A communication connection method between unmanned vehicle and unmanned aerial vehicle based on intelligent link self-healing

Abstract

The present invention focuses on the field of communication technology, and precisely locates the communication connection problems in the scenario of collaborative operation of unmanned vehicles and drones. In practical applications, the communication connection when the two work together is plagued by complex environments, the signal is easily subject to strong interference, the transmission distance is obviously limited, and in terms of data transmission characteristics, it is difficult to take into account the dual requirements of real-time performance and accuracy. The present invention integrates ZigBee, 5G, and satellite communication modules, optimizes the adaptation protocol, and selects the switching channel through real-time channel perception with an intelligent algorithm. It uses directional antennas and beamforming to enhance the signal, and adaptive filtering and error correction coding to resist interference. It uses link monitoring and self-healing mechanisms to ensure stability. This method can ensure the reliable interaction of data between unmanned vehicles and drones in complex environments, and can ensure the real-time and accurate transmission of monitoring data even in the event of strong electromagnetic interference or bad weather.

Description

Technical Field

[0001] The present invention relates to the field of intelligent communication technology, and in particular to an unmanned vehicle-unmanned aerial vehicle communication connection method based on intelligent link self-healing. Background Art

[0002] The statements in this section merely provide background technology related to the present invention and do not necessarily constitute prior art.

[0003] In many areas today, such as grassland monitoring, logistics distribution, and emergency rescue, the collaborative operation of unmanned vehicles and drones is becoming an increasingly important work model. However, the efficient implementation of this collaborative operation is highly dependent on a stable, reliable, and efficient communication connection between the two.

[0004] Currently, existing communication technologies for unmanned vehicles and drones have numerous shortcomings. In complex environments, such as rugged grasslands and mountainous areas, or densely populated urban areas, signal interference is extremely severe, significantly degrading communication quality and making it difficult to ensure the accuracy and integrity of data transmission. Furthermore, limited transmission distance is a common issue. When the distance between the unmanned vehicle and drone exceeds a certain range, signal strength rapidly decays, causing communication to become unstable or even interrupted.

[0005] Furthermore, existing technologies struggle to balance the real-time and accuracy requirements of data transmission. In some applications where real-time performance is crucial, such as emergency rescue missions, data delays can lead to misguided decisions and missed opportunities for rescue. In precision agriculture monitoring, where data accuracy is crucial, inaccurate data can lead to erroneous farming decisions, impacting crop yield and quality.

[0006] Furthermore, existing communication connection technologies lack sufficient adaptability and intelligence, and are unable to automatically optimize communication parameters and switch communication modes based on different operating environments and task requirements. This makes it difficult for the communication system to respond effectively to sudden inclement weather or temporary signal obstruction, thus hindering the smooth progress of the entire collaborative operation. Existing communication connection technologies for collaborative operations between unmanned vehicles and drones are no longer able to meet the growing demands of complex applications. An innovative communication connection method is urgently needed to address these issues and improve the overall effectiveness of collaborative operations. Summary of the Invention

[0007] In order to overcome the many shortcomings of the current communication connection technology between unmanned vehicles and drones in harsh environments, the present invention focuses on the field of communication technology and accurately locates the communication connection problems in the scenario of collaborative operation of unmanned vehicles and drones. Specifically, it is an unmanned vehicle-drone communication connection method based on intelligent link self-healing, integrating ZigBee, 5G, satellite communication module multi-mode communication modules, optimizing adaptation protocols, sensing channels in real time, selecting switching channels with intelligent algorithms, using directional antennas and beamforming to enhance signals, adaptive filtering and error correction coding to resist interference, and ensuring stability with the help of link monitoring and self-healing mechanisms. This method can ensure reliable interaction of data between unmanned vehicles and drones in complex environments, and can ensure real-time and accurate transmission of monitoring data even in the event of strong electromagnetic interference or bad weather.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] The first aspect of the present invention provides a method for connecting an unmanned vehicle to an unmanned aerial vehicle (UAV) based on intelligent link self-healing:

[0010] Unmanned vehicles and drones are equipped with ZigBee, LoRa, and 5G communication modules, as well as satellite-based BeiDou short message communication modules. The TI CC1352P chip is used to build a multi-channel low-power radio frequency integrated circuit, achieving a fusion design of ZigBee and LoRa communication modules and dynamic switching between dual communication modes. The X555G modem and the HX-DU2017D BeiDou RDSS chip developed by China Electronics Technology Group Corporation are used to integrate 5G and BeiDou signals. A field-programmable gate array (FPGA) is used as the management center for the four communication modules, enabling dynamic resource scheduling and protocol adaptation among multiple modules.

