Unmanned aerial vehicle communication method

Abstract

The invention discloses an unmanned aerial vehicle communication method, and relates to the technical field of communication, and the method comprises the steps: S1, constructing an unmanned aerial vehicle communication network architecture, determining the communication relation of each node, and collecting unmanned aerial vehicle task types, data volume demands and real-time position information, the amount of data demand includes pre-amounts of text data, image data, and sensor data. According to the unmanned aerial vehicle communication method provided by the invention, the unmanned aerial vehicle communication network architecture is constructed and the communication relationship of each node is determined, so that the task type, the data volume demand and the real-time position information of the unmanned aerial vehicle are effectively collected and managed, a solid foundation is provided for unmanned aerial vehicle communication, and the resource allocation priority algorithm is designed to improve the communication efficiency of the unmanned aerial vehicle. Communication bandwidth resources can be reasonably allocated according to the task urgency and the data volume demand of the unmanned aerial vehicle, and it is ensured that the high-priority unmanned aerial vehicle preferentially obtains sufficient communication bandwidth.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a communication method for an unmanned aerial vehicle. Background Art

[0002] There are many challenges in existing UAV communication methods. First, the construction of the UAV communication network architecture needs to consider the UAV's mission type, data volume requirements and real-time location information, which is crucial for effectively managing UAV communications.

[0003] However, traditional communication methods often ignore these dynamic factors, resulting in unreasonable allocation of communication resources and failure to meet the communication needs of drones with different missions. The design of resource allocation priority algorithm is a key factor affecting the performance of drone communication system. Under limited communication bandwidth resources, how to reasonably allocate resources according to the mission urgency and data volume requirements of drones to ensure that high-priority drones can obtain sufficient communication bandwidth is an urgent problem to be solved. In addition, drones may encounter poor communication quality during flight, and an effective cooperative communication mechanism is needed to ensure the continuity and stability of communication. Traditional communication methods lack such a flexible cooperative mechanism, resulting in the inability to find suitable relay nodes to assist communication in time when the communication quality deteriorates. In the absence of a reasonable scheduling mechanism, multiple drones may compete for communication resources at the same time, resulting in inefficient resource utilization and even causing communication conflicts. Real-time monitoring of channel capacity and the design of channel switching process are crucial to the stability of drone communication system. However, existing communication methods often lack consideration of these aspects, resulting in the inability to switch to backup channels in time when channel capacity is insufficient, thus affecting the continuity and reliability of communication. With the increasing application of drones, network security threats are also increasing. Traditional communication methods have deficiencies in security protection and are vulnerable to unauthorized device access and malicious attacks. In this regard, we propose a UAV communication method. Summary of the invention

[0004] In order to solve the above technical problems, a UAV communication method is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A communication method for an unmanned aerial vehicle, the method steps are:

[0007] S1: Build the UAV communication network architecture, determine the communication relationship between each node, and collect the UAV mission type, data volume requirements and real-time location information. The UAV missions include: reconnaissance, mapping, transportation and monitoring. The data volume requirements include: the estimated amount of text data, image data and sensor data;

[0008] S2: Design a resource allocation priority algorithm to allocate communication bandwidth resources from high to low priority;

[0009] S3: A distributed cooperative communication mechanism is adopted. When the communication quality of the UAV is poor, the optimal relay UAV is selected to assist in communication. When the suboptimal relay UAV has a stable communication function, the optimal relay UAV is allocated 1 / 3 of the number of UAVs rounded down to assist in communication by the suboptimal relay UAV.

[0010] S4: Establish a UAV communication scheduling mechanism to plan the communication time of each UAV in time slices to avoid multiple UAVs competing for communication resources at the same time and improve resource utilization efficiency;

[0011] S5: Monitor the drone communication channel in real time and calculate the channel capacity. When the channel capacity is lower than the preset value, trigger the channel switching process and select the best channel from the backup channels for switching;

[0012] S6: Provide security protection for the drone communication network, monitor network traffic anomalies, and activate intrusion detection mechanisms to ensure the safe and stable operation of the drone communication system.

