A UAV group communication support method and system

Abstract

The present invention relates to the field of wireless communication technology, and specifically to a method and system for supporting communication of a drone group, comprising the following steps: using a radio frequency sensor to capture the position coordinates and signal quality of each drone, analyzing the signal strength and environmental interference data, calculating the communication quality index based on the data, and obtaining a communication quality score table. In the present invention, by obtaining the position and signal quality of the drone, dynamically adjusting the communication link weight in combination with real-time meteorological and terrain information, improving the flexibility and efficiency of drone group communication, analyzing the signal strength and environmental interference, generating a communication quality score table, selecting the drone with the strongest signal and the least interference as the network base node in real time, optimizing the network structure and ensuring communication stability, adjusting the link weight in real time according to the optimized node data and environmental changes, reducing data delay, improving throughput, simulating the dynamic changes of drones, optimizing the communication path, and enhancing the network scalability and fault recovery capabilities.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and system for supporting communication of a group of unmanned aerial vehicles. Background Art

[0002] The field of wireless communication technology involves the use of electromagnetic waves (such as radio frequency, microwave, etc.) to transmit information in space without the need for physical connection media such as wires. This field includes various forms of communication, such as satellite communication, wireless network, cellular mobile communication, etc. Core technologies include signal modulation, channel coding, signal enhancement, energy efficiency management, etc., which aim to increase data transmission rate, extend communication distance, enhance signal quality and optimize network capacity. Wireless communication technology is the cornerstone of modern communication, supporting a wide range of needs from simple personal devices to complex enterprise-level applications.

[0003] Among them, the drone swarm communication support method involves realizing effective communication and coordination between drones and between drones and the control center in a specific communication network. The uses of this technology include ensuring that drones can achieve real-time data exchange and transmission of flight control instructions when performing tasks (such as search and rescue, geographic data collection, logistics transportation, etc.). This communication support method improves the operational efficiency and safety of drones, allowing drone swarms to work stably in radio interference, signal attenuation or complex environments.

[0004] Existing technologies support a wide range of applications, but they are still insufficient in dealing with dynamic environmental changes and communication stability under complex interference conditions. Traditional UAV communication support methods rely on static network configurations and lack the ability to respond quickly to environmental changes, which is particularly evident in rapidly changing battlefield environments or complex natural disaster sites. For example, when a swarm of drones needs to operate under extreme weather conditions, the static network cannot adjust the communication link weights in a timely manner, resulting in increased communication delays and affecting rescue efficiency. Communication path optimization in existing technologies mostly relies on preset environmental models, which limits its application flexibility in unknown or changing environments. For example, when conducting search and rescue in mountainous areas, due to the complex and changeable terrain, the preset model cannot accurately reflect the actual situation of signal propagation, thereby affecting the communication efficiency of the drone and the safety of mission execution. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a drone swarm communication support method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a UAV group communication support method, comprising the following steps:

[0007] S1: Use radio frequency sensors to capture the location coordinates and signal quality of each drone, analyze signal strength and environmental interference data, calculate the communication quality index based on the data, and obtain a communication quality score table;

[0008] S2: Using the communication quality score table, select the drone with the best signal and the least interference as the network base node, record the node's location and communication data, and obtain the benchmark connection node data;

[0009] S3: Based on the reference connection node data, combined with real-time meteorological data and terrain changes, adjusting the communication link weights in the network, evaluating the environmental impact of signal transmission, and obtaining adjusted communication link weight data;

[0010] S4: using the adjusted communication link weight data, recalculating the optimal path for data transmission, including evaluating the data delay and load capacity of the path, selecting the path with the optimal data throughput, and obtaining the optimal communication path configuration;

[0011] S5: Based on the optimal communication path configuration, simulate the impact of new drones joining and leaving the network, re-evaluate and optimize the communication path, analyze the load changes and connection stability of multiple paths, and generate network dynamic adjustment results;

[0012] S6: Monitor the communication paths that have been dynamically adjusted and optimized through the network, detect communication failures in real time, record the failure types and frequencies, and generate UAV group communication status monitoring results.

[0013] As a further solution of the present invention, the communication quality score table includes location coordinates, signal strength index, and environmental interference index; the benchmark connection node data includes node coordinates, communication quality index, and reference signal value; the adjusted communication link weight data includes weight value, environmental impact index, and signal optimization index; the optimal communication path configuration includes path delay value, path load index, and throughput index; the network dynamic adjustment results include load change index, stability score, and network adjustment impact; the drone swarm communication status monitoring results include fault type, fault frequency index, and fault detection record.

[0014] As a further solution of the present invention, the steps of using a radio frequency sensor to capture the position coordinates and signal quality of each drone, analyzing the signal strength and environmental interference data, and calculating the communication quality index based on the data to obtain the communication quality score table are specifically as follows:

[0015] S101: using a radio frequency sensor to capture the position coordinates and signal quality of each drone, recording the position coordinates and signal quality parameters of each drone within a preset time interval, and generating a position signal data set;

[0016] S102: Screening the data in the position signal data set, removing outliers caused by environmental factors, using the corrected data to evaluate the signal strength differences between drones and the impact of environmental interference, optimizing the signal capture effect by adjusting the receiving frequency and filter settings of the radio frequency sensor, and obtaining preliminary analysis data of communication quality;

[0017] S103: Calculate the communication quality index between the UAVs using the preliminary communication quality analysis data, compare the communication quality between the UAV combinations, merge the related data, formulate a communication quality scoring standard, and generate a communication quality scoring table.

