5G unmanned aerial vehicle beyond visual range video transmission system and method of adaptive network

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and provides a 5G unmanned aerial vehicle beyond visual range video transmission method of an adaptive network, comprising the following steps: capturing and processing a high-definition video; dynamically adjusting the video file in real time according to the current network parameters; packaging the adjusted video file into stream data and pushing the stream data to a network end; an adaptive forward error correction mechanism is introduced, and the proportion of redundant coding is dynamically adjusted according to the current network packet loss rate so as to cope with different network conditions; transmitting the packaged and corrected video stream data to a cloud server through a 5G public network; and the cloud server receives the video stream data, processes the video stream data and generates a unique pull stream URL, so that the video content can be quickly accessed on different terminal devices. According to the invention, the 5G public network is used for transmitting the video data captured by the unmanned aerial vehicle, the beyond-visual-range, wide-coverage and ultra-high-rate transmission capability is realized, the limitation of traditional communication on the video transmission distance is broken through, and meanwhile, the transmission cost is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a 5G unmanned aerial vehicle beyond-visual-range video transmission system and method based on an adaptive network. Background Art

[0002] With the gradual popularization of unmanned aerial vehicles (hereinafter referred to as drones) in my country, aviation safety and public safety are also facing new challenges. At present, drones have been widely used in aerial photography, inspection, reconnaissance, public security, firefighting, rescue, border defense, monitoring, real-time broadcasting and other aspects. There are many ways to transmit camera video / image data collected by drones on the market, but drones have high requirements for the distance to the ground station that receives video information. At the same time, the receiving end is usually a drone ground station or a drone controller, making it difficult to achieve multi-end receiving control.

[0003] Nowadays, when drones cruise over a larger area, the following data communication technologies are commonly used.

[0004] The first is direct link communication. The implementation of direct link is relatively simple. The drone and the ground station conduct direct link communication through a specific frequency band. This communication method is usually limited to communication within the line of sight. At the same time, direct link communication will be blocked by obstacles, resulting in reduced communication reliability. The ground station needs to connect to the gateway to enable the drone to access the public network, which further increases the delay.

[0005] The second is satellite communication, which is the data exchange between the drone and the ground station through satellite relay. This communication method has a large coverage area and can also use satellite signals for navigation and positioning of the drone. However, satellite communication also has some disadvantages. First, the distance between the satellite and the drone and the ground station is too far, which will cause serious transmission loss and delay. For services that are sensitive to delay requirements, this communication method cannot be used.

[0006] The third is Wi-Fi communication, which is the connection between the drone and the ground station through a local area network. This communication method is low-cost and has relatively low latency. However, the coverage range supported by Wi-Fi communication is usually limited to about 2km, which does not meet the requirements of large-scale beyond-line-of-sight communication.

[0007] The fourth type is public network communication, which is to communicate using cellular networks that cover most parts of the world. UAVs and ground stations can communicate in this way to meet the requirements of beyond-line-of-sight. At the same time, cellular networks can perform well in large data transmission services such as video image transmission, and have many advantages such as large bandwidth, low latency, and multiple connections.

[0008] Therefore, how to provide a drone beyond-visual-range video image transmission system based on the 5G public network to realize drone inspection tasks in beyond-visual-range conditions is a technical problem that needs to be urgently solved by technical personnel in this field. Summary of the invention

[0009] In order to effectively solve the above problems, the present invention provides a 5G drone beyond-visual-range video transmission system and method with an adaptive network.

[0010] To achieve the above object, a technical solution of the present invention is:

[0011] A 5G UAV beyond-visual-range video transmission method for an adaptive network comprises the following steps:

[0012] S1, high-definition video capture and processing;

[0013] S2, dynamically adjust the video file in real time according to the current network parameters;

[0014] S3, encapsulating the adjusted video file into streaming data and pushing it to the network end;

[0015] S4, introduce an adaptive forward error correction mechanism to dynamically adjust the proportion of redundant coding according to the current network packet loss rate to cope with different network conditions;

[0016] S5, transmitting the packaged and error-corrected video stream data to the cloud server via the 5G public network;

[0017] S6. The cloud server receives the video stream data, processes it, and generates a unique streaming URL to ensure that the video content can be quickly accessed on different terminal devices.

[0018] In some preferred technical solutions, the step S1 specifically includes starting the high-definition camera device carried by the drone to capture clear video images, and transmitting the collected video files in real time to the drone's onboard computer module for processing.

