A control method and system of a networked unmanned aerial vehicle
By introducing advanced communication protocols and intelligent decision-making algorithms, combined with modular design, precise control and efficient management of networked drones have been achieved, solving the problems of insufficient flexibility and accuracy of existing control systems and improving the operational capabilities of drones in diverse application scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NAT INST OF STANDARDIZATION
- Filing Date
- 2024-11-29
- Publication Date
- 2026-05-29
AI Technical Summary
Existing networked drone control methods are insufficient for achieving precise control and efficient operation in complex environments, and the control systems lack flexibility, making it difficult to meet the needs of diverse application scenarios.
By introducing advanced communication protocols, intelligent decision-making algorithms, and modular design, precise control and efficient management can be achieved through encrypted communication between UAVs and ground control stations, real-time data acquisition and processing, intelligent decision-making, and modular collaborative operations.
It improves the accuracy and safety of drone operations, enhances the system's flexibility and scalability, and can meet the needs of diverse application scenarios.
Smart Images
Figure CN122111058A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology, specifically relating to a control method and system for a networked UAV. Background Technology
[0002] With the rapid development of drone technology, connected drones have been widely used in logistics transportation, environmental monitoring, agricultural plant protection and other fields. However, most existing control methods for connected drones rely on a single communication protocol and simple flight algorithms, which makes it difficult to achieve precise control and efficient operation in complex environments. In addition, existing control systems lack flexibility in module design and cannot meet the needs of diverse application scenarios. Therefore, this invention proposes a control method and system for connected drones. Summary of the Invention
[0003] To address the shortcomings of existing technologies, this invention proposes a control method and system for networked drones. By introducing advanced communication protocols, intelligent decision-making algorithms, and modular design concepts, it achieves precise control and efficient management of networked drones.
[0004] To achieve the above objectives, the present invention provides the following technical solution: a control method for a network-connected unmanned aerial vehicle, comprising the following steps:
[0005] Step S1: Communication Connection Establishment: The UAV establishes a connection with the ground control station through an encrypted wireless communication protocol and exchanges information with other networked devices in the vicinity.
[0006] Step S2, Flight Mission Planning: The ground control station generates a flight mission plan based on user needs and environmental information, which is received by the UAV and processed locally.
[0007] Step S3, Real-time Flight Monitoring: The UAV collects environmental data in real time through sensors and uploads it to the ground control station. The ground control station processes and analyzes the data and generates flight adjustment commands.
[0008] Step S4, Intelligent Decision-Making and Adjustment: The UAV makes autonomous decisions based on real-time environmental data and flight adjustment commands, adjusting its flight attitude and parameters.
[0009] Step S5, Task Execution and Feedback: The UAV completes the designated task according to the flight mission plan and uploads the task execution results to the ground control station.
[0010] Preferably, the communication connection establishment in step S1 specifically includes the following detailed steps:
[0011] Step 1.1: After the drone is powered on, it first searches for available ground control station signals through its built-in wireless communication module (such as 4G / 5G, Wi-Fi, LoRa, etc.).
[0012] Step 1.2: The UAV and the ground control station use an encrypted communication protocol based on TLS (Transport Layer Security) or SSL (Secure Sockets Layer) to ensure the security and stability of data transmission. The two parties confirm the communication encryption method through a handshake protocol and exchange encryption keys.
[0013] Step 1.3: The drone exchanges information with other connected devices in the vicinity (such as sensors, other drones, etc.) via broadcast or request to build a local connected environment, including device ID, location information, status information, etc.
[0014] Preferably, the flight mission planning in step S2 specifically includes the following detailed steps:
[0015] Step 2.1: The ground control station receives the flight mission requirements input by the user, including key information such as takeoff point, waypoint, landing point, flight altitude, and speed.
[0016] Step 2.2: The ground control station uses GIS (Geographic Information System) and meteorological data to make preliminary plans for the flight path, and optimizes it by taking into account factors such as terrain, obstacles, and no-fly zones, and generates a preliminary flight path.
[0017] Step 2.3: After receiving the flight mission plan, the UAV performs localization processing based on its own flight performance and current environmental information (such as wind speed, wind direction, temperature, etc.) to generate specific flight paths and flight parameters.
