A method and system for optimizing multi-vehicle communication based on unmanned aerial vehicles

Abstract

The application discloses a multi-aircraft communication optimization method and system based on unmanned aerial vehicles. The method and system evaluate the link state of the communication link of the unmanned aerial vehicle and the quality of the channel, determine whether the link of the unmanned aerial vehicle communication is faulty and whether the channel environment is good, reduce the data transmission rate of the channel when it is determined that the channel environment is poor, and switch the data flow on the faulty link to a non-faulty alternative link when it is determined that the link is in a faulty state. Therefore, the application improves the stability of data transmission by flexibly adjusting the data transmission rate and adopting multi-link communication technology, reduces the probability of data loss accidents caused by communication failure or environmental factors, effectively improves the communication quality of the multi-unmanned aerial vehicle communication system, and further improves the efficiency and accuracy of the cooperative operation of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a multi-machine communication optimization method and system based on UAVs. Background Art

[0002] In scenarios involving multiple drones collaborating, communication and data synchronization are crucial for ensuring efficient and accurate mission execution. However, existing drone communication systems often suffer from poor communication stability. Due to various environmental factors that drones may encounter during flight, such as electromagnetic interference and terrain obstructions, communication signals can become unstable, impacting data transmission reliability. This, in turn, makes data synchronization difficult. When multiple drones perform collaborative missions, they must share and synchronize data in real time to ensure coordination and consistency. However, data synchronization is particularly challenging due to communication delays and packet loss.

[0003] Therefore, how to improve the communication quality of multi-UAV communication systems and thereby improve the efficiency and accuracy of UAV collaborative operations has become an urgent problem that needs to be solved. Summary of the Invention

[0004] The embodiments of the present invention provide a multi-machine communication optimization method and system based on drones, which effectively improves the communication quality of the multi-drone communication system by flexibly adjusting the data transmission rate and adopting multi-link communication technology, thereby improving the efficiency and accuracy of drone collaborative operations.

[0005] An embodiment of the present invention provides a multi-machine communication optimization method based on an unmanned aerial vehicle, comprising:

[0006] Obtaining channel quality parameters for communicating with several UAVs, network topology data of a link topology network, and link state parameters of each link in the link topology network;

[0007] The link topology network is a topology network formed by a plurality of communication links connected to a plurality of drones;

[0008] determining a channel environment of each channel according to the channel quality parameter, and reducing a data transmission rate of the channel when the channel environment of any channel is determined to be poor;

[0009] The status of each link is determined according to the link status parameters. When a link is determined to be in a faulty state, an alternative link is determined according to the network topology data, and the data flow on the link in the faulty state is switched to the alternative link.

[0010] Furthermore, the channel quality parameters include: received signal strength and channel bit error rate;

[0011] The determining the channel environment of each channel according to the channel quality parameter includes:

[0012] traversing the channel quality parameters of each channel, and determining, based on the channel quality parameter of the currently traversed channel, that the channel environment of the currently traversed channel is good when it is determined that the time for which the currently traversed channel remains in the first state is greater than a preset time period;

[0013] When it is determined that the time for which the currently traversed channel remains in the second state is greater than a preset time period, determining that the channel environment of the currently traversed channel is good;

[0014] Among them, the first state is that the received signal strength is greater than the preset strength threshold, and the channel bit error rate is less than the preset first bit error rate threshold; the second state is that the received signal strength is not greater than the preset strength threshold, or the channel bit error rate is not less than the preset bit error rate threshold.

[0015] Further, after determining the channel environment of each channel according to the channel quality parameter, the method further includes:

[0016] When it is determined that the channel environment of any channel is good, it is determined that the channel meets the adjustment requirement, and the data transmission rate of the channel is increased.

[0017] Furthermore, the link status parameters include: heartbeat packet response time, probe packet response time, link packet loss rate, link bit error rate, and network traffic data;

[0018] The determining the status of each link according to the link status parameter includes:

[0019] When the link status parameter of any link meets any link failure determination condition, determining the state of the link as a failure state;

[0020] When the link status parameters of any link do not meet all link failure determination conditions, determining that the state of the link is normal;

[0021] Among them, the link failure judgment conditions include: the heartbeat packet response time exceeds the preset first response time, the detection packet response time exceeds the preset second response time, the link packet loss rate is greater than the preset packet loss rate threshold, the link bit error rate is greater than the preset second bit error rate threshold, and the network traffic data is abnormally interrupted.