[0013] Preferably, the drone whose assisted communication is taken over by the suboptimal relay drone in step S3 and rounded down to 1 / 3 is the drone with poor communication quality relative to the optimal relay drone;

[0014] If the second-best relay drone cannot take over the drone with relatively poor communication quality, the third-best relay drone will be tried to take over the drone with relatively poor communication quality. If it still cannot be taken over, the best relay drone will still assist in communication.

[0015] Preferably, the resource allocation priority algorithm in step S2 is specifically:

[0016] Based on the established UAV communication network architecture, the information of connected UAVs of each node is obtained, and the UAVs are assigned numbers according to the time series until all UAVs are assigned numbers, where the number format is: i = {1, 2, ..., n};

[0017] Obtaining the mission urgency and data volume demand data of the UAV, and quantifying the mission urgency into a mission urgency coefficient, wherein the mission urgency coefficient is quantified according to the mission type and the importance of the mission;

[0018] Each drone is given a priority based on the mission urgency coefficient and data volume requirement. The priority calculation formula is:

[0019]

[0020] Where Pi Indicates the priority of the UAV labeled i, U i represents the urgency coefficient of the UAV mission numbered i, D i represents the data volume requirement of the UAV mission numbered i, It represents the sum of the products of the mission urgency coefficient and the data volume demand of all drones;

[0021] Based on this priority, communication bandwidth resources are allocated to each drone in order from high to low, ensuring that high-priority drones have priority in obtaining sufficient communication bandwidth.

[0022] Preferably, the distributed collaborative communication mechanism is specifically:

[0023] Collect drone signal indicators to monitor drone communication channels in real time. The signal indicators include signal strength, bit error rate, and signal-to-noise ratio. The signal indicators are compared based on thresholds pre-set in device specifications. When the real-time monitored signal indicators are lower than the pre-set thresholds, it is determined that the drone's communication quality is poor.

[0024] Taking the UAV with poor communication quality as the center, other UAVs within the search range are used as potential relay nodes. For each potential relay UAV, its communication efficiency is calculated according to the distance between it and the UAV with poor communication quality and the ground control center. The communication efficiency calculation formula is:

[0025]

[0026] In the formula, E represents the communication efficiency, r i represents the distance between the potential relay UAV and the source UAV or destination node;

[0027] The communication performance of all potential relay drones is compared, and the drone with the highest communication performance is selected as the optimal relay drone. Once the optimal relay drone is determined, drones with poor communication quality will send data to the relay drone, which will then forward the data to the destination node.

[0028] Preferably, the UAV communication scheduling mechanism is specifically:

[0029] Based on the obtained UAV task priority and data volume demand data in the network, the comprehensive weight of each UAV is calculated:

[0030] W i =P i ·D i

[0031] Where W i represents the comprehensive weight of the UAV;

[0032] Based on the calculated comprehensive weight of the drones, calculate the time slice allocation ratio for each drone:

[0033]

[0034] In the formula, r i Indicates the time slice allocation ratio of the drone, represents the sum of the comprehensive weights of all drones;

[0035] According to the calculated time slice allocation ratio, specific communication time is allocated to each drone:

[0036] t i =r i ·T

[0037] Where, t i represents the allocated UAV communication time, and T represents the total time slice length;

[0038] Monitor the drone in real time. When the drone's mission changes, recalculate the comprehensive weight and time slice allocation ratio, and adjust the time slice allocation.

[0039] Preferably, the formula for calculating the channel capacity is:

[0040]

[0041] In the formula, C represents the channel capacity, B represents the channel bandwidth, S represents the signal strength of the channel, and N represents the noise power of the channel.

[0042] Preferably, monitoring network traffic anomalies and starting the intrusion detection mechanism are specifically as follows:

[0043] Get traffic information within a certain period of time and calculate network traffic anomaly indicators:

[0044]

[0045] In the formula, A represents the network traffic anomaly index, F s represents the flow rate in the time interval, T 流 Indicates the total monitoring time;

[0046] When the indicator exceeds the preset threshold, the intrusion detection mechanism is activated to prevent unauthorized device access and malicious attacks.