[0018] Preferably, the real-time flight monitoring in step S3 specifically includes the following detailed steps:
[0019] Step 3.1: During flight, the drone collects environmental data in real time through built-in sensors (such as GPS, barometer, gyroscope, camera, etc.), including location information, altitude information, speed information, obstacle information, etc.
[0020] Step 3.2: The UAV uploads the collected data to the ground control station through the communication module. The ground control station processes and analyzes the received data, including data verification, filtering, and fusion, to generate more accurate flight status information.
[0021] Step 3.3: The ground control station generates flight adjustment commands based on the processed data, including adjusting flight altitude, speed, and heading, and sends them to the UAV in real time.
[0022] Preferably, the intelligent decision-making and adjustment in step S4 specifically includes the following detailed steps:
[0023] Step 4.1: The UAV's built-in intelligent decision-making module receives flight adjustment commands and real-time environmental data sent by the ground control station.
[0024] Step 4.2: The intelligent decision-making module uses machine learning algorithms or rule engines to process and analyze the received data and generate autonomous decision-making results, including adjusting flight attitude and changing flight parameters.
[0025] Step 4.3: When encountering emergencies (such as obstacles, severe weather, etc.), the intelligent decision-making module can respond quickly, take measures such as obstacle avoidance or emergency landing, and notify the ground control station.
[0026] Preferably, the task execution and feedback in step S5 specifically includes the following detailed steps:
[0027] Step 5.1: The UAV completes the designated tasks (such as shooting, delivery, monitoring, etc.) according to the flight mission plan, and uploads the mission execution results (such as images, videos, data, etc.) to the ground control station.
[0028] Step 5.2: The ground control station processes and analyzes the received mission execution results and generates a mission completion report, including mission completion status, data quality, and anomaly information.
[0029] Step 5.3: The ground control station sends a mission completion report to the user and makes follow-up processing or adjustments based on the user's feedback.
[0030] A control system for the control method of the network-connected drone includes the following modules:
[0031] The communication module is used for encrypted data transmission and communication protocol conversion between the UAV and the ground control station, as well as information exchange with other networked devices in the vicinity;
[0032] The mission planning module is used to generate flight mission plans based on user needs and environmental information, and to optimize and adjust them.
[0033] The flight monitoring module is used to collect environmental data in real time through sensors, process and analyze the data, and generate flight adjustment commands.
[0034] The intelligent decision-making module is used to make autonomous decisions based on real-time environmental data and flight adjustment instructions, adjust flight attitude and flight parameters, and respond quickly in the event of emergencies.
[0035] The mission execution and feedback module is used to complete the designated mission according to the flight mission plan and upload the mission execution results to the ground control station.
[0036] Preferably, the communication module includes: a wireless communication submodule responsible for encrypted data transmission and communication protocol conversion between the UAV and the ground control station; and a network device interaction submodule responsible for information exchange and collaborative operation between the UAV and other surrounding network devices.
[0037] The mission planning module includes: a user requirement processing submodule, which generates a preliminary flight mission plan based on user requirements; and an environmental information analysis submodule, which optimizes and adjusts the flight mission plan based on environmental information.
[0038] The flight monitoring module includes: a data acquisition submodule, which collects environmental data in real time through sensors; and a data processing and analysis submodule, which processes and analyzes the collected data to generate flight adjustment commands.
[0039] The intelligent decision-making module includes: an autonomous decision-making submodule, which makes autonomous decisions based on real-time environmental data and flight adjustment instructions; and an emergency response submodule, which responds quickly and takes appropriate measures when encountering emergencies.
[0040] The mission execution and feedback module includes: a mission execution submodule, which completes the designated mission according to the flight mission plan; and a result feedback submodule, which uploads the mission execution results to the ground control station and generates a mission completion report.
[0041] Compared with the prior art, the beneficial effects of the present invention are:
[0042] 1. The networked drone control method of the present invention, by introducing advanced communication protocols and intelligent decision-making algorithms, achieves precise control and efficient management of networked drones, thereby improving the accuracy and safety of drone operations.
