Multi-address data access method of farm monitoring and operation unmanned aerial vehicle

Abstract

The invention discloses a multi-address data access method for a farm monitoring and operation unmanned aerial vehicle, and relates to the field of unmanned aerial vehicle agricultural automation and wireless communication, and the method comprises the following steps: S1, network architecture design: deploying a plurality of wireless access points in a farm region to form a wireless communication network covering the whole farm, the access points are connected through high-speed wired or wireless links, a stable network topology is constructed, and high-speed transmission of data is ensured; s2, data acquisition: each unmanned aerial vehicle performs data acquisition in a designated area, wherein the data comprises soil, environmental parameters and crop growth state information; an independent communication channel is allocated for each unmanned aerial vehicle through a dynamic channel allocation mechanism, it is ensured that each unmanned aerial vehicle transmits data at different time or frequencies, signal interference is avoided, it is ensured that the unmanned aerial vehicles transmit data in time, communication delay is reduced, the reliability and stability of the network are enhanced, and the high requirement of intelligent agriculture for data communication is met.

Description

Technical Field

[0001] The present invention relates to the field of agricultural automation and wireless communication technology of unmanned aerial vehicles, and in particular to a multi-address data access method for farm monitoring and operation unmanned aerial vehicles. Background Art

[0002] With the development of modern agriculture, drones are playing an increasingly important role in crop monitoring, precision pesticide application, precision fertilization, land surveying, etc. In order to meet the management needs of large-scale farms, multiple drones are often required to work together and transmit the collected data to ground monitoring stations in real time.

[0003] In the existing technology, drones usually use point-to-point communication links or simple outdoor wireless network access methods, which have problems such as limited bandwidth, single access method, large interference, low bandwidth utilization, and high data transmission delay. This limits the collaborative efficiency of drone groups and the data transmission rate and quality, and affects the level of intelligence in agricultural production. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-address data access method for farm monitoring and operation drones to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a multi-address data access method for farm monitoring and operation drones, comprising the following steps: S1: Network architecture design: Deploy multiple wireless access points in the farm area to form a wireless communication network covering the entire farm. Deploy servers to provide network services. Multiple access points and access points to servers are connected through high-speed wired or wireless links to build a stable network topology and ensure high-speed data transmission. S2: Data collection: Each drone is equipped with an environmental collection unit, which collects data in a designated area, including soil, environmental parameters and crop growth status information; S3: Data grouping: The computing device or server carried by the drone groups and packages the collected data according to preset rules to form data packets, and compresses and encodes the data packets according to the communication environment and transmission requirements to reduce the amount of data transmitted; S4: Channel allocation: According to the number of drones and data traffic, channels are dynamically allocated when using wireless communication networks to transmit data. The number of drones is grouped according to the total number of channels. The number of drones in each group can be equal to the number of channels, or there can be a certain number difference within a controllable range, that is, the number of drones can be one more or one less than the number of channels without affecting channel allocation; drones in the same group randomly select any channel to reduce the complexity of channel selection, and drones in different groups that select the same channel use multiple access technology to ensure that each drone transmits data at different time, space or frequency to avoid signal interference; S5: Data transmission: The UAV transmits data packets to the ground control station through the channel allocated by the wireless communication network. The control station receives the data packets and decodes, stores, processes and analyzes the data; S6: Establish coordinates: The drone is equipped with a GPS positioning module to obtain the initial global coordinates of the drone when it is in the ground control station and the three-dimensional global coordinates of the drone at different times, and use the partitioning method to give a number of points on the drone's operating plane. The given points are called generation points or seed points. The partitioning method used can be a dynamic Voronoi partitioning algorithm or other partitioning algorithms; the plane is divided into several areas, so that all points in each area are closer to their corresponding generation points than to other generation points. The coverage area is divided according to the current position and communication range of the drone, and the drone's mission range is adjusted in real time. The drone dynamically adjusts its position within the partition according to the communication load and mission completion; S7: Data reception: When the UAV transmits data, the transceiver link distance from the UAV to the ground control station is calculated according to the global coordinates of the UAV, and the data is received in order from the smallest to the largest transceiver link distance; S8: Feedback mechanism: The ground control station feeds back the data reception status and channel usage to the UAV via the wireless communication network, so that the UAV can select the appropriate communication channel for data transmission according to the channel occupancy, adjust the data transmission strategy in real time, and improve the transmission efficiency; S9: Interference management: The drone adjusts its own transmission power through power control technology to make the signal-to-noise ratio of different receivers consistent during the communication process, so that the communication between different drones or users does not interfere with each other; or the drone quickly switches between multiple frequencies through channel hopping technology, making it difficult for interference to be sustained and effective, reducing communication interference and improving the overall performance of the network; in addition, multi-antenna technology is added to the access point and drone communication module, and multi-antenna technology and beamforming technology are used to improve signal directivity and interference suppression capabilities; S10: Multi-UAV Collaboration and Operation Optimization: Use multi-agent reinforcement learning to optimize path planning and resource allocation through collaborative learning between UAVs. Each UAV acts as an independent agent, and the agents share location, mission status, and channel occupancy information to maximize coverage and minimize path overlap and communication conflicts. S11: Coverage area optimization: Use dynamic Voronoi partitioning algorithm or other partitioning algorithms to divide the coverage area according to the current position and communication range of the drone, and adjust the drone mission range in real time. The drone dynamically adjusts its position within the partition based on the communication load and mission completion.

