Driving control method for urban railway inspection of unmanned aerial vehicle

Abstract

The invention provides a driving control method for urban railway inspection of an unmanned aerial vehicle. The driving control method comprises the following steps: acquiring an inspection task issued by a ground center; driving the unmanned aerial vehicle to inspect the inspection main body according to the inspection route, and detecting whether a sudden working condition occurs in real time; controlling the shortest distance between the unmanned aerial vehicle and the current inspection main body to be greater than a first safety distance corresponding to the current inspection main body under the condition that the sudden working condition does not occur; under the condition that the sudden working condition occurs, the working condition grade of the sudden working condition is determined, a temporary danger avoiding route is generated according to the working condition grade, and the unmanned aerial vehicle is driven to sail according to the temporary danger avoiding route. By presetting the first safety distance corresponding to the inspection main body, it is ensured that the unmanned aerial vehicle always keeps the safety interval with the inspection target under the normal working condition, the collision risk is avoided, and the problem that the safety distance is not accurately controlled in traditional inspection is solved; specific temporary danger avoiding routes are generated according to different grades of sudden working conditions, graded disposal and quick response are achieved, and equipment faults are effectively reduced.

Description

Technical Field

[0001] This disclosure relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a drive control method, electronic equipment, UAV, and UAV inspection system for UAV inspection of urban railways. Background Technology

[0002] With the acceleration of urbanization, urban rail lines are becoming increasingly dense and complex, making traditional manual inspection methods insufficient to meet the demands for safe and efficient operation and maintenance. Drones, with their advantages of flexible deployment and wide field of view, are gradually becoming a key technology for urban rail inspection; however, several pain points remain in their application within the industry. Currently, drone inspection procedures lack unified standards, flight path planning and safety distance settings rely heavily on experience, and the safety protection needs of different inspection entities are not adequately addressed. Furthermore, the emergency response mechanism is inadequate. When faced with unexpected situations such as equipment failure, severe weather, and electromagnetic interference, there is a lack of scientifically sound tiered handling plans, which can easily lead to inspection interruptions, equipment collisions, or even mission failures, seriously affecting the safety and continuity of inspections. Summary of the Invention

[0003] The purpose of this disclosure is to provide a drive control method, electronic device, drone, and drone inspection system for unmanned aerial vehicle (UAV) inspection of urban railways, in order to solve the problems existing in the prior art.

[0004] The embodiments of this disclosure adopt the following technical solution: a drive control method for unmanned aerial vehicle (UAV) inspection of urban railways, applied to UAVs, comprising: acquiring an inspection task issued by a ground center, the inspection task including at least: an inspection route and its electronic fence range, at least one inspection subject to be inspected along the inspection route, and a first safety distance corresponding to each inspection subject; driving the UAV to inspect the inspection subject according to the inspection route, and detecting in real time whether a sudden working condition occurs; if no sudden working condition occurs, controlling the shortest distance between the UAV and the current inspection subject to be greater than the first safety distance corresponding to the current inspection subject; if a sudden working condition occurs, determining the working condition level of the sudden working condition, generating a temporary avoidance route according to the working condition level, and driving the UAV to navigate according to the temporary avoidance route.

[0005] This disclosure also provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and the processor executes the computer program in the memory to implement the steps of the above-described drive control method for unmanned aerial vehicle (UAV) urban railway inspection.

[0006] This disclosure also provides a drone, which includes at least the electronic equipment described above.

[0007] This disclosure also provides an unmanned aerial vehicle (UAV) inspection system, which includes at least: a hangar; a ground center; and at least one UAV as described above.

[0008] The beneficial effects of this disclosed embodiment are as follows: by presetting a first safe distance corresponding to the inspection subject, it ensures that the UAV always maintains a safe distance from the inspection target under normal working conditions, avoids the risk of collision, and solves the problem of inaccurate control of safe distance in traditional inspection; furthermore, it generates exclusive temporary avoidance routes for different levels of emergency situations, realizes graded handling and rapid response, effectively reduces the losses caused by unexpected situations such as equipment failure and sudden environmental changes, and ensures the safety of UAVs and railway facilities during the inspection of urban railways. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of the drive control method for unmanned aerial vehicle (UAV) inspection of urban railways in the first embodiment of this disclosure. Detailed Implementation

[0011] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0012] To address the problems existing in the prior art, the first embodiment of this disclosure provides a drive control method for unmanned aerial vehicle (UAV) inspection of urban railways. This method is mainly applied to inspection UAVs and can serve as the execution algorithm for the core control device of the UAV. Its flowchart is as follows: Figure 1 As shown, it mainly includes steps S10 to S40: S10: Receive inspection tasks issued by the ground center.

