis information of the power grid's intelligent data center, fault inspections are accurately triggered, and UAVs are quickly dispatched to the fault location to carry out special inspections. This significantly shortens the time from fault discovery to response, improves the timeliness of fault handling, effectively reduces power outage time and scope, and enhances the reliability of power distribution network supply.

[0031] 2. Achieve standardized management and secure cross-network transmission of inspection results: Establish a standardized system for the entire process of inspection result data collection, processing, and transmission. Standardize the processing of image results collected by UAVs, including format conversion, data compression, and redundant data removal, to form a unified structured inspection result dataset. At the same time, through encryption processing and LoRa network security exchange equipment, achieve secure transmission of inspection results between the power intranet and the extranet, avoid data leakage and tampering, and enable automatic transmission and multi-platform synchronization of inspection results, solving the problem of chaotic inspection data management and forming a complete inspection data system.

[0032] 3. Improve the accuracy and efficiency of defect identification and reduce reliance on manual labor: The distribution network defect image recognition technology is used to automatically identify defects in inspection results. Through a standardized process of feature extraction, target detection, and defect classification, it accurately identifies defects and safety hazards in equipment such as poles, conductors, fittings, and insulators, replacing manual experience-based judgment. This completely solves the problems of missed and misjudgments, greatly improves the efficiency of defect identification, and can quickly complete the defect analysis of a large number of inspection results, meeting the defect identification needs of large-scale distribution network inspections.

[0033] 4. Achieve integrated management of equipment status and inspection data: Link and store defect identification results with the real-time operating status information of unmanned vehicles, drone hangars, and drones. At the same time, push inspection results and defect identification results to the power grid digital intelligence central platform through a multi-terminal data synchronization mechanism. This enables centralized monitoring and visualization of inspection data, equipment defect data, and equipment status data, providing complete and traceable data support for inspection review, equipment operation and maintenance, and defect handling.

[0034] 5. Adapting to the needs of digital transformation of distribution networks and promoting unmanned inspection operations: This method integrates the drone inspection of distribution networks with the existing PMS3.0 distribution network inspection process of the power grid, realizes intelligent planning, scheduling and management of inspection tasks, reduces manual intervention, promotes the transformation of overhead distribution lines to "multi-dimensional intelligent inspection of sky and ground", and realizes automatic data collection and unmanned management of equipment status in drone hangars during non-inspection periods, which is fully adapted to the development requirements of digital transformation of distribution networks and large-scale application of drones. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the 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.

[0036] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0037] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention:

[0038] Example: Please refer to Figure 1According to an embodiment of the present invention, an automatic feedback method for power distribution network drone inspection results and defect identification is proposed. This embodiment can be applied to intelligent inspection scenarios of overhead power distribution lines. It is implemented based on the automatic inspection system of intelligent driving drone hangar for power distribution networks. The system is deployed in the power enterprise's Internet regional area. Through the LoRa communication platform, the network security guarantee and data communication between the drone platform and the unmanned vehicle platform in the power grid Internet regional area are realized. The drone platform completes the entire process of data interaction with the drone management platform and the power grid digital intelligence hub platform. The unmanned vehicles, drones, and drone hangars adapted to the system all meet the technical requirements of outdoor operation, long-distance endurance, and high-precision operation for power distribution network inspection. The LoRa communication platform includes hardware such as LoRa broadcasting equipment, signal control system integrated machine, internal network VPN switch, and Internet wireless router to realize encrypted transmission of commands, encoding and decoding, and secure network connectivity.

[0039] Step 1: Inspection task planning and unmanned vehicle scheduling, collection of basic inspection data.

[0040] The drone management platform plans the driving path of the unmanned vehicle based on the inspection needs of the overhead power distribution lines and the geographic information of the power grid. It connects the preset pole and line stopping points as turning points to form a complete driving path. At the same time, the drone platform completes the input and verification of the inspection route and uniquely binds the preset route with the unmanned vehicle stopping point and the drone's backup landing point to ensure the location matching of the drone's take-off, inspection and return. The unmanned vehicle, carrying the drone and the drone hangar, departs from the initial stopping point and drives according to the planned path. During the journey, it reports the location information, remaining battery power, driving speed and driving trajectory and other operating status to the vehicle control platform in real time through the wireless communication module until it accurately arrives at the preset stopping point and completes parking, preparing for drone inspection.

[0041] During operation, the unmanned vehicle can stop at any planned stop point according to the needs of the inspection task, and then continue to the next stop point to carry out subsequent inspection tasks after the drone takes off for inspection, thus achieving continuous inspection at multiple points.

[0042] Step 2: The drone takes off for inspection, and the gimbal collects various types of inspection data.

