Unmanned aerial vehicle deployment honeycomb system

Abstract

The invention provides an unmanned aerial vehicle deployment honeycomb system, and relates to the technical field of unmanned aerial vehicle transceiving. S1, a basic adaptation module, wherein the basic adaptation module comprises an unmanned aerial vehicle storage assembly and an unmanned aerial vehicle automatic charging assembly; s2, a flight control module, wherein the flight control module comprises an unmanned aerial vehicle take-off and landing guide assembly, a path planning assembly and a trajectory tracking assembly; s3, a data processing module, wherein the data processing module comprises a task management component, a data transmission component and an information real-time processing component; data of the position of the unmanned aerial vehicle is transmitted to the trajectory tracking assembly through the visual identification positioning device and the positioning system, the trajectory tracking assembly obtains various data of the unmanned aerial vehicle in flight in real time through the kinematics parameter acquisition unit, and when path deviation occurs, deviation signals are fed back to the path planning assembly, and the path planning assembly plans the path. The trajectory tracking assembly and the path planning are matched with each other, so that closed-loop cooperative operation from deviation detection to path correction to tracking execution can be completed.

Description

Technical Field

[0001] This application relates to the field of drone transceiver technology, and more specifically, to a drone deployment cellular system. Background Technology

[0002] Unmanned aerial vehicle (UAV) deployment systems are mainly used in fields such as environmental monitoring and surveying. When using UAV systems, it is necessary to launch and recover the UAVs. However, existing UAV systems require two or more UAV systems to distinguish between launch and recovery tasks. This results in existing UAV deployment systems needing more space to accommodate two systems, which in turn affects the efficiency of UAV deployment tasks. By utilizing a cellular deployment system, the launch and recovery systems can be integrated, thereby reducing the overall operating cost of UAV swarm launch and swarm recovery and improving the overall applicability during use.

[0003] To overcome the aforementioned shortcomings, existing technology 1 (Chinese patent publication number CN118907500A, publication date 2024-11-08) provides a drone deployment box and a drone cellular system, both designed to address the technical problems of existing drone cellular systems occupying large spaces, being inconvenient to transport, and thus having relatively poor concealment, mobility, and flexibility during deployment. The drone deployment box includes a box body with an opening, the opening of which is fitted with a movable cover. The box body contains a launch bay and a parking bay. The launch bay houses the drone launch system, and the parking bay contains parking positions, transfer positions, and a transport system for reciprocating drone transport. The box body also includes a transfer robotic arm, the working range of which covers the transfer position and the launch starting point of the drone launch system. The aforementioned structure integrates the UAV launch system into a single, complete enclosure, reducing the system space occupied by the UAV swarm system and effectively improving the concealment, mobility, and flexibility of the UAV swarm system deployment in swarm launch scenarios. Existing technology two (Chinese patent publication number CN120469443A, publication date 2025-08-12) discloses a UAV swarm deployment system, a vehicle-mounted system, and a UAV swarm deployment method. The system includes a mechanical transport module, a processor, a UAV maintenance area, a load maintenance area, a battery maintenance area, and a UAV launch platform. The processor sends a first transport instruction to the mechanical transport module upon receiving a UAV swarm deployment task instruction. The mechanical transport module receives the first transport instruction and, based on the instruction, transports the UAVs to the UAV launch platform, to the load maintenance area to obtain the task load, and to the battery maintenance area to obtain the battery, installing the task load and battery during UAV transport. The UAV maintenance area holds the UAVs; the load maintenance area holds the task load; the battery maintenance area holds the battery; and the UAV launch platform holds the UAVs equipped with the task load and battery, transported by the mechanical transport module, to complete the UAV swarm deployment. This achieves rapid deployment of the UAV swarm.

[0004] The aforementioned organizations have enabled drones to launch and recover in swarms by deploying cellular systems. However, in actual use, the drones may encounter problems adapting to complex scenarios during inspections due to the different environments at different inspection locations, thus affecting their applicability in use. Summary of the Invention

[0005] This application provides a drone-deployed cellular system that can improve the accuracy of data collection during inspections in complex scenarios.