[0019] In some preferred technical solutions, the step S2 is specifically to use an adaptive bit rate control mechanism and a network congestion perception mechanism according to current network parameters to adjust the bit rate and resolution of the video in real time.

[0020] In some preferred technical solutions, the S3 step is specifically that the video file that has undergone compression encoding and adaptive encoding is encapsulated into a streaming data format to ensure that the video streaming data is stably transmitted in the network and pushed to the cloud server.

[0021] In some better technical solutions, the S4 step is specifically that the streaming media protocol of the streaming end introduces adaptive forward error correction technology, dynamically adjusts the proportion of redundant coding according to the current network packet loss rate, and increases the error correction code ratio to improve the anti-packet loss capability when the network condition is poor, ensuring that even in the case of high packet loss rate, the client can still successfully receive the complete video stream data; when the network condition is good, reduce the redundant coding ratio.

[0022] In some preferred technical solutions, the S6 step is specifically that the cloud server performs decoding, denoising, enhancement, and compression processing on the received video stream data, and generates a unique streaming URL to ensure fast access to the video on different terminals.

[0023] In some preferred technical solutions, the step S6 further includes step S7, where the client device obtains real-time video stream data by pulling the stream URL.

[0024] Some preferred technical solutions also include:

[0025] S8, initializing flight control;

[0026] S9. Real-time data collection.

[0027] The present invention also includes another technical solution, a 5G drone beyond-visual-range video transmission system with an adaptive network, characterized in that the system includes:

[0028] A drone, the drone comprising a high-definition camera, a flight control module, a 5G communication module, an adaptive network encoder and an onboard computer module, the onboard computer module comprising a video processing module and a streaming module, the video processing module being used for video noise reduction, de-jittering and image enhancement, the adaptive network encoder integrating an adaptive bit rate control mechanism and a network congestion perception mechanism, adjusting the video encoding scheme in real time according to the detection result of the network status detection unit, the streaming module being used for pushing video streams to a cloud server, the onboard computer module controlling other modules for receiving video data from the high-definition camera, and transmitting the encoded real-time video stream data and the drone status information to the cloud server through the 5G communication module;

[0029] The cloud server is used to receive the video stream data, perform decoding, denoising, enhancement, and compression processing, and generate a stream pull URL that can be accessed by the client;

[0030] The client obtains video stream data from the cloud in real time through the streaming URL, and performs operations including video playback, playback, screenshots, and PTZ control.

[0031] In some preferred technical solutions, the drone also includes an embedded platform development board, a satellite navigation module, an inertial measurement module and a battery management module, the embedded development board integrates a 5G communication module, a millimeter wave radar module and an airborne computer module, and the client is equipped with a drone monitoring APP.

[0032] Beneficial effects of the present invention:

[0033] The present invention utilizes the 5G public network to transmit video data captured by drones, achieving beyond-line-of-sight, wide coverage, and ultra-high-speed transmission capabilities, breaking through the limitations of traditional direct links and Wi-Fi communications in video transmission distance, while significantly reducing transmission costs.

[0034] In terms of data processing, by real-time collection of current link information and combining the current efficient video encoding technologies H.264 and H.265, the video data collected by the drone is adaptively transmitted over the network bandwidth. It can adapt to video transmission under various network conditions and seamlessly connect to the cloud server through advanced Internet streaming protocols.

[0035] The cloud server is not only responsible for receiving and storing video data, but also has powerful image analysis, collection and processing capabilities. It can support simultaneous access of multiple clients and multiple threads, ensuring that users can monitor the video images and status information sent back by the drone in real time and synchronously, realizing real-time monitoring and efficient management.

[0036] The drone monitoring APP developed by the company can receive high-definition video images and real-time status information transmitted by drones in real time, greatly broadening the application scope of video images in various monitoring scenarios and enabling users to flexibly respond to various monitoring needs.