[0047] Preferably, the communication power of the UAV is dynamically adjusted;

[0048] Obtain real-time distance and channel quality indicator data between the drone and the base station;

[0049] Obtain the initial power, initial distance and initial channel quality index data of the drone;

[0050] According to the real-time distance between the UAV and the base station, the channel quality index, the initial power, the initial distance and the initial channel quality index, the communication power of the UAV is adjusted. The calculation formula of the communication power of the adjusted UAV is:

[0051]

[0052] Where P new Represents the adjusted communication power, P 0 represents the initial power, r 0 represents the initial distance, Q 0 represents the initial channel quality index, r represents the real-time distance between the UAV and the base station, and Q represents the channel quality index;

[0053] By dynamically adjusting the power, energy waste and signal interference caused by excessive power can be avoided while ensuring communication quality.

[0054] Preferably, a frequency planning scheme for multi-UAV communication is designed;

[0055] Divide the available communication frequency band into multiple sub-bands, and assign different sub-bands to drones in different areas or performing different tasks according to the distribution areas and mission types of drones;

[0056] For drones in adjacent areas, the required frequency isolation is calculated to avoid mutual interference between different frequency bands. The calculation formula for frequency isolation is:

[0057]

[0058] In the formula, Δf represents the frequency isolation, c represents the speed of light, and d represents the boundary distance between adjacent areas.

[0059] Preferably, data caching and scheduling are performed during the communication process. A data cache area is set on each unmanned aerial vehicle. When data arrives, it is stored in the cache area and the arrival time and priority are recorded. Scheduling is performed based on data priority and cache area occupancy, with high-priority data being transmitted first. If the cache area occupancy exceeds 80%, data below the threshold priority is discarded, thereby optimizing the data transmission process and improving transmission efficiency and stability.

[0060] Preferably, data caching and scheduling are performed during the communication process, and a data cache area is set on each unmanned aerial vehicle. When the time interval between the first data and the second data is no more than 1 second, and the cache area occupancy is 0%, the first data that arrives does not need to enter the cache area, but is directly transmitted, and the second and subsequent data that arrive enter the cache area;

[0061] Set a validity period for each data. When the data stays in the cache for longer than the validity period and is lower than the threshold priority, the data will be discarded. For data that is about to expire and has a high priority, it will be transmitted first to ensure that important and time-sensitive data is sent in time, thereby improving the effective utilization of data.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The unmanned aerial vehicle communication method proposed in the present invention realizes the effective collection and management of the drone mission type, data volume demand and real-time location information by constructing the drone communication network architecture and determining the communication relationship of each node, thereby providing a solid foundation for drone communication. By designing a resource allocation priority algorithm, it is possible to reasonably allocate communication bandwidth resources according to the mission urgency and data volume demand of the drone, ensuring that high-priority drones have priority in obtaining sufficient communication bandwidth, thereby improving the utilization efficiency of communication resources. When the drone communication quality is poor, it is possible to automatically select the optimal relay drone to assist in communication, thereby enhancing the reliability and stability of communication. By establishing a drone communication scheduling mechanism, the method plans the communication time of each drone in time slices, thereby avoiding the problem of multiple drones competing for communication resources at the same time, further improving the resource utilization efficiency, and having the functions of real-time monitoring, safety protection and dynamic adjustment of communication power for drone communication channels, thereby effectively ensuring the safe and stable operation of the drone communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The present invention is a flow chart of the UAV communication method. DETAILED DESCRIPTION

[0065] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0066] Reference Figure 1 As shown, a UAV communication method, the method steps are:

[0067] In step S1, when constructing the UAV communication network architecture, it is necessary to analyze in detail the connection mode and signal transmission path between each node to clarify their communication relationship. At the same time, carefully collect the various types of tasks performed by the UAV, such as reconnaissance missions to obtain information about the target area, surveying and mapping to accurately draw the terrain, transportation involves the delivery of materials or information, and monitoring is the continuous observation of a specific area or target. It is also necessary to count the expected amount of text data, image data, and sensor data required, and keep track of the real-time location information of the UAV at any time.

