Unmanned vehicle-unmanned aerial vehicle communication connection method based on intelligent link self-healing

Abstract

The method focuses on the technical field of communication, and the communication connection problem under the cooperative operation scene of the unmanned vehicle and the unmanned aerial vehicle is accurately positioned. In practical application, communication connection is troubled by a complex environment, signals are prone to strong interference, the transmission distance is obviously limited, and double requirements of real-time performance and accuracy are difficult to consider in the aspect of data transmission characteristics during cooperative operation of the two. According to the invention, ZigBee, 5G, satellite communication modules and multi-mode communication modules are integrated, an adaptive protocol is optimized, channels are sensed in real time, the channels are selected and switched through an intelligent algorithm, signals are enhanced through directional antennas and beam forming, interference resistance is realized through adaptive filtering and error correction coding, and stability is guaranteed by means of link monitoring and a self-healing mechanism. According to the method, reliable interaction of data between the unmanned vehicle and the unmanned aerial vehicle in a complex environment can be ensured, and real-time and accurate transmission of monitoring data can be ensured even in case of strong electromagnetic interference or severe weather.

Description

Technical Field

[0001] The present invention relates to the field of intelligent body 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 fields today, such as grassland monitoring, logistics distribution, emergency rescue, etc., the collaborative operation of unmanned vehicles and drones is gradually becoming an important working mode. However, the efficient implementation of their collaborative operation is highly dependent on the stable, reliable and efficient communication connection between the two.

[0004] At present, the existing communication connection technology between unmanned vehicles and drones has many shortcomings. In complex environments, such as grasslands and mountains with undulating terrain or urban areas with dense buildings, signal interference is extremely serious, resulting in a significant decline in communication quality and difficulty in ensuring the accuracy and integrity of data transmission. At the same time, limited transmission distance is also a common problem. When the distance between the unmanned vehicle and the drone exceeds a certain range, the signal strength decays rapidly, and the communication becomes unstable or even interrupted.

[0005] In addition, it is difficult for existing technologies to simultaneously take into account the real-time and accuracy requirements of data transmission. In some application scenarios with extremely high real-time requirements, such as emergency rescue missions, data delays may lead to wrong decisions and miss the best time for rescue; and in scenarios where data accuracy is crucial, such as precision agricultural monitoring, inaccurate data may lead to wrong agricultural decisions and affect crop yields and quality.

[0006] Moreover, the existing communication connection technology lacks sufficient adaptability and intelligence, and cannot automatically optimize communication parameters and switch communication modes according to different operating environments and task requirements. When faced with sudden severe weather or temporary signal blocking, the communication system cannot take effective countermeasures in time, thus affecting the smooth progress of the entire collaborative operation. The existing communication connection technology for collaborative operations between unmanned vehicles and drones can no longer meet the growing complex application needs. An innovative communication connection method is urgently needed to solve these problems and improve the overall efficiency of collaborative operations. Summary of the invention