[0043] 2. The networked drone control system of the present invention adopts a modular design concept, with each module being independent yet cooperative, which improves the system's flexibility and scalability, meets the needs of diverse application scenarios, and achieves an improvement and breakthrough in networked drone control technology. Attached Figure Description
[0044] Figure 1 This is a flowchart of the network-connected drone control method of the present invention;
[0045] Figure 2 This is a system framework diagram of the network-connected unmanned aerial vehicle control system of the present invention; Detailed Implementation
[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0047] Example
[0048] Please see Figure 1 and Figure 2 This embodiment of the invention provides a technical solution: a control method for a networked unmanned aerial vehicle (UAV), comprising the following steps:
[0049] Step S1: Communication Connection Establishment: The UAV establishes a connection with the ground control station through an encrypted wireless communication protocol and exchanges information with other networked devices in the vicinity. To provide a high-speed, low-latency communication connection, 5G or future 6G communication technologies can be used, which can ensure stable and efficient data transmission between the UAV and the ground control station. To address the risk of communication link interruption, multiple communication links can be established as backups. When the main link fails, it automatically switches to the backup link to ensure communication continuity. During the communication connection establishment process, a secure authentication mechanism based on blockchain or quantum cryptography is used to enhance communication security and prevent data theft or tampering.
[0050] Step S2, Flight Mission Planning: The ground control station generates a flight mission plan based on user needs and environmental information. The UAV receives the plan and processes it locally. Using deep learning or reinforcement learning algorithms, it automatically generates the optimal flight path based on historical flight data and real-time environmental information, which can significantly improve the accuracy and efficiency of path planning. During flight, the flight path and parameters are dynamically adjusted based on real-time weather data, traffic conditions, and other factors to adapt to the complex and ever-changing environment. For missions that require multiple UAVs to work together, a distributed collaborative planning algorithm is used to ensure that the task allocation and path planning among the UAVs are coordinated and consistent.
[0051] Step S3, Real-time Flight Monitoring: The UAV collects environmental data in real time through sensors and uploads it to the ground control station. The ground control station processes and analyzes the data to generate flight adjustment commands. High-definition video encoding technology is used to transmit the video captured by the UAV to the ground control station in real time for operators to monitor and make decisions. Data from different sensors (such as GPS, radar, cameras, etc.) is fused to improve the accuracy and reliability of the data. Machine learning algorithms are used to analyze sensor data in real time to detect abnormal conditions (such as low battery power, mechanical failure, etc.) and promptly issue alarms to operators.
[0052] Step S4, Intelligent Decision-Making and Adjustment: The UAV makes autonomous decisions based on real-time environmental data and flight adjustment commands, adjusting its flight attitude and parameters. Edge computing devices are deployed on the UAV to achieve real-time data processing and decision-making, which reduces data transmission latency and improves the real-time performance of decisions. An adaptive control algorithm is adopted to dynamically adjust control parameters based on the UAV's flight status and real-time environmental information, ensuring flight stability and safety. Reinforcement learning algorithms are used to train the UAV's obstacle avoidance strategy, enabling it to autonomously avoid obstacles in complex environments.
[0053] Step S5, Task Execution and Feedback: The UAV completes the designated task according to the flight mission plan and uploads the task execution results to the ground control station. Depending on the mission requirements, the UAV is equipped with different mission modules (such as photography module, delivery module, monitoring module, etc.) to achieve diversified task execution. Real-time data during the task execution process (such as images, videos, sensor data, etc.) is fed back to the ground control station for operators to analyze and make decisions. Based on the task execution results and real-time data, the task completion rate is evaluated and a detailed evaluation report is generated.
[0054] In this embodiment, preferably, the communication connection establishment in step S1 includes the following detailed steps:
[0055] Step 1.1: After the drone is powered on, it first searches for available ground control station signals through its built-in wireless communication module (such as 4G / 5G, Wi-Fi, LoRa, etc.).
[0056] Step 1.2: The UAV and the ground control station use an encrypted communication protocol based on TLS (Transport Layer Security) or SSL (Secure Sockets Layer) to ensure the security and stability of data transmission. The two parties confirm the communication encryption method through a handshake protocol and exchange encryption keys.
[0057] Step 1.3: The drone exchanges information with other connected devices in the vicinity (such as sensors, other drones, etc.) via broadcast or request to build a local connected environment, including device ID, location information, status information, etc.