[0037] The system organically combines various modules to achieve real-time video adjustment, optimize video quality, improve user experience, and ensure the efficiency and reliability of drone line-of-sight video transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a workflow diagram of a 5G drone beyond-visual-range video transmission method of an adaptive network in one embodiment of the present invention;

[0039] Figure 2 It is a schematic block diagram of a method for transmitting beyond-visual-range video of a 5G drone using an adaptive network in an embodiment of the present invention;

[0040] Figure 3 It is a structural block diagram of a 5G drone beyond-visual-range video transmission system of an adaptive network in one embodiment of the present invention;

[0041] Figure 4It is a structural block diagram of a drone of a 5G drone beyond-visual-range video transmission system of an adaptive network in one embodiment of the present invention;

[0042] Figure 5 It is a schematic block diagram of a drone embedded development board in a 5G drone beyond-visual-range video transmission system of an adaptive network in one embodiment of the present invention;

[0043] Figure 6 It is one of the UAV monitoring APP page diagrams of the 5G UAV beyond-visual-range video transmission system of the adaptive network in one embodiment of the present invention;

[0044] Figure 7 This is a second diagram of a drone monitoring APP page of a 5G drone beyond-visual-range video transmission system with an adaptive network in an embodiment of the present invention.

[0045] In the figure, 1. UAV; 2. Client; 3. Cloud server; 4. HD camera equipment; 5. Flight control module; 6. Embedded development board; 7. 5G communication module; 8. Adaptive network encoder; 9. Airborne computer module; 10. Video processing module; 11. Streaming module; 12. Network status detection unit; 13. Millimeter wave radar module. DETAILED DESCRIPTION

[0046] 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 the embodiments. 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.

[0047] See also Figure 1 and Figure 2 , a 5G drone beyond-visual-range video transmission method based on an adaptive network, comprising the following steps:

[0048] S1, high-definition video capture and processing;

[0049] S2, dynamically adjust the video file in real time according to the current network parameters;

[0050] S3, encapsulating the adjusted video file into streaming data and pushing it to the network end;

[0051] S4, introduce an adaptive forward error correction mechanism to dynamically adjust the proportion of redundant coding according to the current network packet loss rate to cope with different network conditions;

[0052] S5, transmitting the packaged and error-corrected video stream data to the cloud server;

[0053] S6. The cloud server receives the video stream data, processes it, and generates a unique streaming URL to ensure that the video content can be quickly accessed on different terminal devices. As the data processing center, the cloud server is responsible for receiving the video stream data from the drone. In addition, the cloud server can decode, analyze, and process the received video files (including but not limited to denoising, enhancement, and compression), and generate a streaming URL that can be accessed by the client, further improving the user experience.

[0054] The cloud server is not only responsible for receiving and storing video data, but also has powerful image analysis, collection and processing capabilities. It can support simultaneous access of multiple clients and multiple threads, ensuring that users can monitor the video images and status information sent back by the drone in real time and synchronously, realizing real-time monitoring and efficient management.

[0055] Preferably, the step S1 specifically includes starting a high-definition camera device carried by the drone to capture clear video images, and transmitting the collected video files to the drone onboard computer module in real time for processing.

[0056] Specifically, the step S2 is to use an adaptive bit rate control mechanism and a network congestion awareness mechanism to adjust the bit rate and resolution of the video in real time and optimize the video quality according to current network parameters. The network parameters specifically include but are not limited to network bandwidth, delay, and packet loss rate.

[0057] The adaptive bitrate control mechanism is implemented by an adaptive network encoder. The adaptive network encoder at the streaming end adopts the H.264 and H.265 high-efficiency video compression coding standards and integrates an adaptive bitrate control mechanism and a network congestion perception mechanism. The encoder can dynamically adjust the bitrate and resolution of the video in real time according to the current network status such as network bandwidth, latency, and packet loss rate. When the network bandwidth is insufficient, the adaptive network encoder will automatically reduce the bitrate and resolution of the video to avoid video freezes or frame drops; when the network conditions are good, the adaptive network encoder can increase the bitrate to ensure video quality.

[0058] The network congestion awareness mechanism detects network feedback information and promptly detects changes in network conditions. In particular, when the network is congested, the adaptive network encoder can automatically adjust the video frame rate and bit rate to reduce packet loss and delay. For example, the system will reduce the video frame rate and bit rate to avoid excessive network load, thereby ensuring smooth video transmission.

[0059] In addition, the onboard computer module has a built-in video processing program that has network dynamic adaptation and congestion perception functions. The video processing program processes the video data collected by the drone's high-definition camera in real time and dynamically optimizes the encoding scheme according to the network conditions. First, the system uses network adaptive encoding technology to adjust the video encoding method in real time according to the current network bandwidth and delay parameters to ensure the quality and efficiency of video transmission. When network congestion is detected, the system will automatically reduce the bit rate or adjust the transmission strategy to reduce packet loss and delay.