[0011] During operation, the FPGA's RSSI (Received Signal Strength Indicator) sensor monitors the strength of the drone's data transmission information received by each module in real time. The Welch power spectrum estimation and analysis algorithm assesses the channel's interference noise intensity and spectrum utilization. The Shannon-Hartley theorem is used to calculate channel capacity. Based on capacity indicators and real-time bandwidth requirements, the availability of the current data transmission channel is assessed, and dynamic scheduling and switching of multiple channels are performed.

[0012] The unmanned vehicle and drone are equipped with electronically controlled adjustable antennas based on MEMS (micro-electromechanical systems) technology. The relative position and direction of the two are calculated in real time using the inertial navigation system (INS) and GPS data. Dynamic beam steering technology based on phased array technology is used to focus the communication signal on the transmission direction of the two. At the same time, the LMS (minimum mean square error) adaptive filtering algorithm is adopted in the communication data, and LDPC (low-density parity check) code is introduced to filter high-frequency interference noise and reduce the bit error rate of low-frequency interference noise. When the communication signal is subject to multipath interference, multiple-input multiple-output (MIMO) technology is combined to decouple the interference signal through diversity reception;

[0013] Unmanned vehicles and drones regularly send link detection data packets to each other, analyze parameters such as the round-trip time of the data packets, packet loss rate, and signal strength changes to grasp the health status of the communication link in real time. The central system controls the historical communication data and real-time link data, and establishes a link health status prediction and management model through the long short-term memory network (LSTM) algorithm. Based on the real-time monitoring status of data transmission, it identifies channel interference and signal attenuation, and quickly sends early warning signals within a short time after the abnormality occurs, activates the error correction mechanism, realizes intelligent selection and repair switching of the channel, and adjusts the antenna angle and reduces the transmission rate to achieve self-repair and continuous stability of the communication connection between the unmanned vehicle and the drone.

[0014] As a further limitation of the first aspect of the present invention, the following details are added when integrating and coordinating the communication modules:

[0015] Targeting ZigBee and LoRa modules, the communication protocol stack and signal processing flow are optimized, taking into account their low power consumption and long-distance transmission characteristics. ZigBee's lightweight protocol stack is used to reduce latency in low-power application scenarios. Dynamic frequency adjustment is implemented based on LoRa's Chirp Spread Spectrum (CSS) technology and Adaptive Data Rate (ADR) algorithm to adapt to real-time channel conditions, thereby improving transmission efficiency. Furthermore, the LMS (least mean square error) adaptive filtering algorithm is used to reduce interference noise and ensure stable data transmission.

[0016] Regarding 5G modules, the TCP / IP protocol stack of the Qualcomm X55 5G modem has been optimized to meet high bandwidth and low latency requirements. By adjusting the TCP window size to increase transmission speed, the modem dynamically selects Sub-6GHz or millimeter wave frequency bands to adapt to different communication scenarios based on different task requirements. Furthermore, with the help of 5G network slicing technology, tasks of different priorities are allocated to dedicated virtual resources, ensuring the efficient and parallel transmission of inspection video streams, image streams, and control data collected by unmanned vehicles and drones, respectively.

[0017] In the Beidou short message module, the communication protocol is optimized to ensure reliable transmission of emergency data due to its narrowband and high latency characteristics. The antenna beam steering technology of the HX-DU2017D chip improves the reception capability of Beidou signals in weak coverage areas. At the same time, the LZ77 compression algorithm is used to compress data to reduce bandwidth usage, and FPGA is used for real-time decompression to ensure the integrity and real-time performance of transmitted data.

[0018] The FPGA serves as the management center for the communication modules in the entire system, monitoring the operating status of each module in real time and dynamically allocating hardware resources based on task requirements to avoid resource conflicts between modules. Furthermore, the FPGA uses a protocol self-adaptation mechanism to dynamically load the optimal communication protocol stack based on specific task requirements, ensuring seamless collaboration among modules. Furthermore, the FPGA is equipped with a highly efficient cache system, prioritizing data with high real-time requirements and delaying the caching of low-priority data.