[0018] As a further solution of the present invention, the steps of selecting a drone with the best signal and the least interference as a network base node through the communication quality score table, recording the location and communication data of the node, and obtaining the reference connection node data are specifically as follows:

[0019] S201: by screening the communication quality score table, according to the signal strength and interference index as the screening criteria, determine the drones whose signal strength exceeds the set threshold and whose interference index is lower than the set threshold, record the signal strength and interference data of the drones, and compile the screened drones into the base node candidate list;

[0020] S202: Filter data from the base node candidate list, compare the signal strength and interference data of the drone, select a drone with a strong signal and low interference, and record the spatial position data of the drone, including longitude, latitude and flight altitude, to obtain network node positioning information;

[0021] S203: According to the position recorded in the network node positioning information, the communication signal quality and connection stability data of the node in the target environment are captured to obtain the reference connection node data.

[0022] As a further solution of the present invention, based on the reference connection node data, combined with real-time meteorological data and terrain changes, the communication link weights in the network are adjusted to evaluate the environmental impact of signal transmission, and the steps of obtaining the adjusted communication link weight data are specifically as follows:

[0023] S301: after deploying radio frequency sensors in the monitoring area according to the reference connection node data, synchronously capturing real-time meteorological data and terrain change information, and generating an environmental synchronization data set by setting a data collection frequency and adjusting the sensitivity of the radio frequency sensors;

[0024] S302: Utilizing the environmental synchronization data set, by drawing a node environmental impact map, analyzing the relationship between signal strength and environmental factors, including terrain and weather conditions, adjusting the frequency response range of the radio frequency sensor, and generating a signal environmental response analysis result;

[0025] S303: According to the signal environment response analysis result, the weight values ​​are adjusted link by link to match the environment compatibility and communication requirements, and the link configuration is optimized to obtain adjusted communication link weight data.

[0026] As a further solution of the present invention, the optimal path for data transmission is recalculated using the adjusted communication link weight data, including evaluating the data delay and load capacity of the path, selecting the path with the optimal data throughput, and obtaining the optimal communication path configuration in the following steps:

[0027] S401: Based on the adjusted communication link weight data, a path simulation algorithm is applied to perform data delay evaluation on each path, by sending test data packets at regular intervals and recording the transmission time of each path, calculating the delay index of the path, and combining the transmission time with the influence of environmental variables to obtain path delay performance data;

[0028] S402: using the path delay performance data, evaluating the data load capacity of each path, testing the load limit of each path by simulating the transmission of differentiated data volumes, determining the stability and data throughput of the path, and obtaining path load capacity data;

[0029] S403: Filtering a path with high throughput and low latency from the path load capacity data, optimizing the communication efficiency of the selected path by setting network parameters, and obtaining an optimal communication path configuration.

[0030] As a further solution of the present invention, the formula of the path simulation algorithm is as follows:

[0031]

[0032] Calculate the delay index of each path and get the delay performance value, where D represents the path delay performance index and W represents the path delay performance index. s Represents the weight coefficient of transmission time, T represents the transmission time of each path, W e represents the weight coefficient of the environmental variable, E represents the real-time data of the environmental variable, |V p |Represents the absolute value of the path complexity.

[0033] As a further solution of the present invention, based on the optimal communication path configuration, the steps of simulating the impact of newly joining and leaving drones on the network, re-evaluating and optimizing the communication path, analyzing the load changes and connection stability of multiple paths, and generating the network dynamic adjustment results are specifically as follows:

[0034] S501: According to the optimal communication path configuration, on the current drone network structure, monitor the access points of newly added drones and the nodes of exiting drones, record the state changes of the nodes one by one, including the timestamps of access and exit, adjust the network topology map, and generate a network node update table;

[0035] S502: using the network node update table, analyzing the data traffic and transmission rate of multiple communication paths, recording the utilization and response time of each path, marking the heavy load path and the low latency path, performing a preliminary evaluation on the communication paths, and generating a preliminary analysis table of the communication paths;

[0036] S503: According to the preliminary analysis table of the communication path, the data flow direction is adjusted, the data transmission path in the network is optimized by adjusting the number of node connections, the load is balanced, and the network is reconfigured to maintain the stability of the connection, thereby generating a network dynamic adjustment result.

[0037] As a further solution of the present invention, the steps of monitoring the communication path after the dynamic adjustment result and optimization of the network, detecting communication failures in real time, recording the failure types and frequencies, and generating the monitoring results of the communication status of the drone group are specifically as follows:

[0038] S601: According to the network dynamic adjustment result, monitoring hardware is installed to capture data transmission activities on the optimized communication path in real time, and by continuously monitoring network signals, information on each data interruption and transmission delay, including the start and end time of the event, is recorded to generate a real-time communication fault log;

[0039] S602: extracting fault events from the real-time communication fault log and classifying the fault events, including signal interruption, data packet loss frequency and network conditions when the fault occurs, and generating fault event classification results by analyzing common causes and characteristics of the fault through information;

[0040] S603: Analyze the overall communication status of the drone group using the fault event classification results, evaluate the severity and frequency of the fault impact, and evaluate the communication stability to obtain the drone group communication status monitoring results.