[0006] To achieve the above objectives, this application provides the following technical solution: a drone deployment cellular system, comprising the following modules: S1, a basic adaptation module, which completes the storage and energy replenishment of drones. The basic adaptation module includes a drone storage component and a drone automatic charging component. The drone storage component provides storage space for drones in a non-recovery state through a sealed cellular cabinet, and uses a temperature control mechanism to regulate the internal environment of the cabinet to prevent temperature changes from causing damage to the drones. The drone automatic charging component realizes the positional docking of the drone with the sealed cellular cabinet through an automatic docking mechanism, and then automatically replenishes the energy of drones that have completed inspection tasks through an intelligent charging management mechanism. Yes; S2, Flight Control Module: The flight control module enables precise takeoff and landing and flight trajectory control of the UAV. The flight control module includes a UAV takeoff and landing guidance component, a path planning component, and a trajectory tracking component. The UAV takeoff and landing guidance component utilizes visual recognition and positioning equipment and a positioning system to provide takeoff and landing guidance, avoiding the safety risks of manual takeoff and landing. The path planning component is based on gridded map modeling, enabling dynamic obstacle recognition and real-time path correction, while also optimizing energy consumption. The trajectory tracking component is equipped with PID and model predictive control algorithms, acquiring UAV flight data in real time through a kinematic parameter acquisition unit. When a path deviation occurs, the deviation signal is fed back to the path planning component. The system completes a closed-loop collaborative operation of deviation detection, path correction, and tracking execution. S3, the data processing module, manages inspection tasks and processes data. This module includes a task management component, a data transmission component, and a real-time information processing component. The task management component is linked to the sealed drone housing to plan, share, distribute, and track inspection tasks, and selects drones with compatible sensors based on requirements. The data transmission component uses wireless communication lines to transmit image and video data collected by the drone inspection in real time, ensuring data transmission stability in complex scenarios. The real-time information processing component integrates a lightweight image recognition algorithm to process the collected data. Preliminary processing and environmental defect identification: When a serious defect is detected, the defect location and level are fed back to the task management component, triggering a priority return-to-home command; S4, Monitoring and maintenance module: This module enables multi-scenario adaptation and status monitoring. It includes a multi-scenario adaptation component and a status monitoring and maintenance component. The multi-scenario adaptation component optimizes the adaptation parameters between the sealed drone housing and the drone for different inspection environments such as power transmission lines, substations, and railway lines. The status monitoring and maintenance component monitors the drone's battery status, wear and tear, and the internal operating environment of the drone housing in real time. When severe wear and tear or abnormalities in the drone housing are detected, a maintenance prompt is triggered.

[0007] Preferably, the sealed storage cabinet has dustproof and waterproof functions, which can reduce the impact of the external environment on the storage status of the drone. The temperature adjustment range of the temperature control mechanism is set according to the storage requirements of the drone to ensure that the drone is in a suitable storage environment. The drone storage components also have shock-resistant and buffering functions, which can effectively reduce the damage caused by external vibrations to the drone. When encountering external vibrations, its internal buffer structure can absorb and disperse the vibration energy, ensuring that the drone's precision components are not affected. Moreover, the storage cabinet is made of high-strength and corrosion-resistant materials, which can not only resist the damage of natural environments such as wind, rain, acid and alkali corrosion, but also prevent human collisions and malicious damage, further improving the protection of the drone.

[0008] Preferably, the raster map modeling process of the path planning component needs to construct an initial map by combining terrain data and obstacle distribution data of the inspection scene. During the UAV flight, the map data is updated in real time to match dynamic obstacle changes, ensuring the real-time performance and accuracy of path planning. When a path deviation occurs, the deviation signal is fed back to the path planning component. The cooperation between the trajectory tracking component and the path planning component can complete a closed-loop collaborative operation from deviation detection to path correction to tracking execution. This closed-loop collaborative operation greatly improves the accuracy and stability of the UAV inspection path. After receiving the deviation signal, the path planning component will quickly replan a more reasonable flight path according to the preset algorithm and strategy, combined with the current environmental information and task requirements, to ensure that the newly planned path can both avoid potential dangers and efficiently complete the inspection task.

[0009] Preferably, the kinematic parameter acquisition unit of the trajectory tracking component acquires UAV flight data including flight speed, flight altitude, heading angle, attitude angle and position coordinates, with an acquisition frequency of not less than 5Hz to ensure timely deviation detection.

[0010] Preferably, the wireless communication line used by the data transmission component supports multi-band adaptive switching. When the signal of a certain frequency band is interfered with, it automatically switches to a frequency band with a stable signal to ensure the continuity of data transmission.

[0011] Preferably, the lightweight image recognition algorithm of the real-time information processing component processes the collected image data through a pre-trained defect recognition model. The training samples of the defect recognition model cover common defect types in different inspection scenarios, and the model recognition accuracy is not less than 90%.

[0012] Preferably, the optimized adaptation parameters of the multi-scenario adaptation component include the drone's flight speed threshold, sensor sampling frequency, take-off and landing platform size of the drone's nest cabinet, and charging interface adaptation specifications. The adaptation parameters for different scenarios are stored in a scenario parameter database, and the corresponding parameters are obtained by directly matching the scenario type when called.

[0013] Preferably, the monitoring data of the status monitoring and maintenance component is displayed in real time through a data dashboard. When the drone battery power is below 20%, the body wear exceeds a preset threshold, or the internal temperature of the drone's housing exceeds the temperature control range of ±5℃, an audible and visual alarm is triggered.

[0014] Preferably, when issuing inspection tasks, the task management component can automatically allocate task priorities based on the remaining battery power, load status, and historical task completion status of the drone, and prioritize scheduling the drone with the best status to execute high-priority tasks.

