Unmanned aerial vehicle network system and method for communication recovery in unmanned areas or disaster areas

Abstract

The present application relates to the field of drone network communication technology, and discloses a drone network system and method for communication recovery in uninhabited areas or disaster areas, the method comprising: performing geographic information analysis on the uninhabited areas or disaster areas to obtain a drone deployment plan and an adaptive network routing model; controlling multiple drones equipped with satellite communication equipment to establish an initial communication network with a communication satellite network; performing self-organizing networking on multiple drones to obtain a mesh network between drones; delivering multi-band network signals to portable ground stations and emergency communication terminals to generate a ground coverage network; optimizing the high-speed data forwarding path of the ground coverage network to obtain a target data transmission path; executing a drone rotation plan and dynamic resource allocation based on the target data transmission path to obtain a final high-coverage communication network, thereby achieving fast, efficient, and lasting communication recovery.

Description

Technical Field

[0001] The present application relates to the field of drone network communication technology, and in particular to a drone network system and method for communication recovery in uninhabited areas or disaster areas. Background Art

[0002] When natural disasters or emergencies occur in remote and uninhabited areas, communication infrastructure is often severely damaged or lacking, resulting in communication interruptions, which seriously affects rescue work and information transmission. Traditional communication recovery methods, such as deploying temporary base stations or using satellite phones, often have problems such as long deployment time, limited coverage, and insufficient communication capacity, and cannot meet the needs of large-scale, rapid-response communication recovery.

[0003] In recent years, the rapid development of drone technology has provided new possibilities for solving this problem. Drones have the advantages of strong mobility, rapid deployment, and wide coverage, and can be used as aerial communication nodes to quickly build temporary communication networks. However, in practical applications, there are still challenges such as how to effectively coordinate the deployment of multiple drones, realize self-organizing networks, optimize resource allocation, and ensure the long-term stable operation of the network. In addition, due to the complex terrain of disaster areas or uninhabited areas and the dynamic changes in communication needs, how to dynamically adjust the network structure according to actual conditions, optimize the data transmission path, and achieve long-term operation of the network under limited energy conditions are all key issues that need to be solved. Summary of the invention

[0004] The present application provides a drone network system and method for communication recovery in uninhabited areas or disaster areas, thereby achieving rapid, efficient and lasting communication recovery.

[0005] In a first aspect, the present application provides a drone network method for communication restoration in an unmanned area or a disaster area, and the drone network method for communication restoration in an unmanned area or a disaster area includes:

[0006] Analyze geographic information of uninhabited areas or disaster areas to obtain UAV deployment plans and adaptive network routing models;

[0007] According to the drone deployment plan, multiple drones equipped with satellite communication equipment are controlled to establish links with a communication satellite network to obtain an initial communication network;

[0008] Based on the initial communication network, the plurality of drones are self-organized to obtain a mesh network between the drones;

[0009] Delivering multi-band network signals to portable ground stations and emergency communication terminals through the mesh network between the drones to generate a ground coverage network;

[0010] By using the adaptive network routing model, the high-speed data forwarding path of the ground coverage network is optimized to obtain a target data transmission path;

[0011] Based on the target data transmission path, drone rotation plan and dynamic resource allocation are executed to obtain a final high-coverage communication network.

[0012] A second aspect of the present application provides a drone network device for communication restoration in an unmanned area or a disaster area, wherein the drone network device for communication restoration in an unmanned area or a disaster area comprises:

[0013] The analysis module is used to analyze geographic information of uninhabited areas or disaster areas to obtain UAV deployment plans and adaptive network routing models;

[0014] An establishment module, used to control multiple drones equipped with satellite communication equipment to establish links with a communication satellite network according to the drone deployment plan to obtain an initial communication network;

[0015] A self-organizing network module, used for self-organizing the plurality of drones based on the initial communication network to obtain a mesh network between the drones;

[0016] A generation module, used to deliver multi-band network signals to portable ground stations and emergency communication terminals through the mesh network between the drones to generate a ground coverage network;

[0017] An optimization module, configured to optimize the high-speed data forwarding path of the ground coverage network through the adaptive network routing model to obtain a target data transmission path;

[0018] The allocation module is used to execute the drone rotation plan and dynamic resource allocation based on the target data transmission path to obtain the final high-coverage communication network.

[0019] The third aspect of the present application provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned drone network method for communication restoration in uninhabited areas or disaster areas.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is executed on a computer, the computer executes the above-mentioned drone network method for communication restoration in uninhabited areas or disaster areas.

[0021] Compared with the prior art, the present application has the following beneficial effects: by analyzing geographic information of unmanned areas or disaster areas, a drone deployment scheme and an adaptive network routing model are obtained, which realizes the rapid deployment and initialization of the drone network and improves the efficiency of communication recovery. The initial communication network is constructed by establishing links between multiple drones and a communication satellite network, which enhances the communication capability and coverage of the system. Based on the initial communication network, a drone self-organizing network is realized to form a mesh network structure, which improves the robustness and adaptability of the network. Multi-band network signals are delivered to the ground through the drone mesh network to generate a ground coverage network, which expands the communication coverage and enhances the compatibility of the system with different communication devices. The ground coverage network is optimized by using an adaptive network routing model to optimize the high-speed data forwarding path, which improves the data transmission efficiency and network performance. The drone rotation plan and dynamic resource allocation are implemented to achieve long-term stable operation of the network and efficient use of resources. The communication link sleep and activation strategy is adopted to effectively reduce the system energy consumption and extend the working time of the network. Through real-time monitoring and dynamic adjustment, the system can adapt to complex and changeable communication environments and ensure the stability of communication quality.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a UAV network method for communication recovery in an unmanned area or disaster area provided by an embodiment of the present invention;

[0025] Figure 2 It is a schematic block diagram of the structure of a drone network device for communication recovery in an unmanned area or a disaster area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0028] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0029] It should be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 , an embodiment of the drone network method for communication recovery in an unmanned area or disaster area in the embodiment of the present application includes:

[0030] Step 100: Analyze geographic information of uninhabited areas or disaster areas to obtain a UAV deployment plan and an adaptive network routing model;

[0031] It is understandable that the execution subject of the present application can be a drone network device used for communication restoration in an unmanned area or a disaster area, or a terminal or a server, which is not specifically limited here. The present application embodiment takes a server as the execution subject as an example for explanation.