[0058] In this embodiment, preferably, the flight mission planning in step S2 includes the following detailed steps:
[0059] Step 2.1: The ground control station receives the flight mission requirements input by the user, including key information such as takeoff point, waypoint, landing point, flight altitude, and speed.
[0060] Step 2.2: The ground control station uses GIS (Geographic Information System) and meteorological data to make preliminary plans for the flight path, and optimizes it by taking into account factors such as terrain, obstacles, and no-fly zones, and generates a preliminary flight path.
[0061] Step 2.3: After receiving the flight mission plan, the UAV performs localization processing based on its own flight performance and current environmental information (such as wind speed, wind direction, temperature, etc.) to generate specific flight paths and flight parameters.
[0062] In this embodiment, preferably, the real-time flight monitoring in step S3 includes the following detailed steps:
[0063] Step 3.1: During flight, the drone collects environmental data in real time through built-in sensors (such as GPS, barometer, gyroscope, camera, etc.), including location information, altitude information, speed information, obstacle information, etc.
[0064] Step 3.2: The UAV uploads the collected data to the ground control station through the communication module. The ground control station processes and analyzes the received data, including data verification, filtering, and fusion, to generate more accurate flight status information.
[0065] Step 3.3: The ground control station generates flight adjustment commands based on the processed data, including adjusting flight altitude, speed, and heading, and sends them to the UAV in real time.
[0066] In this embodiment, preferably, the intelligent decision-making and adjustment in step S4 specifically includes the following detailed steps:
[0067] Step 4.1: The UAV's built-in intelligent decision-making module receives flight adjustment commands and real-time environmental data sent by the ground control station.
[0068] Step 4.2: The intelligent decision-making module uses machine learning algorithms or rule engines to process and analyze the received data and generate autonomous decision-making results, including adjusting flight attitude and changing flight parameters.
[0069] Step 4.3: When encountering emergencies (such as obstacles, severe weather, etc.), the intelligent decision-making module can respond quickly, take measures such as obstacle avoidance or emergency landing, and notify the ground control station.
[0070] In this embodiment, preferably, the task execution and feedback in step S5 includes the following detailed steps:
[0071] Step 5.1: The UAV completes the designated tasks (such as shooting, delivery, monitoring, etc.) according to the flight mission plan, and uploads the mission execution results (such as images, videos, data, etc.) to the ground control station.
[0072] Step 5.2: The ground control station processes and analyzes the received mission execution results and generates a mission completion report, including mission completion status, data quality, and anomaly information.
[0073] Step 5.3: The ground control station sends a mission completion report to the user and makes follow-up processing or adjustments based on the user's feedback.
[0074] A control system for a control method applied to connected drones includes the following modules:
[0075] The communication module is used for encrypted data transmission and communication protocol conversion between the UAV and the ground control station, as well as information exchange with other networked devices in the vicinity;
[0076] The mission planning module is used to generate flight mission plans based on user needs and environmental information, and to optimize and adjust them.
[0077] The flight monitoring module is used to collect environmental data in real time through sensors, process and analyze the data, and generate flight adjustment commands.
[0078] The intelligent decision-making module is used to make autonomous decisions based on real-time environmental data and flight adjustment instructions, adjust flight attitude and flight parameters, and respond quickly in the event of emergencies.
[0079] The mission execution and feedback module is used to complete the designated mission according to the flight mission plan and upload the mission execution results to the ground control station.
[0080] In this embodiment, preferably, the communication module includes: a wireless communication submodule: responsible for encrypted data transmission and communication protocol conversion between the UAV and the ground control station, the submodule including an encryption key management unit, a communication protocol parsing unit and a data sending / receiving unit; and a network device interaction submodule: responsible for information exchange and collaborative operation between the UAV and other surrounding network devices, the submodule including a device discovery unit, an information exchange protocol processing unit and a data synchronization unit.
[0081] The mission planning module includes: a user requirements processing submodule, which generates a preliminary flight mission plan based on user requirements. This submodule includes a user requirements analysis unit, a mission plan generation unit, and a plan optimization unit; and an environmental information analysis submodule, which optimizes and adjusts the flight mission plan by combining environmental information. This submodule includes a GIS data interface unit, a meteorological data interface unit, and a path optimization algorithm unit. These units acquire data through interfaces and use algorithms to optimize paths.