[0015] Preferably, after the drone automatic charging component completes automatic docking, the intelligent charging management mechanism first detects the health status of the drone battery, and then selects constant current charging or constant voltage charging mode according to the battery health status. After charging is completed, the charging connection is automatically disconnected to avoid overcharging and damaging the battery.

[0016] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0017] 1. The drone storage components housed within the storage cabinet protect drones when not in use. The internal temperature of the cabinet is controlled by a temperature control system, preventing damage caused by temperature fluctuations during storage. The sealed cabinet provides stable dust and water protection, minimizing the impact of external temperatures on drone lifespan. The adjustable internal temperature ensures a suitable storage environment for drones when not in use, reducing wear and tear. Furthermore, the storage unit features shock absorption and cushioning, effectively mitigating damage from external vibrations. Its internal cushioning structure absorbs and disperses vibration energy, protecting delicate drone components. The cabinet is constructed from high-strength, corrosion-resistant materials, resisting not only everyday elements like wind, rain, and acid / alkali corrosion, but also human impact. To further enhance protection against malicious damage, the storage cabinet is equipped with an advanced dehumidification system that monitors and regulates internal humidity in real time. When humidity exceeds a suitable range, the dehumidification system automatically activates to remove excess moisture, preventing rust and short circuits caused by damp environments. Additionally, the cabinet features an intelligent access control system, allowing only authorized personnel to open it, ensuring the safety and confidentiality of the drones and preventing unauthorized access and operation, thus reducing potential risks. The cabinet's internal layout is meticulously designed with clearly defined areas, facilitating drone delivery and pickup as well as routine maintenance and repair. Each drone has an independent storage location within the storage components, equipped with corresponding identification and positioning systems, allowing staff to quickly and accurately locate the required drone. Furthermore, the storage components have reserved space for future upgrades or the addition of new equipment, providing excellent scalability and adaptability.

[0018] Furthermore, after completing its inspection mission, the drone can automatically dock its charging port using an automatic charging component, eliminating the need for manual intervention. This automatic docking extends the drone's actual operating time and significantly improves charging efficiency, reducing waiting time. In traditional manual charging, operators must manually perform the process, which is time-consuming, labor-intensive, and prone to inaccurate docking, affecting charging effectiveness. The automatic charging component, with its precise docking technology, can quickly and stably dock the charging port, ensuring a smooth charging process. The stability and reliability of the automatic charging component also provide strong support for the drone's continuous operation. It can adapt to various environmental conditions, accurately completing docking and charging tasks in harsh weather or complex terrain. This enables drones to maintain good working condition in various scenarios, further expanding their application scope. The automatic charging component also has intelligent monitoring functions. During the charging process, it can monitor the battery's charging status, temperature, and other parameters in real time. Once an abnormality is detected, it can take timely measures to adjust or issue an alarm, avoiding battery damage or other safety hazards caused by charging problems. This not only protects the drone's battery but also improves the safety and stability of the entire system. Through the application of the automatic charging component, the deployment of cellular systems for drones can operate more efficiently and stably, providing a solid foundation for the large-scale application and continuous operation of drones, and promoting the widespread application and development of drones in various fields.

[0019] Furthermore, different sensors can be installed on the drone, allowing for more stable data collection from the inspection scene and ensuring more accurate information during data acquisition. These sensors cover various types, including optical, infrared, and lidar. Optical sensors provide high-resolution image data for identifying the appearance features and details of target objects. Infrared sensors detect infrared radiation emitted by objects, capturing thermal imaging information of targets even at night or in low-light conditions, helping to discover potential safety hazards or anomalies. LiDAR sensors accurately measure distance and the three-dimensional shape of objects, building precise 3D maps for the drone, facilitating navigation and obstacle avoidance in complex environments. The cooperation of different types of sensors makes the drone's inspection process more comprehensive, through optical transmission... Sensors can be used to detect damage or deformation on the appearance of power transmission lines; infrared sensors can detect temperature changes in the lines, promptly identifying potential overheating faults; lidar sensors can accurately scan the terrain and obstacles around the lines, ensuring flight safety for drones during inspections; the sensors on the drone also have adaptive adjustment capabilities, automatically adjusting their operating parameters and acquisition modes according to the different characteristics and needs of the inspection scenario. In low-light environments, optical sensors can automatically increase their sensitivity; in complex terrain, lidar sensors can increase their scanning frequency to obtain more detailed terrain information. This adaptive adjustment function enables drones to collect data efficiently and accurately in various complex inspection scenarios. By installing different sensors, drones can play a greater role in inspection work. It not only improves the accuracy and efficiency of data collection but also reduces the cost and risk of manual inspections.