[0013] When not assigned a mission, drones are typically stored in hangars, which handle their takeoff and landing, storage, maintenance, charging, data storage, and communication relay. The ground control center acts as the upper-level platform, coordinating the control of both the drones and the hangars. When a railway section needs inspection, the ground control center generates an inspection task based on the requirements. This task includes at least: the inspection route and its electronic fence coverage, at least one inspection target along the route, and a first safe distance for each inspection target. In this embodiment, the main inspection subjects include bridge structures, external equipment, and subway protection zones along the urban railway line to be inspected. Images of these subjects are collected by drones to determine if any critical facilities along the railway line may have issues affecting railway operation. Considering the image and video acquisition quality and safety of the inspection subjects, different initial safety distances are pre-set for different inspection subjects as navigation restrictions during drone inspections. The electronic fence range needs to match the drone's three-dimensional flight characteristics and the complex inspection environment, taking into account the actual situation of the inspection subject, surrounding obstacles, and safety buffer zones. This ensures that the drone uses the electronic fence range as a safe inspection boundary during inspections, avoiding impact on the operation of other equipment or facilities in the urban area. The electronic fence range can be updated according to actual needs.

[0014] When actually issuing inspection tasks, the configuration and battery status of drones currently in standby mode can be obtained from the hangar. Drones with hardware conditions that meet the inspection requirements and sufficient battery power can be selected to perform the inspection task.

[0015] S20: Drive the drone to inspect the main body according to the inspection route and detect in real time whether any sudden working conditions occur; if no sudden working conditions occur, proceed to step S30; if a sudden working condition occurs, proceed to step S40.

[0016] After receiving an inspection task, the drone can inspect the target subject according to the inspection route. This includes, but is not limited to, capturing images and videos of the target subject, and detecting damage using methods such as LiDAR and infrared. Typically, there are multiple targets to inspect along the route, and they can be inspected sequentially. During the inspection, various sensors on the drone can collect real-time environmental data to determine if any unexpected situations occur and respond promptly to prevent abnormal drone navigation or collisions with surrounding obstacles that could cause damage.

[0017] Typically, a drone is equipped with at least a wind speed sensor, a rainfall sensor, an attitude sensor, and a visibility sensor to collect and determine wind speed, rainfall level, drone sway, and visibility conditions, respectively. In this embodiment, during drone flight, the system continuously monitors whether the drone's current environment meets any of the following conditions. If any of these conditions are met, an emergency situation is determined to have occurred: (1) The wind speed measurement sensor collects data on the current wind force level, which is greater than level 4; (2) The rainfall sensor detects that the current rainfall level is higher than moderate rain; (3) The attitude sensor detects that the current UAV body sway angle is greater than 15°; (4) The visibility sensor collects the current horizontal visibility as less than or equal to 1000 meters.

[0018] Specifically, high wind speeds can affect the stability of drone flight; high rainfall levels not only impact flight stability but can also cause corrosion to onboard equipment due to moisture; aircraft swaying can be caused by external environmental factors (such as gusts or wind between buildings) or by drone malfunctions; visibility affects image acquisition and radar scanning results, preventing drones from collecting data on the main inspection targets. Furthermore, both fog and dust storms can impair visibility, affecting the drone's perception of its surroundings and potentially leading to collisions. Therefore, by setting these conditions, the drone can quickly detect whether it is in an emergency, enabling rapid response.

[0019] S30: Control the drone to maintain a minimum distance greater than the first safe distance corresponding to the current inspection subject.

[0020] Under normal operating conditions, the UAV should inspect each inspection subject sequentially according to the cruise route issued by the ground center, and should maintain a safe distance from each inspection subject during the inspection process. However, due to the influence of the external environment and the differences in the shape of the inspection subject, the UAV needs to maintain a distance between itself and the inspection subject that exceeds the first safe distance corresponding to the inspection subject at all times during the flight. In this embodiment, different first safe distances can be set according to the type of inspection subject. For example, for bridge structures, considering structural protrusions and corresponding cables and other ancillary facilities, the first safe distance is usually set to 5 meters; for external equipment such as contact networks and communication towers, in order to prevent cable entanglement and discharge risks, the corresponding first safe distance should be set to more than 8 meters; and in order to avoid electromagnetic interference and physical intrusion around the railway line, the first safe distance in the subway protection zone should be set to at least 10 meters, which ensures the safety distance and also facilitates the UAV to photograph problems such as illegal construction, overloading, pipeline damage, subway tunnel settlement, and surface deformation in the area along the line.

[0021] In some embodiments, the first safe distance can also be calculated using a safe distance formula, which combines the physical properties of the UAV and the type of the inspection target. Specifically, the safe distance formula is as follows:

[0022] in, Indicates the first safe distance; This represents the main risk coefficient, corresponding to the type of inspection subject. For example, the coefficient for a bridge structure can be 1.2, the coefficient for a subway protection zone can be 1.8, and the coefficient for an elevated outer line can be 2. This indicates the length of the drone's fuselage, in meters. In actual calculations, the average value of 1.5 meters for mainstream inspection drones can be selected. The maximum protrusion size of the main body to be inspected is specified, for example, 2 meters for bridges, 3 meters for subway protection zones, and 2.5 meters for elevated outer lines. This is a speed correction factor, typically set to 0.3, to balance inspection efficiency and safety. This indicates the drone's inspection speed, expressed in m / s, with 10 m / s taken under normal operating conditions.

[0023] For example, when a bridge structure inspection is required, the corresponding first safety distance is: In practice, 7 meters can be rounded up as the first safe distance.