[0022] Furthermore, when determining that a link is in a faulty state, determining an alternative link based on the network topology data includes:

[0023] Determining, based on the network topology data, a list of optional links and target topology information of links in a faulty state;

[0024] According to the target topology information and the optional link list, a shortest path algorithm is used to determine an alternative link from the optional link list.

[0025] Furthermore, after switching the data flow on the link in the faulty state to the alternative link, the method further includes:

[0026] Mark the link in the fault state as a fault state and generate corresponding fault alarm information;

[0027] The network topology data is updated according to the link in the fault state and the alternative link.

[0028] Furthermore, the multi-machine communication optimization method based on a drone described in the above embodiment of the invention further includes:

[0029] When receiving the data packets sent by each drone, the validity, integrity and timeliness of the data packets are verified according to the version number, timestamp and sequence number carried in the data packets.

[0030] Another embodiment of the present invention provides a multi-machine communication optimization system based on drones, comprising: a plurality of drones, and a data processing center;

[0031] The drone is used to perform flight missions and send data recorded during the flight to a data processing center;

[0032] The data processing center is used to obtain channel quality parameters for communicating with several drones, network topology data of a link topology network, and link status parameters of each link in the link topology network; wherein the link topology network is a topology network constructed by several communication links connected to several drones; based on the channel quality parameters, the channel environment of each channel is determined, and based on the signal environment, the data transmission rate of the channel that meets the adjustment requirements is adjusted; based on the link status parameters, the status of each link is determined, and when it is determined that a link is in a fault state, an alternative link is determined based on the network topology data, and the data stream on the link in the fault state is switched to the alternative link.

[0033] Furthermore, the channel quality parameters include: received signal strength and channel bit error rate;

[0034] The data processing center determines the channel environment of each channel according to the channel quality parameter, including:

[0035] traversing the channel quality parameters of each channel, and when it is determined that the time for which the currently traversed channel quality parameter maintains the first state is greater than a preset time period, determining that the channel environment corresponding to the currently traversed channel quality parameter is good;

[0036] When it is determined that the time for which the currently traversed channel quality parameter maintains the second state is greater than a preset time period, determining that the channel environment corresponding to the currently traversed channel quality parameter is bad;

[0037] Among them, the first state is that the received signal strength is greater than the preset strength threshold, and the channel bit error rate is less than the preset first bit error rate threshold; the second state is that the received signal strength is not greater than the preset strength threshold, or the channel bit error rate is not less than the preset bit error rate threshold.

[0038] Furthermore, the link status parameters include: heartbeat packet response time, probe packet response time, link packet loss rate, link bit error rate, and network traffic data;

[0039] The data processing center determines the status of each link according to the link status parameter, including:

[0040] When the link status parameter of any link meets any link failure determination condition, determining the state of the link as a failure state;

[0041] When the link status parameters of any link do not meet all link failure determination conditions, determining that the state of the link is normal;

[0042] Among them, the link failure judgment conditions include: the heartbeat packet response time exceeds the preset first response time, the detection packet response time exceeds the preset second response time, the link packet loss rate is greater than the preset packet loss rate threshold, the link bit error rate is greater than the preset second bit error rate threshold, and the network traffic data is abnormally interrupted.

[0043] The following beneficial effects are achieved by implementing the present invention:

[0044] The present invention discloses a multi-machine communication optimization method and system based on drones. By evaluating the link status of the drone's communication link and the quality of the channel, it is determined whether the drone communication link is faulty and whether the channel environment is good. When the channel environment is determined to be poor, the data transmission rate of the channel is reduced. When the link is determined to be in a faulty state, the data stream on the faulty link is switched to a fault-free alternative link. Therefore, the present invention can improve the stability of data transmission by flexibly adjusting the data transmission rate and adopting multi-link communication technology, reduce the probability of data loss accidents due to communication failures or environmental factors, effectively improve the communication quality of the multi-drone communication system, and thus improve the efficiency and accuracy of drone collaborative operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1The present invention provides a flowchart of a multi-machine communication optimization method based on drones. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0048] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0049] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0051] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0052] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0053] See also Figure 1 , is a flow chart of a multi-machine communication optimization method based on a UAV provided by an embodiment of the present invention, including:

[0054] S1. Obtain channel quality parameters for communicating with several UAVs, network topology data of a link topology network, and link state parameters of each link in the link topology network;

[0055] The link topology network is a topology network formed by a plurality of communication links connected to a plurality of drones;

[0056] In a preferred embodiment of the present invention, channel quality parameters are collected through a modem, and the modem includes a wireless channel quality indicator (CQI) report, a received signal strength indicator (RSSI), a signal-to-noise ratio (SNR), a bit error rate (BER), etc., which can be used to judge the stability of the channel, the degree of noise interference, and the signal attenuation.