[0020] 2. The UAV's position data is transmitted to the trajectory tracking component via visual recognition and positioning equipment and a positioning system. During the inspection process, the trajectory tracking component acquires various data of the UAV in real time through the kinematic parameter acquisition unit. When path deviation occurs, the deviation signal is fed back to the path planning component. The cooperation between the trajectory tracking component and the path planning component can complete a closed-loop collaborative operation from deviation detection to path correction to tracking execution. This closed-loop collaborative operation greatly improves the accuracy and stability of the UAV's inspection path. After receiving the deviation signal, the path planning component quickly replans a more reasonable flight path based on preset algorithms and strategies, combined with current environmental information and task requirements. It comprehensively considers various factors, such as the distribution of power transmission lines, the location of surrounding obstacles, and weather conditions, to ensure that the newly planned path can avoid potential dangers and efficiently complete the inspection task. The replanned path is promptly sent back to the trajectory tracking component, which then precisely controls the UAV to adjust its flight attitude and direction based on the new path information, gradually correcting the deviation and bringing the UAV back to the correct inspection path. During this process, the kinematic parameter acquisition unit continuously acquires data. This closed-loop collaborative operation collects various flight data from drones and monitors their flight status in real time, ensuring they fly strictly according to the newly planned path. It also possesses self-optimization and learning capabilities. As drone inspection missions continue, the system records and analyzes each deviation and correction process, summarizing lessons learned. Through learning and processing large amounts of data, the path planning component continuously optimizes its algorithms and strategies, improving the accuracy and efficiency of path planning. The trajectory tracking component can also more precisely control the drone's flight, reducing the probability of deviations. In actual inspection work, this closed-loop collaborative operation effectively copes with various complex and changing situations. When significant path deviations occur, the closed-loop collaborative operation responds quickly and corrects the path in a timely manner, ensuring the drone completes the inspection mission safely and stably. The closed-loop collaborative operation enhances the drone's autonomous decision-making ability, enabling it to react quickly and appropriately to emergencies, reducing the need for manual intervention. Through the closed-loop collaborative operation between the trajectory tracking component and the path planning component, the drone can fly more accurately and stably during inspections, greatly improving the quality and efficiency of inspection work and providing a more reliable guarantee for the safe operation of power transmission lines.

[0021] Furthermore, the drone takeoff and landing guidance component can guide the takeoff and landing of drones, avoiding the safety risks associated with manual takeoff and landing. The path planning component requires first creating a raster map model and then dynamically identifying obstacles in real time to generate detour paths, thus avoiding obstacle-prone sections while optimizing energy consumption. This prevents insufficient energy and mission interruptions caused by detours during inspections. The trajectory tracking component continuously monitors and compares the actual flight trajectory with the planned path during drone flight. If a deviation is detected, it immediately calculates correction commands based on the deviation, controlling the drone to adjust its flight attitude and direction to ensure it returns to the planned path. This component can also intelligently adjust the drone's flight speed, selecting the most suitable speed under different terrain and environmental conditions to ensure both inspection efficiency and flight safety. The data acquisition and processing component efficiently collects various data obtained by the drone during the inspection process. According to data collected, such as images, videos, temperature, and humidity of power transmission lines, the collected data is transmitted to the system in real time. After processing and analysis by professional algorithms and models, it can accurately identify and warn of potential faults and equipment malfunctions on the power transmission lines, providing a basis for subsequent maintenance and repair work. The communication component is responsible for information exchange between the UAV and the ground control center, as well as between various components. Utilizing wireless communication technology, it ensures the stability and timeliness of data transmission, maintaining smooth communication even in complex electromagnetic environments. This allows ground control personnel to monitor the UAV's flight status, mission execution, and collected data in real time. Simultaneously, the ground control center can also send commands to the UAV through the communication component, enabling remote control and scheduling. The maintenance and management component provides a solid guarantee for the stable operation of the entire UAV deployment cellular system. It can monitor the UAV's hardware in real time, including battery level, motor status, and sensor performance. When an abnormality is detected, it will promptly issue an alarm and provide corresponding maintenance suggestions. It also manages and statistically analyzes data such as the UAV's mission execution and flight records, providing data support for system optimization and upgrades.

[0022] In summary, the technical solution adopted in this application can improve the accuracy of data collection during inspections in complex scenarios. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a drone deployment cellular system provided in this application;

[0025] Figure 2 This is a modular structure diagram of the drone-deployed cellular system provided in this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] This application provides a drone deployment cellular system. Its core is drone scheduling to complete inspection tasks and collect data from inspection scenarios. Before a task, drones equipped with different sensors can be selected based on different needs, improving task accuracy during inspection. After completing a task, the drones can be automatically retrieved and recharged using an automatic docking mechanism, improving charging efficiency and avoiding efficiency drops caused by manual intervention. This also avoids situations where untimely intervention leads to insufficient drone energy. Furthermore, during the inspection process, path planning and trajectory tracking components enable the drones to acquire more comprehensive inspection data in complex scenarios. While acquiring data, the drones can also avoid dynamic obstacles, preventing unnecessary wear and tear. When avoiding dynamic obstacles, the system calculates its own payload to optimize energy consumption, preventing subsequent task interruptions.