[0068] For step S2, when designing a resource allocation priority algorithm, it is necessary to comprehensively consider multiple factors such as the urgency and importance of the task, and reasonably allocate communication bandwidth resources in order of priority from high to low, to ensure that important tasks are given priority and sufficient resources.

[0069] In step S3, a distributed collaborative communication mechanism is adopted. When the drone is in an area with poor communication quality, such as when the signal is interfered or the distance is far, the system will select the best relay drone based on the drone's performance, location and other conditions to assist it in communication and ensure smooth information transmission. When the suboptimal relay drone has a stable communication function, the best relay drone allocates 1 / 3 of the number of drones rounded down to assist in communication by the suboptimal relay drone. When the drone is in an area with poor communication, the best relay can be selected to ensure smooth information transmission and avoid mission interruption. On the basis of the best relay drone, when the suboptimal relay drone has a stable communication function, it can also help share some communication tasks, further improving communication efficiency and stability.

[0070] In step S4, a UAV communication scheduling mechanism is established to plan the communication time in time slices and allocate exclusive time slices to each UAV to avoid them competing for communication resources at the same time and improve the overall utilization efficiency of resources.

[0071] In step S5, the drone communication channel is monitored in real time, and the channel capacity is calculated using professional formulas and algorithms. Once the channel capacity is lower than the preset value, the switching process is immediately triggered and the most suitable channel is selected from the backup channels for switching.

[0072] In step S6, we must do a good job in the security protection of the drone communication network, closely monitor the network traffic, and when abnormal traffic is found, such as a sudden increase in traffic or an abnormal data pattern, activate the intrusion detection mechanism to ensure the safe and stable operation of the multi-drone communication system.

[0073] The drone that the suboptimal relay drone takes over 1 / 3 of the assisted communication in step S3 is the drone with poor communication quality relative to the optimal relay drone;

[0074] If the second-best relay drone cannot take over the drone with relatively poor communication quality, the third-best relay drone will be tried to take over the drone with relatively poor communication quality. If it still cannot take over, it will still be handed over to the best relay drone for communication assistance. When the communication quality of the best relay drone is limited, the drones with relatively poor communication quality rounded down to 1 / 3 will be assigned to the second-best relay, which can make full use of the communication capacity of the second-best relay, reduce the burden of the best relay, and improve the overall communication efficiency and stability. If the second-best relay cannot take over, try the third-best relay. This progressive approach maximizes the potential of each relay drone and avoids waste of resources. When all levels of relays cannot take over, they will still be assisted by the best relay, which ensures the bottom-line capability of the communication system, ensures that information transmission will not be interrupted due to relay allocation problems, and maintains the reliability and resilience of the system.

[0075] The resource allocation priority algorithm in step S2 is specifically:

[0076] Based on the established UAV communication network architecture, the information of connected UAVs of each node is obtained, and the UAVs are assigned numbers according to the time series until all UAVs are assigned numbers, where the number format is: i = {1, 2, ..., n};

[0077] Obtaining the mission urgency and data volume demand data of the UAV, and quantifying the mission urgency into a mission urgency coefficient, wherein the mission urgency coefficient is quantified according to the mission type and the importance of the mission;

[0078] Each drone is given a priority based on the mission urgency coefficient and data volume requirement. The priority calculation formula is:

[0079]

[0080] Where P i Indicates the priority of the UAV labeled i, U i represents the urgency coefficient of the UAV mission numbered i, D i represents the data volume requirement of the UAV mission numbered i, It represents the sum of the products of the mission urgency coefficient and the data volume demand of all drones;

[0081] Based on this priority, communication bandwidth resources are allocated to each drone in order from high to low, ensuring that high-priority drones have priority in obtaining sufficient communication bandwidth.

[0082] By assigning labels to drones, they can be managed clearly and orderly. Quantifying the urgency of tasks as coefficients can objectively evaluate the importance and urgency of tasks. Using the priority calculation formula, the task urgency coefficient and data volume requirements are comprehensively considered to make resource allocation more scientific and reasonable. Allocating communication bandwidth resources from high to low priority can ensure that important and urgent tasks are given priority, ensure that high-priority drones obtain sufficient bandwidth, and avoid critical tasks being affected by uneven resource allocation. This helps to improve the task execution efficiency of the entire drone communication system, ensure smooth communication, enable the drone communication network to better serve various tasks, and improve the overall performance of the system.