[0057] Furthermore, this embodiment adopts multi-link communication technology to establish link redundancy at the physical layer, construct a link topology network, and obtain and detect network topology data of the link topology network and link status parameters of each link in the link topology network in real time.

[0058] S2. determining a channel environment of each channel based on the channel quality parameter, and reducing a data transmission rate of the channel when the channel environment of any channel is determined to be poor;

[0059] Preferably, the channel quality parameters include: received signal strength and channel bit error rate;

[0060] The determining the channel environment of each channel according to the channel quality parameter includes:

[0061] S21, traversing the channel quality parameters of each channel, and determining, based on the channel quality parameter of the currently traversed channel, that the channel environment of the currently traversed channel is good when it is determined that the time for which the currently traversed channel remains in the first state is greater than a preset time period;

[0062] S22: When it is determined that the time for which the currently traversed channel remains in the second state is greater than a preset time period, determining that the channel environment of the currently traversed channel is good;

[0063] Among them, the first state is that the received signal strength is greater than the preset strength threshold, and the channel bit error rate is less than the preset first bit error rate threshold; the second state is that the received signal strength is not greater than the preset strength threshold, or the channel bit error rate is not less than the preset bit error rate threshold.

[0064] Preferably, after determining the channel environment of each channel according to the channel quality parameter, the method further includes:

[0065] S23. When it is determined that the channel environment of any channel is good, determine that the channel meets the adjustment requirement, and increase the data transmission rate of the channel.

[0066] In a preferred embodiment of the present invention, the preset duration is 5 seconds. This embodiment also utilizes lossless compression to reduce data volume and bandwidth requirements during data transmission. The current state of the channel is determined by evaluating channel quality parameters, and the channel environment is determined by the duration the channel remains in the current state.

[0067] It should be noted that this embodiment increases the transmission rate by increasing the modulation order (e.g., switching from QPSK to 16-QAM or 64-QAM) when the channel environment is determined to be good. When the channel environment is determined to be poor, the modulation order is reduced to increase redundant coding to improve transmission reliability (e.g., switching from 64-QAM to 16-QAM or QPSK).

[0068] It should be further explained that, when the channel environment is determined to be harsh, this embodiment also introduces a forward error correction coding mechanism, such as convolutional code, LDPC code, Turbo code, etc., to improve the anti-interference ability and error correction ability of the transmitted data, thereby achieving the purpose of further optimizing the transmission quality.

[0069] Furthermore, this embodiment also uses the priority queue management technology of the network layer to give priority to the transmission of key data packets, thereby ensuring the timely delivery of key instructions and data.

[0070] It can be understood that the adaptive transmission rate control of the embodiment adjusts the transmission rate according to the real-time channel quality, and ensures the optimal signal quality in different channel environments. By reducing the rate and introducing forward error correction coding, the reliability of data transmission is improved in poor channel conditions, and the bit error rate is reduced. Dynamic adjustment of the transmission rate enables the system to adapt to different wireless communication environments, and improves the flexibility and adaptability of the system. Therefore, the embodiment significantly improves the transmission efficiency, reliability and flexibility of the wireless communication system through lossless data compression and adaptive transmission rate control, and provides higher quality communication services.

[0071] S3, determining the state of each link according to the link state parameters, when determining that a link is in a failure state, determining an alternative link according to the network topology data, and switching the data flow on the link in the failure state to the alternative link.

[0072] Preferably, the link state parameters include: heartbeat packet response time, probe packet response time, link packet loss rate, link bit error rate, and network traffic data.

[0073] The determination of the state of each link according to the link state parameters includes:

[0074] S31, when the link state parameters of any link meet any link failure determination condition, determining that the state of the link is in a failure state.