[0060] Specifically, the S3 step is to encapsulate the compressed and adaptively encoded video files into a streaming data format to ensure that the video streaming data is stably transmitted in the network and pushed to the cloud server;

[0061] Specifically, step S4 introduces adaptive forward error correction technology into the streaming media protocol of the streaming end, dynamically adjusts the proportion of redundant coding according to the current network packet loss rate, and increases the proportion of error correction code to improve the anti-packet loss capability when the network condition is poor, ensuring that the client can still smoothly receive the complete video stream data even in the case of high packet loss rate; when the network condition is good, reduces the proportion of redundant coding to improve transmission efficiency.

[0062] Steps S2-S4 are implemented by self-compiling FFMpeg and optimizing the SRT video streaming protocol. Adaptive network control is implemented based on the BBR congestion control algorithm to improve the defect of the SRT protocol that lacks dynamic bit rate control.

[0063] Specifically, step S5 transmits the encapsulated video stream data to the cloud server via a high-speed and encrypted 5G public network. The high-speed and low-latency characteristics of the 5G public network enable fast and secure transmission of long-distance real-time video, ensuring the efficiency and security of encoded video data transmission. The use of the 5G public network to transmit video data captured by drones achieves beyond-line-of-sight, wide coverage, and ultra-high-speed transmission capabilities, breaking through the limitations of traditional direct links and Wi-Fi communications in video transmission distance, while significantly reducing transmission costs.

[0064] Specifically, step S6 is that the cloud server performs decoding, denoising, enhancement, and compression on the received video stream data, and generates a unique stream pull URL to ensure fast access to the video on different terminals.

[0065] The step S6 also includes S7, where the client device obtains real-time video stream data by pulling the stream URL. After receiving the video stream, the client performs decapsulation and decoding operations to realize remote monitoring and real-time viewing functions. Users can seamlessly play high-definition real-time video images transmitted by drones, providing a smooth multi-platform interactive experience. Furthermore, the client uses an APP designed specifically for drone monitoring, which supports real-time acquisition of video stream data collected by drones from cloud servers through pulling the stream URL, realizes remote monitoring of drone images, and provides functions such as video playback, screenshots, and pan / tilt control. The drone monitoring APP supports multi-platform playback, such as mobile device clients and PC clients. The drone monitoring APP application can receive high-definition video images and real-time status information transmitted by drones in real time, greatly broadening the application scope of video images in various monitoring scenarios, allowing users to flexibly respond to various monitoring needs.

[0066] The transmission method further includes:

[0067] S8, initializing flight control;

[0068] S9. Real-time data collection.

[0069] In one implementation scenario, S8, initializing flight control: starting the UAV flight control system and establishing a stable connection between the flight control system and the onboard computer module through the serial port connector;

[0070] S9. Real-time data acquisition: The flight control detector accurately captures the UAV operation data and transmits this information to the UAV onboard computer module in real time through the serial port.

[0071] By integrating flight control and data acquisition, not only the flight stability and path planning of the drone are optimized, but also efficient video acquisition and transmission are ensured. By controlling flight and acquiring real-time data, key support is provided for the video transmission system, while the adaptive bit rate control mechanism and network congestion perception mechanism further improve the transmission quality under different network conditions. The organic combination of these functions enables drones to perform beyond-line-of-sight video transmission tasks efficiently and stably in various complex environments.

[0072] See also Figure 3 and Figure 4 , a 5G drone beyond-line-of-sight video transmission system with an adaptive network, the system comprising:

[0073] A drone, the drone comprising a high-definition camera, a flight control module, a 5G communication module, an adaptive network encoder, and an onboard computer module;

[0074] The drone's high-definition motion camera equipment is designed with high resolution and high frame rate, which can capture clear and smooth video images;

[0075] The flight control module is the core control system of the drone, responsible for stable control of flight attitude and path planning. When initializing flight control, start the drone flight control module and establish a stable connection between the flight control system and the onboard computer module through the serial port connector; Real-time data acquisition: The detector of the flight control module accurately captures the drone operation data and transmits the information to the drone onboard computer module in real time through the serial port to ensure the timeliness and accuracy of real-time data acquisition.

[0076] The 5G communication module provides ultra-high-speed, low-latency data transmission capabilities, enabling high-definition video streams to be transmitted to the cloud server in real time and stably.