[0006] Preferably, the acquisition unit in S2 includes a temperature sensor, a humidity sensor, a light sensor, a digital camera, a hyperspectral camera and a multispectral camera. The environmental parameters collected by each drone in a designated area include temperature, humidity and light, and the collected crop growth status information includes visible light images, videos, hyperspectral images and multispectral images.

[0007] Preferably, the acquisition unit in S2 adds an edge computing unit (such as an embedded GPU / TPU) to the drone communication module to perform preliminary data processing and feature extraction locally to reduce the amount of original data transmission. The data transmission priority is based on the importance of the task, with high-priority data transmitted in real time and low-priority data transmitted in buffer.

[0008] Preferably, an adaptive compression algorithm based on deep learning is introduced in S3 (such as a variational autoencoder) to perform hierarchical compression on hyperspectral, multispectral images and sensor data, and key features are transmitted preferentially. The length of the data packet in S3 is 1-10000000 baud Byte, and the data packet contains the drone ID, timestamp, data type, data length and data content.

[0009] Preferably, in S4, a reinforcement learning method is used to dynamically optimize channel allocation. In specific operations, a Q-Learning or deep reinforcement learning method can be used to dynamically optimize channel allocation.

[0010] Preferably, the multiple access technology in S4 includes: Orthogonal frequency division multiple access technology: divides spectrum resources into multiple orthogonal subcarriers. When different groups of drones select the same channel, they occupy subcarriers of different subchannels for communication, thus avoiding subchannel spectrum conflicts. Code Division Multiple Access technology: When different groups of drones select the same channel, a unique code sequence is assigned to each drone, and multiple drones can communicate simultaneously in the same frequency band through spread spectrum technology; Time Division Multiple Access Technology: Divide time into multiple time slots. Different groups of drones send data in different time slots when selecting the same channel to avoid signal interference.

[0011] Air interface multiple access technology: The air interface of the drone data transmitter is divided into multiple air interfaces or multiple antennas. When different groups of drones select the same channel, they send data on different air interfaces or antennas to avoid signal interference.

[0012] In addition, multi-agent reinforcement learning is used to optimize path planning and resource allocation through collaborative learning between drones. Each drone is treated as an independent agent, and agents share information, namely location, mission status, and channel occupancy, to maximize coverage area and minimize path overlap and communication conflicts.

[0013] Preferably, the power control technology in S9 means that the drone can optimize the power allocation according to the distance and channel loss indicators of different drones or users in combination with a dynamic programming algorithm to reduce interference with other drones.