[0028] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a drone deployment cellular system according to this embodiment of the application. In actual use, task planning is first performed, and a suitable drone is selected for inspection by scheduling drones. Then, data collection of the inspection and the scene is carried out. The task is dynamically adjusted according to different situations. After the task is completed, the drone is retrieved into the cellular cabinet and replenished with energy through the drone automatic charging component. Finally, the data collected during the inspection is archived, completing the overall drone inspection and reception process.

[0029] The drone storage components inside the drone storage cabinet protect the drones when not in use. The temperature inside the cabinet is controlled by a temperature control component to prevent damage caused by temperature fluctuations while the drones are stored. The task management component receives task commands from the ground control station, including the task scenario type, inspection range, and required accuracy. These commands are then transmitted to a multi-scenario adaptation component, which matches the appropriate storage cabinet and selects a suitable drone. Different sensors can be installed on the drone, allowing for more stable and accurate data collection during inspections. The adaptation results are then fed back to the task management component, providing a scenario-based foundation for subsequent path planning.

[0030] After the mission management component issues the takeoff command, the UAV is pushed to the takeoff and landing platform by the takeoff and landing guidance component. At the same time, the automatic charging component separates from the UAV. Then, the UAV's own position data is transmitted to the trajectory tracking component through the visual recognition positioning device and positioning system. During the inspection process, the trajectory tracking component acquires various data of the UAV in real time through the kinematic parameter acquisition unit. When a path deviation occurs, the deviation signal is fed back to the path planning component. The cooperation between the trajectory tracking component and the path planning component can complete the closed-loop collaborative operation from deviation detection to path correction to tracking execution.

[0031] The data collected by the drone is transmitted back in real time through the data transmission component. After the data arrives at the drone's nest, the real-time information processing component can identify defects in the data. When a relatively obvious serious defect is found, the data transmission component can promptly feed back the defect location and defect level to the task management component, triggering a priority return command.

[0032] Example 2: This application provides a drone-deployed cellular system, referencing... Figure 2As shown in the figure, this is a modular structure diagram of a drone deployment cellular system according to this embodiment of the application, including the following modules: S1, a basic adaptation module, which completes the storage and energy replenishment of drones. The basic adaptation module includes a drone storage component and a drone automatic charging component. The drone storage component provides storage space for drones in a non-recovery state through a sealed cellular cabinet, and uses a temperature control mechanism to regulate the internal environment of the cabinet to avoid damage to drones caused by temperature changes. The drone automatic charging component realizes the positional docking of drones with the sealed cellular cabinet through an automatic docking mechanism, and then automatically charges drones that have completed inspection tasks through an intelligent charging management mechanism. Energy replenishment; S2, Flight control module, which enables precise take-off and landing and flight trajectory control of the UAV. The flight control module includes a UAV take-off and landing guidance component, a path planning component, and a trajectory tracking component. The UAV take-off and landing guidance component uses visual recognition positioning equipment and a positioning system to provide take-off and landing guidance, avoiding the safety risks of manual take-off and landing. The path planning component is based on gridded map modeling to achieve dynamic obstacle recognition and real-time path correction, while also taking into account energy consumption optimization. The trajectory tracking component is equipped with PID and model predictive control algorithms, and acquires UAV flight data in real time through a kinematic parameter acquisition unit. When a path deviation occurs, the deviation signal is fed back to the path planning component. The system completes a closed-loop collaborative operation of deviation detection, path correction, and tracking execution; S3, the data processing module, which manages and processes inspection tasks. This module includes a task management component, a data transmission component, and a real-time information processing component. The task management component is linked to the sealed drone housing to plan, share, distribute, and track inspection tasks, and selects drones with compatible sensors based on requirements. The data transmission component uses wireless communication lines to transmit image and video data collected by the drone inspection in real time, ensuring data transmission stability in complex scenarios. The real-time information processing component integrates a lightweight image recognition algorithm to process the collected data. Preliminary processing and environmental defect identification: When a serious defect is detected, the defect location and level are fed back to the task management component, triggering a priority return-to-home command; S4, Monitoring and maintenance module: This module enables multi-scenario adaptation and status monitoring. It includes a multi-scenario adaptation component and a status monitoring and maintenance component. The multi-scenario adaptation component optimizes the adaptation parameters between the sealed drone housing and the drone for different inspection environments such as power transmission lines, substations, and railway lines. The status monitoring and maintenance component monitors the drone's battery status, wear and tear, and the internal operating environment of the drone housing in real time. When severe wear and tear or abnormalities in the drone housing are detected, a maintenance prompt is triggered.