[0083] The distributed collaborative communication mechanism is specifically:

[0084] Collect drone signal indicators to monitor drone communication channels in real time. The signal indicators include signal strength, bit error rate, and signal-to-noise ratio. The signal indicators are compared based on thresholds pre-set in device specifications. When the real-time monitored signal indicators are lower than the pre-set thresholds, it is determined that the drone's communication quality is poor.

[0085] Taking the UAV with poor communication quality as the center, other UAVs within the search range are used as potential relay nodes. For each potential relay UAV, its communication efficiency is calculated according to the distance between it and the UAV with poor communication quality and the ground control center. The communication efficiency calculation formula is:

[0086]

[0087] In the formula, E represents the communication efficiency, r i represents the distance between the potential relay UAV and the source UAV or destination node;

[0088] The communication performance of all potential relay drones is compared, and the drone with the highest communication performance is selected as the optimal relay drone. Once the optimal relay drone is determined, drones with poor communication quality will send data to the relay drone, which will then forward the data to the destination node.

[0089] On the one hand, by real-time monitoring of drone signal indicators, the communication quality of the drone can be judged in time to ensure effective control of the communication status. On the other hand, by searching for potential relay nodes and calculating the communication efficiency with drones with poor communication quality as the center, the optimal relay drone can be found flexibly, which improves the adaptability of the system. Using the communication efficiency calculation formula and comprehensively considering the distance factor, the selection of relay drones is more scientific and reasonable. Ultimately, the continuity and reliability of communication can be guaranteed, and information transmission interruption due to communication quality problems can be avoided. The communication capability of drones in complex environments is improved, which helps to complete various complex tasks.

[0090] The UAV communication scheduling mechanism is specifically as follows:

[0091] Based on the obtained UAV task priority and data volume demand data in the network, the comprehensive weight of each UAV is calculated:

[0092] W i =P i ·D i

[0093] Where W i represents the comprehensive weight of the UAV;

[0094] Based on the calculated comprehensive weight of the drones, calculate the time slice allocation ratio for each drone:

[0095]

[0096] In the formula, r i Indicates the time slice allocation ratio of the drone, represents the sum of the comprehensive weights of all drones;

[0097] According to the calculated time slice allocation ratio, specific communication time is allocated to each drone:

[0098] t i =r i ·T

[0099] Where, t i represents the allocated UAV communication time, and T represents the total time slice length;

[0100] Monitor the drone in real time. When the drone's mission changes, recalculate the comprehensive weight and time slice allocation ratio, and adjust the time slice allocation.

[0101] It takes into account the task priority and data volume requirements by calculating the comprehensive weight to ensure that resource allocation is more in line with the actual task requirements. The time slice allocation ratio is calculated based on the comprehensive weight to achieve a reasonable division of communication time. When the drone mission changes, the time slice allocation can be recalculated and adjusted to ensure that the scheduling is always adapted to the task status. The overall efficiency of communication resource utilization is improved, avoiding multiple machines competing for resources, ensuring that different tasks obtain communication time as required, and enhancing the stability of system operation.

[0102] The formula for calculating channel capacity is:

[0103]

[0104] In the formula, C represents the channel capacity, B represents the channel bandwidth, S represents the signal strength of the channel, and N represents the noise power of the channel.

[0105] Monitor network traffic anomalies and start intrusion detection mechanisms as follows:

[0106] Get traffic information within a certain period of time and calculate network traffic anomaly indicators:

[0107]

[0108] In the formula, A represents the network traffic anomaly index, F s represents the flow rate in the time interval, T 流 Indicates the total monitoring time;

[0109] When the indicator exceeds the preset threshold, the intrusion detection mechanism is activated to prevent unauthorized device access and malicious attacks.