[0032] Specifically, the geographic information and communication demand data of the uninhabited area or disaster area are integrated and processed, and the communication coverage priority map of the target area is obtained by combining the geographical environment characteristics and emergency communication needs. The map determines the communication coverage priority of each area by analyzing the urgency of communication needs, terrain characteristics, population density distribution, and potential disaster impacts in different areas. Based on the communication coverage priority map, multiple potential drone deployment points are calculated to obtain the initial drone deployment coordinate set. In this process, the communication coverage effect, terrain height difference, and possible obstacle locations between different points are analyzed by algorithms to ensure that the deployment of drones can maximize the coverage of communication blind spots and reduce the degradation of communication quality caused by terrain obstruction. The initial drone deployment coordinate set is analyzed for terrain obstacles, and the impact of mountains, buildings, and trees on signal propagation is considered to optimize a practical drone deployment plan. A network topology map is generated according to the drone deployment plan to clarify the communication links between drones and the connection relationship with ground stations and satellite networks. In order to ensure the self-organizing network capability of the network, preset self-organizing network protocol parameters are designed according to the topology map. These parameters include communication frequency between nodes, bandwidth allocation strategy, routing priority, etc. Based on the preset parameters of the self-organizing network protocol, the possible future network traffic is predicted, including the changes in communication demand and the increase or decrease in data transmission volume that may occur after a disaster or in an uninhabited area. Through the prediction, a dynamic network load distribution diagram is obtained, which reflects the load of each node in the network in real time and helps identify potential network congestion points and communication bottlenecks. An adaptive routing model is constructed based on the dynamic network load distribution diagram. The model dynamically adjusts the routing strategy by analyzing the load of each drone node, the quality of the communication link, and the overall traffic trend of the network to ensure that the communication data can be transmitted in the network along the optimal path, thus obtaining an efficient and reliable adaptive network routing model.

[0033] Step 200: Control multiple drones equipped with satellite communication equipment to establish links with a communication satellite network according to the drone deployment plan to obtain an initial communication network;

[0034] Specifically, satellite visibility analysis is performed for each deployment point in the UAV deployment plan. Taking into account the geographical location, terrain features and satellite orbit data of the deployment point, a list of available satellites that can receive signals at each deployment point within a given time is determined. The optimal satellite link is assigned to each UAV based on the list of available satellites. Considering the visibility of the satellite, and taking into account factors such as the satellite's communication load, signal strength, and link stability, it is ensured that each UAV can establish an efficient and stable communication link with the most suitable satellite, and a UAV-satellite matching table is obtained to clarify the satellite that each UAV is paired with. According to the UAV-satellite matching table, the optimal pointing angle of the satellite communication antenna of each UAV is calculated to ensure that the antenna can accurately align with the selected satellite, maximize the signal reception strength and link quality, and obtain a set of antenna pointing control parameters, which are used to guide the attitude adjustment of the UAV so that it can optimize the communication link with the satellite. The attitude of the satellite communication equipment of each UAV is adjusted based on the antenna pointing control parameters, including changing the flight attitude of the UAV, the pitch angle of the antenna, the azimuth angle, etc., until each UAV reaches the optimized signal reception state. The signal strength measurement and link quality evaluation are performed on the optimized signal reception state. The initial link performance data is generated by real-time detection of key performance indicators such as signal strength, data transmission rate, bit error rate, etc. of each link. Based on the initial link performance data, the satellite links of multiple drones are load balanced to avoid communication bottlenecks caused by excessive loads on some links, optimize the performance of the entire communication network, and obtain the initial communication network topology.

[0035] Step 300: Ad hoc networking is performed on multiple drones based on the initial communication network to obtain a mesh network between the drones;

[0036] It should be noted that the initial communication network topology is analyzed to identify the neighboring node list of each drone. The neighboring node list reflects the possibility of direct communication between each drone and other drones around it. The communication link quality between each pair of drones is calculated based on the neighboring node list to obtain a link quality matrix. This matrix integrates multiple indicators such as signal strength, bandwidth utilization, and bit error rate to reflect the communication link status between drones. Based on the link quality matrix, the topology of the entire drone network is optimized, and the connection relationship between nodes is adjusted to ensure that the entire network achieves the best balance between communication efficiency and stability, and a preliminary mesh network structure is obtained. The preliminary mesh network structure is analyzed by distributed routing protocols to generate a routing table for each drone. The routing table includes the optimal path information to reach other nodes, and also takes into account the dynamic change characteristics of the network, such as node movement, link quality fluctuations, etc., so that the network can flexibly respond to different communication needs and environmental changes. Based on the generated routing table, a network traffic distribution model is constructed and an initial data flow distribution plan is formulated. This plan determines how data is transmitted between drone nodes in the mesh network to distribute the network load as evenly as possible and prevent some nodes from being overloaded. Perform load balancing calculations on the initial data flow allocation plan to obtain an optimized data flow allocation plan. Construct a network resource allocation diagram based on the optimized data flow allocation plan to depict the allocation of resources for each node in the network, including bandwidth, computing power, etc. Perform fault detection and self-healing processing based on the network resource allocation diagram. By monitoring possible node failures or link interruptions in the network, trigger the self-healing mechanism in time, adjust routing and resource allocation, and maintain the normal operation of the network. Through these steps, a mesh network between drones is obtained.

[0037] Step 400: Deliver multi-band network signals to portable ground stations and emergency communication terminals through a mesh network between drones to generate a ground coverage network;

[0038] Specifically, the coverage area of ​​the mesh network between drones is divided to determine the signal delivery range of each drone. This division is based on the flight altitude, coverage radius and relative position of the drones to ensure that the ground coverage area is maximized without overlap and seamless connection. The multi-band signal parameters of each drone are calculated based on the signal delivery range, and a frequency band allocation plan is formulated. The communication characteristics, potential interference and receiving capabilities of each frequency band are considered to optimize the efficiency of frequency band use and avoid waste of spectrum resources. The multi-band network signal conversion device of each drone is configured according to the frequency band allocation plan to obtain a signal transmission parameter set. These parameters include the frequency, bandwidth, transmission power, etc. of the signal, which directly affect the quality and range of drone signal coverage. Based on the signal transmission parameter set, the signal strength of the ground area is simulated to generate a theoretical coverage map. By simulating the path of signal propagation in the air, considering the effects of multipath attenuation, environmental reflection and scattering, the possible signal strength distribution of each ground area is predicted. The terrain attenuation model is applied to the theoretical coverage map. The model combines geographic information, such as terrain features such as mountains, buildings, and forests, to predict the attenuation of the signal and obtain a more realistic actual signal coverage prediction map. According to the actual signal coverage prediction map, the hovering position and antenna pointing of the drone are adjusted to optimize the signal delivery strategy. Through adjustment, the drone can avoid terrain obstruction, enhance the uniformity and effectiveness of signal coverage, and obtain an optimized signal delivery strategy. Based on this strategy, perform simultaneous broadcasting of multi-band signals to establish an initial ground coverage network. Perform real-time signal quality monitoring on the initial coverage network. By monitoring key parameters such as real-time signal strength, bit error rate, and delay, potential communication quality issues can be identified and addressed. At the same time, dynamic spectrum management is performed to dynamically adjust frequency band allocation according to actual communication needs and network load to prevent signal interference, maximize the utilization efficiency of network resources, and obtain a ground coverage network.