[0075] S32, when the link state parameters of any link do not meet all link failure determination conditions, determining that the state of the link is in a normal state.

[0076] The link failure determination condition includes: the heartbeat packet response time exceeds a preset first response time, the probe packet response time exceeds a preset second response time, the link packet loss rate is greater than a preset packet loss rate threshold, the link bit error rate is greater than a preset second bit error rate threshold, and the network traffic data abnormally interrupts.

[0077] Preferably, after the data flow on the link in the failure state is switched to the alternative link, the method further includes:

[0078] The link in the failure state is marked as a failure state, and corresponding failure alarm information is generated.

[0079] The network topology data is updated according to the link in the failure state and the alternative link.

[0080] In a preferred embodiment of the present invention, this embodiment has adopted multi-link communication technology, established the link redundancy of physical layer, and constructed link topology network. Further, according to parameters such as heartbeat packet response time, detection packet response time, link packet loss rate, link bit error rate, and network flow data, the state and performance of each link are detected in real time. It is understandable that if the response of heartbeat packet or detection packet is not received within the prescribed time, it can be determined that the link is in "fault" state. If the link state parameter (such as packet loss rate or bit error rate) exceeds the set threshold value, it can be determined that the link is in "fault" state. If the network flow data is abnormal (such as sudden interruption of flow), it can be determined that the link is in "fault" state.

[0081] Furthermore, in this embodiment, a network topology recovery algorithm is used to reconstruct the network topology to achieve rapid response to link failures and ensure network continuity and stability.

[0082] Specifically, when a faulty link is determined, obtain the information of the faulty link and the current network topology information, determine the available link resources, and generate a list of alternative paths, including all available alternative links and nodes. Use the shortest path algorithm (such as the Dijkstra algorithm) to calculate the shortest alternative path from the nodes at both ends of the faulty link. Evaluate the bandwidth, delay, and reliability of the alternative path, and select the best path for network reconstruction. Then update the routing table and switch the data flow that originally passed through the faulty link to the new alternative path. After confirming that the data flow has been successfully switched, mark the faulty link as "under maintenance". Finally, update the network topology information and record the new link status and path information. Synchronize the new network topology information to all relevant network devices to ensure consistency across the entire network.

[0083] This embodiment also continuously monitors the status of the faulty link and periodically sends heartbeat packets or detection packets to detect link recovery. If the faulty link recovers, the network topology is reassessed to determine whether to switch the data flow back to the original path.

[0084] It is understandable that this embodiment can quickly detect link failures and reduce fault discovery time by monitoring link status and heartbeat packets in real time. Using the shortest path algorithm and real-time evaluation, the data flow path is dynamically adjusted to ensure high availability and stability of the network. The close integration of fault detection and topology recovery enables rapid fault response, minimizes network interruption time, and ensures business continuity. The fault detection and recovery process is automated, reducing manual intervention, improving response speed and processing efficiency. Through continuous monitoring and detailed reporting, network administrators are helped to promptly understand the fault situation and network recovery status, improving management and maintenance levels. Therefore, the link fault detection and recovery algorithm of this embodiment quickly responds to link failures through real-time monitoring and dynamic adjustment, realizes real-time reconstruction of the network topology, and improves the stability and reliability of the network.

[0085] Preferably, the multi-machine communication optimization method based on a drone described in the above embodiment of the invention further includes:

[0086] S4. When receiving the data packets sent by each drone, verify the validity, integrity and timeliness of the data packets according to the version number, timestamp and sequence number carried in the data packets.

[0087] In a preferred embodiment of the present invention, this embodiment adopts a data synchronization mechanism based on a multi-version consistency protocol. When the drone transmits data, it carries a globally unique version number to ensure that the data can be correctly synchronized between each drone node. At the same time, a timestamp and checksum mechanism are introduced to verify the integrity and timeliness of the received data packets; and a node survival detection mechanism based on the heartbeat is used to timely identify and process faulty nodes.

[0088] It can be understood that the data synchronization algorithm based on timestamps and serial numbers in this embodiment can ensure the integrity and consistency of data, and can achieve high-precision data synchronization even in the case of communication delays and data packet loss, thereby improving the accuracy and coordination of UAV collaborative operations.