[0019] As a further limitation of the first aspect of the present invention, when the status of each communication channel is perceived and used in real time, the following details are added:

[0020] Based on the results of real-time channel availability analysis, when channel interference or signal quality degradation is detected, a fuzzy logic-based weighted evaluation algorithm is used to score the four current communication channels. The channel with the highest score is selected as the target switching channel for rapid switching. During the switching process, the Fast Handover for Mobile IPv6 protocol is used to minimize switching time and reduce data loss.

[0021] When a channel switch is triggered, the system uses a channel redundancy mechanism to simultaneously maintain the connection between the original and target channels. This means that parallel communication between the original and target channels is maintained for a short period of time after the channel switch. This dual-channel parallel mechanism allows for dual data transmission during the initial switchover phase, ensuring uninterrupted data transmission even when the target channel is not yet fully stable. This also facilitates the subsequent use of XOR check technology to verify and compare data packets on the two channels, ensuring the integrity and consistency of data packets during the switchover process.

[0022] During the handover process, an intelligent buffer management strategy is activated to temporarily store and prioritize incoming data packets, determining the transmission order based on the importance and timeliness of the data. Before the target channel stabilizes, an adaptive flow control algorithm is used to dynamically adjust the data flow distribution ratio between the two channels to ensure that critical data is delivered first and securely transmitted and stored.

[0023] After the target channel is stable, the three-way handshake confirmation mechanism is used to confirm that the switch is successful. At the same time, the original channel is gradually released and resources are cleared. After the system confirms that the channel switch is successful, it notifies the interactive devices on the drone and unmanned vehicle by sending a confirmation response packet to ensure the synchronization of the two ends.

[0024] During the entire switching process, data transmission is monitored in real time, and the switching process is recorded in detail through transmission logs and data packet analysis tools. The support vector machine (SVM) machine learning algorithm is used to analyze switching historical data and optimize the switching strategy.

[0025] As a further limitation of the first aspect of the present invention, the following additional details are provided regarding beamforming technology:

[0026] The MEMS electrically controlled adjustable antenna dynamically adjusts the antenna beam width and direction. Combining inertial navigation system (INS) and GPS data, the system calculates the relative distance, azimuth, and pitch angle between the unmanned vehicle and drone in real time. A dynamic beamforming model is then built to dynamically adjust the antenna beam width to meet communication needs. When the two are close, the beam width is narrowed to improve signal strength and directionality. When the distance is greater, the beam width is expanded to cover a larger communication range and ensure signal stability.

[0027] During the beamforming process, the system optimizes the antenna's input impedance based on its impedance matching characteristics. Combining real-time map data and environmental information, the system uses shortest path search and dynamic environment modeling to develop an optimal beam path selection algorithm, selecting the best signal propagation path and minimizing signal attenuation and interference caused by obstacles or complex terrain.

[0028] When a communication link is subject to multipath interference, the communication system incorporates MIMO technology to simultaneously utilize four antenna channels for signal diversity reception, improving transmission stability through spatial diversity. At the receiving end, a phase adjustment algorithm is used to time-align and phase-correct multipath signals to eliminate signal interference caused by path differences. Furthermore, the system incorporates Maximum Ratio Combining (MRC) technology to weightedly combine received signals to improve the signal-to-noise ratio, thereby increasing the robustness and bandwidth utilization of the communication link.

[0029] In LDPC decoding, the confidence of the received bits is dynamically evaluated through a soft decision algorithm to improve the bit error rate correction capability. For burst errors in data transmission, a cyclic redundancy check (CRC) mechanism is used to ensure the integrity and accuracy of data transmission.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. This invention ensures the high stability of the communication connection between the unmanned vehicle and the drone through a series of innovative technologies such as multi-mode communication module integration, intelligent channel selection and switching mechanism, and real-time monitoring and self-healing mechanism of communication links. It can still maintain reliable communication in complex and changing environments, greatly reducing the risk of communication interruption and ensuring the continuity of collaborative operations.