[0039] Step 500: Optimize the high-speed data forwarding path of the ground coverage network through an adaptive network routing model to obtain a target data transmission path;

[0040] Specifically, the user access data in the ground coverage network is collected to obtain real-time user distribution information, including the number of users, location distribution, changes in communication needs, etc. in each area, to accurately reflect the current network usage. Based on the real-time user distribution information, the communication needs of each area are calculated to obtain a regional communication load diagram. The load diagram shows the communication pressure in different areas, which helps to identify high-load areas and possible bottlenecks. According to the generated regional communication load diagram, the coverage of the drone is dynamically divided to obtain a multi-drone collaborative coverage plan. By adjusting the coverage area of ​​each drone, it is ensured that the high-load area can obtain sufficient communication resources, while the resources in the low-load area can be appropriately reduced to optimize the resource allocation of the overall network. The multi-drone collaborative coverage plan is analyzed for network topology, and a network link status table is generated to record the real-time status of each link in the network, such as bandwidth utilization, delay, bit error rate, etc. Based on the network link status table, a network traffic model is constructed to predict the distribution of future data traffic and generate data traffic distribution prediction results. According to the data traffic distribution prediction results, preliminary network resource allocation is performed to generate an initial resource allocation plan. The plan aims to reasonably allocate bandwidth, computing resources, etc. in the network to meet the communication needs of different areas. Perform load balancing analysis on the initial resource allocation plan to identify and adjust possible resource allocation imbalances and generate an optimized resource allocation strategy. Based on the optimized resource allocation strategy, generate a set of data forwarding rules, formulate a high-speed data forwarding plan, determine the optimal data transmission path in the network, and ensure the effective use of resources to prevent bottlenecks or congestion in the network. At the same time, in order to improve the robustness and reliability of the network, perform path redundancy analysis on the high-speed data forwarding plan and generate a backup routing table. The backup routing table provides backup options for each data transmission path to deal with possible network failures or emergencies. Update the network node configuration according to the high-speed data forwarding plan and the backup routing table to obtain the target data transmission path.

[0041] Step 600: Execute the drone rotation plan and dynamic resource allocation based on the target data transmission path to obtain the final high-coverage communication network.

[0042] Specifically, the network topology analysis of the target data transmission path is performed to obtain the UAV node connection relationship diagram, which shows the connection status of each UAV with other nodes, as well as their position and role in the entire network structure. Through topological analysis, redundant links in the network are identified, and then a redundant communication link list is generated, listing the communication links that can be temporarily closed or dormant under the current network configuration. Based on the redundant communication link list, a communication link dormancy sequence is formulated, and a network resource saving scheme is designed. This scheme reduces unnecessary communication resource consumption by putting some redundant links into dormant state, while avoiding link overload. In order to ensure that the overall performance of the network is not negatively affected, the communication capacity of the network resource saving scheme is evaluated to obtain the optimized network topology. Based on the optimized network topology, energy consumption analysis is performed to generate a UAV energy consumption status table, which records the current energy consumption of each UAV, including its battery power, communication load and flight status. According to the UAV energy consumption status table, which communication links should be activated or rotated is reasonably planned, and a communication link rotation plan is obtained. This plan extends the battery life of the overall network by dynamically adjusting the communication links between UAVs, and avoids network crashes caused by excessive power consumption of individual UAVs. Perform timing simulation on the communication link rotation plan to generate a link switching schedule to avoid network interruption or data loss during the switching process. Based on the link switching schedule, redistribute network traffic to adapt to the new round of link configuration changes and form a dynamic traffic distribution plan. With the redistribution of traffic, some communication resources may become redundant. The redundant resources are redistributed to adjacent nodes to generate a load balancing strategy. This strategy balances the communication load in the network to avoid overloading of some nodes or waste of resources, thereby improving the overall efficiency of the network. The load balancing strategy is used to schedule network resources, and after multiple adjustments and optimizations, a high-coverage communication network is finally formed.

[0043] In the embodiment of the present application, by analyzing the geographic information of the unmanned area or the disaster area, the UAV deployment scheme and the adaptive network routing model are obtained, the rapid deployment and initialization of the UAV network are realized, and the efficiency of communication recovery is improved. The initial communication network is constructed by establishing links between multiple UAVs and the communication satellite network, which enhances the communication capability and coverage of the system. Based on the initial communication network, the UAV self-organizing network is realized to form a mesh network structure, which improves the robustness and adaptability of the network. Through the UAV mesh network, multi-band network signals are delivered to the ground to generate a ground coverage network, which expands the communication coverage and enhances the compatibility of the system with different communication devices. The adaptive network routing model is used to optimize the high-speed data forwarding path of the ground coverage network, which improves the data transmission efficiency and network performance. The UAV rotation plan and dynamic resource allocation are executed to achieve long-term stable operation of the network and efficient use of resources. The communication link sleep and activation strategy is adopted to effectively reduce the system energy consumption and extend the working time of the network. Through real-time monitoring and dynamic adjustment, the system can adapt to complex and changeable communication environments and ensure the stability of communication quality.

[0044] In a specific embodiment, the process of executing step 100 may specifically include the following steps:

[0045] Integrate and process geographic information and communication demand data of uninhabited areas or disaster areas to obtain a communication coverage priority map of the target area;

[0046] Calculate multiple potential UAV deployment points based on the communication coverage priority map to obtain an initial UAV deployment coordinate set;

[0047] Perform terrain obstacle analysis on the initial UAV deployment coordinate set to obtain the UAV deployment plan;

[0048] Generate a network topology diagram based on the UAV deployment plan and obtain the preset self-organizing network protocol parameters;

[0049] Based on the preset self-organizing network protocol parameters, network traffic prediction is performed to obtain a dynamic network load distribution diagram;

[0050] An adaptive routing model is constructed for the dynamic network load distribution graph to obtain an adaptive network routing model.