[0041] A drone swarm communication support system, the drone swarm communication support system is used to execute the drone swarm communication support method, the system comprises:

[0042] The position and signal quality capture module uses radio frequency sensors to capture the position coordinates and signal quality of each drone, analyzes the signal strength and environmental interference, and generates key morphological features by comparing and analyzing the communication capabilities of the drones;

[0043] The network base node selection module selects the UAV with the best signal quality and the least interference according to the key morphological features, records the location and communication quality data of the selected UAV as the network base node, calculates the reference connection node data, and establishes the node selection dynamics;

[0044] The communication link weight adjustment module performs real-time adjustment of the communication link weight based on the node selection dynamic and real-time meteorological and terrain change information, analyzes the potential environmental impact in signal transmission, optimizes link weight allocation, and obtains link weight adjustment records;

[0045] The optimal communication path configuration module uses the link weight adjustment record to evaluate the data delay and load capacity of the current communication path, recalculate the optimal path for data transmission, and construct an optimized communication path configuration;

[0046] The network dynamic adjustment and monitoring module simulates the impact of new drones joining and leaving the network based on the optimized communication path configuration, performs real-time evaluation and optimization of the communication path, analyzes and adjusts the load changes and connection stability of multiple paths, and generates drone group communication status monitoring results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by using radio frequency sensors to capture the position coordinates and signal quality of each drone, combined with real-time meteorological data and terrain change information, the weight of the communication link is dynamically adjusted, which greatly improves the flexibility and efficiency of drone group communication. By analyzing the signal strength and environmental interference data between each pair of drones, calculating the communication quality index and generating a communication quality score sheet, it is helpful to identify and select the drone with the strongest signal and the least interference as the network base node in real time, which not only optimizes the network infrastructure, but also ensures the stability of communication in complex environments. Based on the optimized benchmark connection node data, the weight of the communication link can be adjusted in real time according to environmental changes, so that the data transmission path can adapt quickly when facing environmental challenges, reduce data delays and increase data throughput. By simulating the impact of new drones joining and leaving the network, the communication path can be continuously optimized to improve the scalability and fault recovery capabilities of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a UAV group communication support method, comprising the following steps:

[0060] S1: Use radio frequency sensors to capture the location coordinates and signal quality of each drone, analyze the signal strength and environmental interference data between each pair of drones, calculate the communication quality index based on the data, and obtain a communication quality score table;

[0061] S2: Select the drone with the best signal strength and the least interference as the network base node through the communication quality score table, record the node location and communication quality data, and use the recorded data as a reference for network connection to obtain the benchmark connection node data;

[0062] S3: Based on the benchmark connection node data, combined with real-time meteorological data and terrain change information, the communication link weights in the network are adjusted, including evaluating the potential environmental impact of signal transmission, and reallocating the weights to obtain adjusted communication link weight data;

[0063] S4: using the adjusted communication link weight data, recalculating the optimal path for data transmission, including evaluating the data delay and load capacity of the path, selecting the path with the optimal data throughput, and obtaining the optimal communication path configuration;

[0064] S5: Based on the optimal communication path configuration, simulate the impact of new drones joining and leaving the network, re-evaluate and optimize the communication path, analyze the load changes and connection stability of multiple paths, and generate network dynamic adjustment results;

[0065] S6: Monitor the communication paths that have undergone dynamic network adjustment and optimization, detect communication failures in real time, record the failure types and frequencies, and generate UAV swarm communication status monitoring results.

[0066] The communication quality score table includes location coordinates, signal strength index, and environmental interference index. The benchmark connection node data includes node coordinates, communication quality indicators, and reference signal values. The adjusted communication link weight data includes weight values, environmental impact indicators, and signal optimization indexes. The optimal communication path configuration includes path delay values, path load indicators, and throughput indicators. The network dynamic adjustment results include load change index, stability score, and network adjustment impact. The drone swarm communication status monitoring results include fault types, fault frequency indicators, and fault detection records.

[0067] Specifically, if Figure 2 As shown in FIG. 1 , the steps of using a radio frequency sensor to capture the position coordinates and signal quality of each drone, analyzing the signal strength and environmental interference data, and calculating the communication quality index based on the data to obtain the communication quality score sheet are as follows:

[0068] S101: Using a radio frequency sensor to capture the position coordinates and signal quality of each drone, recording the position coordinates and signal quality parameters of each drone within a preset time interval, and generating a position signal data set. The execution process is as follows;

[0069] Radio frequency sensors are used to capture the position coordinates and signal quality of each drone. Based on the real-time position and signal quality of each drone captured by the equipment, the position coordinates and signal quality parameters of the drone are recorded within the preset time interval. The dataset accurately obtains the real-time position information of the drone through advanced sensing technology. By recording the position coordinates, the dynamic changes of the drone within the specified time can be accurately tracked. Radio frequency sensors play a key role in this process. Their receiving sensitivity and transmission capacity directly affect the accuracy of the position data and the stability of the signal. By analyzing the position data, the movement characteristics and operating environment of the drone group can be intuitively grasped, providing data support for the operation safety and scheduling management of the drone, and generating a position signal dataset.

[0070] S102: Screen the data in the position signal data set, remove outliers caused by environmental factors, use the corrected data to evaluate the signal strength differences between drones and the impact of environmental interference, and optimize the signal capture effect by adjusting the receiving frequency and filter settings of the radio frequency sensor. The execution process of obtaining preliminary analysis data of communication quality is as follows;

[0071] The position signal data set is screened to remove outliers caused by environmental factors, and the signal strength differences between drones and the impact of environmental interference are evaluated according to the formula:

[0072]

[0073] Calculate the signal strength difference S ij , where P i represents the signal strength of drone i, P j represents the signal strength of UAV j, and n represents the number of samples;

[0074] Collect the signal strength data of the drones, set n as the total number of measurements, calculate the sum of the squares of the difference in signal strength between the two drones in each measurement, and finally divide it by the number of measurements n to get the average signal strength difference S ij , if there are n = 100 measurements, the signal strength of drone i is P i =-70dBm, the signal strength of drone j is P j =-75dBm, then calculate S ij :