[0033] The sealed storage cabinet is dustproof and waterproof, reducing the impact of the external environment on the drone's storage status. The temperature control mechanism's temperature adjustment range is set according to the drone's storage requirements, ensuring the drone is in a suitable storage environment. The gridded map modeling process of the path planning component needs to combine terrain data and obstacle distribution data of the inspection scene to construct an initial map. During drone flight, the map data is updated in real time to match dynamic obstacle changes, ensuring the real-time performance and accuracy of path planning. The kinematic parameter acquisition unit of the trajectory tracking component collects drone flight data including flight speed, flight altitude, heading angle, attitude angle, and position coordinates at a frequency of no less than 5Hz to ensure timely deviation detection. The wireless communication line used by the data transmission component supports multi-band adaptive switching. When a certain frequency band signal is interfered with, it automatically switches to a stable frequency band to ensure the continuity of data transmission. The lightweight image recognition algorithm of the real-time information processing component processes the collected image data through a pre-trained defect recognition model. The training samples of the defect recognition model cover a wide range of data. For common defect types in the same inspection scenario, the model recognition accuracy is no less than 90%. The optimized adaptation parameters of the multi-scenario adaptation component include the drone's flight speed threshold, sensor sampling frequency, take-off and landing platform size of the drone's nest cabinet, and charging interface adaptation specifications. Adaptation parameters for different scenarios are stored in a scenario parameter database. When called, the corresponding parameters are obtained by directly matching the scenario type. The monitoring data of the status monitoring and maintenance component is displayed in real time through a data dashboard. When the drone's battery power is below 20%, the fuselage wear exceeds a preset threshold, or the internal temperature of the drone's nest cabinet exceeds the temperature control range of ±5℃, an audible and visual alarm is triggered. When the task management component issues inspection tasks, it can automatically allocate task priorities based on the drone's remaining power, load status, and historical task completion status, prioritizing the drone with the best status to perform high-priority tasks. After the drone automatic charging component completes automatic docking, the intelligent charging management mechanism first detects the health status of the drone's battery, and then selects constant current charging or constant voltage charging mode based on the battery health status. After charging is completed, the charging connection is automatically disconnected to avoid overcharging and damaging the battery.

[0034] In this example, the drone storage component also features shock absorption and cushioning, effectively reducing damage caused by external vibrations. When encountering external vibrations, its internal cushioning structure absorbs and disperses the vibration energy, ensuring the drone's delicate components remain unaffected. Furthermore, the storage cabinet is made of high-strength, corrosion-resistant materials, which not only withstand daily wind, rain, acid and alkali corrosion, and other environmental damage, but also prevent human-caused collisions and malicious damage, further enhancing the drone's protection. The storage cabinet is equipped with an advanced dehumidification device that monitors and adjusts internal humidity in real time. When the humidity exceeds a suitable range, the dehumidification device automatically activates to drain excess moisture, preventing the drone from rusting or short-circuiting due to a damp environment. Additionally, the storage cabinet is equipped with an intelligent access control system. Only authorized personnel can open the storage unit, ensuring the safety and confidentiality of the drones and preventing unauthorized access and operation, thus reducing potential risks. The internal layout of the storage unit is meticulously designed with clearly defined areas, facilitating both the launch and reception of drones and routine maintenance and repair. Each drone has its own independent storage location within the storage unit, equipped with corresponding identification and positioning systems, allowing staff to quickly and accurately locate the required drone. Furthermore, the storage unit has reserved space for future upgrades or the addition of new equipment, offering excellent scalability and adaptability. In traditional manual charging, staff must operate manually, which is time-consuming, labor-intensive, and prone to inaccurate docking, affecting charging efficiency. The automatic charging unit, with its precise docking technology, can quickly and stably complete the charging port docking, ensuring a smooth charging process. The stability and reliability of the automatic charging unit also provide strong support for continuous drone operation. It can adapt to different environmental conditions, accurately completing docking and charging tasks in harsh weather or complex terrain. This enables drones to maintain good working condition in various scenarios, further expanding their application scope. The automatic charging component also has intelligent monitoring functions. During the charging process, it can monitor the battery's charging status, temperature, and other parameters in real time. Once an abnormality is detected, it can take timely measures to adjust or issue an alarm, avoiding battery damage or other safety hazards caused by charging problems. This not only protects the drone's battery but also improves the safety and stability of the entire system. Through the application of the automatic charging component, the deployment of cellular systems for drones can operate more efficiently and stably, providing a solid foundation for the large-scale application and continuous operation of drones, and promoting the widespread application and development of drones in various fields.