[0051] Specifically, by collecting geographic information of uninhabited areas or disaster areas, key data such as regional terrain features, obstacle distribution, and altitude are obtained. Communication demand data is determined through post-disaster assessments or emergency response plans, including the number and distribution of rescuers and trapped personnel, the location of key communication nodes, and other information. The geographic information and communication demand data of uninhabited areas or disaster areas are integrated and processed, and a communication coverage priority map of the target area is generated through a weighted algorithm. For example, communication needs may be more urgent in certain rescue centers, medical points, or densely populated areas. The priority of each area is calculated using a weighted formula:

[0052] ;

[0053] in, Indicates area Communication coverage priority, It is a region The complexity of the terrain, It is a region The user density, It is a region The urgency of communication needs. , , are the weight coefficients of these factors respectively. The selection of coefficients needs to be adjusted according to the actual situation. For example, in different terrains such as mountains or cities, the terrain complexity The weight of may be higher. Based on the generated communication coverage priority map, multiple potential drone deployment points are calculated through coverage algorithms and optimization methods. The algorithm usually adopts a coverage model. For example, when optimizing the deployment location of drones, consider using K-means clustering or Voronoi diagram segmentation methods to divide the entire area into several sub-areas to ensure that the communication needs in each sub-area can be covered by the least number of drones. Perform terrain obstacle analysis on the initial drone deployment coordinate set. Combined with geographic information system (GIS) data, terrain obstacles near the deployment point, such as mountains, buildings or trees, may hinder the propagation of drone communication signals. Based on these analyses, the deployment plan may need to be adjusted, such as by changing the flight altitude of the drone or adjusting the deployment location of the drone to ensure that the signal can cover the target area to the maximum extent and reduce the signal blind spots caused by terrain obstacles. Generate a network topology map based on the drone deployment plan to describe the connection relationship between each drone and the communication link between the drone and the ground station. The core of network topology generation is to determine the communication links between nodes and configure self-organizing network protocol parameters for these links. The parameters of the self-organizing network protocol include communication frequency, bandwidth, routing selection strategy, etc. These parameters will directly affect the self-organizing network capability and communication efficiency of the network. Usually, these network parameters are optimized based on the shortest path algorithm (such as Dijkstra algorithm) or the maximum flow algorithm to ensure that the network can be quickly formed and stably operated in the complex environment of disaster areas or uninhabited areas. Based on the preset self-organizing network protocol parameters, the possible network traffic in the future is predicted. Network traffic prediction usually involves a lot of statistical analysis and simulation calculations. In order to simplify the analysis, a basic network traffic prediction model is introduced. For example, assume that the traffic prediction model is as follows:

[0054] ;

[0055] in, Representation Node Expected network traffic at Representation Node The user density at Representation Node and nodes The distance between them. Through the formula, the traffic of each node in the network is preliminarily predicted, and a dynamic network load distribution diagram is generated. According to the dynamic network load distribution diagram, an adaptive routing model is constructed. The goal of the adaptive routing model is to dynamically adjust the data transmission path according to the load situation in the network to ensure that the traffic distribution of the entire network is balanced and to avoid bottleneck problems caused by excessive load on some nodes. Adaptive routing is usually implemented in conjunction with dynamic routing protocols (such as AODV or DSDV), which can respond to changes in network topology in real time and select the optimal path for data transmission.

[0056] In a specific embodiment, the process of executing step 200 may specifically include the following steps:

[0057] Perform satellite visibility analysis on each deployment point in the UAV deployment plan to obtain a list of available satellites for each deployment point;

[0058] Assign the best satellite link to each UAV based on the list of available satellites to obtain a UAV-satellite matching table;

[0059] The satellite communication antenna pointing angle of each UAV is calculated according to the UAV-satellite matching table to obtain the antenna pointing control parameters;

[0060] The satellite communication equipment of each UAV is adjusted in attitude based on the antenna pointing control parameters to obtain the optimized signal reception status;

[0061] Perform signal strength measurement and link quality evaluation on the optimized signal reception state to obtain initial link performance data;

[0062] The satellite links of multiple UAVs are load balanced according to the initial link performance data to obtain the initial communication network topology.

[0063] Specifically, satellite visibility analysis is performed for each deployment point in the drone deployment plan, and the geographic coordinates and satellite orbit data of the deployment point are calculated to determine which satellites are within the visible range of the deployment point within a given time and can provide sufficient signal strength to maintain communication. To achieve this goal, the following formula is used to calculate the elevation angle of the satellite relative to the ground deployment point:

[0064] ;

[0065] in, is the elevation angle of the satellite relative to the ground deployment point, is the radius of the Earth, is the altitude of the satellite, is the satellite's horizon angle (the geocentric angle between the ground station and the satellite). By calculating the elevation angle of each satellite, determine whether it is above the horizon (usually an elevation angle greater than 10 is required to be considered an available satellite), and finally obtain a list of available satellites. Assign the optimal satellite link to each drone based on the list of available satellites. Consider multiple factors, including the satellite's signal strength, link quality, bandwidth availability, and satellite load. For example, select the satellite with the highest signal strength and the smallest current load for each drone to maximize the stability of the communication link. The optimization goal can be defined by the following objective function:

[0066] ;

[0067] in, For optimal satellite selection, is the received signal power, is the link load. By solving the objective function, the optimal satellite link is assigned to each drone and a drone-satellite matching table is generated. The satellite communication antenna pointing angle of each drone is calculated according to the drone-satellite matching table to ensure that the antenna can accurately align with the selected satellite. The azimuth and elevation angles of the antenna are calculated based on the position of the drone and the orbital parameters of the selected satellite. These parameters are called antenna pointing control parameters, and the calculation formula is:

[0068] ;

[0069] in, is the azimuth of the antenna, and are the longitudes of the satellite and deployment point, respectively. is the latitude of the satellite. By calculating these angles, accurate pointing parameters are provided for the drone's antenna. Based on the antenna pointing control parameters, the drone can adjust its attitude to optimize the communication link with the satellite. Attitude adjustment usually includes adjusting the antenna's pitch, roll, and yaw angles to ensure that the antenna can stably align with the satellite. After the attitude adjustment is completed, the signal strength measurement and link quality evaluation are performed on the optimized signal reception state. The measurement usually includes key indicators such as received signal strength (RSSI), signal-to-noise ratio (SNR), and bit error rate (BER). These data are used to evaluate the initial performance of each link and detect whether further adjustments are needed. Based on the initial link performance data, the satellite links of multiple drones are load balanced to ensure that each satellite link can evenly carry the communication load and avoid performance degradation of some links due to overuse. Load balancing can be achieved by adjusting the forwarding path of data packets or dynamically redistributing communication tasks to obtain a balanced initial communication network topology. For example, suppose that among several drones deployed in the disaster area, the communication link between drone A and satellite 1 performs well, but its load is high, while the link between drone B and satellite 2 has a light load but a slightly weak signal. Through the load balancing algorithm, part of the communication tasks of drone A are transferred to drone B to relieve the load pressure of A and ensure the stability and efficiency of the entire network.