[0014] Preferably, in said S9, the signal is denoised at the ground control station, and digital signal processing algorithms and devices, such as wavelet transform denoising, adaptive filtering denoising and spectral subtraction denoising, can be used to remove noise from the signal.

[0015] Preferably, the S9 uses a deep learning model to predict future channel states, allocates resources in advance, and introduces a Kalman filter or a long short-term memory model to model and compensate for channel aging in a high-speed mobile environment of the UAV, introduces enhanced mobile broadband or ultra-high reliability and low-latency communication technology into the data transmission protocol, adopts a low-latency communication protocol, and introduces enhanced mobile broadband or ultra-high reliability and low-latency communication technology into the data transmission protocol to reduce data transmission delay and improve reliability.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The multi-address data access method for farm monitoring and operation drones divides drones into groups through a dynamic channel allocation mechanism. Drones in the same group randomly select any channel. Drones in different groups transmit data at different times, spaces or frequencies through multiple access technology. An independent communication channel is allocated to each drone to avoid signal interference, ensure that drones transmit data in a timely manner, reduce communication delays, enhance network reliability and stability, and meet the high requirements of smart agriculture for data communication.

[0017] 2. The multi-access data access method for farm monitoring and operation drones can effectively utilize channel resources and reduce data conflicts by using multiple access technology, thus realizing efficient data transmission of drones in agricultural production. It can realize simultaneous and efficient access of multiple drones to the wireless network, enhance system flexibility, support multiple drones to work simultaneously, and improve operation efficiency.

[0018] 3. The multi-address data access method for farm monitoring and operation drones improves the efficiency and reliability of data transmission, reduces communication delays, and enhances the ability of drone groups to work collaboratively by optimizing network architecture and communication protocols. It is of great significance to the intelligent development of modern agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0022] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] like Figure 1 As shown, Example 1: The multi-address data access method for farm monitoring and operation drones in this embodiment includes the following steps: S1: Network deployment: Install wireless access points that support multiple access points in key areas of the farm. These access points can support multiple communication technologies, such as Wi-Fi6, Wi-Fi7, 5GNR and LoRaWAN; S2: UAV equipment modification: Hardware support is to equip the UAV with a communication module that supports multiple accesses and is compatible with a variety of access technologies and frequency bands; software protocol is to develop or adopt a communication protocol stack that supports multiple accesses to ensure that the UAV can correctly process the signals and data of multiple accesses; S3: Access process: Initialization: After the drone is started, it scans the surrounding wireless access points to obtain channel and load information; Selection strategy: According to the preset dynamic channel allocation algorithm, multiple drones are grouped and the access points and communication channels with the best signal strength and the least load are selected; Authentication access: The drone performs identity authentication and key exchange with the selected access point to establish a secure communication connection. Blockchain technology is used to perform distributed verification of the drone identity to prevent forgery. Channel usage history is recorded in channel allocation to prevent malicious occupation. Quantum key distribution technology is used in high-security scenarios, such as military and agricultural applications, to ensure the unconditional security of data communication. Data transmission: Data is exchanged with access points according to the multiple access protocol, including task instructions and status feedback; Record channel usage history during channel allocation to prevent malicious occupation; Data compression technology: Efficiently compress monitoring data to reduce the amount of transmitted data and increase transmission speed; Transport protocol optimization: Use lightweight and efficient transport protocols (UDP, dedicated protocols) to reduce transmission overhead and latency; Cache mechanism: In case of communication interruption or weak signal, data is temporarily cached and transmitted immediately after communication is restored to ensure that data is not lost; S4: Dynamic Adjustment: Roaming switching: When the drone moves to a new area, it automatically switches to a new access point to maintain the continuity of the wireless link connection; Channel optimization: Real-time monitoring of channel quality, and switching channels or frequency adjustments when necessary to avoid interference. Reinforcement learning is used to optimize channel allocation. The execution steps can be: (1) Setting status: indicating channel quality, task priority, drone location, and data volume; (2) Control action: allocate channels or time slots; (3) Setting rewards: maximizing network throughput and minimizing latency; The method obtains the optimal channel allocation strategy through training, adapts to dynamic task changes in complex environments, and reserves a dedicated resource pool for urgent tasks or high-priority data, and dynamically adjusts the channel resource allocation strategy according to the priority of the task; obtains the optimal channel allocation strategy through training, adapts to dynamic task changes in complex environments, and reserves a dedicated resource pool for urgent tasks or high-priority data, such as a fixed time slot or subcarrier, and dynamically adjusts the channel resource allocation strategy in combination with the priority of the task; S5: Network Management: Load balancing: The network system monitors the load of each access point and dynamically adjusts the access distribution of drones; Fault handling: When an access point fails or the signal is abnormal, quickly switch to the backup access point to ensure uninterrupted communication; Channel monitoring: UAVs and access points monitor channel usage and interference levels in real time; Dynamic allocation: Dynamically allocate communication channels or time slots according to real-time network conditions to optimize spectrum utilization; Channel switching: When the quality of the current channel decreases, it supports fast channel switching to maintain communication stability.