[0032] 2. By optimizing and adapting different communication modules and applying signal enhancement and anti-interference technologies, efficient data transmission is achieved. Whether it is large-capacity image data, massive environmental data collected by sensors, or control instructions with extremely high real-time requirements, they can all be transmitted between unmanned vehicles and drones in a fast and accurate manner. This makes data interaction in collaborative operations more timely and accurate, and improves the response speed and work efficiency of the entire operation system.

[0033] 3. The communication connection method of the present invention has excellent environmental adaptability and can automatically adjust the communication strategy according to different operating environments. This adaptability enables the collaborative operation of unmanned vehicles and drones to be widely used in various complex environments, expanding its application scope.

[0034] 4. The application of intelligent decision-making algorithms gives the system intelligent channel selection and switching capabilities, and can automatically optimize communication paths based on real-time channel status. At the same time, the real-time monitoring and self-healing mechanism of the communication link enables it to automatically detect and repair faults. When a link anomaly occurs, the system can quickly take corresponding measures to repair it.

[0035] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0037] Figure 1 This is a diagram of the key technical architecture of an unmanned vehicle-to-drone communication connection method based on intelligent link self-healing provided by the present invention; DETAILED DESCRIPTION

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0040] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0041] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0042] The technical solutions of the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0043] A method for connecting an unmanned vehicle to an unmanned aerial vehicle (UAV) based on intelligent link self-healing comprises the following steps:

[0044] S10, equipping unmanned vehicles and drones with ZigBee, LoRa, and 5G communication modules, as well as satellite-based BeiDou short message communication modules. Using the TI CC1352P chip to build a multi-channel low-power RF integrated circuit, this system integrates the ZigBee and LoRa communication modules and dynamically switches between the two communication modes. The X555G modem and the HX-DU2017D BeiDou RDSS chip developed by China Electronics Technology Group Corporation (CETC) are used to integrate 5G and BeiDou signals. A field-programmable gate array (FPGA) serves as the management center for the four communication modules, enabling dynamic resource scheduling and protocol adaptation across multiple modules.

[0045] During S20 operation, the FPGA's RSSI (Received Signal Strength Indicator) sensor monitors the strength of the drone data transmission information received by each module in real time. The Welch power spectrum estimation and analysis algorithm is used to evaluate the channel's interference noise intensity and spectrum utilization. The Shannon-Hartley theorem is used to calculate channel capacity. The availability of the current data transmission channel is assessed based on capacity indicators and real-time bandwidth requirements, and dynamic scheduling and switching of multiple channels are performed.

[0046] S30. Equip unmanned vehicles and drones with electronically controlled adjustable antennas based on MEMS (micro-electromechanical systems) technology. Use inertial navigation system (INS) and GPS data to calculate the relative position and orientation of the two vehicles in real time. Utilize dynamic beam steering technology based on phased array technology to focus communication signals in the direction of transmission of the two vehicles. Simultaneously, employ an LMS (minimum mean square error) adaptive filtering algorithm in communication data, and introduce LDPC (low-density parity check) codes to filter high-frequency interference noise and reduce the bit error rate of low-frequency interference noise. When communication signals are subject to multipath interference, combine multiple-input multiple-output (MIMO) technology to decouple interference signals through diversity reception.

[0047] S40, unmanned vehicles and drones regularly send link detection data packets to each other, analyze parameters such as the round-trip time of the data packets, packet loss rate, and signal strength changes to grasp the health status of the communication link in real time. The central system controls the historical communication data and real-time link data, and establishes a link health status prediction and management model through the long short-term memory network (LSTM) algorithm. According to the real-time monitoring status of data transmission, it identifies channel interference and signal attenuation, and quickly sends early warning signals within a short time after the abnormality occurs, activates the error correction mechanism, realizes intelligent selection and repair switching of the channel, and adjusts the antenna angle and reduces the transmission rate to achieve self-repair and continuous stability of the communication connection between the unmanned vehicle and the drone.

[0048] Preferably, S10 further includes the following steps:

[0049] S11. Optimizing the communication protocol stack and signal processing flow for ZigBee and LoRa modules, taking into account their low power consumption and long-distance transmission characteristics, ZigBee's lightweight protocol stack reduces latency in low-power applications. Dynamic frequency adjustment is implemented based on LoRa's Chirp Spread Spectrum (CSS) technology and Adaptive Data Rate (ADR) algorithm to adapt to real-time channel conditions, thereby improving transmission efficiency. Furthermore, the LMS (least mean square error) adaptive filtering algorithm is used to mitigate interference noise and ensure stable data transmission.