[0070] In a specific embodiment, the process of executing step 300 may specifically include the following steps:

[0071] Analyze the initial communication network topology to obtain the neighboring node list of each UAV;

[0072] Calculate the communication link quality between each pair of drones based on the neighboring node list to obtain the link quality matrix;

[0073] The topology of the UAV network is optimized according to the link quality matrix to obtain the initial mesh network structure;

[0074] Perform distributed routing protocol analysis on the initial mesh network structure to obtain the routing table of each drone;

[0075] A network traffic distribution model is constructed based on the routing table to obtain an initial data flow distribution plan, and a load balancing calculation is performed on the initial data flow distribution plan to obtain an optimized data flow distribution plan;

[0076] A network resource allocation graph is constructed according to the optimized data flow allocation scheme, and fault detection and self-healing processing are performed based on the network resource allocation graph to obtain a mesh network between drones.

[0077] Specifically, the topology of the initial communication network is analyzed to determine the neighboring node list of each drone. The neighboring node list refers to the set of other drones that each drone can directly communicate with within its communication range. In order to generate this list, the location information and communication range of each drone are obtained, and then the distance calculation is used to identify those nodes within the specified communication range. Set the drone and drones The distance is ,if is less than or equal to the maximum communication range R of the drone, then the drone Considered to be a drone The communication link quality between each pair of drones is calculated based on the neighboring nodes. The communication link quality depends on many factors, including signal strength, bandwidth, interference level, and delay. The link quality can usually be represented by a matrix, which is the link quality matrix. The link quality can be approximately represented by the following formula:

[0078] ;

[0079] in, It's a drone and drones The link quality between represents the signal-to-noise ratio, is the bandwidth, represents the link delay, and It indicates the interference degree of the link. By calculating the link quality between all pairs of drones, a link quality matrix is ​​generated to reflect the communication quality between different nodes in the entire network. The topology of the drone network is optimized according to the link quality matrix. A mesh network structure is constructed by selecting high-quality communication links to maximize the reliability and efficiency of the network. Based on the link quality matrix, the system can use algorithms such as the minimum spanning tree algorithm or other network optimization algorithms to select the best links and connect all drones into an efficient mesh network. The mesh network structure ensures that even if some links fail, data can still be transmitted through other paths to ensure the robustness of the network. The preliminary mesh network structure is analyzed by distributed routing protocols to generate routing tables for each drone. The analysis process of the distributed routing protocol includes determining the optimal path between drones and how to quickly update the routing table when the network topology changes. A commonly used routing protocol is AODV (On-demand Distance Vector Routing Protocol), which can dynamically adjust the path according to demand and generate a detailed routing table for each drone, including the optimal path from the current drone to all other nodes in the network and its related parameters. Based on the generated routing table, a network traffic distribution model is constructed. The core of this model is to determine how to reasonably distribute the data flow in the network to each link to ensure that each communication link in the network is not overloaded. Network traffic distribution usually involves multi-objective optimization algorithms, such as linear programming or genetic algorithms, to select the optimal solution from multiple possible traffic distribution schemes. The preliminary data flow distribution scheme will take into account the current network topology, link quality, and traffic requirements of each node. Load balancing calculation is performed on the initial data flow distribution scheme. By redistributing data traffic, bottlenecks caused by excessive traffic on some links can be avoided, thereby improving the overall performance of the entire network. Load balancing calculations are usually combined with real-time network status information, such as the current link utilization rate, latency, etc., to dynamically adjust the data flow distribution strategy. The optimized data flow distribution scheme can significantly improve the efficiency and stability of the network. According to the optimized data flow distribution scheme, a network resource allocation diagram is constructed to describe the resource usage of each drone node in the entire network, including the allocation of key resources such as bandwidth and processing power. Fault detection and self-healing processing are performed based on the network resource allocation diagram. The purpose of fault detection is to monitor the link status in the network in real time. When problems occur in certain links or nodes, the system can identify and take corresponding self-healing measures in a timely manner. Self-healing processing usually includes recalculating routes, adjusting traffic distribution, and even activating backup links to ensure an efficient and robust mesh network between drones. For example, suppose that during network operation, a main link weakens due to the change in the position of the drone, resulting in a decrease in communication quality. In this case, the fault detection system will promptly identify the quality change of the link and quickly adjust the transmission path of the data stream through the self-healing processing module to avoid network interruption or data loss.

[0080] In a specific embodiment, the process of executing step 400 may specifically include the following steps:

[0081] Divide the coverage area of ​​the mesh network between drones to obtain the signal delivery range of each drone;

[0082] Calculate the multi-band signal parameters of each drone based on the signal delivery range and obtain the frequency band allocation plan;

[0083] According to the frequency band allocation plan, the multi-band network signal conversion device of each UAV is configured to obtain a signal transmission parameter set;

[0084] Based on the signal transmission parameter set, the signal strength of the ground area is simulated to obtain a theoretical coverage map;

[0085] Apply terrain attenuation model to theoretical coverage map to get actual signal coverage prediction map;

[0086] Adjust the drone's hovering position and antenna pointing according to the actual signal coverage prediction map to obtain an optimized signal delivery strategy;

[0087] Based on the optimized signal delivery strategy, multi-band signal synchronous broadcasting is performed to obtain the initial coverage network, and real-time signal quality monitoring and dynamic spectrum management are performed on the initial coverage network to obtain the ground coverage network.