[0007] In order to overcome the many shortcomings of the communication connection technology between unmanned vehicles and drones in the current harsh environment, the present invention focuses on the field of communication technology, accurately locates the communication connection problem in the scenario of unmanned vehicles and drones working together, and is specifically a communication connection method between unmanned vehicles and drones based on intelligent link self-healing, integrating ZigBee, 5G, satellite communication module multi-mode communication module, optimizing adaptation protocol, 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 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.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: The first aspect of the present invention provides an unmanned vehicle-unmanned aerial vehicle communication connection method based on intelligent link self-healing: Unmanned vehicles and drones are equipped with ZigBee, LoRa, 5G communication modules and satellite Beidou short message communication modules respectively. TI CC1352P chips are used to build multi-channel low-power radio frequency integrated circuits to achieve the integrated design of ZigBee and LoRa communication modules and dynamic switching of dual communication modes. X555G modem and HX-DU2017D Beidou RDSS chip developed by China Electronics Technology Group Corporation are selected to integrate 5G signals and Beidou signals. Field programmable gate array (FPGA) is used as the management center of the four communication modules to achieve dynamic resource scheduling and protocol adaptation among multiple modules. During the operation of the unmanned vehicle, the RSSI (received signal strength indication) sensor of the FPGA monitors the strength of the drone data transmission information received by each module in real time, and uses the Welch power spectrum estimation analysis algorithm to evaluate the interference noise intensity and spectrum utilization of the channel. The Shannon-Hartley theorem is used to calculate the channel capacity, and the availability of the current data transmission channel is evaluated based on the capacity index and real-time bandwidth requirements, and multiple channels are dynamically scheduled and switched. Equip unmanned vehicles and drones with electronically controlled adjustable antennas based on MEMS (micro-electromechanical systems) technology, calculate the relative position and direction of the unmanned vehicles and drones in real time through inertial navigation systems (INS) and GPS data, and use dynamic beam control technology based on phased array technology to focus communication signals on the transmission direction of the two. At the same time, use LMS (minimum mean square error) adaptive filtering algorithm in communication data, and introduce LDPC (low-density parity check code) 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, combine multiple-input multiple-output (MIMO) technology to decouple the interference signal through diversity reception; Unmanned vehicles and drones regularly send link detection data packets to each other, analyze data packet round-trip time, packet loss rate, signal strength changes and other parameters 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 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 channels, and adjusts the antenna angle and reduces the transmission rate at the same time to realize self-repair and continuous stability of the communication connection between unmanned vehicles and drones.

[0009] As a further limitation of the first aspect of the present invention, when the communication modules are integrated and used in coordination, the following details are added: For ZigBee and LoRa modules, the communication protocol stack and signal processing flow are optimized in combination with their low power consumption and long-distance transmission characteristics. ZigBee's lightweight protocol stack is used to reduce the delay in low-power application scenarios, and dynamic frequency adjustment is implemented based on LoRa's Chirp Spread Spectrum (CSS) technology and Adaptive Data Rate (ADR) algorithm to adapt to the real-time channel status, thereby improving transmission efficiency. At the same time, the LMS (minimum mean square error) adaptive filtering algorithm is used to weaken interference noise and ensure the stability of data transmission. In terms of 5G modules, the TCP / IP protocol stack of the Qualcomm X55 5G modem is optimized to meet the requirements of high bandwidth and low latency. The transmission speed is increased by adjusting the TCP window size, so that it can 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 assigned to exclusive virtual resources to ensure the parallel and efficient transmission of multiple tasks of inspection video streams, image streams and control data collected by unmanned vehicles and drones respectively; In the Beidou short message module, the communication protocol is optimized to ensure the reliable transmission of emergency data in view of its narrowband and high latency characteristics. The antenna beam control technology of the HX-DU2017D chip is used to improve the reception capability of Beidou signals in weak coverage areas. At the same time, the LZ77 compression algorithm is used to compress the data to reduce bandwidth occupancy, and FPGA is used to achieve real-time decompression to ensure the integrity and real-time performance of the transmitted data. FPGA acts as the management center of the communication module in the entire system. It 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. In addition, FPGA dynamically loads the optimal communication protocol stack according to specific task requirements based on the protocol self-adaptation mechanism to ensure seamless collaboration among modules. At the same time, FPGA is equipped with an efficient cache system, which gives priority to data with high real-time requirements and delays caching of low-priority data.