[0035] These sensors encompass various types, including optical, infrared, and lidar. Optical sensors provide high-resolution image data for identifying the appearance features and details of target objects. Infrared sensors detect infrared radiation emitted by objects, capturing thermal imaging information of targets even at night or in low-light conditions, helping to detect potential safety hazards or anomalies. LiDAR sensors accurately measure distance and the three-dimensional shape of objects, building precise 3D maps for drones, facilitating navigation and obstacle avoidance in complex environments. The combined use of different types of sensors makes the drone's inspection process more comprehensive. Optical sensors can be used to detect damage or deformation on the appearance of power transmission lines, while infrared sensors can detect temperature changes in the lines, promptly identifying potential overheating faults. The lidar sensor can accurately scan the terrain and obstacles around the line, ensuring the flight safety of the drone during inspection. The sensors on the drone also have adaptive adjustment functions. According to the different characteristics and needs of the inspection scenario, the sensor can automatically adjust its working parameters and acquisition mode. In the low light environment, the optical sensor can automatically increase the light sensitivity. In complex terrain, the lidar sensor can increase the scanning frequency to obtain more detailed terrain information. This adaptive adjustment function enables the drone to collect data efficiently and accurately in various complex inspection scenarios. By installing different sensors, the drone can play a greater role in the inspection work. It can not only improve the accuracy and efficiency of data collection, but also reduce the cost and risk of manual inspection.

[0036] Closed-loop collaborative operation significantly improves the accuracy and stability of UAV inspection paths. Upon receiving a deviation signal, the path planning component quickly replans a more reasonable flight path based on preset algorithms and strategies, combined with current environmental information and task requirements. It comprehensively considers various factors, such as the distribution of power lines, the location of surrounding obstacles, and weather conditions, to ensure that the newly planned path avoids potential hazards while efficiently completing the inspection task. The replanned path is promptly sent back to the trajectory tracking component, which then precisely controls the UAV to adjust its flight attitude and direction based on the new path information, gradually correcting deviations and returning the UAV to the correct inspection path. During this process, the kinematic parameter acquisition unit continuously collects various flight data from the UAV, monitoring its flight status in real time to ensure it strictly follows the newly planned path. Closed-loop collaborative operation also possesses self-optimization and learning capabilities; as the UAV inspection task continues... The system records and analyzes each deviation and correction process, summarizing lessons learned. Through learning and processing large amounts of data, the path planning component continuously optimizes its algorithms and strategies, improving the accuracy and efficiency of path planning. The trajectory tracking component can also more precisely control the drone's flight, reducing the probability of deviations. In actual inspection work, this closed-loop collaborative operation can effectively cope with various complex and changing situations. When a large path deviation occurs, the closed-loop collaborative operation can respond quickly and correct the path in a timely manner, ensuring that the drone completes the inspection task safely and stably. The closed-loop collaborative operation can improve the drone's autonomous decision-making ability, enabling it to make reasonable responses quickly when encountering emergencies, reducing the need for manual intervention. Through the closed-loop collaborative operation between the trajectory tracking component and the path planning component, the drone can fly more accurately and stably during the inspection process, greatly improving the quality and efficiency of the inspection work and providing a more reliable guarantee for the safe operation of power transmission lines.

[0037] The trajectory tracking component continuously monitors and compares the actual flight trajectory with the planned path during the drone's flight. If a deviation is detected, it immediately calculates correction commands based on the deviation, controlling the drone to adjust its flight attitude and direction to ensure it returns to the planned path. Simultaneously, this component can intelligently adjust the drone's flight speed, selecting the most suitable speed under different terrain and environmental conditions to ensure both inspection efficiency and flight safety. The data acquisition and processing component efficiently collects various data acquired by the drone during the inspection, such as images, videos, and environmental parameters like temperature and humidity of the power transmission lines. The collected data is transmitted to the system in real time, where it is processed and analyzed using professional algorithms and models to identify potential faults on the power transmission lines. The system can accurately identify and warn of potential hazards and equipment malfunctions, providing a solid basis for subsequent maintenance and repair work. The communication component is responsible for information exchange between the UAV, the ground control center, and other components. Utilizing wireless communication technology, it ensures stable and timely data transmission, maintaining uninterrupted communication even in complex electromagnetic environments. This allows ground control personnel to monitor the UAV's flight status, mission execution, and collected data in real time. Simultaneously, the ground control center can send commands to the UAV through the communication component, enabling remote control and scheduling. The maintenance and management component provides a robust guarantee for the stable operation of the entire UAV deployment cellular system. It can monitor the UAV's hardware in real time, including battery level, motor status, and sensor performance. When equipment malfunctions are detected, it will promptly issue alarms and provide corresponding maintenance suggestions. It also manages and statistically analyzes data such as the UAV's mission execution and flight records, providing data support for system optimization and upgrades.