[0088] Specifically, the coverage area of ​​the mesh network between drones is divided, and an independent communication coverage range is assigned to each drone to ensure that the entire target area can be fully covered, while reducing signal overlap and interference, and obtaining the signal delivery range of each drone. The division of coverage areas is usually based on Voronoi diagrams or other geometric segmentation methods. Through these algorithms, the coverage range of the drone is divided into several non-overlapping sub-areas, and each sub-area corresponds to the signal delivery range of a drone. The multi-band signal parameters of each drone are calculated based on the signal delivery range. Select an appropriate communication frequency band and assign a specific frequency range to each drone. Since the propagation characteristics and interference conditions of different frequency bands are different, the allocation of frequency bands must take these factors into account. In order to achieve frequency band allocation, optimization algorithms, such as linear programming or genetic algorithms, are used to maximize spectrum efficiency and minimize signal interference. The formula can be expressed as:

[0089] ;

[0090] in, For the optimized frequency band allocation scheme, represents the signal power, represents the interference power, Represents the noise power. By optimizing this formula, the best frequency band combination is assigned to each drone to achieve efficient communication. According to the frequency band allocation plan, the multi-band network signal conversion device of each drone is configured to generate a signal transmission parameter set, including key technical parameters such as transmission power, modulation method, bandwidth, etc. for each frequency band. For example, if there are many obstacles in a certain area, it may be necessary to increase the transmission power or select a low frequency band to enhance penetration. Based on the signal transmission parameter set, the signal strength of the ground area is simulated to generate a theoretical coverage map. Signal strength simulation usually uses ray tracing or other electromagnetic wave propagation models that can simulate the propagation path of the signal in free space and take into account effects such as path loss, reflection, and refraction. The formula is as follows:

[0091] ;

[0092] in, is the received signal strength, is the transmission power, For distance The path loss at each location is calculated. A theoretical coverage map is generated to reflect the signal strength in different areas. The theoretical coverage map is corrected by applying the terrain attenuation model. The terrain attenuation model takes into account the attenuation effect of geographical factors such as mountains, buildings, and vegetation on the signal. This process is usually implemented through GIS data or DEM (digital elevation model). The corrected coverage map can more accurately reflect the actual signal coverage and generate an actual signal coverage prediction map. According to the actual signal coverage prediction map, the hovering position and antenna pointing of each drone are adjusted to optimize the signal delivery strategy to ensure that each drone can maximize its signal coverage and reduce blind spots and interference. The adjustments may include changing the height of the drone, adjusting the pitch angle and azimuth angle of the antenna, etc. Through these adjustments, full coverage of the target area is achieved. Based on the optimized signal delivery strategy, multi-band signal simultaneous broadcasting is performed to generate an initial ground coverage network. The advantage of simultaneous broadcasting is that multiple frequency bands can be used for communication at the same time, which increases the data transmission rate and enhances the robustness of the network. In this process, coordination and spectrum management between drones are particularly important to prevent frequency band conflicts and interference. To ensure the performance of the initial coverage network, real-time signal quality monitoring and dynamic spectrum management are performed. Signal quality monitoring includes monitoring of indicators such as RSSI (received signal strength indicator), SNR (signal-to-noise ratio), and BER (bit error rate). These data can help identify potential problems in the network, such as signal attenuation and increased interference, and take timely remedial measures. Dynamic spectrum management ensures the best use of network resources by adjusting the allocation and use strategies of frequency bands in real time.

[0093] In a specific embodiment, the process of executing step 500 may specifically include the following steps:

[0094] Collect user access data in the ground coverage network to obtain real-time user distribution information;

[0095] Calculate the communication demand of each area based on the real-time user distribution information and obtain the regional communication load diagram;

[0096] Dynamically divide the drone coverage area according to the regional communication load diagram to obtain a multi-drone collaborative coverage solution;

[0097] Conduct network topology analysis on the multi-UAV collaborative coverage solution and obtain the network link status table;

[0098] Build a network traffic model based on the network link state table to generate data traffic distribution prediction results;

[0099] Preliminarily allocate network resources according to the data traffic distribution prediction results to obtain an initial resource allocation plan;

[0100] Perform load balancing analysis on the initial resource allocation plan to obtain an optimized resource allocation strategy, and generate data forwarding rules based on the optimized resource allocation strategy to obtain a high-speed data forwarding plan;

[0101] The high-speed data forwarding scheme is subjected to path redundancy analysis to obtain a backup routing table, and the network node configuration is updated according to the high-speed data forwarding scheme and the backup routing table to obtain a target data transmission path.

[0102] Specifically, user access data in the ground coverage network is collected, including the user's device ID, location, communication behavior, signal strength, and data usage. By analyzing this data, user distribution information is obtained in real time to understand the user density and communication needs in each area. Based on the real-time user distribution information, the communication needs of each area are calculated to generate a regional communication load diagram. The regional communication load diagram reflects the communication pressure in different areas and can be expressed by the formula:

[0103] ;

[0104] in, Indicates area The communication load, Is a user In the area The amount of data required, Is a user The required bandwidth. Through calculation, the load of each area is determined, and the areas with high communication demand and possible network bottlenecks are identified. According to the regional communication load map, the coverage of the drone is dynamically divided to form a multi-drone collaborative coverage plan. The position and coverage radius of the drone are adjusted to ensure that the high-load area can get more communication resources, while the low-load area can appropriately reduce resource allocation. Dynamic division can be achieved through optimization algorithms, with the goal of maximizing the coverage efficiency and communication quality of the overall network. The network topology analysis of the multi-drone collaborative coverage plan is performed to generate a network link status table to record the status of the communication link between each drone, including information such as link bandwidth, delay, packet loss rate, and current load. A network traffic model is constructed based on the network link status table to predict future traffic distribution. The network traffic model generates data traffic distribution prediction results through statistical analysis and historical data prediction. The model can help the system predict which links may be at risk of overload and which areas need more communication resources. According to the data traffic distribution prediction results, the network resources are preliminarily allocated to obtain the initial resource allocation plan. Based on the current network topology and traffic prediction, the plan reasonably allocates bandwidth, computing resources, and storage resources to ensure load balancing of each link and node. Perform load balancing analysis on the initial resource allocation scheme. By adjusting the resource allocation strategy, balance the communication load in the network to avoid overload on some links or nodes. Generate an optimized resource allocation strategy through optimization algorithm. Generate data forwarding rules based on the optimized resource allocation strategy to form a high-speed data forwarding scheme. The high-speed data forwarding scheme defines how to efficiently transmit data in the network, including the forwarding path of data packets, priority processing, etc. Through reasonable forwarding rules, delays can be effectively reduced and the reliability and speed of data transmission can be improved. In order to enhance the robustness of the network, the high-speed data forwarding scheme is analyzed for path redundancy and a backup routing table is generated. The path redundancy analysis takes into account possible network failures or emergencies and designs backup paths for each main transmission path. The backup path can be quickly enabled when the main path fails to ensure uninterrupted data transmission. Update the configuration of the network node according to the high-speed data forwarding scheme and the backup routing table to form the final target data transmission path. After multiple rounds of optimization and redundancy design, the transmission path can provide stable and efficient data transmission services in complex environments such as unmanned areas or disaster areas.