[0043] After the unmanned vehicle arrives at the preset parking point and parks, the drone platform issues an inspection start command to the drone hangar. The drone hangar automatically opens its door, and the drone takes off autonomously from the hangar, performing a full-process inspection of the power distribution network overhead lines according to the preset route. During the inspection, the gimbal on the drone simultaneously collects inspection data, including inspection images and videos. The images cover wide-angle visible light, telephoto visible light, and infrared imaging in multiple dimensions, while the videos are continuous image data of the entire inspection route, meeting the needs for multi-dimensional identification of equipment defects.

[0044] During drone inspections, the drone platform obtains the drone's flight status in real time through the 4G / 5G power grid. The flight status includes the drone's real-time location, remaining battery power, flight speed, and waypoint execution progress. If the drone experiences abnormalities such as insufficient battery power or waypoint deviation, the platform can issue commands to control the drone to hover or accurately return to the drone hangar, ensuring the safety of the inspection operation.

[0045] Step 3: Data transmission and standardization of inspection results to achieve secure transmission.

[0046] After completing the full-route inspection, the drone receives a return command from the drone platform and accurately returns to the drone hangar on the unmanned vehicle. The drone hangar automatically closes its doors. The drone platform establishes a stable communication connection with the drone hangar through a 4G / 5G dedicated power wireless communication network, extracts the inspection results data from the drone, and performs standardized processing on the data. The processing includes format conversion, data compression, and redundant data removal. The format conversion is unified to the common data format for power grid inspection, data compression reduces transmission bandwidth usage, and redundant data removal improves data validity. After processing, a structured inspection results dataset is formed.

[0047] The drone platform encrypts the processed inspection data. The encrypted inspection data is then transmitted securely between the power grid intranet and the external network via a LoRa network security exchange device, and finally sent back to the drone management platform. This ensures the confidentiality and integrity of the data transmission process and prevents data leakage or tampering.

[0048] Step 4: AI image recognition of distribution network defects to generate standardized defect identification results.

[0049] After receiving the transmitted inspection results dataset, the drone management platform uses power distribution network defect image recognition technology to automatically identify defects in the inspection data. Specifically, this includes feature extraction, target detection, and defect classification of the inspection images. Feature extraction extracts key features such as the appearance, shape, and temperature of the equipment. Target detection locates core equipment such as poles, conductors, fittings, and insulators. Defect classification identifies equipment defects and safety hazards such as pole defects, conductor defects, fitting defects, and insulator defects based on power grid equipment defect standards. At the same time, it accurately marks the defect level and defect location to generate standardized defect identification results.

[0050] The drone management platform associates and stores defect identification results with the real-time operating status information of unmanned vehicles, drone hangars, and drones, storing them in a relational database and file storage system. This enables integrated management of inspection data, equipment defect data, and equipment status data, providing complete data support for subsequent defect handling and inspection review.

[0051] Step 5: Proactively trigger fault inspection to achieve targeted and precise inspection of fault points.

[0052] This embodiment also includes a fault inspection triggering step to adapt to the emergency inspection needs of sudden power outages in the power grid:

[0053] The power grid digital intelligence central platform obtains power outage assessment information from the distribution automation system. After locating the fault location of the distribution network line through big data analysis, it accurately pushes fault information such as fault location, fault type, and fault impact range to the drone management platform through the power intranet.

[0054] The drone management platform automatically generates fault inspection task work orders based on fault information and sends the work orders to the drone platform.

[0055] The drone platform generates unmanned vehicle dispatch instructions and drone inspection instructions based on the work order. After encrypting and encoding the unmanned vehicle dispatch instructions, it sends them in broadcast form through the LoRa broadcasting device. The vehicle-side LoRa receiving device collects the encoded instructions and sequentially decodes and decrypts them. The decrypted dispatch instructions are then sent to the vehicle control platform, which executes the unmanned vehicle dispatch and simultaneously issues drone inspection instructions to the drone hangar. The unmanned vehicles and drones are dispatched to quickly go to the fault location to carry out special inspections. The process of collecting, transmitting, and identifying results in steps 2-4 above is repeated to achieve rapid fault location and accurate defect identification.

[0056] Step 6: Synchronize inspection results data across multiple devices to achieve centralized monitoring and display.

[0057] This embodiment also includes a step for multi-terminal synchronization of inspection results data, enabling power grid-wide sharing and visual monitoring of inspection data:

[0058] The drone management platform synchronizes inspection data and defect identification results to the power management information regional drone management platform, which then pushes the data to the power grid digital intelligence central platform via the power intranet. The power grid digital intelligence central platform visualizes the data, including inspection path, defect location, defect level, and equipment status, enabling centralized monitoring and unified management of distribution network line inspection data. Power grid operation and maintenance personnel can view the inspection results in real time through the platform and quickly issue defect handling instructions.

[0059] Step 7: Monitor equipment status during non-inspection periods to achieve all-day equipment management.