[0010] 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: According to the results of real-time channel availability analysis, when channel interference or signal quality degradation is detected, a fuzzy logic-based weight evaluation algorithm 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. During the switching process, the Fast Handover for Mobile IPv6 protocol is used to minimize the switching time and reduce data loss. When the channel switching task is triggered, the channel redundancy mechanism is adopted, and the system maintains the connection between the original channel and the target channel at the same time, that is, the parallel communication between the original channel and the target channel is maintained for a short time after the channel switching. Through the dual-channel parallel mechanism, the data is transmitted twice in the early stage of the switching, ensuring that the data transmission is uninterrupted even if the target channel is not completely stable. It is also convenient to use the XOR check technology (XOR Check) to check and compare the data packets of the two channels in the subsequent process to ensure the integrity and consistency of the data packets during the switching process; During the switching process, the intelligent buffer management strategy is enabled to temporarily store and prioritize the data packets to be sent, and the transmission order is determined based on the importance and timeliness of the data. Before the target channel is stable, the adaptive flow control algorithm is combined to dynamically adjust the distribution ratio of the data flow between the two channels to ensure that key data is delivered first and transmitted and stored safely. 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 both ends. During the entire switching process, data transmission is monitored in real time, the switching process is recorded in detail through transmission logs and data packet analysis tools, and the support vector machine (SVM) machine learning algorithm is used to analyze the switching history data and optimize the switching strategy.

[0011] As a further limitation of the first aspect of the present invention, at the beamforming technology level, the following details are added: Through the MEMS electrically controlled adjustable antenna, the antenna beam width and direction can be dynamically adjusted. Combined with the inertial navigation system (INS) and GPS data, the relative distance, azimuth and pitch angle between the unmanned vehicle and the drone are calculated in real time, and a dynamic beamforming model is built to dynamically adjust the antenna beam width to meet communication needs. When the distance between the two is close, the beam width is narrowed to improve signal strength and directivity. When the distance is far, the beam width is expanded to cover a larger communication range to ensure signal stability. During the beamforming process, the system optimizes the input impedance of the antenna based on the impedance matching characteristics of the antenna, combines real-time map data and environmental information, and uses the shortest path search and dynamic environment modeling to fuse the optimal beam path selection algorithm to select the best signal propagation path and reduce signal attenuation and interference caused by obstacles or complex terrain. When the communication link is subject to multipath interference, the communication system combines MIMO technology to use four antenna channels for diversity reception of signals at the same time, and improves the stability of transmission through spatial diversity technology. At the receiving end, the phase adjustment algorithm is used to time align and phase correct the multipath signals to eliminate the signal interference caused by path differences. In addition, the system introduces maximum ratio combining (MRC) technology to weightedly combine the received signals to improve the signal-to-noise ratio, increase the robustness of the communication link and increase the bandwidth utilization; 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.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention ensures the high stability of the communication connection between the unmanned vehicle and the UAV 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 a complex and changing environment, greatly reducing the risk of communication interruption and ensuring the continuity of collaborative operations.

[0013] 2. Efficient data transmission is achieved through the optimization and adaptation of different communication modules and the application of signal enhancement and anti-interference technology. Whether it is large-capacity image data, massive environmental data collected by sensors, or control instructions with extremely high real-time requirements, they can 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.

[0014] 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 scope of application.

[0015] 4. The application of intelligent decision-making algorithms gives the system intelligent channel selection and switching capabilities, and can automatically optimize the communication path according to the 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 abnormality occurs, the system can quickly take corresponding measures to repair it.

[0016] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings in the specification, 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.

[0018] Figure 1 A key technical architecture diagram of an unmanned vehicle-unmanned aerial vehicle communication connection method based on intelligent link self-healing provided by the present invention; DETAILED DESCRIPTION

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0021] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0022] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0023] The technical solution of the embodiment of the present application will be described below in conjunction with the drawings in the embodiment of the present application.