[0024] In this embodiment, the UAV carries a Beidou positioning module and a GPS positioning module. It uses the dual positioning system to accurately locate its own position with an accuracy of <0.5m and obtain the UAV coordinates of its current location in real time. These coordinates can be represented based on the geodetic coordinate system. At the same time, it scans the target outline of the current inspection subject based on the lidar and extracts the feature point coordinates of multiple feature points located in different directions on the target outline. When selecting feature points, priority can be given to the parts of the target with the highest collision risk, such as the edges of bridge supports, beam corners, auxiliary cable joints, overhead contact line joints, communication tower connectors, and the lowest point of cable sag. These parts are often protruding, sharp, or easily entangled structures, and the minimum distance between them and the drone directly determines the collision risk. Selecting them as feature points can accurately reflect the safe distance under the most dangerous conditions. Alternatively, points with obvious geometric changes on the target outline that are easily identified by LiDAR and have stable positioning can be selected as feature points, including but not limited to corners, inflection points, and vertices. These points have stronger laser reflection signals and are less likely to be confused with surrounding areas during scanning, which can improve the accuracy of coordinate acquisition. Alternatively, multiple feature points can be evenly distributed in the current target outline area according to the target outline area corresponding to the drone's flight trajectory, so that it covers the entire horizontal and vertical dimensions and avoids misjudgment of distance due to omission of local areas.

[0025] It is important to note that the coordinates of the aforementioned feature points should also be converted to the geodetic coordinate system, or the UAV coordinates and feature point coordinates should be converted to any identical coordinate system to facilitate subsequent calculations. Before actual calculations, noise reduction or removal of obvious outliers can be performed on the feature point coordinates to avoid affecting the calculation accuracy. In addition, it should be ensured that the UAV coordinates and feature point coordinates are acquired at the same time to avoid calculation errors caused by time differences.

[0026] After determining the coordinates of the UAV and the feature points, the straight-line distance between the UAV coordinates and each feature point coordinate is calculated based on the distance formula between two points in a Cartesian coordinate system. Then, all straight-line distances are iterated through, and the smallest straight-line distance is selected as the shortest distance between the UAV and the inspection target. Subsequently, the relationship between the shortest distance and the first safe distance is checked. If the shortest distance is greater than or equal to the first safe distance, it indicates that the current UAV position meets the safety requirements and no additional adjustment is needed. If the shortest distance is less than the first safe distance, there may be a safety risk. In this case, the UAV is driven to move away from the current inspection target, and the shortest distance is continuously recalculated during the movement away until the shortest distance meets the first safe distance requirement.

[0027] In some embodiments, in addition to meeting the first safe distance requirement between the drone and the inspection subject, a safe distance from surrounding obstacles should also be met. This is especially important after the drone has flown away from the current inspection subject, as this can easily result in an excessively short distance from obstacles in that direction, affecting the drone's flight or creating other collision risks. Therefore, the distance between the drone and surrounding obstacles can be further determined. The determination is also made through scanning with LiDAR, combined with the drone's coordinates and the distance formula between two points to complete the calculation. This immediately triggers a secondary flight path adjustment, by driving the drone to make lateral or altitude adjustments until... And during the process of photographing or scanning the inspection subject, the required distance must be maintained between the inspection subject and surrounding obstacles at all times.

[0028] S40 determines the condition level of the emergency, generates a temporary evacuation route based on the condition level, and drives the drone to fly along the temporary evacuation route.

[0029] When a sudden emergency occurs, the UAV in this embodiment should promptly determine the emergency level and respond accordingly to ensure the safety of the UAV and surrounding facilities. Specifically, wind speed, rainfall, attitude, and visibility sensors will sense and clarify the specific conditions of the external environment in real time. By acquiring the sensor data collected by these sensors and combining it with the triggering conditions for Level 1, Level 2, and Level 3 emergency situations, the level of the emergency can be evaluated, and its emergency level can be determined, facilitating graded response.

[0030] In this embodiment, the triggering conditions for Level 1 operating conditions include at least the following: the wind speed sensor detects a current wind force level greater than level 4, the rainfall sensor detects a current rainfall level higher than moderate rain, the attitude sensor detects a current drone body sway angle greater than 15° and less than or equal to 25°, and the visibility sensor detects a current horizontal visibility less than or equal to 1000 meters; the triggering conditions for Level 2 operating conditions include at least the following: the wind speed sensor detects a current wind force level greater than level 6, the rainfall sensor detects a current rainfall level higher than heavy rain, the attitude sensor detects a current drone body sway angle greater than 25° and less than or equal to 35°, and the visibility sensor detects a current horizontal visibility less than or equal to 500 meters; the triggering conditions for Level 3 operating conditions include at least the following: the wind speed sensor detects a current wind force level greater than level 8, the rainfall sensor detects a current rainfall level higher than torrential rain, the attitude sensor detects a current drone body sway angle greater than 35°, and the visibility sensor detects a current horizontal visibility less than or equal to 300 meters.

[0031] In actual performance evaluation, if the sensor data meets any one of the triggering conditions for a Level 1 operating condition, the sudden operating condition is determined to be Level 1. If the sensor data meets any one of the triggering conditions for a Level 2 operating condition or at least two of the Level 1 operating conditions, the sudden operating condition is determined to be Level 2. If the sensor data meets any one of the triggering conditions for a Level 3 operating condition or at least two of the Level 2 operating conditions, the sudden operating condition is determined to be Level 3. It should be noted that if the sensor data simultaneously meets multiple triggering conditions of different levels, the highest level of operating condition will be used as the determination result.