[0060] During non-inspection periods, the drone hangar continuously collects equipment status information through its onboard on-site monitoring equipment and environmental sensors. This information includes hangar temperature and humidity, external wind speed and rainfall, drone location status, and hangar cabin anomalies (smoke, vibration, water immersion). The hangar proactively uploads this information to the drone platform via a 4G / 5G dedicated power grid, ensuring the platform has real-time access to the equipment's operational status. Simultaneously, the hangar pushes drone video and on-site monitoring video to the drone platform using national standard transmission methods. The platform enables real-time preview, historical playback, and storage of the video. If any abnormal equipment condition occurs, the platform can trigger an alarm, achieving 24 / 7 unattended management of the equipment.

[0061] Step 8: Inspection mission concludes, unmanned vehicle autonomously returns to base.

[0062] Once a certain area or the entire process inspection task is completed, the UAV platform sends a return command to the vehicle control platform. The UAV then autonomously travels along the planned path back to its initial parking point or charging point, completing the inspection task. The UAV management platform summarizes and archives all the data from the inspection, forming an inspection task report that includes core information such as the inspection scope, inspection duration, number of defects, defect level, and equipment status, providing a complete basis for the operation and maintenance of overhead power distribution lines.

[0063] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification, characterized in that, Includes the following steps: S1. The unmanned vehicle, carrying a drone and a drone hangar, travels to the preset pole and line stopping point. The drone takes off from the drone hangar and performs inspections of the overhead power distribution lines according to the preset route. The inspection results data are collected by the gimbal mounted on the drone, including inspection pictures and inspection videos. S2. After completing the inspection, the drone returns to the drone hangar, where the drone platform processes the collected inspection data and transmits the processed data back to the drone management platform. S3. The drone management platform uses power distribution network defect image recognition technology to identify defects in the returned inspection data, identify equipment defects and safety hazards in the overhead power distribution lines, and generate defect identification results. S4. The drone management platform synchronizes the defect identification results to the power grid digital intelligence hub platform and associates and stores the operating status information of unmanned vehicles, drone hangars, and drones with the defect identification results.

2. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, In S1, the unmanned vehicle plans its driving path with preset stopping points as turning points. During the driving process, it reports its location and operating status to the vehicle control platform in real time via wireless communication. The operating status includes the vehicle's remaining battery power, driving speed, and driving trajectory.

3. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, In S1, the preset flight path of the drone is entered, modified and managed by the drone platform. The preset flight path is bound to the unmanned vehicle docking point and the drone alternate landing point. During the drone inspection process, the drone platform acquires the drone's flight status in real time, including the drone's location, remaining battery power, flight speed, and waypoint execution progress.

4. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, In S2, the processing of inspection results data includes format conversion, data compression, and redundant data removal; the UAV platform establishes a communication connection with the UAV hangar through a dedicated power wireless communication network to realize the real-time acquisition of inspection results data.

5. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, In S2, when the drone platform interacts with the drone management platform, the inspection results data is encrypted; the encrypted inspection results data is then transmitted securely between the power intranet and the extranet via a network security exchange device.

6. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, In S3, the power distribution network defect image recognition technology includes feature extraction, target detection, and defect classification of inspection images; the identified equipment defects and safety hazards include tower defects, conductor defects, hardware defects, and insulator defects.

7. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, It also includes fault inspection triggering steps: The power grid digital intelligence central platform obtains power outage analysis information from the distribution network, locates the fault location of the distribution network line, and then pushes the fault information to the drone management platform through the power intranet. The drone management platform generates inspection task work orders based on fault information and sends them to the drone platform; The drone platform sends unmanned vehicle dispatch instructions to the vehicle control platform via wireless communication, and simultaneously sends drone inspection instructions to the drone hangar, dispatching unmanned vehicles and drones to the fault location to carry out special inspections.

8. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 7, characterized in that, The drone platform encrypts and encodes the unmanned vehicle dispatch instructions and then broadcasts them. The vehicle receives the encoded instructions and decodes and decrypts them in sequence. The vehicle then sends the decrypted dispatch instructions to the vehicle control platform, which executes the unmanned vehicle dispatch.

9. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, It also includes the step of synchronizing inspection results data across multiple devices: The drone management platform synchronizes inspection data and defect identification results to the drone management platform of the power management information zone, and then pushes them to the power grid digital intelligence hub platform through the power intranet, realizing centralized monitoring and display of distribution network line inspection data.

10. The method for automatic transmission of power distribution network unmanned aerial vehicle (UAV) inspection results and defect identification according to claim 1, characterized in that, During non-inspection periods, the drone hangar collects equipment status information through on-site monitoring equipment. This equipment status information includes hangar temperature and humidity, ambient wind speed and rainfall, drone on-site status, and cabin anomaly information. The hangar actively uploads the equipment status information to the drone platform and pushes drone videos and on-site monitoring videos to the drone platform for preview and storage via national standard transmission methods.

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