[0024] A method for connecting an unmanned vehicle to an unmanned aerial vehicle based on intelligent link self-healing comprises the following steps: S10. Equip unmanned vehicles and drones with ZigBee, LoRa, 5G communication modules and satellite Beidou short message communication modules respectively. Use TI CC1352P chip to build multi-channel low-power RF integrated circuits to achieve the integrated design of ZigBee and LoRa communication modules and dynamic switching of dual communication modes. Select X555G modem and HX-DU2017D Beidou RDSS chip developed by China Electronics Technology Group Corporation to integrate 5G signals and Beidou signals. Use field programmable gate array (FPGA) as the management center of the four communication modules to achieve dynamic resource scheduling and protocol adaptation among multiple modules. S20. During the operation of the unmanned vehicle, the RSSI (received signal strength indication) sensor of the FPGA monitors the strength of the UAV data transmission information received by each module in real time, and the Welch power spectrum estimation analysis algorithm is used to evaluate the interference noise intensity and spectrum utilization of the channel. The Shannon-Hartley theorem is used to calculate the channel capacity, and the availability of the current data transmission channel is evaluated according to the capacity index and real-time bandwidth requirements, and multiple channels are dynamically scheduled and switched. S30. Equip unmanned vehicles and drones with electronically controlled adjustable antennas based on MEMS (micro-electromechanical systems) technology, calculate the relative position and direction of the unmanned vehicles and drones in real time through inertial navigation systems (INS) and GPS data, and use dynamic beam control technology based on phased array technology to focus communication signals on the transmission direction of the two. At the same time, use LMS (minimum mean square error) adaptive filtering algorithm in communication data, and introduce LDPC (low-density parity check code) 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, combine multiple-input multiple-output (MIMO) technology to decouple the interference signal through diversity reception; S40, unmanned vehicles and drones regularly send link detection data packets to each other, analyze data packet round-trip time, packet loss rate, signal strength changes and other parameters 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, starts the error correction mechanism, realizes intelligent selection and repair switching of channels, and adjusts the antenna angle and reduces the transmission rate at the same time to realize self-repair and continuous stability of the communication connection between unmanned vehicles and drones.

[0025] Preferably, S10 also includes the following steps: S11. For ZigBee and LoRa modules, the communication protocol stack and signal processing flow are optimized in combination with their low power consumption and long-distance transmission characteristics. ZigBee's lightweight protocol stack is used to reduce the delay in low-power application scenarios, and dynamic frequency adjustment is implemented based on LoRa's Chirp Spread Spectrum (CSS) technology and Adaptive Data Rate (ADR) algorithm to adapt to the real-time channel status, thereby improving transmission efficiency. At the same time, the LMS (minimum mean square error) adaptive filtering algorithm is used to weaken interference noise and ensure the stability of data transmission. S12. In terms of 5G modules, the TCP / IP protocol stack of the Qualcomm X55 5G modem is optimized to meet the requirements of high bandwidth and low latency. The transmission speed is improved by adjusting the TCP window size, so that it can 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 assigned to exclusive virtual resources to ensure the parallel and efficient transmission of multiple tasks 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 the reliable transmission of emergency data in view of its narrowband and high latency characteristics. The antenna beam control technology of the HX-DU2017D chip is used to improve the reception capability of Beidou signals in weak coverage areas. At the same time, the LZ77 compression algorithm is used to compress the data to reduce bandwidth occupancy, and FPGA is used to achieve real-time decompression to ensure the integrity and real-time performance of the transmitted data. S14, FPGA acts as the management center of the communication module in the entire system, 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. In addition, FPGA dynamically loads the optimal communication protocol stack according to specific task requirements based on the protocol self-adaptation mechanism to ensure seamless cooperation among modules. At the same time, FPGA is equipped with an efficient cache system, which gives priority to data with high real-time requirements and delays the cache of low-priority data; Preferably, S20 also includes the following steps: S21. According to the results of the real-time evaluation of the channel availability analysis, when channel interference or signal quality degradation is detected, the current four communication channels are scored using a weight evaluation algorithm based on fuzzy logic, and 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 the switching time and reduce data loss. S22. When the channel switching task is triggered, the channel redundancy mechanism is adopted, and the system maintains the connection between the original channel and the target channel at the same time, that is, the parallel communication between the original channel and the target channel is still maintained for a short time after the channel switching. Through the dual-channel parallel mechanism, the data is double-transmitted in the early stage of the switching, ensuring that the data transmission is uninterrupted even if the target channel is not completely stable. It is also convenient to use the XOR check technology (XOR Check) to check and compare the data packets of the two channels in the subsequent process to ensure the integrity and consistency of the data packets during the switching process; S23. During the switching process, the intelligent buffer management strategy is enabled to temporarily store and prioritize the data packets to be sent, and the transmission order is determined based on the importance and timeliness of the data. Before the target channel is stable, the adaptive flow control algorithm is combined to dynamically adjust the distribution ratio of the data flow between the two channels to ensure that the key data is delivered first and transmitted and stored safely. 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 both 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 algorithm to analyze switching history data and optimize switching strategies.