[0110] By calculating the traffic anomaly index, the degree of network traffic anomaly can be accurately quantified. Once the index exceeds the preset threshold, the detection mechanism is activated to detect unauthorized device access in time to prevent it from stealing key information or interfering with drone communications. At the same time, it can also respond quickly to malicious attacks, effectively preventing hackers from tampering with mission instructions, destroying data transmission, etc., to fully protect the drone communication network security, ensure the smooth execution of drone missions, and maintain the stable operation of the entire system.

[0111] Dynamically adjust the UAV communication power;

[0112] Obtain real-time distance and channel quality indicator data between the drone and the base station;

[0113] Obtain the initial power, initial distance and initial channel quality index data of the drone;

[0114] According to the real-time distance between the UAV and the base station, the channel quality index, the initial power, the initial distance and the initial channel quality index, the communication power of the UAV is adjusted. The calculation formula of the communication power of the adjusted UAV is:

[0115]

[0116] Where P new Represents the adjusted communication power, P 0 represents the initial power, r 0 represents the initial distance, Q 0 represents the initial channel quality index, r represents the real-time distance between the UAV and the base station, and Q represents the channel quality index;

[0117] By dynamically adjusting the power, energy waste and signal interference caused by excessive power can be avoided while ensuring communication quality.

[0118] The communication power of the drone is dynamically adjusted to accurately control the power according to the real-time distance and channel quality. For example, when the drone is close to the base station and the channel quality is good, reducing the power can reduce energy consumption and extend the drone's flight time. When it is far away from the base station or the channel is not good, appropriately increasing the power can ensure stable communication quality. At the same time, it avoids signal interference to other devices due to excessive power, maintains the purity of the communication environment, and ensures the efficient, stable and energy-saving operation of the entire drone communication system.

[0119] Design frequency planning scheme for multi-UAV communication;

[0120] Divide the available communication frequency band into multiple sub-bands, and assign different sub-bands to drones in different areas or performing different tasks according to the distribution areas and mission types of drones;

[0121] For drones in adjacent areas, the required frequency isolation is calculated to avoid mutual interference between different frequency bands. The calculation formula for frequency isolation is:

[0122]

[0123] In the formula, Δf represents the frequency isolation, c represents the speed of light, and d represents the boundary distance between adjacent areas.

[0124] In the communication process of unmanned aerial vehicles, data caching and scheduling mechanisms play a key role. Each unmanned aerial vehicle is equipped with a dedicated data cache area, which is like a temporary warehouse for data. When data arrives, it is immediately stored in the cache area, and the system accurately records its arrival time and priority. The priority of data is determined based on factors such as the urgency of the task and the importance of the data, which provides an important basis for subsequent data scheduling.

[0125] In the data scheduling phase, the system will flexibly arrange according to the data priority and the occupancy of the cache area. It always adheres to the principle of giving priority to the transmission of high-priority data to ensure that key data can be quickly delivered to the destination. Once the occupancy of the cache area exceeds 80%, it means that the warehouse is about to be full. At this time, the system will decisively discard the data below the threshold priority to ensure that the cache area has enough space to receive new important data, optimize the data transmission process, and improve transmission efficiency and stability.

[0126] Data caching and scheduling are performed during the communication process. A data cache area is set on each unmanned aerial vehicle. When the time interval between the first and second data is no more than 1 second, and the cache area occupancy is 0%, the first data that arrives does not need to enter the cache area, but is directly transmitted. The second and above data that arrive enter the cache area. When the time interval between the two data does not exceed 1 second and the cache area is idle, the first data is directly transmitted, which can speed up the transmission speed of key data and reduce delays. Subsequent data enters the cache area, which can manage the data in an orderly manner, avoid data congestion, ensure the efficiency and stability of the communication process, and improve the communication performance of the unmanned aerial vehicle.

[0127] In addition, to further improve the scientific nature of data management, an expiration date is set for each data. When the data stays in the cache for longer than the expiration date and its priority is lower than the threshold, it will be discarded by the system to prevent invalid data from occupying valuable cache space for a long time. However, for data that is about to expire but has a higher priority, the system will activate the emergency mechanism and transmit this data first to ensure that important and time-sensitive data can be sent out in a timely manner, greatly improving the effective utilization of data and allowing the communication system of the unmanned aerial vehicle to always operate efficiently.