[0075] S103: Using the preliminary communication quality analysis data, the communication quality index between the UAVs is calculated, and by comparing the communication quality between the UAV combinations, the associated data is merged and the communication quality scoring standard is formulated, and the execution process of generating the communication quality scoring table is as follows;

[0076] Using the preliminary communication quality analysis data, the communication quality index between drones is calculated. The generation of the communication quality score sheet involves a comprehensive evaluation of the communication quality between drone combinations, merging related data and formulating a communication quality scoring standard. The calculation of the communication quality index is based on a comprehensive analysis of signal strength and environmental interference. The index can effectively reflect the communication efficiency of the drone group in a specific environment. By analyzing the communication quality between differentiated drone combinations, potential signal interference problems or signal blind spots can be discovered. The formulated scoring standard aims to provide a standardized evaluation method for the communication scheduling of drone groups. The final communication quality score sheet will serve as a key tool for evaluating the communication efficiency of drone groups and will be used for drone maintenance and optimization decisions.

[0077] Specifically, if Figure 3As shown in the figure, through the communication quality score table, the drone with the best signal and the least interference is selected as the network base node, the node location and communication data are recorded, and the steps for obtaining the benchmark connection node data are as follows:

[0078] S201: By screening the communication quality score table, based on the signal strength and interference index as the screening criteria, determine the drones whose signal strength exceeds the set threshold and whose interference index is lower than the set threshold, record the signal strength and interference data of the drones, and compile the screened drones into the base node candidate list. The execution process is as follows;

[0079] By screening the communication quality score table, drones that meet the conditions are screened according to their signal strength and interference index, which involves quantitative measurement of signal strength and precise calculation of the interference index. The signal strength is obtained after processing the data captured by the radio sensor through a specific algorithm, and the interference index is calculated by analyzing the noise and external interference factors in the signal. Drones that meet the screening criteria are considered to have better communication efficiency and lower environmental interference. The signal strength and interference data of the drones are recorded for further analysis and use. The screened drones are included in the base node candidate list, providing potential efficient communication nodes for subsequent network construction.

[0080] S202: Filter data from the base node candidate list, compare the signal strength and interference data of the drone, select the drone with strong signal and small interference, and record the spatial position data of the drone, including longitude, latitude and flight altitude. The execution process of obtaining the network node positioning information is as follows;

[0081] Compare the signal strength of the drone with the interference data, select the drone with strong signal and small interference, according to the formula:

[0082] Q=Sk·I

[0083] Calculate the communication quality Q, where S represents the signal strength, I represents the interference index, and k represents the interference suppression coefficient;

[0084] Data is screened from the base node candidate list, the signal strength S and interference data I of the drone are evaluated, and the interference suppression coefficient k is set according to the actual environment to balance the influence of signal strength and interference. For example, if the signal strength S of the drone is 75dBm, the interference index I is 20dBm, and k is 0.5, then the communication quality Q is 75-0.5·20=65dBm, which is higher than the set threshold of communication quality, indicating that the drone is an ideal choice for the network base node.

[0085] S203: According to the location recorded in the network node positioning information, the communication signal quality and connection stability data of the node in the target environment are captured, and the execution process of obtaining the reference connection node data is as follows;

[0086] Based on the obtained network node positioning information, the communication signal quality and connection stability data of the node in the target environment are captured, including the spatial location data of the node, such as longitude, latitude and flight altitude. The acquisition of data depends on a high-precision positioning system and stable signal transmission technology. By recording the location data, the communication performance of the communication nodes in differentiated geographical locations can be analyzed in detail, including signal coverage and signal quality changes, so as to obtain the benchmark connection node data. The benchmark connection node data is the key to evaluating network coverage efficiency and optimizing communication paths. Through the data, the layout of the UAV network can be effectively planned and adjusted to ensure the efficient and stable operation of the network under various environmental conditions.

[0087] Specifically, if Figure 4 As shown, based on the benchmark connection node data, combined with real-time meteorological data and terrain changes, the communication link weights in the network are adjusted to evaluate the environmental impact of signal transmission. The steps for obtaining the adjusted communication link weight data are as follows:

[0088] S301: After deploying radio frequency sensors in the monitoring area according to the benchmark connection node data, real-time meteorological data and terrain change information are captured synchronously, and the execution process of generating an environmental synchronization data set by setting the data collection frequency and radio frequency sensor sensitivity adjustment is as follows;

[0089] According to the benchmark connection node data, after deploying radio frequency sensors in the monitoring area, real-time meteorological data and terrain change information are captured synchronously. This process involves precise data collection frequency setting and sensor sensitivity adjustment. The deployment of radio frequency sensors determines the optimal location based on the benchmark connection node data to ensure the integrity and accuracy of the data. Real-time meteorological data includes temperature, humidity, wind speed, etc., while terrain change information includes terrain height, terrain change and other data. The synchronous capture of data is crucial for analyzing the communication performance of drones in differentiated environments. By setting the appropriate data collection frequency and sensor sensitivity, the data collection efficiency and accuracy can be optimized, thereby generating a high-quality environmental synchronization data set.