[0026] Preferably, S30 also includes the following steps: S31. Through the MEMS electrically controlled adjustable antenna, the antenna beam width and direction are dynamically adjusted. Combined with the inertial navigation system (INS) and GPS data, the relative distance, azimuth and pitch angle between the unmanned vehicle and the drone are calculated in real time, and a dynamic beamforming model is constructed to dynamically adjust the antenna beam width to meet communication needs. When the distance between the two is close, the beam width is narrowed to improve signal strength and directivity. When the distance is far, the beam width is expanded to cover a larger communication range to ensure signal stability. S32. During the beamforming process, the system optimizes the input impedance of the antenna based on the impedance matching characteristics of the antenna, combines real-time map data and environmental information, and uses the shortest path search and dynamic environment modeling to fuse the optimal beam path selection algorithm to select the best propagation path for the signal and reduce signal attenuation and interference caused by obstacles or complex terrain; S33. When the communication link is subject to multipath interference, the communication system combines MIMO technology to use four antenna channels for diversity reception of signals at the same time, and improves the stability of transmission through spatial diversity technology. At the receiving end, the phase adjustment algorithm is used to time align and phase correct the multipath signals to eliminate the signal interference caused by path differences. In addition, the system introduces maximum ratio combining (MRC) technology to weightedly combine the received signals to improve the signal-to-noise ratio, increase the robustness of the communication link and increase the bandwidth utilization; S34. In LDPC decoding, the confidence of the received bits is dynamically evaluated through the 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.