[0032] In Level 1 operating conditions, the external environment may affect the normal flight of the drone, but the drone still possesses autonomous control capabilities, and there is no immediate risk of collision in the surrounding environment. The handling strategy in this case can be as follows: using a preset multiple of the first safety distance as the second safety distance, the drone ceases its normal inspection mission, takes its current location as the starting point, and the nearest stable airspace as the endpoint. Combining the second safety distance with the limitations of the electronic fence range, a Level 1 temporary avoidance route is generated. It should be understood that the preset multiple of the first safety distance can also vary depending on the type of inspection subject. For example, for external wiring equipment, the preset multiple can be 2 times, ensuring that the cables will not become entangled with the drone when they are unstable in windy weather. For bridge structures and subway protection zones, the preset multiple can be 1.5 times, ensuring that the safety distance between the drone and the inspection subject is increased in unstable conditions, reducing the possibility of collision. The most recent stable airspace can be one of the multiple preset airspaces issued by the ground center when issuing inspection tasks. The preset airspace can be pre-set for the surrounding environment of different inspection subjects, allowing the UAV to temporarily hover under Level 1 operating conditions; or the stable airspace can be temporarily determined by the UAV on site, combining lidar scanning and camera shooting to avoid surrounding tall obstacles and determine a stable position as a temporary hovering area.

[0033] In some embodiments, during the response process of the first-level working condition, the UAV continues to perceive the external environment in real time. If the sudden working condition disappears at a certain moment and does not reappear within a certain period of time (e.g., 5 minutes), it can be considered that the current environment allows the normal inspection task to be resumed. The UAV can resume the inspection operation on the inspection subject according to the original inspection task content and the progress already completed.

[0034] Under Level 2 operating conditions, if a single indicator moderately exceeds the limit or multiple indicators slightly overlap, the drone's control accuracy decreases, and positioning errors increase, posing a high risk to continued operation. In this case, immediately initiate a speed-limited return-to-home procedure, using the drone's hangar as the destination, and generate a Level 2 temporary obstacle avoidance route. During flight, a safe distance must be maintained from obstacles along the route. In actual Level 2 operating conditions, the drone can shut down non-core loads, prioritizing flight control power supply and obstacle avoidance functions, while locking the hangar as the sole return-to-home target, prohibiting the drone from changing its destination mid-flight.

[0035] In Level 3 conditions, where harsh external environments prevent the drone from flying normally, an emergency landing procedure should be initiated immediately. This involves quickly identifying a flat landing area within a 200-meter radius of the drone's current location and generating a Level 3 temporary escape route using the nearest flat landing area as the endpoint. The drone should then land in this flat landing area, and its location should be transmitted to the ground control center for assistance. During landing, all non-essential sensors should be shut down to reduce energy consumption and ensure stable power during descent. Open areas such as playgrounds, open spaces, and rooftop platforms are preferred for landing, or pre-set landing points in the vicinity should be provided when the inspection mission is initiated.

[0036] This embodiment ensures that the drone maintains a safe distance from the inspection target under normal operating conditions by pre-setting a first safe distance corresponding to the inspection subject, thus avoiding the risk of collision and solving the problem of inaccurate control of safe distance in traditional inspections. Furthermore, it generates exclusive temporary avoidance routes for different levels of emergencies, enabling graded handling and rapid response, effectively reducing losses caused by unexpected situations such as equipment failure and sudden environmental changes, and ensuring the safety of drones and railway facilities during the inspection of urban railways.

[0037] Based on the same inventive concept, the second embodiment of this disclosure provides an electronic device, including at least a memory and a processor. The memory stores a computer program, and when the processor executes the computer program in the memory, it implements the steps of the drive control method for UAV urban railway inspection according to the first embodiment of this disclosure. The electronic device of this embodiment can serve as a control device for a UAV to achieve drive control of the UAV. The UAV should also include equipment to drive the UAV to perform routine tasks such as flight and inspection, which can be implemented using conventional technology and will not be described in this embodiment.

[0038] Based on the same inventive concept, the third embodiment of this disclosure provides a drone, which includes at least the electronic equipment described in the second embodiment of this disclosure. Meanwhile, the drone should also have conventional flight-related structures, data acquisition equipment, sensing equipment, a positioning system, a power supply unit, etc. In a preferred example, the drone includes an integrated high-definition zoom gimbal camera, a multispectral line detection camera, an obstacle avoidance and recognition vision system, a night inspection supplementary light, an infrared thermal imaging sensing system, a built-in emergency charging module, a flight positioning indicator light, an aircraft status indicator light, a high-torque motor, a wind-resistant propeller, an arm with an integrated multi-band antenna, a USB-C inspection parameter debugging interface, a long-endurance intelligent flight battery, a power control button, a battery remaining power indicator light, a battery quick-fix buckle, a battery safety lock, an inspection image storage microSD card slot, a night long-distance navigation light, a high-precision GNSS / RTK line positioning antenna, an inspection equipment expansion E-Port interface, and a long-range image transmission enhancement module compartment. It can automatically execute patrol plans and detailed image acquisition tasks for urban power lines, communication optical cables, and rail transit lines. Through high-definition zoom cameras and infrared thermal imaging systems, it can quickly acquire full-area image data of the lines and accurately identify line anomalies such as insulator damage, broken conductor strands, tower tilting, foreign object entanglement, and joint overheating, providing aerial precision monitoring support for urban line inspection.