[0090] S302: Using the environment synchronization data set, by drawing a node environment impact map, analyzing the relationship between signal strength and environmental factors, including terrain and weather conditions, adjusting the frequency response range of the radio frequency sensor, and generating a signal environment response analysis result, the execution process is as follows;

[0091] By drawing the node environmental impact diagram, the relationship between signal strength and environmental factors is analyzed according to the formula:

[0092] R=α·Sc+β·Ec

[0093] Calculate the signal response R, where Sc represents the signal strength, Ec represents the quantitative impact value of the environmental factor, and α and β are the weight coefficients of the signal and environmental factors respectively;

[0094] Through the environmental synchronization data set, the node environmental impact map of the drone operation area was drawn using analysis software. The graph expresses the relationship between the signal strength Sc and environmental factors E such as terrain and weather. The signal weight coefficient α is set to 0.7 and the environmental factor weight coefficient β is set to 0.3. If the signal strength of a node is Sc = 50dBm and the quantitative impact value of the environmental factor is Ec = 20, the signal environment response is calculated as R = 0.7·50+0.3·20 = 41dB m ;

[0095] This calculation process shows the node's signal response strength under current environmental conditions and is used to adjust the frequency response range of the radio frequency sensor.

[0096] S303: According to the signal environment response analysis result, the weight value is adjusted link by link to match the environment compatibility and communication requirements, and the link configuration is optimized to obtain the adjusted communication link weight data. The execution process is as follows;

[0097] According to the results of the signal environment response analysis, the weight values ​​are adjusted link by link to match the environment compatibility and communication requirements. The weight adjustment of each link takes into account the changes in environmental factors and communication requirements. The adjustment of link weights is based on the detailed data of the signal environment response analysis results. The purpose of the adjustment is to optimize the link configuration and improve communication efficiency and stability. By accurately calculating the weight data of each link, the optimal performance of the communication link under various environmental conditions can be ensured. The adjusted communication link weight data provides important decision-making support for network management and ensures the reliability and efficiency of the network.

[0098] Specifically, if Figure 5 As shown, the optimal path for data transmission is recalculated using the adjusted communication link weight data, including evaluating the data delay and load capacity of the path, selecting the path with the optimal data throughput, and obtaining the optimal communication path configuration in the following specific steps:

[0099] S401: Based on the adjusted communication link weight data, a path simulation algorithm is applied to perform data delay evaluation on each path. The delay index of the path is calculated by sending test data packets at regular intervals and recording the transmission time of each path. The execution flow of obtaining the path delay performance data by combining the transmission time and the influence of environmental variables is as follows;

[0100] The formula of the path simulation algorithm is as follows:

[0101]

[0102] Calculate the delay index of each path and get the delay performance value, where D represents the path delay performance index and W represents the path delay performance index. s Represents the weight coefficient of transmission time, T represents the transmission time of each path, W e represents the weight coefficient of the environmental variable, E represents the real-time data of the environmental variable, |V p |Represents the absolute value of the path complexity.

[0103] Detailed explanation of the formula and the process of formula calculation and derivation:

[0104] This formula is used to evaluate the delay performance of a data communication path, where D represents the path delay performance index, and the calculation of this value requires reference to the influence of transmission time, environmental variables, and path complexity;

[0105] Transmission time T: obtained by sending test data packets at regular intervals and recording the transmission time of each path. The transmission time of a communication path is set to 120 milliseconds;

[0106] Environmental variable E: Real-time data of environmental variables, such as the degree of signal interference, is obtained through environmental sensor monitoring. In this example, the environmental variable value obtained through monitoring is 15dB;

[0107] Path complexity |V p |: The complexity of the path is determined by the number of routers and switches and the network topology. Here, it is set to include 5 network nodes, that is, the path complexity is 5;

[0108] WS: The weight coefficient of transmission time, which is set according to the impact of transmission time on delay performance. When the network conditions are stable, it is set to 0.7;

[0109] W e : The weight coefficient of the impact of environmental variables is determined by the frequency of environmental changes and the impact on transmission quality. If the impact of environmental changes on communication quality is large, this parameter is higher and is set to 0.3;

[0110] Calculate W for the numerator s ·T+W e ·E: 0.7·120+0.3·15=84+4.5=88.5;

[0111] Calculate the square root of the denominator, which is

[0112] Substitute the results of these two parts into the formula to calculate D:

[0113] The results show that after considering the comprehensive factors of transmission time, environmental variables and path complexity, the delay performance index of this path is 39.58. This value reflects the efficiency of data transmission under the current network conditions and environment. A lower delay index value means better communication performance, that is, data can be transmitted faster on this path, indicating the effectiveness of path selection and network design.

[0114] S402: using the path delay performance data, evaluating the data load capacity of each path, testing the load limit of each path by simulating the transmission of differentiated data volumes, determining the stability and data throughput of the path, and the execution process of obtaining the path load capacity data is as follows;

[0115] Using the path delay performance data, first collect the delay performance data of each communication path under differentiated time and differentiated data load conditions, including average delay, maximum delay and delay fluctuation rate. Apply a gradually increasing amount of data to each path in a controlled environment and observe its impact on the data packet transmission rate and the data packet loss rate under high load conditions to evaluate its load limit. Use specific monitoring tools to record the data transmission rate and delay of each test, as well as any possible errors or interruptions. Analyze the data and compare the performance of differentiated paths under the same test conditions. Calculate the data throughput and stability indicators of each path, such as the average throughput and stability index. The indicators help determine the performance limit and operational stability of each path, thereby providing a basis for the next path selection.