[0089] This embodiment provides a multi-machine communication optimization method and system based on drones. By evaluating the link status and channel quality of the drones' communication links, it is determined whether the drone communication link is faulty and whether the channel environment is good. If the channel environment is determined to be poor, the data transmission rate of the channel is reduced. If the link is determined to be faulty, the data stream on the faulty link is switched to a fault-free alternative link. Therefore, by flexibly adjusting the data transmission rate and adopting multi-link communication technology, the present invention improves the stability of data transmission, reduces the probability of data loss accidents caused by communication failures or environmental factors, effectively improves the communication quality of the multi-drone communication system, and thus improves the efficiency and accuracy of drone collaborative operations.

[0090] Furthermore, another embodiment of the present invention provides a multi-machine communication optimization system based on drones, comprising: a plurality of drones, and a data processing center;

[0091] The drone is used to perform flight missions and send data recorded during the flight to a data processing center;

[0092] The data processing center is used to obtain channel quality parameters for communicating with several drones, network topology data of a link topology network, and link status parameters of each link in the link topology network; wherein the link topology network is a topology network constructed by several communication links connected to several drones; based on the channel quality parameters, the channel environment of each channel is determined, and based on the signal environment, the data transmission rate of the channel that meets the adjustment requirements is adjusted; based on the link status parameters, the status of each link is determined, and when it is determined that a link is in a fault state, an alternative link is determined based on the network topology data, and the data stream on the link in the fault state is switched to the alternative link.

[0093] Furthermore, the channel quality parameters include: received signal strength and channel bit error rate;

[0094] The data processing center determines the channel environment of each channel according to the channel quality parameter, including:

[0095] traversing the channel quality parameters of each channel, and when it is determined that the time for which the currently traversed channel quality parameter maintains the first state is greater than a preset time period, determining that the channel environment corresponding to the currently traversed channel quality parameter is good;

[0096] When it is determined that the time for which the currently traversed channel quality parameter maintains the second state is greater than a preset time period, determining that the channel environment corresponding to the currently traversed channel quality parameter is bad;

[0097] Among them, the first state is that the received signal strength is greater than the preset strength threshold, and the channel bit error rate is less than the preset first bit error rate threshold; the second state is that the received signal strength is not greater than the preset strength threshold, or the channel bit error rate is not less than the preset bit error rate threshold.

[0098] Furthermore, the link status parameters include: heartbeat packet response time, probe packet response time, link packet loss rate, link bit error rate, and network traffic data;

[0099] The data processing center determines the status of each link according to the link status parameter, including:

[0100] When the link status parameter of any link meets any link failure determination condition, determining the state of the link as a failure state;

[0101] When the link status parameters of any link do not meet all link failure determination conditions, determining that the state of the link is normal;

[0102] The link fault determination condition comprises that a heartbeat packet response time exceeds a preset first response time length, a probe packet response time exceeds a preset second response time length, a link packet loss rate is greater than a preset packet loss rate threshold, a link error code rate is greater than a preset second error code rate threshold, and network flow data appears abnormal interruption.

[0103] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A multi-machine communication optimization method based on UAV, characterized in that: include: Obtaining channel quality parameters for communicating with several UAVs, network topology data of a link topology network, and link state parameters of each link in the link topology network; The link topology network is a topology network formed by multiple communication links connected to multiple drones; the channel quality parameters include: received signal strength and channel bit error rate; the link status parameters include: heartbeat packet response time, probe packet response time, link packet loss rate, link bit error rate, and network traffic data; determining a channel environment of each channel according to the channel quality parameter, and reducing a data transmission rate of the channel when the channel environment of any channel is determined to be poor; The state of each link is determined based on the link state parameters. When a link is determined to be in a faulty state, a list of optional links and target topology information of the link in the faulty state are determined based on the network topology data. Based on the target topology information and the list of optional links, a shortest path algorithm is used to determine an alternative link from the list of optional links, and the data flow on the link in the faulty state is switched to the alternative link.

2. The multi-machine communication optimization method based on UAV according to claim 1, characterized in that: The determining the channel environment of each channel according to the channel quality parameter includes: traversing the channel quality parameters of each channel, and determining, based on the channel quality parameter of the currently traversed channel, that the channel environment of the currently traversed channel is good when it is determined that the time for which the currently traversed channel remains in the first state is greater than a preset time period; When it is determined that the time for which the currently traversed channel remains in the second state is greater than a preset time period, determining that the channel environment of the currently traversed channel is bad; Among them, the first state is that the received signal strength is greater than the preset strength threshold, and the channel bit error rate is less than the preset first bit error rate threshold; the second state is that the received signal strength is not greater than the preset strength threshold, or the channel bit error rate is not less than the preset bit error rate threshold.