Claims

1. A method for unmanned vehicle-unmanned aerial vehicle communication connection based on intelligent link self-healing, characterized in that: The following steps are involved: S10. Equip unmanned vehicles and drones with ZigBee, LoRa, 5G, and Beidou communication modules respectively, use TICC1352P chips to build multi-channel low-power radio frequency integrated circuits, realize the integrated design of ZigBee and LoRa communication modules and the dynamic switching of dual communication modes, select X55 5G modem and Beidou RDSS chip to integrate 5G signals and Beidou signals, and use field programmable gate array (FPGA) as the management center of the four communication modules to realize dynamic resource scheduling and protocol adaptation among multiple modules; S20. During the operation of the unmanned vehicle, the sensor monitors the strength of the UAV data transmission information received by each module in real time, and uses the Welch power spectrum estimation analysis algorithm to evaluate the interference noise intensity and spectrum utilization of the channel, and uses the Shannon-Hartley theorem to calculate the channel capacity. According to the capacity index and real-time bandwidth requirements, the availability of the current data transmission channel is evaluated, and dynamic scheduling and switching of multiple channels are carried out; S30. Equip unmanned vehicles and drones with electrically controlled adjustable antennas, calculate the relative position and direction of the unmanned vehicles and drones in real time through the inertial navigation system (INS) and GPS data, and use dynamic beam control technology based on phased array technology to focus the communication signals on the transmission direction of the two. At the same time, use the minimum mean square error (LMS) adaptive filtering algorithm in the communication data, and introduce low-density parity check code (LDPC) to filter high-frequency interference noise and reduce the bit error rate in low-frequency interference noise. When the communication signal is subject to multipath interference, combine multiple-input multiple-output (MIMO) technology to decouple the interference signal 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. A link health status prediction and management model is established through the long short-term memory network (LSTM) algorithm. 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 also includes: S11. For ZigBee and LoRa modules, the communication protocol stack and signal processing flow are optimized in combination with their low power consumption and long-distance transmission characteristics. ZigBee's lightweight protocol stack is used to reduce the delay in low-power application scenarios, and dynamic frequency adjustment is implemented based on LoRa's Chirp Spread Spectrum (CSS) technology and Adaptive Data Rate (ADR) algorithm to adapt to the real-time channel status, thereby improving transmission efficiency. At the same time, the minimum mean square error (LMS) adaptive filtering algorithm is used to weaken interference noise and ensure the stability of data transmission. S12. In terms of 5G modules, in response to high bandwidth and low latency requirements, the transmission speed is improved by adjusting the TCP window size, so that it can dynamically select Sub-6GHz or millimeter wave frequency bands 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 exclusive virtual resources to ensure the parallel and efficient transmission of multiple tasks 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 the reliable transmission of emergency data in view of its narrowband and high latency characteristics. The antenna beam control technology of the HX-DU2017D chip is used to improve the reception capability of Beidou signals in weak coverage areas. At the same time, the LZ77 compression algorithm is used to compress the data to reduce bandwidth occupancy, and FPGA is used to achieve real-time decompression to ensure the integrity and real-time performance of the transmitted data. S14, monitor the working status of each communication module in real time, and dynamically allocate hardware resources according to task requirements to avoid resource conflicts between modules; FPGA dynamically loads the optimal communication protocol stack according to specific task requirements based on the protocol self-adaptation mechanism to ensure seamless collaboration of each module. At the same time, FPGA is equipped with an efficient cache system, which gives priority to 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 also includes: S21. According to the availability analysis results of the real-time evaluation channels, when channel interference or signal quality degradation is detected, a weight 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 the channel switching task is triggered, the channel redundancy mechanism is adopted to maintain the short-term parallel communication between the original channel and the target channel after the channel switching; S23. During the switching process, the intelligent buffer management strategy is enabled to temporarily store and prioritize the data packets to be sent, and the transmission order is determined based on the importance and timeliness of the data. Before the target channel is stable, the adaptive flow control algorithm is combined to dynamically adjust the distribution ratio of the data flow between the two channels to ensure that the key data is delivered first and transmitted and stored safely. 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 both 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 algorithm 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 also includes: S31. Through the MEMS electrically controlled adjustable antenna, the antenna beam width and direction are dynamically adjusted. Combined with the inertial navigation system (INS) and GPS data, the relative distance, azimuth and pitch angle between the unmanned vehicle and the drone are calculated in real time, and a dynamic beamforming model is constructed to dynamically adjust the antenna beam width to meet communication needs. When the distance between the two is close, the beam width is narrowed to improve signal strength and directivity. When the distance is far, the beam width is expanded to cover a larger communication range to ensure signal stability. S32. During the beamforming process, the system optimizes the input impedance of the antenna based on the impedance matching characteristics of the antenna, combines real-time map data and environmental information, and uses the shortest path search and dynamic environment modeling to fuse the optimal beam path selection algorithm to select the best propagation path for the signal; S33. When the communication link is subject to multipath interference, the communication system combines MIMO technology to use four antenna channels for diversity reception of signals at the same time, and improves the stability of transmission through spatial diversity technology. At the receiving end, the phase adjustment algorithm is used to time align and phase correct the multipath signals to eliminate the signal interference caused by path differences. In addition, the system introduces maximum ratio combining (MRC) technology to weightedly combine the received signals to improve the signal-to-noise ratio, increase the robustness of the communication link and increase the bandwidth utilization; S34. In LDPC decoding, the confidence of the received bits is dynamically evaluated through the 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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