[0039] Based on the same inventive concept, the fourth embodiment of this disclosure provides a drone inspection system, which includes at least a hangar, a ground center, and at least one drone as described in the third embodiment.

[0040] In practical implementation, the hangar can be an automated hangar equipped with wind speed and direction monitors (adapted to determine the flight environment for line inspections), hangar perimeter security cameras, camera auxiliary lighting, rain gauges for rainfall monitoring, automatically opening and closing protective canopies, visual markers for precise drone take-off and landing, a dedicated landing pad for line inspections, aircraft take-off and landing orientation guidance markers, PoE power output ports, AC power adapter output ports, safety grounding devices, intelligent power distribution control cabinets, stable city network input ports, multi-scenario installation feet (adapted to areas near poles, inspection stations, etc.), emergency stop control buttons, canopy operation status indicator lights, high-speed communication network card bays, line positioning auxiliary RTK modules, dedicated inspection equipment mounting positions, drone data interaction E-Port interfaces, and hangar drainage channels. It can achieve core functions such as automatic take-off and landing of inspection drones, safe storage, fast charging, remote communication within the city area, local storage and synchronization of inspection data, and intelligent preprocessing of abnormal data. Relying on this automated hangar, drones can complete take-off, landing, and energy replenishment for urban route inspections without human intervention. They can also seamlessly connect to the urban route inspection management platform via API, achieving full automation of the inspection task assignment, data transmission, and result analysis, completely replacing the traditional mode of manual on-site drone operation.

[0041] The ground center can be equipped with a multi-node server cluster, distributed storage array, high-speed core switch, virtualization layer, container orchestration module, elastic computing power scheduling module, multi-tenant permission isolation module, off-site disaster recovery backup node, AI model training accelerator card, security gateway, log auditing module, and dedicated cloud storage pool for inspection data. It can achieve core functions such as centralized storage and unified management of massive amounts of data (images, thermal imaging, location data) from city-wide line inspections, large-scale defect identification AI model training and inference, real-time data collaboration across multiple terminals, elastic scaling of computing resources, and off-site disaster recovery backup of data. Based on this cloud computing unit, a dedicated cloud platform for city-wide line inspections can be built, centrally receiving data uploaded from various inspection stations and hangars across the entire region. Through standardized interfaces, it connects to the line inspection management system, enabling unified dispatch of inspection tasks, cross-regional data collaborative analysis, and historical data trend analysis—all processes are cloud-based, completely replacing the traditional distributed data storage and manual aggregation and analysis model, supporting the large-scale and intensive management of city-level inspection operations.

[0042] Furthermore, the ground center's system service layer comprises four major platform management modules: service management, user management, task management, and equipment management, as well as three service interfaces: bridge and ancillary facilities, high-voltage power lines, and subway protection zone detection. Its system application layer includes an integrated service platform, a flight management platform, and an algorithm scheduling platform.

[0043] (1) Service Management Module Core functions: To coordinate the entire lifecycle management of system services, ensure the stable operation and efficient collaboration of inspection business processes, and serve as the core hub connecting management modules and service interfaces.

[0044] Specific functions: Service scheduling: Based on the priority of inspection tasks, automatically allocate "drone + automated hangar" resources and match the corresponding bridge and ancillary facilities, high-voltage power lines, and subway protection zone inspection service interfaces; Service monitoring: Real-time monitoring of the running status of each service interface (such as data transmission rate, detection task completion rate) and resource usage (CPU, memory, bandwidth), and generation of service health reports; Service configuration: Supports custom service parameters to adapt to different inspection needs; Fault handling: Automatically identify service anomalies, trigger SMS and platform pop-up alarm mechanisms, and provide fault location logs and automatic recovery solutions; Service Upgrade: Supports online iterative updates of service functions, retains the ability to roll back historical versions, and avoids interruptions to ongoing inspection tasks during upgrades.

[0045] (2) User Management Module Core function: To implement access control and operational standards for users with multiple roles, and to ensure the security and compliance of system use.

[0046] Specific functions: Account Management: Supports user registration, account cancellation, account freezing / unfreezing, and records basic account information (name, organization, contact information) and login status (last login time, login IP). Role assignment: Preset roles such as "system administrator, inspection dispatcher, and grassroots maintenance worker" are available, and the scope of role permissions can be customized; Access control: Assign operation permissions based on the "principle of least privilege"; Operation audit: Automatically record all user operations, generate traceable audit logs, and retain them for no less than one year; Password management: Supports password strength verification (uppercase and lowercase letters + numbers + special symbols), regular password reset reminders, and password retrieval function if forgotten.

[0047] (3) Task Management Module Core function: To manage and control inspection tasks throughout the entire process, achieving standardized and automated management from creation to closed-loop.