[0024] S6: Ground monitoring station design: High-performance receiving equipment: Configure high-performance receiving equipment that can receive data from multiple drones at the same time and support multiple access protocols; Data processing and display: Real-time processing and analysis of monitoring data sent back by drones, providing an intuitive visualization interface to support agricultural management decisions.

[0025] Embodiment 2: The multi-address data access method of the farm monitoring and operation drone of this embodiment includes the following steps: S1: Wireless network deployment: Base station setup: Install OFDMA-supported base stations at key locations within the farm to ensure wireless signal coverage of the entire operating area; Network topology design: The base station is connected to the ground monitoring station through a high-speed wired or wireless backhaul link to form a stable network topology and ensure high-speed data transmission; S2: UAV communication module design: Hardware support: Each drone is equipped with a wireless communication module that supports OFDMA, including a radio frequency front end and a baseband processing unit, which can modulate and demodulate OFDMA signals; Software protocol: Develop or adopt a communication protocol stack that supports OFDMA, including the physical layer and media access control layer, to ensure that the drone can correctly process OFDMA signals and data; S3: Dynamic subcarrier allocation: Subcarrier pool: Divide the available spectrum resources into multiple subcarriers to form a subcarrier pool; Resource allocation algorithm: The base station uses a resource allocation algorithm (proportional fairness algorithm) to dynamically allocate subcarriers based on the real-time network status and the needs of the drone; Feedback mechanism: The drone regularly feeds back channel status information to the base station to assist the base station in optimizing the allocation of subcarriers; S4: Synchronization and frame structure design: Downlink synchronization: The base station sends a downlink synchronization signal to the drone to ensure that the time and frequency of the drone are synchronized with the base station; Frame structure: Design a frame structure suitable for UAV communication, including the arrangement of pilot, control channel and data channel, to meet the requirements of high mobility and real-time performance; S5: Data transfer optimization: Data compression technology: Efficiently compress monitoring data (images and sensor data) to reduce data volume and improve transmission efficiency; Transport protocol optimization: Use lightweight and efficient transport protocols (UDP, dedicated real-time transport protocol) to reduce transmission overhead and latency.

[0026] Priority processing: Set different transmission priorities according to the importance of data to ensure real-time transmission of critical data; S6: Interference Management and Power Control: Adaptive power control: The drone dynamically adjusts the transmission power according to the distance from the base station and the channel quality, reducing interference to other drones while saving energy; Interference suppression technology: Advanced signal processing algorithms such as interference cancellation and beamforming are used at the receiving end to improve the signal-to-noise ratio; Spectrum sensing: UAVs have spectrum sensing capabilities, which can detect surrounding interference and assist base stations in resource allocation; S7: Ground monitoring station design: High-performance receiving equipment: The ground monitoring station is equipped with high-performance OFDMA receiving equipment, which supports simultaneous reception and processing of data from multiple drones; Data processing and display: Real-time processing of monitoring data sent back by drones, providing intuitive visualization interfaces and data analysis tools to support agricultural management decisions; S8: Security and Encryption: Identity authentication: Establish a two-way identity authentication mechanism between the drone and the base station to prevent illegal devices from accessing the network; Data encryption: Encrypt the transmitted data to protect the security of sensitive information and prevent data from being stolen or tampered with.