[0077] The onboard computer module includes a video processing module and a streaming module. The video processing module is used for video noise reduction, de-jittering and image enhancement. The adaptive network encoder integrates an adaptive bit rate control mechanism and a network congestion perception mechanism. The network status detection unit detects the result and adjusts the video encoding scheme in real time. The network status monitoring unit specifically includes a network bandwidth detection module, a network congestion detection module and a network delay detection module. The streaming module is used to push the video stream to the cloud server. The onboard computer module controls other modules to receive video data from the high-definition camera device and transmits the encoded real-time video stream data and the drone status information to the cloud server through the 5G communication module. The onboard computer module serves as a control center and is responsible for complex data processing tasks, including video encoding, image recognition and path planning.

[0078] The cloud server is used to receive the video stream data, perform decoding, denoising, enhancement, and compression processing, and generate a stream pull URL that can be accessed by the client;

[0079] The client obtains video stream data from the cloud in real time through the streaming URL, and performs operations including video playback, playback, screenshots, and PTZ control.

[0080] The adaptive bitrate control mechanism is implemented by an adaptive network encoder. The adaptive network encoder at the streaming end adopts the H.264 and H.265 high-efficiency video compression coding standards and integrates an adaptive bitrate control mechanism and a network congestion perception mechanism. The encoder can dynamically adjust the bitrate and resolution of the video in real time according to the current network status such as network bandwidth, latency, and packet loss rate. When the network bandwidth is insufficient, the adaptive network encoder will automatically reduce the bitrate and resolution of the video to avoid video freezes or frame drops; when the network conditions are good, the adaptive network encoder can increase the bitrate to ensure video quality.

[0081] The network congestion awareness mechanism detects network feedback information and promptly detects changes in network conditions. In particular, when the network is congested, the adaptive network encoder can automatically adjust the video frame rate and bit rate to reduce packet loss and delay. For example, the system will reduce the video frame rate and bit rate to avoid excessive network load, thereby ensuring smooth video transmission.

[0082] The onboard computer module has a built-in video processing program, which has the functions of network dynamic adaptation and congestion perception, and is used to efficiently process the video data collected by the drone's high-definition motion camera. First, the system uses network adaptive coding technology to adjust the video coding scheme in real time according to the current network bandwidth and delay parameters to ensure the quality and efficiency of video transmission. At the same time, the program has the function of network congestion perception, which can monitor the network status in time. When network congestion is detected, the system will automatically reduce the bit rate or optimize the transmission strategy to reduce packet loss and delay. Subsequently, the encoded video data is encapsulated into a streaming media format suitable for network transmission. The streaming media protocol contains an adaptive forward error correction mechanism, which can dynamically adjust the proportion of redundant coding according to the current network packet loss rate to ensure that the client can smoothly receive the complete video stream data. Finally, the onboard computer pushes the encapsulated and processed video data to the cloud server through the public network for subsequent processing and distribution.

[0083] The invention provides a new type of streaming end of a drone video image transmission system for 5G public network transmission. Different clients actively pull video stream data from the server by accessing the streaming URL generated by the cloud server, and decapsulate and decode it, and finally present the video content to the user.

[0084] The drone also includes an embedded platform development board, a satellite navigation module, an inertial measurement module and a battery management module. Figure 5 The embedded development board integrates 5G communication module, millimeter wave radar module and airborne computer module.

[0085] See also Figure 6 and Figure 7 The client is equipped with a drone monitoring APP. The application can receive high-definition video images and real-time status information transmitted by drones in real time, greatly broadening the application scope of video images in various monitoring scenarios, allowing users to flexibly respond to various monitoring needs.

[0086] The millimeter-wave radar module enhances the drone's environmental perception capability, can monitor surrounding obstacles in real time, improve obstacle avoidance accuracy and flight safety, and effectively prevent drone flight failures from causing video transmission interruptions.

[0087] The satellite navigation module and inertial measurement module together constitute the UAV's precise positioning system, ensuring that the UAV can fly accurately according to the predetermined trajectory.

[0088] In the design of the UAV streaming module of the beyond-visual-range video transmission system, the UAV onboard computer uses FFmpeg as the video development program. In order to further improve the encoding efficiency, the development board has a hard-coded module, which can effectively improve the encoding speed by calling the hardware acceleration function. The beyond-visual-range video transmission system is implemented by self-compiling FFMpeg, and the SRT video streaming protocol is optimized. Specifically, adaptive network control is realized based on the idea of ​​BBR congestion control algorithm, which improves the defect of the SRT protocol lacking dynamic bit rate control.

[0089] In the beyond-line-of-sight video transmission system, the cloud server is responsible for receiving the video stream from the drone's streaming module, analyzing and processing these video files, and generating a streaming URL so that the client can access and play the video content in real time.