[0048] Specific functions: Task creation: Supports manual entry (selecting inspection area, equipment type, and testing indicators) or template import (preset report templates such as "Monthly Inspection of High Voltage External Lines" and "Line and Bridge Structure Inspection") to create tasks; Task dispatch: Automatically assigns tasks to corresponding execution units based on task type, and supports manual adjustment of dispatch results; Mission execution monitoring: Real-time tracking of mission progress (time consumed in each stage of "takeoff → cruise → data acquisition → return → analysis"), displaying drone location, remaining battery power, and real-time preview of acquired data; Task result processing: Automatically receive data returned from the detection interface, generate task reports (including anomaly point markings and defect level assessments), and support export in PDF / Excel format; Task closure: Mark the processing status of abnormal issues in the report ("Pending rectification → Rectification in progress → Accepted"), link rectification records with review tasks, and form a management closure; Task optimization: Based on historical task data (such as completion efficiency and anomaly detection rate), intelligently optimize task routes (avoid no-fly zones and shorten cruise distances) and execution time (avoid periods of severe weather).

[0049] (4) Equipment Management Module Core function: To achieve full lifecycle monitoring and management of hardware equipment such as drones and automated hangars.

[0050] Specific functions: Equipment ledger: Records basic equipment information (model, serial number, purchase time, warranty period) and deployment location (latitude and longitude, inspection area), and supports batch import and update of equipment information; Status monitoring: Real-time collection of equipment operating parameters (UAVs: battery level, flight attitude, sensor status; hangar: door open / close status, charging / battery swapping progress, temperature and humidity), and identification of equipment status (normal / warning / fault) through red, green and yellow colors; Maintenance Management: Generate maintenance reminders based on equipment runtime and failure frequency, and record maintenance content and personnel; Remote control: Supports remote operation of the hangar and remote parameter configuration of the drone; Interface adaptation: Provides standardized interfaces for hardware device integration, supporting rapid integration and compatibility testing of new device models.

[0051] (5) Bridge and ancillary facility inspection interface Functional positioning: To connect with high-definition cameras and multispectral sensors carried by drones to realize intelligent detection data interaction and analysis of bridge structures and ancillary facilities.

[0052] Core competencies: Data acquisition: Receive bridge appearance images (bridge deck, beams, supports) and multispectral data (concrete cracks, steel corrosion detection, high-voltage foreign object detection) transmitted back by drones; Defect identification: Built-in AI algorithm model automatically identifies common bridge defects (cracks, spalling, exposed reinforcement, support displacement), and marks the defect location, size and level (general / severe). Data output: Generate bridge inspection reports (including defect distribution maps and video evidence), support integration with the city-wide bridge operation and maintenance management system, and push defect rectification tasks; Special adaptations: Supports segmented inspection data splicing for long-span bridges, adapting to the inspection needs of special structures such as cable-stayed bridges and suspension bridges.

[0053] (6) High-voltage external line detection interface Functional positioning: Focusing on high-voltage transmission line inspection scenarios, it realizes the transmission, analysis and early warning of detection data of lines and auxiliary equipment.

[0054] Core competencies: Data acquisition: Receives UAV infrared thermal imaging data (conductor joint temperature, insulator heating), visible light images (conductor strand breakage, foreign object entanglement, tower tilt), and lidar data (conductor sag, tree obstacle distance); Anomaly monitoring: Real-time data analysis triggers real-time alarms for overheating (joint temperature > 80℃), tree obstruction (distance from conductor < 5 meters), and equipment defects (insulator damage, loose bolts); Data integration: Supports integration with power dispatching systems and power transmission and transformation operation and maintenance platforms to synchronize inspection data and line operating status, assisting in dispatching decisions; Historical comparison: Stores inspection data from previous years, supports analysis of defect trends at different times for the same line, and predicts equipment aging risks.

[0055] (7) Subway protection zone detection interface Functional positioning: To enable the interaction and management of monitoring data on construction disturbance and environmental risks in the protected area around subway lines (generally within 30 meters on both sides of the line).

[0056] Core competencies: Data acquisition: Receive images of the protected area transmitted by drones (illegal construction, overloading, pipeline damage) and GNSS displacement monitoring data (subway tunnel settlement, surface deformation). Risk identification: Automatically identify violations and determine the settlement risk level (minor / moderate / severe) based on displacement data; Joint early warning: Connects with the subway operation command center to push risk warning information through the interface, and synchronizes the warning location and handling suggestions; Boundary Management: Supports importing electronic fence data for subway protection zones, and automatically alerts when drone inspections exceed the fence range, ensuring accurate detection range.

[0057] (8) Integrated service platform Core positioning: A "one-stop operation portal" for all users of urban line inspection, integrating core capabilities such as data display, task management, and business collaboration to achieve visualized, process-oriented, and collaborative management of inspection business.