[0027] To solve advanced threats to communication security, such as man-in-the-middle attacks and signal forgery, you can use the following methods to upgrade security: (1) Blockchain technology combined with distributed identity authentication: Use blockchain technology to perform distributed verification of drone identities to prevent forgery; record channel usage history during channel allocation to prevent malicious occupation.

[0028] (2) Quantum Key Distribution (QKD): In high-security scenarios (such as military and agricultural applications), quantum key distribution technology is used to ensure unconditional security of data communications.

[0029] The specific steps are as follows: S1: Mission initialization: The farm manager sets the flight mission and monitoring parameters of the drone at the ground monitoring station; S2: Drone takeoff: Multiple drones take off according to the set route and start data collection; S3: Dynamic resource allocation: The base station dynamically allocates OFDMA subcarriers based on the number and location of drones to ensure that each drone has sufficient communication resources; S4: Real-time data transmission: The UAV transmits the collected monitoring data to the ground monitoring station in real time through the OFDMA wireless network; S5: Data processing and decision-making: The ground monitoring station analyzes the received data and generates crop growth status reports to assist managers in making agricultural decisions; S6: Drone returns: After the mission is completed, the drone returns to the starting point according to instructions to complete charging and maintenance.

[0030] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-address data access method for farm monitoring and operation drones, characterized in that: The following steps are involved: S1: Network architecture design: Deploy multiple wireless access points in the farm area to form a wireless communication network covering the entire farm. Deploy servers to provide network services. Multiple access points and access points to servers are connected through high-speed wired or wireless links to build a stable network topology to ensure high-speed data transmission. S2: Data collection: Each drone is equipped with an environmental collection unit, which collects data in a designated area, including soil, environmental parameters and crop growth status information; S3: Data grouping: The computing device or server carried by the drone groups and packages the collected data according to preset rules to form data packets, and compresses and encodes the data packets according to the communication environment and transmission requirements to reduce the amount of data transmitted; S4: Channel allocation: According to the number of drones and data traffic, channels are dynamically allocated when using wireless communication networks to transmit data. The number of drones is grouped according to the total number of channels, and drones in the same group randomly select any channel to reduce the complexity of channel selection. The channels are selected and drones in different groups use multiple access technology to ensure that each drone transmits data at different time, space or frequency to avoid signal interference; S5: Data transmission: The UAV transmits data packets to the ground control station through the channel allocated by the wireless communication network. The control station receives the data packets and decodes, stores, processes and analyzes the data; S6: Establish coordinates: The drone is equipped with a GPS positioning module to obtain the initial global coordinates of the drone when it is in the ground control station and the three-dimensional global coordinates of the drone at different times, and use the partitioning method to give a number of points on the drone's operating plane. The given points are called generation points or seed points. The plane is divided into several areas, so that all points in each area are closer to their corresponding generation points than to other generation points. The coverage area is divided according to the current position and communication range of the drone, and the drone's mission range is adjusted in real time. The drone dynamically adjusts its position within the partition according to the communication load and mission completion; S7: Data reception: When the UAV transmits data, the transceiver link distance from the UAV to the ground control station is calculated according to the global coordinates of the UAV, and the data is received in order from the smallest to the largest transceiver link distance; S8: Feedback mechanism: The ground control station feeds back the data reception status and channel usage to the UAV via the wireless communication network, so that the UAV can select the appropriate communication channel for data transmission according to the channel occupancy, adjust the data transmission strategy in real time, and improve the transmission efficiency; S9: Interference management: The drone adjusts its transmission power through power control technology to make the signal-to-noise ratio of different receivers consistent during the communication process, so that the communication between different drones or users does not interfere with each other; or the drone quickly switches between multiple frequencies through channel hopping technology, making it difficult for interference to be sustained and effective, reducing communication interference and improving the overall performance of the network; in addition, multiple antennas are configured in the network access point equipment and drone communication modules, and multi-antenna technology and beamforming technology are used to improve signal directivity and interference suppression capabilities; S10: Multi-UAV Collaboration and Operation Optimization: Use multi-agent reinforcement learning to optimize path planning and resource allocation through collaborative learning between UAVs. Each UAV acts as an independent agent, and the agents share location, mission status, and channel occupancy information to maximize coverage and minimize path overlap and communication conflicts. S11: Coverage area optimization: The coverage area is divided according to the current position and communication range of the drone, and the drone mission range is adjusted in real time. The drone dynamically adjusts its position within the partition based on the communication load and mission completion.