[0105] In a specific embodiment, the process of executing step 600 may specifically include the following steps:

[0106] Perform network topology analysis on the target data transmission path to obtain the UAV node connection relationship diagram;

[0107] Calculate network redundancy based on the drone node connection relationship graph and obtain a list of redundant communication links;

[0108] Generate a communication link sleep sequence according to the redundant communication link list to obtain a network resource saving solution;

[0109] Evaluate the communication capacity of the network resource saving scheme to obtain the optimized network topology;

[0110] Based on the optimized network topology, energy consumption analysis is performed to obtain the UAV energy consumption status table;

[0111] Formulate a link activation rotation strategy based on the UAV energy consumption status table to obtain a communication link rotation plan;

[0112] Perform timing simulation on the communication link rotation plan to obtain the link switching schedule;

[0113] Redistribute network traffic based on the link switching schedule to obtain a dynamic traffic allocation solution;

[0114] According to the dynamic traffic allocation scheme, the surplus communication resources are adjacently reallocated to obtain a load balancing strategy;

[0115] The load balancing strategy is used to schedule network resources to obtain the final high-coverage communication network.

[0116] Specifically, the network topology analysis of the target data transmission path is performed to generate a drone node connection diagram, which reflects the connection status between each drone node and how they are connected to each other through communication links. The network redundancy is calculated based on the drone node connection diagram to evaluate the reliability and fault tolerance of the network. Redundancy reflects the number of backup communication links in the network, which can provide alternative paths when the main link fails. The formula can be expressed as:

[0117] ;

[0118] in, is the network redundancy, is the actual number of links in the current network, is the minimum number of links that meet network connectivity. By calculating the network redundancy, determine which links are redundant in the current situation, and these redundant links constitute a list of redundant communication links. Based on the list of redundant communication links, generate a communication link sleep sequence and formulate a network resource saving plan. The sleep sequence determines which redundant links can be temporarily shut down to reduce energy consumption while ensuring that the overall connectivity and performance of the network are not significantly affected. This process requires comprehensive consideration of the importance and load of the links to ensure that the service quality of the network is not seriously affected when some links are shut down. Perform a communication capacity assessment on the network resource saving plan to determine whether the network can still meet the communication needs of all users after shutting down some redundant links. The core of the assessment is to check whether the capacity of the remaining links is sufficient to bear the entire communication load. The formula can be expressed as:

[0119] ;

[0120] in, is the total communication capacity of the network, The remaining link The capacity, is the communication demand of each user. Through evaluation, it is ensured that the network can still operate normally even if some links enter the dormant state, and an optimized network topology is generated. Based on the optimized network topology, energy consumption analysis is performed to generate a drone energy consumption status table. The energy consumption analysis takes into account factors such as the current power, communication load and expected endurance of each drone. According to the energy consumption status table, a link activation rotation strategy is formulated. By dynamically activating and dormant different communication links, the energy consumption of drones is balanced and the operation time of the entire network is extended. In order to verify the effectiveness of the link activation rotation strategy, timing simulation is performed to generate a link switching schedule. The timing simulation determines the optimal link switching time by simulating the energy consumption and network status of drones at different time points. The link switching schedule ensures that the link switching process can proceed smoothly and avoids communication interruption or performance degradation caused by improper switching. Based on the link switching schedule, network traffic is redistributed to adapt to the new link configuration. The core of network traffic redistribution is to redistribute data traffic to ensure that each activated link can be used efficiently and avoid overloading of some links. Through this process, a dynamic traffic allocation plan is generated to ensure smooth data transmission in the network after link switching. According to the dynamic traffic allocation plan, the surplus communication resources are redistributed to the neighboring nodes to form a load balancing strategy. The load balancing strategy optimizes the performance of the network by allocating excess resources to those nodes or links with heavy loads, preventing the operation of the entire network from being affected by excessive local loads. Based on the load balancing strategy, network resources are scheduled to form the final high-coverage communication network.

[0121] The above describes the drone network method for communication recovery in an unmanned area or disaster area in the embodiment of the present application. The following describes the drone network device 10 for communication recovery in an unmanned area or disaster area in the embodiment of the present application. Figure 2 In one embodiment of the present application, a drone network device 10 for communication recovery in an unmanned area or a disaster area includes:

[0122] The analysis module 11 is used to analyze geographic information of uninhabited areas or disaster areas to obtain a UAV deployment plan and an adaptive network routing model;

[0123] Establishing module 12, used for controlling multiple drones equipped with satellite communication equipment to establish links with the communication satellite network according to the drone deployment plan to obtain an initial communication network;

[0124] A self-organizing network module 13, used to conduct self-organizing networking for multiple drones based on the initial communication network to obtain a mesh network between the drones;

[0125] A generation module 14 is used to deliver multi-band network signals to portable ground stations and emergency communication terminals through a mesh network between drones to generate a ground coverage network;

[0126] An optimization module 15 is used to optimize the high-speed data forwarding path of the ground coverage network through an adaptive network routing model to obtain a target data transmission path;

[0127] The allocation module 16 is used to execute the drone rotation plan and dynamic resource allocation based on the target data transmission path to obtain the final high-coverage communication network.

[0128] Through the cooperation of the above components, the UAV deployment plan and adaptive network routing model are obtained by analyzing the geographic information of unmanned areas or disaster areas, realizing the rapid deployment and initialization of the UAV network and improving the efficiency of communication recovery. The initial communication network is constructed by establishing links between multiple UAVs and the communication satellite network, which enhances the communication capability and coverage of the system. Based on the initial communication network, the UAV self-organizing network is realized to form a mesh network structure, which improves the robustness and adaptability of the network. Through the UAV mesh network, multi-band network signals are delivered to the ground to generate a ground coverage network, which expands the communication coverage and enhances the compatibility of the system with different communication equipment. The adaptive network routing model is used to optimize the high-speed data forwarding path of the ground coverage network, which improves the data transmission efficiency and network performance. The UAV rotation plan and dynamic resource allocation are implemented to achieve long-term stable operation of the network and efficient use of resources. The communication link sleep and activation strategy is adopted to effectively reduce the system energy consumption and extend the working time of the network. Through real-time monitoring and dynamic adjustment, the system can adapt to complex and changing communication environments and ensure the stability of communication quality.

[0129] The present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the drone network method for communication recovery in uninhabited areas or disaster areas in the above-mentioned embodiments.