[0058] Specific functions: Full-area inspection data dashboard: Real-time display of key indicators such as the inspection coverage area within the city (divided by line type / region), daily task completion rate, number and distribution of abnormal defects detected, and equipment online rate through a visual large screen, and supports drill-down to view detailed data of a single line or a single hangar; Full-process task management: Provides closed-loop management functions for task creation (supports template import and custom area drawing), dispatch (automatic allocation according to hangar location / route affiliation), tracking (real-time display of drone flight progress and data acquisition status), acceptance (supports manual review of defect labeling results), and archiving (automatic generation of inspection reports and association with historical data), adapting to different scenarios such as monthly routine inspections and emergency fault inspections; Multi-role collaborative interaction: Differentiated functional permissions are provided for roles such as "managers, inspection dispatchers, and maintenance personnel". Managers can view global statistical reports and approve task plans; maintenance personnel can receive equipment fault alarms and report maintenance records; data analysts can export raw data and generate trend analysis charts. Inspection resource scheduling: Linked to the "Equipment Management Module" of the service layer, it displays the real-time occupancy status of drones, hangars, and cloud computing resources, and supports manual adjustment of resource allocation (such as temporarily scheduling its tasks to nearby idle hangars when a hangar fails). Data query and statistics: Provides multi-dimensional data retrieval functions (filter by line name, inspection time, defect type, etc.), supports automatic generation of "Quarterly Inspection Report of City Lines", "Defect Rectification Rate Analysis Report", etc., and data can be exported to Excel and PDF formats, and can be connected to government supervision platforms or enterprise internal management systems.

[0059] (9) Flight Management Platform Core positioning: Focusing on intelligent management and control of the entire drone flight process, connecting automated hangars and cloud computing units to ensure the safety, accuracy and efficiency of inspection flights.

[0060] Specific functions: Intelligent flight plan generation: Supports automatic generation of cruise paths based on route direction (avoiding no-fly zones and obstacles, with data collection points set by default at "50 meters between poles"). Flight altitude, speed (8 m / s for routine inspection, 5 m / s for fine inspection) and camera parameters can be manually adjusted. The generated plan must pass "no-fly zone verification and weather adaptability judgment" before it can be submitted for execution. Real-time flight monitoring and intervention: Displays drone location, flight path, remaining battery power, battery temperature, image transmission signal strength, and other parameters on a map in real time; supports receiving real-time images transmitted from the drone (visible light / infrared dual-view switching); in case of emergency, remotely issues "hover, return to home, or forced landing" commands, or takes over flight control. Takeoff and landing and energy management: Linked with an automated hangar, it realizes full automation of the drone's "pre-takeoff self-check (battery power, sensor status) → automatic opening of hangar door → precise takeoff and landing positioning" process; real-time monitoring of hangar battery inventory and charging status, and automatic scheduling of charging mode when the drone returns to base; Flight safety management: Integrated weather warning function (connects to meteorological platform, automatically suspends flight plans when wind speed > 12m / s or rainfall intensity > moderate rain); built-in no-fly zone / restricted flight zone database (updated with municipal construction areas, airport airspace, etc.), automatically triggers three-level protection of "deceleration, alarm, and return" when the drone approaches a no-fly zone; records complete flight logs (including position, attitude, and command response time) for flight accident tracing and optimization; Multi-drone collaborative scheduling: Supports multiple drones to conduct segmented inspections along the same route, avoiding drone collisions through time synchronization and path planning, and improving the efficiency of large-area inspections.

[0061] (10) Algorithm scheduling platform Core positioning: As the "technology engine" of intelligent inspection, it integrates AI algorithm resources and links with cloud computing units to realize automated analysis and defect identification of inspection data, supporting the upgrade of inspection business from "manual interpretation" to "intelligent analysis".

[0062] Specific functions: Algorithm library management: Build a dedicated algorithm library for city-wide route inspection, covering three major categories of core algorithms; Line defect categories: Insulator damage identification, conductor strand breakage / foreign object entanglement detection, tower tilt assessment; Facility status category: Bridge crack identification, sound barrier damage identification, and detection of illegal construction in subway protection zones (identifying targets such as excavators and construction waste piles); Environmental anomalies: Tree obstruction distance calculation (accuracy ±0.3 meters), wire joint overheating monitoring (temperature identification error ±2℃); Supports algorithm version management (retains historical versions and allows rollback) and third-party algorithm integration (provides standardized API interfaces). Intelligent algorithm scheduling: Automatically matches the optimal algorithm based on the type of inspection task, and supports manual algorithm specification (to adapt to special detection requirements). Model training and optimization: It links the GPU cluster of the cloud computing unit to support iterative training of the model based on newly collected inspection data (labeled defect samples), improves the algorithm's adaptability to complex scenarios (defect recognition accuracy in hazy weather and backlighting environments), and provides model performance evaluation tools (outputting accuracy, recall, and F1 score) to assist in algorithm optimization. Algorithm result processing: Defects identified by the algorithm are automatically marked on the inspection images (marking location, defect type, risk level: general / relatively serious / severe), generating a "defect details sheet" (including coordinates, image evidence, and handling suggestions); manual correction is supported for misidentification results (such as misidentifying a bird's nest as a foreign object), and the corrected data is automatically synchronized to the algorithm library for model optimization; Algorithm performance monitoring: Real-time monitoring of algorithm running status. When the algorithm's recognition accuracy falls below the threshold or runs abnormally, an alarm is automatically triggered and the algorithm is switched to a backup algorithm to ensure that the analysis process is not interrupted.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A drive control method for unmanned aerial vehicle (UAV) inspection of urban railways, applied to UAVs, characterized in that, include: Obtain inspection tasks issued by the ground center. The inspection tasks include at least: inspection routes and their electronic fence ranges, at least one inspection subject to be inspected along the inspection routes, and a first safe distance for each inspection subject. The drone is driven to inspect the main body according to the inspection route, and to detect in real time whether any sudden working conditions occur. In the absence of the aforementioned emergency situation, the shortest distance between the drone and the current inspection subject shall be greater than the first safe distance corresponding to the current inspection subject; In the event of the aforementioned emergency, the emergency condition level is determined, a temporary evacuation route is generated based on the emergency condition level, and the drone is driven to fly along the temporary evacuation route.