[0116] S403: Filtering a path with high throughput and low latency from the path load capacity data, optimizing the communication efficiency of the selected path by setting network parameters, and obtaining the optimal communication path configuration. The execution process is as follows;

[0117] Paths with high throughput and low latency are selected from the path load capacity data. By accurately measuring and recording the performance of the selected path under differentiated network conditions and communication protocol settings, key network parameters that affect communication efficiency, such as transmission window size, packet priority setting, and routing algorithm selection, are captured. The parameters are adjusted one by one, and the performance of each adjusted network is tested using simulation software to optimize data transmission efficiency. After each adjustment, the network's response time, data packet loss rate, and quality of service (QoS) are evaluated to ensure that the adjusted parameters can indeed improve communication efficiency without causing negative effects. The optimization measures are applied to the actual network environment, and their effects are confirmed through real-time monitoring tools and performance tests to ensure that the selected path configuration can provide the best communication efficiency in actual applications and obtain the optimal communication path configuration.

[0118] Specifically, if Figure 6As shown in the figure, based on the optimal communication path configuration, the impact of new drones joining and leaving the network is simulated, the communication path is re-evaluated and optimized, the load changes and connection stability of multiple paths are analyzed, and the steps to generate the network dynamic adjustment results are as follows:

[0119] S501: According to the optimal communication path configuration, on the current drone network structure, monitor the access points of newly added drones and the nodes of exiting drones, record the state changes of the nodes one by one, including the timestamps of access and exit, adjust the network topology map, and generate the execution process of the network node update table as follows;

[0120] According to the optimal communication path configuration, the access points of new drones and the nodes of drones exiting the current drone network structure are monitored. This involves real-time monitoring and recording of the status changes of each node, including the timestamps of access and exit. This monitoring is essential to maintaining the structure and function of the network. The status record of each node is automatically completed through the network management system, including the accurate record of timestamps, ensuring the integrity and accuracy of the data. This data is critical for network administrators because it assists administrators in tracking changes in the network and responding in a timely manner. Through recording, the network topology map can be adjusted to reflect the latest status of the network, thereby generating a network node update table, which provides basic data for further analysis and optimization of the network.

[0121] S502: Use the network node update table to analyze the data traffic and transmission rate of multiple communication paths, record the utilization and response time of each path, mark the heavy load path and the low latency path, perform a preliminary evaluation of the communication paths, and generate the execution process of the preliminary analysis table of the communication paths as follows;

[0122] Analyze the data traffic and transmission rate of multiple communication paths, record the utilization and response time of each path, according to the formula:

[0123]

[0124] Calculate the path utilization U, where D t Represents the actual amount of data transmitted, D m Represents the maximum amount of data carried by the path;

[0125] Get the data flow D of each communication path from the network node update table t and the maximum carrying capacity of the path D m , set the actual transmission data volume of a path to be D t =300GB, the maximum amount of data carried is D m =500GB, then the utilization of the path or 60%;

[0126] This calculation assists network administrators in identifying high-load paths and low-latency paths, thereby making a preliminary assessment and generating a preliminary analysis table of communication paths, which is used for further decision support and network optimization.

[0127] S503: According to the preliminary analysis table of the communication path, the data flow direction is adjusted, the data transmission path in the network is optimized by adjusting the number of node connections, the load is balanced, and the network is reconfigured to maintain the stability of the connection. The execution process of generating the network dynamic adjustment result is as follows;

[0128] According to the preliminary analysis table of the communication path, adjust the data flow direction by adjusting the number of connections of the node. This involves optimizing the data transmission path in the network. The purpose is to balance the load and reconfigure the network to maintain the stability of the connection. The adjustment process includes analyzing the load situation and communication needs of each node, adjusting the number of connections according to the load situation, reducing the pressure on heavily loaded nodes, and improving the efficiency and response speed of the entire network. Through this dynamic adjustment, network administrators can ensure that the network can maintain efficient operation under various operating conditions. The generated network dynamic adjustment results will be used to guide future network maintenance and upgrade strategies to ensure the long-term stability and reliability of the network.

[0129] Specifically, if Figure 7 As shown in the figure, the steps of monitoring the communication path after dynamic network adjustment and optimization, detecting communication failures in real time, recording the failure types and frequencies, and generating the UAV group communication status monitoring results are as follows:

[0130] S601: According to the result of the dynamic adjustment of the network, monitoring hardware is installed to capture the data transmission activities on the optimized communication path in real time, and the information of each data interruption and transmission delay, including the start and end time of the event, is recorded by continuously monitoring the network signal, and the execution process of generating a real-time communication fault log is as follows;

[0131] According to the results of dynamic network adjustment, monitoring hardware is installed to capture data transmission activities on the optimized communication path in real time. Monitoring hardware is deployed at key nodes to capture data flow and any potential communication failures. By continuously monitoring network signals, the activity status of data transmission is recorded in real time, including detailed information of each data interruption and transmission delay, such as the start and end time of the event. The records are compiled into the real-time communication fault log, which records the timestamps and duration of all relevant events in detail, providing instant feedback on communication quality, which is crucial for the network operation and maintenance team because it allows rapid response and resolution of problems that occur, ensuring the optimal performance and stability of the communication path.

[0132] S602: Extract fault events from the real-time communication fault log and classify the fault events, including signal interruption, data packet loss frequency and network conditions when the fault occurs. Analyze the common causes and characteristics of the fault through information to generate the fault event classification results. The execution process is as follows;

[0133] Extract fault events from real-time communication fault logs and classify the fault events according to the formula:

[0134]

[0135] Calculate the fault frequency F, where N f Represents the number of failure events within a specific time, T d represents the observation time;

[0136] Using the data extracted from the real-time communication fault log, the fault events are classified, such as signal interruption and data packet loss, and the observation time T is set. d = 24 hours, during which the number of fault events recorded is N f =10, then the fault frequency Second hour;

[0137] Through this calculation, the common causes and characteristics of failures can be analyzed and fault event classification results can be generated. The results provide network technicians with detailed insights into network conditions, helping them make more targeted adjustments in future operations and maintenance.