[0050] S12. Regarding 5G modules, the TCP / IP protocol stack of the Qualcomm X55 5G modem has been optimized to meet high bandwidth and low latency requirements. By adjusting the TCP window size to increase transmission speed, the modem dynamically selects Sub-6GHz or millimeter wave frequency bands to adapt to different communication scenarios based on different task requirements. Furthermore, with the help of 5G network slicing technology, tasks of different priorities are allocated to dedicated virtual resources, ensuring the efficient and parallel transmission of multiple tasks such as inspection video streams, image streams, and control data collected by unmanned vehicles and drones.

[0051] S13. In the Beidou short message module, the communication protocol is optimized to ensure reliable transmission of emergency data due to its narrowband and high latency characteristics. The antenna beam steering technology of the HX-DU2017D chip improves the reception capability of Beidou signals in weak coverage areas. At the same time, the LZ77 compression algorithm is used to compress data to reduce bandwidth usage, and FPGA is used for real-time decompression to ensure the integrity and real-time performance of transmitted data.

[0052] S14. FPGA acts as the management center for the communication modules in the entire system, monitoring the working status of each communication module in real time and dynamically allocating hardware resources based on task requirements to avoid resource conflicts between modules. Furthermore, the FPGA uses a protocol self-adaptation mechanism to dynamically load the optimal communication protocol stack based on specific task requirements, ensuring seamless collaboration between modules. Furthermore, the FPGA is equipped with an efficient cache system, prioritizing data with high real-time requirements and delaying the caching of low-priority data.

[0053] Preferably, S20 further includes the following steps:

[0054] S21. Based on the real-time channel availability analysis results, when channel interference or signal quality degradation is detected, a fuzzy logic-based weighted evaluation algorithm is used to score the four current communication channels. The channel with the highest score is selected as the target switching channel for rapid switching. During the switching process, the Fast Handover for Mobile IPv6 protocol is used to minimize switching time and reduce data loss.

[0055] S22. When a channel switching task is triggered, the system uses a channel redundancy mechanism to simultaneously maintain the connection between the original channel and the target channel. That is, parallel communication between the original channel and the target channel is briefly maintained after the channel switch. Through the dual-channel parallel mechanism, dual data transmission is performed in the initial switching stage. This ensures uninterrupted data transmission even if the target channel is not yet fully stable. This also facilitates the subsequent use of XOR check technology to verify and compare data packets on the two channels, ensuring the integrity and consistency of data packets during the switching process.

[0056] During the handover process, an intelligent buffer management strategy is activated to temporarily store and prioritize data packets to be sent, determining the transmission order based on the importance and timeliness of the data. Before the target channel stabilizes, an adaptive flow control algorithm is used to dynamically adjust the data flow distribution ratio between the two channels to ensure that critical data is delivered first and securely transmitted and stored.

[0057] S24. After the target channel is stable, the three-way handshake confirmation mechanism is used to confirm that the switch is successful. At the same time, the original channel is gradually released and resources are cleared. After the system confirms that the channel switch is successful, it notifies the interactive devices on the drone and the unmanned vehicle by sending a confirmation response packet to ensure the synchronization of the two ends.

[0058] S25. Monitor data transmission in real time during the entire switching process, record the switching process in detail through transmission logs and data packet analysis tools, and use support vector machine (SVM) machine learning algorithms to analyze switching history data and optimize switching strategies.

[0059] Preferably, S30 further includes the following steps:

[0060] S31. Dynamic adjustment of the antenna beam width and direction is achieved through a MEMS electrically controlled adjustable antenna. Combining inertial navigation system (INS) and GPS data, the relative distance, azimuth, and pitch angle between the unmanned vehicle and drone are calculated in real time. A dynamic beamforming model is then built to dynamically adjust the antenna beam width to meet communication needs. When the two are close, the beam width is narrowed to improve signal strength and directionality. When the distance is greater, the beam width is expanded to cover a larger communication range and ensure signal stability.