2. The multiple access data access method for farm monitoring and operation drones according to claim 1, characterized in that: The acquisition unit in S2 includes a temperature sensor, a humidity sensor, a light sensor, a digital camera, a hyperspectral camera and a multispectral camera. The environmental parameters collected by each drone in a designated area include temperature, humidity and light, and the collected crop growth status information includes visible light images, videos, hyperspectral images and multispectral images.

3. The multi-address data access method for farm monitoring and operation drones according to claim 1, characterized in that: The acquisition unit in S2 adds an edge computing unit to the drone communication module to perform preliminary data processing and feature extraction locally to reduce the amount of original data transmission. The data transmission priority is set based on the importance of the task, high-priority data is transmitted in real time, and low-priority data is transmitted in buffer.

4. The multi-address data access method for farm monitoring and operation drones according to claim 1, characterized in that: In the S3, the hyperspectral, multispectral images and sensor data are compressed in layers, and key features are transmitted first. The length of the data packet in S3 is 1-1000000 baud Byte, and the data packet contains the drone ID, timestamp, data type, data length and data content.

5. The multi-address data access method for farm monitoring and operation drones according to claim 1, characterized in that: In S4, a reinforcement learning method is used to dynamically optimize channel allocation.

6. The multiple access data access method for farm monitoring and operation drones according to claim 1, characterized in that: The multiple access technology in S4 includes: Orthogonal frequency division multiple access technology: divides spectrum resources into multiple orthogonal subcarriers. When different groups of drones select the same channel, they occupy subcarriers of different subchannels for communication, thus avoiding subchannel spectrum conflicts. Code Division Multiple Access technology: When different groups of drones select the same channel, a unique code sequence is assigned to each drone, and multiple drones can communicate simultaneously in the same frequency band through spread spectrum technology; Time Division Multiple Access technology: divides time into multiple time slots, and different groups of drones send data in different time slots when selecting the same channel to avoid signal interference; Air interface multiple access technology: The air interface of the drone data transmitter is divided into multiple air interfaces or multiple antennas. When different groups of drones select the same channel, they send data on different air interfaces or antennas to avoid signal interference.

7. The multi-address data access method for farm monitoring and operation drones according to claim 1, characterized in that: The power control technology in S9 refers to the UAV optimizing the power allocation according to the distance and channel loss indicators of different UAVs or users, combined with the dynamic programming algorithm, to reduce interference with other UAVs.

8. The multi-address data access method for farm monitoring and operation drones according to claim 1, characterized in that: In the S9, the signal is denoised at the ground control station; the denoising method is wavelet transform denoising, adaptive filtering denoising or spectral subtraction denoising.

9. The method for multiple access data of a farm monitoring and operation drone according to claim 1, characterized in that: The S9 uses a deep learning model to predict future channel states, allocate resources in advance, and uses a Kalman filter or a long short-term memory model to model and compensate for channel aging in a high-speed mobile environment of the UAV. It introduces enhanced mobile broadband or ultra-high reliability and low-latency communication technology into the data transmission protocol to reduce data transmission delay and improve reliability.

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