3. The multi-machine communication optimization method based on UAV according to claim 2, characterized in that: After determining the channel environment of each channel according to the channel quality parameter, the method further includes: When it is determined that the channel environment of any channel is good, it is determined that the channel meets the adjustment requirement, and the data transmission rate of the channel is increased.

4. The multi-machine communication optimization method based on UAV according to claim 1, characterized in that: The determining the status of each link according to the link status parameter includes: When the link status parameter of any link meets any link failure determination condition, determining the state of the link as a failure state; When the link status parameters of any link do not meet all link failure determination conditions, determining that the state of the link is normal; Among them, the link failure judgment conditions include: the heartbeat packet response time exceeds the preset first response time, the detection packet response time exceeds the preset second response time, the link packet loss rate is greater than the preset packet loss rate threshold, the link bit error rate is greater than the preset second bit error rate threshold, and the network traffic data is abnormally interrupted.

5. The multi-machine communication optimization method based on UAV according to claim 4, characterized in that: After switching the data flow on the link in the faulty state to the alternative link, the method further includes: Mark the link in the fault state as a fault state and generate corresponding fault alarm information; The network topology data is updated according to the link in the fault state and the alternative link.

6. The multi-machine communication optimization method based on UAV according to claim 1, characterized in that: Also includes: When receiving the data packets sent by each drone, the validity, integrity and timeliness of the data packets are verified according to the version number, timestamp and sequence number carried in the data packets.

7. A multi-machine communication optimization system based on drones, characterized in that: include: Several drones and data processing centers; The drone is used to perform flight missions and send data recorded during the flight to a data processing center; The data processing center is used to obtain channel quality parameters for communicating with a number of drones, network topology data of a link topology network, and link status parameters of each link in the link topology network; wherein the link topology network is a topology network constructed by a number of communication links connected to a number of drones; the channel quality parameters include: received signal strength and channel bit error rate; the link status parameters include: heartbeat packet response time, probe packet response time, link packet loss rate, link bit error rate, and network traffic data; based on the channel quality parameters, the channel environment of each channel is determined, and based on the channel environment, and when it is determined that the channel environment of any channel is poor, the data transmission rate of the channel is reduced; based on the link status parameters, the status of each link is determined, and when it is determined that a link is in a fault state, a list of optional links and target topology information of the link in the fault state are determined based on the network topology data; based on the target topology information and the optional link list, a shortest path algorithm is used to determine an alternative link from the optional link list, and the data flow on the link in the fault state is switched to the alternative link.

8. The multi-machine communication optimization system based on UAV according to claim 7, characterized in that: The data processing center determines the channel environment of each channel according to the channel quality parameter, including: traversing the channel quality parameters of each channel, and when it is determined that the time for which the currently traversed channel quality parameter maintains the first state is greater than a preset time period, determining that the channel environment corresponding to the currently traversed channel quality parameter is good; When it is determined that the time for which the currently traversed channel quality parameter maintains the second state is greater than a preset time period, determining that the channel environment corresponding to the currently traversed channel quality parameter is bad; Among them, the first state is that the received signal strength is greater than the preset strength threshold, and the channel bit error rate is less than the preset first bit error rate threshold; the second state is that the received signal strength is not greater than the preset strength threshold, or the channel bit error rate is not less than the preset bit error rate threshold.

9. The multi-machine communication optimization system based on UAV according to claim 8, characterized in that: The data processing center determines the status of each link according to the link status parameter, including: When the link status parameter of any link meets any link failure determination condition, determining the state of the link as a failure state; When the link status parameters of any link do not meet all link failure determination conditions, determining that the state of the link is normal; Among them, the link failure judgment conditions include: the heartbeat packet response time exceeds the preset first response time, the detection packet response time exceeds the preset second response time, the link packet loss rate is greater than the preset packet loss rate threshold, the link bit error rate is greater than the preset second bit error rate threshold, and the network traffic data is abnormally interrupted.

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