[0061] S32. During the beamforming process, the system optimizes the antenna's input impedance based on the antenna's impedance matching characteristics. It then combines real-time map data and environmental information, using shortest path search and dynamic environment modeling to develop an optimal beam path selection algorithm. This algorithm selects the optimal signal propagation path and reduces signal attenuation and interference caused by obstacles or complex terrain.

[0062] S33. When a communication link is subject to multipath interference, the communication system incorporates MIMO technology to simultaneously utilize four antenna channels for signal diversity reception, using spatial diversity to improve transmission stability. At the receiving end, a phase adjustment algorithm is used to time-align and phase-correct multipath signals to eliminate signal interference caused by path differences. Furthermore, the system introduces maximum ratio combining (MRC) technology to weightedly combine received signals to improve the signal-to-noise ratio, thereby increasing the robustness and bandwidth utilization of the communication link.

[0063] S34. In LDPC decoding, a soft decision algorithm is used to dynamically evaluate the confidence of received bits, improve the bit error rate correction capability, and use a cyclic redundancy check (CRC) mechanism to address burst errors in data transmission and ensure the integrity and accuracy of data transmission.

Claims

1. A method for connecting unmanned vehicles and unmanned aerial vehicles (UAVs) based on intelligent link self-healing, characterized in that: The following steps are involved: S10, equipped unmanned vehicles and drones with ZigBee, LoRa, 5G, and BeiDou communication modules, respectively. TICC1352P chips were used to build multi-channel low-power radio frequency integrated circuits, enabling the fusion design of ZigBee and LoRa communication modules and dynamic switching of dual communication modes. X55 5G modems and BeiDou RDSS chips were selected to integrate 5G and BeiDou signals. A field-programmable gate array (FPGA) was used as the management center for the four communication modules, enabling dynamic resource scheduling and protocol adaptation among multiple modules. During S20 operation, sensors monitor the intensity of drone data transmission information received by each module in real time. The Welch power spectrum estimation and analysis algorithm is used to assess the channel interference noise intensity and spectrum utilization. The Shannon-Hartley theorem is used to calculate the channel capacity. The availability of the current data transmission channel is evaluated based on capacity indicators and real-time bandwidth requirements, and dynamic scheduling and switching of multiple channels are performed. S30. Equip the unmanned vehicle and drone with electronically controlled adjustable antennas. Use the inertial navigation system (INS) and GPS data to calculate the relative position and direction of the two in real time. Utilize dynamic beam steering technology based on phased array technology to focus communication signals on the transmission direction of the two. Simultaneously, employ a minimum mean square error (LMS) adaptive filtering algorithm in the communication data, and introduce low-density parity-check (LDPC) codes to filter high-frequency interference noise and reduce the bit error rate of low-frequency interference noise. When communication signals are subject to multipath interference, combine multiple-input multiple-output (MIMO) technology to decouple interference signals through diversity reception. S40, unmanned vehicles and drones regularly send link detection data packets to each other, analyze the round-trip time, packet loss rate, and signal strength change parameters of the data packets, and grasp the health status of the communication link, the historical communication data of the central control system, and the real-time link data. Through the long short-term memory network (LSTM) algorithm, a link health status prediction and management model is established. According to the real-time monitoring status of data transmission, channel interference and signal attenuation are identified, and early warning signals are quickly sent within a short time after the abnormality occurs. The error correction mechanism is activated to realize intelligent selection and repair switching of the channel. At the same time, the antenna angle is adjusted and the transmission rate is reduced to realize self-repair and continuous stability of the communication connection between the unmanned vehicle and the drone.