[0130] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the drone network method for communication recovery in an uninhabited area or a disaster area.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0133] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A drone network method for communication recovery in unmanned areas or disaster areas, characterized in that: The method comprises: Performing geographic information analysis on uninhabited areas or disaster areas to obtain a drone deployment plan and an adaptive network routing model; specifically comprising: integrating and processing the geographic information and communication demand data of the uninhabited areas or disaster areas to obtain a communication coverage priority map of the target area; calculating multiple potential drone deployment points based on the communication coverage priority map to obtain an initial drone deployment coordinate set; performing terrain obstacle analysis on the initial drone deployment coordinate set to obtain a drone deployment plan; generating a network topology map based on the drone deployment plan to obtain preset self-organizing network protocol parameters; performing network traffic prediction based on the preset self-organizing network protocol parameters to obtain a dynamic network load distribution map; constructing an adaptive routing model on the dynamic network load distribution map to obtain an adaptive network routing model; According to the drone deployment plan, multiple drones equipped with satellite communication equipment are controlled to establish links with the communication satellite network to obtain an initial communication network; specifically including: performing satellite visibility analysis on each deployment point in the drone deployment plan to obtain a list of available satellites for each deployment point; allocating an optimal satellite link to each drone based on the list of available satellites to obtain a drone-satellite matching table; calculating the pointing angle of the satellite communication antenna of each drone based on the drone-satellite matching table to obtain antenna pointing control parameters; adjusting the attitude of the satellite communication equipment of each drone based on the antenna pointing control parameters to obtain an optimized signal reception state; performing signal strength measurement and link quality evaluation on the optimized signal reception state to obtain initial link performance data; performing load balancing on the satellite links of multiple drones based on the initial link performance data to obtain an initial communication network topology; Based on the initial communication network, the multiple drones are self-organized to obtain a mesh network between the drones; specifically including: analyzing the topological structure of the initial communication network to obtain a list of neighboring nodes for each drone; calculating the quality of the communication link between each pair of drones based on the neighboring node list to obtain a link quality matrix; topologically optimizing the drone network according to the link quality matrix to obtain an initial mesh network structure; performing distributed routing protocol analysis on the initial mesh network structure to obtain a routing table for each drone; constructing a network traffic distribution model based on the routing table to obtain an initial data flow distribution plan, and performing load balancing calculation on the initial data flow distribution plan to obtain an optimized data flow distribution plan; constructing a network resource allocation diagram according to the optimized data flow distribution plan, and performing fault detection and self-healing processing based on the network resource allocation diagram to obtain a mesh network between drones; A multi-band network signal is delivered to a portable ground station and an emergency communication terminal through the mesh network between the drones to generate a ground coverage network; specifically, the method includes: dividing the coverage area of ​​the mesh network between the drones to obtain the signal delivery range of each drone; calculating the multi-band signal parameters of each drone based on the signal delivery range to obtain a frequency band allocation plan; configuring the multi-band network signal conversion device of each drone according to the frequency band allocation plan to obtain a signal transmission parameter set; performing signal strength simulation on the ground area based on the signal transmission parameter set to obtain a theoretical coverage map; applying a terrain attenuation model to the theoretical coverage map to obtain an actual signal coverage prediction map; adjusting the hovering position and antenna pointing of the drone according to the actual signal coverage prediction map to obtain an optimized signal delivery strategy; performing multi-band signal synchronous broadcasting based on the optimized signal delivery strategy to obtain an initial coverage network, and performing real-time signal quality monitoring and dynamic spectrum management on the initial coverage network to obtain a ground coverage network; Through the adaptive network routing model, the high-speed data forwarding path of the ground coverage network is optimized to obtain the target data transmission path; specifically, the following steps are performed: collecting user access data in the ground coverage network to obtain real-time user distribution information; calculating the communication requirements of each area based on the real-time user distribution information to obtain a regional communication load diagram; dynamically dividing the drone coverage range according to the regional communication load diagram to obtain a multi-drone collaborative coverage plan; performing network topology analysis on the multi-drone collaborative coverage plan to obtain a network link status table; constructing a network traffic model based on the network link status table to generate a data traffic distribution prediction result; performing a preliminary allocation of network resources according to the data traffic distribution prediction result to obtain an initial resource allocation plan; performing a load balancing analysis on the initial resource allocation plan to obtain an optimized resource allocation strategy, and generating data forwarding rules based on the optimized resource allocation strategy to obtain a high-speed data forwarding plan; performing a path redundancy analysis on the high-speed data forwarding plan to obtain a backup routing table, and updating the network node configuration according to the high-speed data forwarding plan and the backup routing table to obtain a target data transmission path; Based on the target data transmission path, a drone rotation plan and dynamic resource allocation are executed to obtain a final high-coverage communication network; specifically, the following steps are performed: a network topology analysis is performed on the target data transmission path to obtain a drone node connection relationship diagram; network redundancy is calculated based on the drone node connection relationship diagram to obtain a redundant communication link list; a communication link sleep sequence is generated according to the redundant communication link list to obtain a network resource saving plan; a communication capacity evaluation is performed on the network resource saving plan to obtain an optimized network topology structure; an energy consumption analysis is performed based on the optimized network topology structure to obtain a drone energy consumption status table; a link activation rotation strategy is formulated according to the drone energy consumption status table to obtain a communication link rotation plan; a timing simulation is performed on the communication link rotation plan to obtain a link switching schedule; network traffic is redistributed based on the link switching schedule to obtain a dynamic traffic allocation plan; neighboring redistribution of surplus communication resources is performed according to the dynamic traffic allocation plan to obtain a load balancing strategy; network resource scheduling is performed on the load balancing strategy to obtain a final high-coverage communication network.

2. A drone network device for communication recovery in unmanned areas or disaster areas, characterized in that: The device is used to execute the drone network method for communication recovery in an unmanned area or a disaster area as claimed in claim 1, wherein the device comprises: The analysis module is used to analyze geographic information of uninhabited areas or disaster areas to obtain UAV deployment plans and adaptive network routing models; An establishment module, used to control multiple drones equipped with satellite communication equipment to establish links with a communication satellite network according to the drone deployment plan to obtain an initial communication network; A self-organizing network module, used to conduct self-organizing networking for the plurality of drones based on the initial communication network to obtain a mesh network between the drones; A generation module, used to deliver multi-band network signals to portable ground stations and emergency communication terminals through the mesh network between the drones to generate a ground coverage network; An optimization module, used to optimize the high-speed data forwarding path of the ground coverage network through the adaptive network routing model to obtain a target data transmission path; The allocation module is used to execute the drone rotation plan and dynamic resource allocation based on the target data transmission path to obtain the final high-coverage communication network.

3. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes the drone network method for communication restoration in uninhabited areas or disaster areas as described in claim 1.

4. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the drone network method for communication recovery in uninhabited areas or disaster areas as described in claim 1 is implemented.

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