[0090] For the above embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the order of the actions described, because according to the invention, some steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0091] It should be noted that the same or similar parts between the various embodiments can be referred to each other. For the device-type and method-type embodiments, since they are highly similar in implementation principles, functional structures, etc., they can be referred to each other during description. Specifically, similar functional modules, steps and their implementation methods in the device-type embodiments and method-type embodiments can be supplemented by referring to each other.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A 5G UAV beyond-line-of-sight video transmission method based on an adaptive network, characterized in that : Includes the following steps: S1, high-definition video capture and processing; S2, dynamically adjust the video file in real time according to the current network parameters; S3, encapsulating the adjusted video file into streaming data and pushing it to the network end; S4, introduce an adaptive forward error correction mechanism to dynamically adjust the proportion of redundant coding according to the current network packet loss rate to cope with different network conditions; S5, transmitting the packaged and error-corrected video stream data to the cloud server via the 5G public network; S6. The cloud server receives the video stream data, processes it, and generates a unique streaming URL to ensure that the video content can be quickly accessed on different terminal devices.

2. According to the adaptive network 5G drone beyond-visual-range video transmission method of claim 1, it is characterized in that: The step S1 specifically includes starting the high-definition camera device carried by the drone to capture clear video images, and transmitting the collected video files to the drone onboard computer module in real time for processing.

3. According to the adaptive network 5G drone beyond-visual-range video transmission method of claim 1, it is characterized in that: The step S2 specifically includes, according to the current network parameters, using an adaptive bit rate control mechanism and a network congestion awareness mechanism to adjust the bit rate and resolution of the video in real time.

4. The method for transmitting beyond-visual-range video of a 5G drone using an adaptive network according to claim 1, characterized in that: Specifically, the S3 step is to encapsulate the video file after compression encoding and adaptive encoding into a stream data format to ensure that the video stream data is stably transmitted in the network and pushed to the cloud server.

5. The adaptive network 5G drone beyond-visual-range video transmission method according to claim 1 is characterized in that: Specifically, the S4 step is to introduce adaptive forward error correction technology into the streaming media protocol of the streaming end, dynamically adjust the proportion of redundant coding according to the current network packet loss rate, and increase the error correction code proportion to improve the anti-packet loss capability when the network condition is poor, to ensure that the client can still smoothly receive the complete video stream data even in the case of high packet loss rate; when the network condition is good, reduce the redundant coding proportion.

6. The adaptive network 5G drone beyond-visual-range video transmission method according to claim 1 is characterized in that: Specifically, the cloud server performs decoding, denoising, enhancement, and compression on the received video stream data, and generates a unique stream pull URL to ensure fast access to the video on different terminals.

7. The adaptive network 5G drone beyond-visual-range video transmission method according to claim 1 is characterized in that: The step S6 also includes S7, where the client device obtains real-time video stream data by pulling the stream URL.

8. The adaptive network 5G drone beyond-visual-range video transmission method according to claim 1 is characterized in that: Also includes, S8, initializing flight control; S9. Real-time data collection.

9. A 5G drone beyond-visual-range video transmission system with an adaptive network, characterized in that: The system comprises: A drone, comprising a high-definition camera, a flight control module, a 5G communication module, an adaptive network encoder and an onboard computer module, wherein the onboard computer module comprises a video processing module and a streaming module, wherein the video processing module is used for video noise reduction, de-jittering and image enhancement, wherein the adaptive network encoder integrates an adaptive bit rate control mechanism and a network congestion perception mechanism, and adjusts the video encoding scheme in real time according to the detection result of the network status detection unit, wherein the streaming module is used for pushing video streams to a cloud server, wherein the onboard computer module controls other modules for receiving video data from the high-definition camera, and transmitting the encoded real-time video stream data and the drone status information to the cloud server through the 5G communication module; The cloud server is used to receive the video stream data, perform decoding, denoising, enhancement, and compression processing, and generate a stream pull URL that can be accessed by the client; The client obtains video stream data from the cloud in real time through the streaming URL, and performs operations including video playback, playback, screenshots, and PTZ control.

10. A 5G drone beyond-line-of-sight video transmission system with an adaptive network, characterized in that: The drone also includes an embedded platform development board, a satellite navigation module, an inertial measurement module and a battery management module. The embedded development board integrates a 5G communication module, a millimeter wave radar module and an airborne computer module. The client is equipped with a drone monitoring APP.

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