[0138] S603: Analyze the overall communication status of the drone group using the fault event classification results, evaluate the severity and frequency of the fault impact, and evaluate the communication stability. The execution process of obtaining the drone group communication status monitoring results is as follows;

[0139] Using the fault event classification results, the overall communication status of the drone swarm is analyzed. By assessing the severity and frequency of the fault impact and evaluating the communication stability, the process includes in-depth analysis of differentiated types of communication faults and their specific impact on drone operations. The analysis results assist in identifying weaknesses and potential risks in communications. By quantitatively assessing the impact of the fault, the network technology team can determine which problems are most urgent and need to be resolved immediately, thereby optimizing communication strategies and network configurations. The final drone swarm communication status monitoring results will provide strong data support for the operational safety and efficiency of the drone swarm.

[0140] like Figure 8 As shown, a UAV group communication support system, the system includes:

[0141] The position and signal quality capture module uses radio frequency sensors to capture the position coordinates and signal quality of each drone, analyzes the signal strength and environmental interference, and generates key morphological features by comparing and analyzing the communication capabilities of the drones;

[0142] The network base node selection module selects the UAVs with the best signal quality and the least interference according to the key morphological features, records the location and communication quality data of the selected UAVs as network base nodes, calculates the benchmark connection node data, and establishes node selection dynamics;

[0143] The communication link weight adjustment module adjusts the communication link weight in real time based on the node selection dynamics and real-time weather and terrain change information, analyzes the potential environmental impact in signal transmission, optimizes link weight allocation, and obtains link weight adjustment records;

[0144] The optimal communication path configuration module uses the link weight adjustment record to evaluate the data delay and load capacity of the current communication path, recalculate the optimal path for data transmission, and build an optimized communication path configuration;

[0145] The network dynamic adjustment and monitoring module optimizes the communication path configuration, simulates the impact of new drones joining and leaving the network, conducts real-time evaluation and optimization of the communication path, analyzes and adjusts the load changes and connection stability of multiple paths, and generates drone group communication status monitoring results.

[0146] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A drone group communication support method, characterized in that: The following steps are involved: The radio frequency sensor is used to capture the location coordinates and signal quality of each drone, the signal strength and environmental interference data are analyzed, and the communication quality index is calculated based on the data to obtain a communication quality score table; Through the communication quality score table, the drone with the best signal and the least interference is selected as the network base node, the location and communication data of the node are recorded, and the benchmark connection node data is obtained; Based on the reference connection node data, combined with real-time meteorological data and terrain changes, the communication link weights in the network are adjusted, the environmental impact of signal transmission is evaluated, and the adjusted communication link weight data is obtained; Recalculating the optimal path for data transmission using the adjusted communication link weight data, including evaluating the data delay and load capacity of the path, selecting the path with the optimal data throughput, and obtaining the optimal communication path configuration; Based on the optimal communication path configuration, simulate the impact of new drones joining and leaving the network, re-evaluate and optimize the communication path, analyze the load changes and connection stability of multiple paths, and generate network dynamic adjustment results; Monitor the dynamic adjustment results and optimized communication paths of the network, detect communication failures in real time, record the types and frequencies of failures, and generate UAV group communication status monitoring results.

2. The drone group communication support method according to claim 1, characterized in that: The communication quality score table includes location coordinates, signal strength index, and environmental interference index; the benchmark connection node data includes node coordinates, communication quality index, and reference signal value; the adjusted communication link weight data includes weight value, environmental impact index, and signal optimization index; the optimal communication path configuration includes path delay value, path load index, and throughput index; the network dynamic adjustment results include load change index, stability score, and network adjustment impact; the drone swarm communication status monitoring results include fault type, fault frequency index, and fault detection record.

3. The drone group communication support method according to claim 1, characterized in that: The steps of using radio frequency sensors to capture the location coordinates and signal quality of each drone, analyzing the signal strength and environmental interference data, and calculating the communication quality index based on the data to obtain the communication quality score table are as follows: Using a radio frequency sensor to capture the position coordinates and signal quality of each drone, recording the position coordinates and signal quality parameters of each drone within a preset time interval, and generating a position signal data set; Screening the data in the position signal data set to remove outliers caused by environmental factors, using the corrected data to evaluate the signal strength differences between drones and the impact of environmental interference, optimizing the signal capture effect by adjusting the receiving frequency and filter settings of the radio frequency sensor, and obtaining preliminary analysis data on communication quality; The communication quality index between UAVs is calculated by using the preliminary communication quality analysis data. By comparing the communication quality between UAV combinations, the associated data is merged and a communication quality scoring standard is formulated to generate a communication quality scoring table.

4. The drone group communication support method according to claim 1, characterized in that: Through the communication quality score table, the drone with the best signal and the least interference is selected as the network base node, the location and communication data of the node are recorded, and the steps of obtaining the benchmark connection node data are as follows: By screening the communication quality score table, based on the signal strength and interference index as screening criteria, determining the drones whose signal strength exceeds the set threshold and whose interference index is lower than the set threshold, recording the signal strength and interference data of the drones, and compiling the screened drones into the base node candidate list; Filter data from the base node candidate list, compare the signal strength and interference data of the drone, select the drone with strong signal and small interference, and record the spatial position data of the drone, including longitude, latitude and flight altitude, to obtain network node positioning information; According to the position recorded in the network node positioning information, the communication signal quality and connection stability data of the node in the target environment are captured to obtain the benchmark connection node data.