[0128] The use process of the present invention is as follows: constructing a network architecture, collecting task and location information; designing a priority algorithm, allocating communication resources; collaborative communication, selecting relay drones; establishing a scheduling mechanism, planning communication time; monitoring channel capacity, triggering a switching process; security protection, monitoring traffic anomalies

[0129] To sum up, the advantages of the present invention are: it ensures the optimal configuration of UAV communication resources and improves communication efficiency by establishing an efficient communication network architecture and resource allocation algorithm. The method adopts a distributed collaboration mechanism, which can automatically select the optimal relay when the communication quality is poor, thereby enhancing the reliability of communication. At the same time, its communication scheduling mechanism effectively avoids resource conflicts and further improves resource utilization. The method also has real-time monitoring, safety protection and dynamic power adjustment functions, which comprehensively guarantees the stable operation of the UAV communication system.

[0130] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A communication method for an unmanned aerial vehicle, characterized in that: The method steps are: S1: Build the UAV communication network architecture, determine the communication relationship between each node, and collect the UAV mission type, data volume requirements and real-time location information. The UAV missions include: reconnaissance, mapping, transportation and monitoring. The data volume requirements include: the estimated amount of text data, image data and sensor data; S2: Design a resource allocation priority algorithm to allocate communication bandwidth resources from high to low priority; S3: A distributed cooperative communication mechanism is adopted. When the communication quality of the UAV is poor, the optimal relay UAV is selected to assist in communication. When the suboptimal relay UAV has a stable communication function, the optimal relay UAV allocates 1 / 3 of the number of UAVs rounded down to the suboptimal relay UAV for communication assistance. S4: Establish a UAV communication scheduling mechanism to plan the communication time of each UAV in time slices to avoid multiple UAVs competing for communication resources at the same time and improve resource utilization efficiency; S5: Monitor the drone communication channel in real time and calculate the channel capacity. When the channel capacity is lower than the preset value, trigger the channel switching process and select the best channel from the backup channels for switching; S6: Provide security protection for the drone communication network, monitor network traffic anomalies, and activate intrusion detection mechanisms to ensure the safe and stable operation of the drone communication system.

2. The unmanned aerial vehicle communication method according to claim 1, characterized in that: The drone that the suboptimal relay drone takes over 1 / 3 of the assisted communication in step S3 is the drone with poor communication quality relative to the optimal relay drone; If the second-best relay drone cannot take over the drone with relatively poor communication quality, the third-best relay drone will be tried to take over the drone with relatively poor communication quality. If it still cannot be taken over, the best relay drone will still assist in communication.

3. The unmanned aerial vehicle communication method according to claim 1, characterized in that: The resource allocation priority algorithm in step S2 is specifically: Based on the established UAV communication network architecture, the information of connected UAVs of each node is obtained, and the UAVs are assigned numbers according to the time series until all UAVs are assigned numbers, where the number format is i = {1, 2, ..., n}; Obtaining the mission urgency and data volume demand data of the UAV, and quantifying the mission urgency into a mission urgency coefficient, wherein the mission urgency coefficient is quantified according to the mission type and the importance of the mission; Each drone is given a priority based on the mission urgency coefficient and data volume requirement. The priority calculation formula is: Where P i Indicates the priority of the UAV labeled i, U i represents the urgency coefficient of the UAV mission numbered i, D i represents the data volume requirement of the UAV mission numbered i, It represents the sum of the products of the mission urgency coefficient and the data volume demand of all drones; Based on this priority, communication bandwidth resources are allocated to each drone in order from high to low, ensuring that high-priority drones have priority in obtaining sufficient communication bandwidth.