[0082] The flight monitoring module includes: a data acquisition submodule, which collects environmental data in real time through sensors. This submodule includes a sensor interface unit, a data acquisition algorithm unit, and a data preprocessing unit. The sensor interface unit supports the access of multiple sensors, and the data acquisition algorithm unit collects data in real time according to the sampling frequency of the sensors. The data preprocessing unit performs preliminary processing on the data. The data processing and analysis submodule processes and analyzes the collected data to generate flight adjustment commands. This submodule includes a data verification unit, a filtering algorithm unit, a data fusion algorithm unit, and a command generation unit. These units further process and analyze the preprocessed data to generate more accurate flight status information and adjustment commands.
[0083] The intelligent decision-making module includes: an autonomous decision-making submodule: which makes autonomous decisions based on real-time environmental data and flight adjustment commands. This submodule includes a machine learning model running unit, a rule engine execution unit, and a decision result generation unit. The machine learning model running unit processes and analyzes the data using a trained model, the rule engine execution unit makes decisions based on preset rules, and the decision result generation unit combines the decision results of both to generate the final autonomous decision result; and an emergency response submodule: which responds quickly and takes appropriate measures when encountering emergencies. This submodule includes an emergency response algorithm unit, an obstacle avoidance algorithm unit, and an emergency landing algorithm unit. These units quickly generate emergency response plans based on real-time environmental data and the UAV's flight status and control the UAV to perform corresponding actions.
[0084] The mission execution and feedback module includes: a mission execution submodule: This submodule completes the designated mission according to the flight mission plan. It includes a mission execution algorithm unit, a data acquisition and uploading unit, and a status monitoring unit. The mission execution algorithm unit controls the UAV's flight and photography operations according to the flight mission plan. The data acquisition and uploading unit collects mission execution results in real time and uploads them to the ground control station. The status monitoring unit monitors and provides feedback on the UAV's flight status in real time. The result feedback submodule: This submodule uploads the mission execution results to the ground control station and generates a mission completion report. It includes a data uploading unit, a report generation unit, and a user feedback processing unit. The data uploading unit uploads the mission execution results to the ground control station's database or server. The report generation unit generates a mission completion report based on the mission execution results. The user feedback processing unit receives user feedback and processes and improves the system.
[0085] In summary, the control methods and systems for connected drones achieve remote control and autonomous flight management of connected drones through efficient communication connections, precise mission planning, real-time flight monitoring, intelligent decision-making and adjustment, and accurate mission execution and feedback.
[0086] Although embodiments of the invention have been shown and described (see the detailed description above), it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A control method for a network-connected unmanned aerial vehicle (UAV), characterized in that: Includes the following steps: Step S1: Communication Connection Establishment: The UAV establishes a connection with the ground control station through an encrypted wireless communication protocol and exchanges information with other networked devices in the vicinity. Step S2, Flight Mission Planning: The ground control station generates a flight mission plan based on user needs and environmental information, which is received by the UAV and processed locally. Step S3, Real-time Flight Monitoring: The UAV collects environmental data in real time through sensors and uploads it to the ground control station. The ground control station processes and analyzes the data and generates flight adjustment commands. Step S4, Intelligent Decision-Making and Adjustment: The UAV makes autonomous decisions based on real-time environmental data and flight adjustment commands, adjusting its flight attitude and parameters. Step S5, Task Execution and Feedback: The UAV completes the designated task according to the flight mission plan and uploads the task execution results to the ground control station.
2. The control method for a networked unmanned aerial vehicle according to claim 1, characterized in that: The establishment of the communication connection in step S1 includes the following detailed steps: Step 1.1: After the drone is powered on, it first searches for available ground control station signals through its built-in wireless communication module; Step 1.2: The UAV and the ground control station use an encrypted communication protocol based on TLS (Transport Layer Security) or SSL (Secure Sockets Layer Security) to ensure the security and stability of data transmission. The two parties confirm the encryption method through a handshake protocol and exchange encryption keys. Step 1.3: The drone exchanges information with other connected devices in the vicinity via broadcast or request to build a local connected environment, including device ID, location information, and status information.