5. The drone group communication support method according to claim 1, characterized in that: Based on the reference connection node data, combined with real-time meteorological data and terrain changes, the communication link weights in the network are adjusted to evaluate the environmental impact of signal transmission. The steps of obtaining the adjusted communication link weight data are as follows: After deploying radio frequency sensors in the monitoring area according to the benchmark connection node data, real-time meteorological data and terrain change information are synchronously captured, and an environmental synchronization data set is generated by setting a data collection frequency and radio frequency sensor sensitivity adjustment; Using the environmental synchronization data set, by drawing a node environmental impact map, analyzing the relationship between signal strength and environmental factors, including terrain and weather conditions, adjusting the frequency response range of the radio frequency sensor, and generating a signal environmental response analysis result; According to the signal environment response analysis result, the weight values ​​are adjusted link by link to match the environment compatibility and communication requirements, the link configuration is optimized, and the adjusted communication link weight data is obtained.

6. The drone group communication support method according to claim 1, characterized in that: The steps of recalculating the optimal path for data transmission by using the adjusted communication link weight data, including evaluating the data delay and load capacity of the path, selecting the path with the optimal data throughput, and obtaining the optimal communication path configuration are specifically as follows: Based on the adjusted communication link weight data, a path simulation algorithm is applied to evaluate the data delay of each path, and the delay index of the path is calculated by sending test data packets at regular intervals and recording the transmission time of each path. The path delay performance data is obtained by combining the transmission time with the influence of environmental variables; Using the path delay performance data, the data load capacity of each path is evaluated, the load limit of each path is tested by simulating the transmission of differentiated data volumes, the stability and data throughput of the path are determined, and the path load capacity data is obtained; Paths with high throughput and low latency are selected from the path load capacity data, and the communication efficiency of the selected paths is optimized by setting network parameters to obtain the optimal communication path configuration.

7. The drone group communication support method according to claim 1, characterized in that: The formula of the path simulation algorithm is as follows: Calculate the delay index of each path and get the delay performance value, where D represents the path delay performance index and W represents the path delay performance index. s Represents the weight coefficient of transmission time, T represents the transmission time of each path, W e represents the weight coefficient of the environmental variable, E represents the real-time data of the environmental variable, |V p |Represents the absolute value of the path complexity.

8. The drone group communication support method according to claim 1, characterized in that: Based on the optimal communication path configuration, the steps of simulating the impact of new joining and leaving drones on the network, re-evaluating and optimizing the communication path, analyzing the load changes and connection stability of multiple paths, and generating the network dynamic adjustment results are as follows: According to the optimal communication path configuration, on the current drone network structure, monitor the access points of newly added drones and the nodes of drones exiting, record the state changes of the nodes one by one, including the timestamps of access and exit, adjust the network topology map, and generate a network node update table; Using the network node update table, analyzing the data traffic and transmission rate of multiple communication paths, recording the utilization and response time of each path, marking the heavily loaded paths and the low latency paths, performing a preliminary evaluation of the communication paths, and generating a preliminary analysis table of the communication paths; According to the preliminary analysis table of the communication path, the data flow direction is adjusted, the data transmission path in the network is optimized by adjusting the number of node connections, the load is balanced, and the network is reconfigured to maintain the stability of the connection, thereby generating a network dynamic adjustment result.

9. The drone group communication support system according to claim 1, characterized in that: The specific steps of monitoring the communication path after the dynamic adjustment result and optimization of the network, detecting communication failures in real time, recording the failure types and frequencies, and generating the UAV group communication status monitoring results are as follows: According to the dynamic adjustment result of the network, monitoring hardware is installed to capture the data transmission activities on the optimized communication path in real time, and by continuously monitoring the network signal, the information of each data interruption and transmission delay, including the start and end time of the event, is recorded to generate a real-time communication fault log; Extracting fault events from the real-time communication fault log and classifying the fault events, including signal interruption, data packet loss frequency and network conditions when the fault occurs, and generating fault event classification results by analyzing the common causes and characteristics of the fault through information; The fault event classification results are used to analyze the overall communication status of the drone swarm. By evaluating the severity and frequency of the fault impact and evaluating the communication stability, the drone swarm communication status monitoring results are obtained.

10. A drone group communication support system, characterized in that: According to the drone group communication support method according to any one of claims 1 to 9, the system comprises: The position and signal quality capture module uses radio frequency sensors to capture the position coordinates and signal quality of each drone, analyzes the signal strength and environmental interference, and generates key morphological features by comparing and analyzing the communication capabilities of the drones; The network base node selection module selects the UAV with the best signal quality and the least interference according to the key morphological features, records the location and communication quality data of the selected UAV as the network base node, calculates the reference connection node data, and establishes the node selection dynamics; The communication link weight adjustment module performs real-time adjustment of the communication link weight based on the node selection dynamic and real-time meteorological and terrain change information, analyzes the potential environmental impact in signal transmission, optimizes link weight allocation, and obtains link weight adjustment records; The optimal communication path configuration module uses the link weight adjustment record to evaluate the data delay and load capacity of the current communication path, recalculate the optimal path for data transmission, and construct an optimized communication path configuration; the network dynamic adjustment and monitoring module simulates the impact of new joining and leaving drones on the network based on the optimized communication path configuration, performs real-time evaluation and optimization of the communication path, analyzes and adjusts the load changes and connection stability of multiple paths, and generates drone group communication status monitoring results.

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