2. The drive control method according to claim 1, characterized in that, The inspection subjects include: bridge structures, external equipment, and subway protection zones along the urban railway line to be inspected.

3. The drive control method according to claim 1, characterized in that, The real-time detection of whether an emergency occurs includes: The system detects whether the current environment of the drone meets any of the following conditions. If any of the following conditions are met, an emergency situation is determined to have occurred: The wind speed sensor collects data indicating that the current wind force level is greater than level 4. The rain gauge detected that the current rainfall level is higher than moderate rain. The attitude sensor detects that the current drone's body sway angle is greater than 15°; The visibility sensor collects data indicating that the current horizontal visibility is less than or equal to 1000 meters.

4. The drive control method according to claim 1, characterized in that, The control of the drone to maintain a minimum distance greater than the first safe distance corresponding to the inspection subject includes: Obtain the drone's current coordinates in real time; Based on the laser radar scanning of the target outline of the current inspection subject, the feature point coordinates of multiple feature points located in different directions on the target outline are extracted. Calculate the shortest distance between the drone and the current inspection subject based on the drone coordinates and the coordinates of each feature point; The system detects whether the shortest distance is greater than or equal to the first safe distance. If the shortest distance is less than the first safe distance, the system drives the drone to travel away from the current inspection subject until the shortest distance is greater than or equal to the first safe distance.

5. The drive control method according to claim 3, characterized in that, Determining the condition level of the emergency includes: Acquire sensor data collected by the wind speed sensor, the rainfall sensor, the attitude sensor, and the visibility sensor; Based on the triggering conditions for Level 1, Level 2, and Level 3 operating conditions, the sensor data is evaluated to determine the operating condition level of the sudden operating condition; wherein, The triggering conditions for the first-level operating condition include at least the following: the wind speed sensor detects that the current wind force level is greater than level 4; the rain sensor detects that the current rainfall level is higher than moderate rain; the attitude sensor detects that the current drone's body sway angle is greater than 15° and less than or equal to 25°; and the visibility sensor detects that the current horizontal visibility is less than or equal to 1000 meters. The triggering conditions for the secondary operating condition include at least the following: the wind speed sensor detects that the current wind force level is greater than level 6; the rain sensor detects that the current rainfall level is higher than heavy rain; the attitude sensor detects that the current drone's body sway angle is greater than 25° and less than or equal to 35°; and the visibility sensor detects that the current horizontal visibility is less than or equal to 500 meters. The triggering conditions for the Level 3 operating conditions include at least the following: the wind speed sensor detects that the current wind force level is greater than level 8; the rain sensor detects that the current rainfall level is higher than heavy rain; the attitude sensor detects that the current drone's body sway angle is greater than 35°; and the visibility sensor detects that the current horizontal visibility is less than or equal to 300 meters.

6. The drive control method according to claim 5, characterized in that, The step of evaluating the sensor data based on Level 1, Level 2, and Level 3 trigger conditions to determine the level of the sudden operating condition includes: If the sensing data satisfies any one of the first-level operating condition triggering conditions, the operating condition level of the sudden operating condition is determined to be a first-level operating condition. If the sensing data satisfies any one of the secondary operating condition triggering conditions or at least two of the primary operating conditions, the operating condition level of the sudden operating condition is determined to be a secondary operating condition. If the sensing data satisfies any one of the three-level operating condition triggering conditions or at least two of the two-level operating conditions, the operating condition level of the sudden operating condition is determined to be a three-level operating condition.

7. The drive control method according to claim 6, characterized in that, The step of generating a temporary evacuation route based on the operational condition level and driving the UAV to fly along the temporary evacuation route includes: When the emergency situation is classified as Level 1, a Level 1 temporary evacuation route is generated, with the nearest stable airspace as the endpoint and the electronic fence range and the second safe distance as navigation restrictions. The second safe distance is a preset multiple of the first safe distance. When the emergency situation is classified as Level 2, a Level 2 temporary evacuation route is generated, with the hangar of the UAV as the endpoint. When the emergency situation is classified as Level 3, a level 3 temporary evacuation route is generated by scanning a flat emergency landing area within a radius of 200 meters from the current location of the UAV, and using the nearest flat emergency landing area as the endpoint.

8. An electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program on the memory, it implements the steps of the drive control method for unmanned aerial vehicle (UAV) urban railway inspection as described in any one of claims 1 to 7.

9. A drone, characterized in that, It includes at least the electronic device as described in claim 8.

10. A drone inspection system, characterized in that, At least including: hangar; Ground center; And at least one drone as described in claim 9.

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