3. The control method for a networked unmanned aerial vehicle according to claim 1, characterized in that: The flight mission planning in step S2 includes the following detailed steps: Step 2.1: The ground control station receives the flight mission requirements input by the user, including key information such as takeoff point, waypoint, landing point, flight altitude, and speed; Step 2.2: The ground control station uses the GIS system and meteorological data to make a preliminary plan for the flight path, and optimizes it by taking into account factors such as terrain, obstacles and no-fly zones, to generate a preliminary flight path; Step 2.3: After receiving the flight mission plan, the UAV performs localization processing based on its own flight performance and current environmental information, including wind speed, wind direction, and temperature, to generate specific flight paths and flight parameters.
4. The control method for a networked unmanned aerial vehicle according to claim 1, characterized in that: The real-time flight monitoring in step S3 specifically includes the following detailed steps: Step 3.1: During flight, the drone collects environmental data in real time through its built-in sensors, including location information, altitude information, speed information, and obstacle information; Step 3.2: The UAV uploads the collected data to the ground control station through the communication module. The ground control station processes and analyzes the received data, including data verification, filtering, and fusion, to generate more accurate flight status information. Step 3.3: The ground control station generates flight adjustment commands based on the processed data, including adjusting flight altitude, speed, and heading, and sends them to the UAV in real time.
5. The control method for a networked unmanned aerial vehicle according to claim 1, characterized in that: The intelligent decision-making and adjustment in step S4 specifically includes the following detailed steps: Step 4.1: The UAV's built-in intelligent decision-making module receives flight adjustment commands and real-time environmental data sent by the ground control station; Step 4.2: The intelligent decision-making module uses machine learning algorithms or rule engines to process and analyze the received data and generate autonomous decision-making results, including adjusting flight attitude and changing flight parameters; Step 4.3: In the event of an emergency, the intelligent decision-making module can respond quickly, take measures such as obstacle avoidance or emergency landing, and notify the ground control station.
6. The control method for a networked unmanned aerial vehicle according to claim 1, characterized in that: The task execution and feedback in step S5 specifically includes the following detailed steps: Step 5.1: The UAV completes the designated task according to the flight mission plan and uploads the mission execution results to the ground control station; Step 5.2: The ground control station processes and analyzes the received mission execution results and generates a mission completion report, including mission completion status, data quality, and anomaly information; Step 5.3: The ground control station sends a mission completion report to the user and makes follow-up processing or adjustments based on the user's feedback.
7. A control system applied to the control method of a networked unmanned aerial vehicle as described in any one of claims 1-6, characterized in that: Includes the following modules: The communication module is used for encrypted data transmission and communication protocol conversion between the UAV and the ground control station, as well as information exchange with other networked devices in the vicinity; The mission planning module is used to generate flight mission plans based on user needs and environmental information, and to optimize and adjust them. The flight monitoring module is used to collect environmental data in real time through sensors, process and analyze the data, and generate flight adjustment commands. The intelligent decision-making module is used to make autonomous decisions based on real-time environmental data and flight adjustment instructions, adjust flight attitude and flight parameters, and respond quickly in the event of emergencies. The mission execution and feedback module is used to complete the designated mission according to the flight mission plan and upload the mission execution results to the ground control station.
8. The control system for a networked unmanned aerial vehicle according to claim 7, characterized in that: The communication module includes: a wireless communication submodule, responsible for encrypted data transmission and communication protocol conversion between the UAV and the ground control station; and a network device interaction submodule, responsible for information exchange and collaborative operation between the UAV and other surrounding network devices. The mission planning module includes: a user requirement processing submodule, which generates a preliminary flight mission plan based on user requirements; and an environmental information analysis submodule, which optimizes and adjusts the flight mission plan based on environmental information. The flight monitoring module includes: a data acquisition submodule, which collects environmental data in real time through sensors; and a data processing and analysis submodule, which processes and analyzes the collected data to generate flight adjustment commands. The intelligent decision-making module includes: an autonomous decision-making submodule, which makes autonomous decisions based on real-time environmental data and flight adjustment instructions; and an emergency response submodule, which responds quickly and takes appropriate measures when encountering emergencies. The mission execution and feedback module includes: a mission execution submodule, which completes the designated mission according to the flight mission plan; and a result feedback submodule, which uploads the mission execution results to the ground control station and generates a mission completion report.