4. The unmanned aerial vehicle communication method according to claim 1, characterized in that: The distributed collaborative communication mechanism is specifically: Collect drone signal indicators to monitor drone communication channels in real time. The signal indicators include signal strength, bit error rate, and signal-to-noise ratio. The signal indicators are compared based on thresholds pre-set in device specifications. When the real-time monitored signal indicators are lower than the pre-set thresholds, it is determined that the drone's communication quality is poor. Taking the UAV with poor communication quality as the center, other UAVs within the search range are used as potential relay nodes. For each potential relay UAV, its communication efficiency is calculated according to the distance between it and the UAV with poor communication quality and the ground control center. The communication efficiency calculation formula is: In the formula, E represents the communication efficiency, r i represents the distance between the potential relay UAV and the source UAV or destination node; The communication performance of all potential relay drones is compared, and the drone with the highest communication performance is selected as the optimal relay drone. Once the optimal relay drone is determined, drones with poor communication quality will send data to the relay drone, which will then forward the data to the destination node.

5. The unmanned aerial vehicle communication method according to claim 1, characterized in that: The UAV communication scheduling mechanism is specifically as follows: Based on the obtained UAV task priority and data volume demand data in the network, the comprehensive weight of each UAV is calculated: W i =P i ·D i Where W i represents the comprehensive weight of the UAV; Based on the calculated comprehensive weight of the drones, calculate the time slice allocation ratio for each drone: In the formula, r i Indicates the time slice allocation ratio of the drone, represents the sum of the comprehensive weights of all drones; According to the calculated time slice allocation ratio, specific communication time is allocated to each drone: t i =r i ·T Where, t i represents the allocated UAV communication time, and T represents the total time slice length; Monitor the drone in real time. When the drone's mission changes, recalculate the comprehensive weight and time slice allocation ratio, and adjust the time slice allocation.

6. The unmanned aerial vehicle communication method according to claim 1, characterized in that: The formula for calculating channel capacity is: In the formula, C represents the channel capacity, B represents the channel bandwidth, S represents the signal strength of the channel, and N represents the noise power of the channel.

7. The unmanned aerial vehicle communication method according to claim 1, characterized in that: Monitor network traffic anomalies and start intrusion detection mechanisms as follows: Get traffic information within a certain period of time and calculate network traffic anomaly indicators: In the formula, A represents the network traffic anomaly index, F s represents the flow rate in the time interval, T 流 Indicates the total monitoring time; When the indicator exceeds the preset threshold, the intrusion detection mechanism is activated to prevent unauthorized device access and malicious attacks.

8. The unmanned aerial vehicle communication method according to claim 1, characterized in that: Dynamically adjust the UAV communication power; Obtain real-time distance and channel quality indicator data between the drone and the base station; Obtain the initial power, initial distance and initial channel quality index data of the drone; According to the real-time distance between the UAV and the base station, the channel quality index, the initial power, the initial distance and the initial channel quality index, the communication power of the UAV is adjusted. The calculation formula of the communication power of the adjusted UAV is: Where P new represents the adjusted communication power, P0 represents the initial power, r0 represents the initial distance, Q0 represents the initial channel quality index, r represents the real-time distance between the drone and the base station, and Q represents the channel quality index; By dynamically adjusting the power, energy waste and signal interference caused by excessive power can be avoided while ensuring communication quality.

9. The unmanned aerial vehicle communication method according to claim 1, characterized in that: Design frequency planning scheme for multi-UAV communication; Divide the available communication frequency band into multiple sub-bands, and assign different sub-bands to drones in different areas or performing different tasks according to the distribution areas and mission types of drones; For drones in adjacent areas, the required frequency isolation is calculated to avoid mutual interference between different frequency bands. The calculation formula for frequency isolation is: In the formula, Δf represents the frequency isolation, c represents the speed of light, and d represents the boundary distance between adjacent areas.

10. The unmanned aerial vehicle communication method according to claim 1, characterized in that: Data caching and scheduling are performed during the communication process. A data cache area is set on each unmanned aerial vehicle. When the time interval between the first data and the second data is no more than 1 second, and the cache area occupancy is 0%, the first data that arrives does not need to enter the cache area, but is directly transmitted, and the second and subsequent data that arrive enter the cache area; Set a validity period for each data. When the data stays in the cache for longer than the validity period and is lower than the threshold priority, the data will be discarded. For data that is about to expire and has a high priority, it will be transmitted first to ensure that important and time-sensitive data is sent in time, thereby improving the effective utilization of data.

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