2. The unmanned vehicle-unmanned aerial vehicle communication connection method according to claim 1, characterized in that: The step S10 further includes: S11. Optimizing the communication protocol stack and signal processing flow for ZigBee and LoRa modules, taking into account their low power consumption and long-distance transmission characteristics, ZigBee's lightweight protocol stack reduces latency in low-power applications. Dynamic frequency adjustment is implemented based on LoRa's Chirp Spread Spectrum (CSS) technology and Adaptive Data Rate (ADR) algorithm to adapt to real-time channel conditions, thereby improving transmission efficiency. Furthermore, the Least Mean Square Error (LMS) adaptive filtering algorithm is used to mitigate interference noise and ensure stable data transmission. S12. In terms of 5G modules, to meet the needs of high bandwidth and low latency, the transmission speed is improved by adjusting the TCP window size, allowing it to dynamically select the Sub-6GHz or millimeter wave frequency band to adapt to different communication scenarios under different task requirements. In addition, with the help of 5G network slicing technology, tasks of different priorities are allocated to dedicated virtual resources, ensuring the parallel and efficient transmission of inspection video streams, image streams, and control data collected by unmanned vehicles and drones respectively. S13. In the Beidou short message module, the communication protocol is optimized to ensure reliable transmission of emergency data due to its narrowband and high latency characteristics. The antenna beam steering technology of the HX-DU2017D chip improves the reception capability of Beidou signals in weak coverage areas. The LZ77 compression algorithm is used to compress data to reduce bandwidth usage, and FPGA is used for real-time decompression to ensure the integrity and real-time performance of transmitted data. S14 monitors the working status of each communication module in real time and dynamically allocates hardware resources according to task requirements to avoid resource conflicts between modules. Based on the protocol self-adaptation mechanism, FPGA dynamically loads the optimal communication protocol stack according to specific task requirements to ensure seamless collaboration among modules. At the same time, FPGA is equipped with an efficient cache system, which prioritizes data with high real-time requirements and delays caching of low-priority data.

3. The unmanned vehicle-unmanned aerial vehicle communication connection method according to claim 1, characterized in that: The step S20 further includes: S21. Based on the real-time channel availability analysis results, when channel interference or signal quality degradation is detected, a weighted evaluation algorithm based on fuzzy logic is used to score the current four communication channels, and the channel with the highest score is selected as the target switching channel for rapid switching; S22. When a channel switching task is triggered, a channel redundancy mechanism is used to maintain short-term parallel communication between the original channel and the target channel after the channel switching. During the handover process, an intelligent buffer management strategy is activated to temporarily store and prioritize data packets to be sent, determining the transmission order based on the importance and timeliness of the data. Before the target channel stabilizes, an adaptive flow control algorithm is used to dynamically adjust the data flow distribution ratio between the two channels to ensure that critical data is delivered first and securely transmitted and stored. S24. After the target channel is stable, the three-way handshake confirmation mechanism is used to confirm that the switching is successful. At the same time, the original channel is gradually released and resources are cleared. After the system confirms that the channel switching is successful, it notifies the interactive devices on the drone and the unmanned vehicle by sending a confirmation response packet to ensure the synchronization of the two ends. S25. Monitor data transmission in real time during the entire switching process, record the switching process in detail through transmission logs and data packet analysis tools, and use support vector machine (SVM) machine learning algorithms to analyze switching history data and optimize switching strategies.

4. The unmanned vehicle-unmanned aerial vehicle communication connection method according to claim 1, characterized in that: The step S30 further includes: S31. Dynamic adjustment of the antenna beam width and direction is achieved through a MEMS electrically controlled adjustable antenna. Combining inertial navigation system (INS) and GPS data, the relative distance, azimuth, and pitch angle between the unmanned vehicle and drone are calculated in real time. A dynamic beamforming model is then built to dynamically adjust the antenna beam width to meet communication needs. When the two are close, the beam width is narrowed to improve signal strength and directionality. When the distance is greater, the beam width is expanded to cover a larger communication range and ensure signal stability. S32. During the beamforming process, the system optimizes the antenna's input impedance based on the antenna's impedance matching characteristics. It then combines real-time map data and environmental information, using the shortest path search and dynamic environment modeling to develop an optimal beam path selection algorithm and select the best signal propagation path. S33. When a communication link is subject to multipath interference, the communication system incorporates MIMO technology to simultaneously utilize four antenna channels for signal diversity reception, using spatial diversity to improve transmission stability. At the receiving end, a phase adjustment algorithm is used to time-align and phase-correct multipath signals to eliminate signal interference caused by path differences. Furthermore, the system introduces maximum ratio combining (MRC) technology to weightedly combine received signals to improve the signal-to-noise ratio, thereby increasing the robustness and bandwidth utilization of the communication link. S34. In LDPC decoding, the confidence of the received bits is dynamically evaluated through a soft decision algorithm to improve the bit error rate correction capability. For burst errors in data transmission, a cyclic redundancy check (CRC) mechanism is used to ensure the integrity and accuracy of data transmission.

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