[0038] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0039] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0040] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A drone-deployed cellular system, characterized in that, Includes the following modules; S1. Basic Adaptation Module: This module handles the storage and energy replenishment of the drone. It includes a drone storage component and an automatic drone charging component. The drone storage component provides storage space for drones in non-recovery mode through a sealed cabinet and uses a temperature control mechanism to regulate the internal environment of the cabinet, preventing damage to the drones due to temperature changes. The automatic drone charging component uses an automatic docking mechanism to dock the drone with the sealed cabinet and then uses an intelligent charging management mechanism to automatically replenish the energy of the drones that have completed their inspection tasks. S2. Flight control module: The flight control module enables precise take-off and landing and flight trajectory control of the UAV. The flight control module includes a UAV take-off and landing guidance component, a path planning component, and a trajectory tracking component. The UAV take-off and landing guidance component uses visual recognition positioning equipment and a positioning system to provide take-off and landing guidance, avoiding the safety risks of manual take-off and landing. The path planning component is based on gridded map modeling to achieve dynamic obstacle recognition and real-time path correction, while also taking into account energy consumption optimization; the trajectory tracking component is equipped with PID and model predictive control algorithms, and acquires UAV flight data in real time through the kinematic parameter acquisition unit. When path deviation occurs, the deviation signal is fed back to the path planning component to complete the closed-loop collaborative operation of deviation detection, path correction and tracking execution. S3. Data Processing Module: This module manages and processes inspection tasks. It includes a task management component, a data transmission component, and a real-time information processing component. The task management component is linked to the sealed drone housing to plan, share, distribute, and track the progress of inspection tasks, and selects drones with compatible sensors as needed. The data transmission component uses wireless communication lines to transmit image and video data collected by the drone during inspections in real time, ensuring data transmission stability in complex scenarios. The real-time information processing component integrates a lightweight image recognition algorithm to perform preliminary processing of the collected data and identify environmental defects. When a serious defect is detected, the defect location and level are fed back to the task management component, triggering a priority return command. S4. Monitoring and Maintenance Module: This module enables multi-scenario adaptation and status monitoring. It includes a multi-scenario adaptation component and a status monitoring and maintenance component. The multi-scenario adaptation component optimizes the adaptation parameters between the sealed drone housing and the drone for inspection environments such as power transmission lines, substations, and railway lines. The status monitoring and maintenance component monitors the drone's battery status, wear and tear, and internal operating environment of the drone housing in real time. When severe wear and tear on the drone or abnormalities in the drone housing are detected, a maintenance prompt is triggered.

2. The UAV deployment cellular system as described in claim 1, characterized in that, The sealed cabinet is dustproof and waterproof, reducing the impact of the external environment on the storage status of the drone. The temperature control mechanism is set according to the storage requirements of the drone to ensure that the drone is in a suitable storage environment.

3. A drone deployment cellular system as described in claim 2, characterized in that, The raster map modeling process of the path planning component needs to combine terrain data and obstacle distribution data of the inspection scene to build an initial map. During the flight of the UAV, the map data is updated in real time to match dynamic obstacle changes, so as to ensure the real-time performance and accuracy of path planning.

4. A drone deployment cellular system as described in claim 3, characterized in that, The kinematic parameter acquisition unit of the trajectory tracking component collects UAV flight data including flight speed, flight altitude, heading angle, attitude angle and position coordinates, with an acquisition frequency of not less than 5Hz to ensure timely deviation detection.

5. A drone deployment cellular system as described in claim 4, characterized in that, The data transmission component uses a wireless communication line that supports multi-band adaptive switching. When a signal in a certain frequency band is interfered with, it automatically switches to a frequency band with a stable signal to ensure the continuity of data transmission.

6. A drone deployment cellular system as described in claim 5, characterized in that, The lightweight image recognition algorithm of the real-time information processing component processes the collected image data through a pre-trained defect recognition model. The training samples of the defect recognition model cover common defect types in different inspection scenarios, and the model recognition accuracy is not less than 90%.

7. A drone deployment cellular system as described in claim 6, characterized in that, The optimized adaptation parameters of the multi-scenario adaptation component include the drone's flight speed threshold, sensor sampling frequency, take-off and landing platform size of the drone's nest cabinet, and charging interface adaptation specifications. Adaptation parameters for different scenarios are stored in a scenario parameter database, and the corresponding parameters are obtained by directly matching the scenario type when called.

8. A drone deployment cellular system as described in claim 7, characterized in that, The monitoring data from the status monitoring and maintenance component is displayed in real time through a data dashboard. When the drone's battery power is below 20%, the wear and tear on the fuselage exceeds a preset threshold, or the internal temperature of the drone's housing exceeds the temperature control range of ±5℃, an audible and visual alarm is triggered.

9. A drone deployment cellular system as described in claim 8, characterized in that, When issuing inspection tasks, the task management component automatically allocates task priorities based on the drone's remaining battery power, load status, and historical task completion status, prioritizing the drone with the best status to execute high-priority tasks.

10. A drone deployment cellular system as described in claim 9, characterized in that, After the automatic docking of the drone's automatic charging component is completed, the intelligent charging management mechanism first detects the health status of the drone's battery, and then selects either constant current charging or constant voltage charging mode based on the battery's health status. After charging is completed, the charging connection is automatically disconnected to avoid overcharging and damaging the battery.

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