Unmanned aerial vehicle distribution scheduling method and system applied to communication interruption area

Abstract

The application provides a kind of unmanned aerial vehicle distribution scheduling method and system applied to communication interruption area, the method includes: determining unmanned aerial vehicle information for communication interruption area, and air networking is carried out to all unmanned aerial vehicles according to unmanned aerial vehicle information;After successful networking, the coordinate set of all unmanned aerial vehicles participating in networking is defined, and a logarithmic path loss model is constructed according to the coordinate set, and the signal propagation intensity of any position point in the communication interruption area is obtained according to the path loss model;According to the signal propagation intensity, a signal coverage rate objective function is constructed, and a network average link quality objective function and a node energy consumption balance objective function are constructed to obtain a comprehensive objective function;With the goal of maximizing the comprehensive objective function, an improved particle swarm optimization algorithm is used to solve, to obtain the deployment position of all unmanned aerial vehicles in the communication interruption area.The application can significantly improve the deployment efficiency and reliability of the disaster area communication system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of unmanned aerial vehicle distribution scheduling, in particular to an unmanned aerial vehicle distribution scheduling method and system applied to a communication interruption area. BACKGROUND

[0002] In extreme situations such as natural disasters, traditional communication facilities are vulnerable to damage, leading to large-scale communication interruption and seriously affecting rescue efficiency. Although some unmanned aerial vehicle platforms have been applied to emergency communication, they are difficult to operate for a long time in complex environments due to limited endurance and stability, and it is urgent to solve the problem of how to quickly and effectively restore and guarantee communication services.

[0003] Unmanned aerial vehicles are considered as an ideal choice for providing temporary communication services in communication interruption areas due to their flexible deployment and rapid response. However, how to efficiently schedule multiple unmanned aerial vehicles to achieve optimal communication coverage, link quality and energy consumption balance is a technical challenge currently faced. Traditional unmanned aerial vehicle scheduling methods often ignore special environmental factors in communication interruption areas, such as complex terrain and uneven signal attenuation, resulting in poor scheduling effect. SUMMARY

[0004] The purpose of the present application is to provide an unmanned aerial vehicle distribution scheduling method and system applied to a communication interruption area, aiming to solve the problem of lack of unmanned aerial vehicle scheduling method or poor scheduling effect in traditional technology for providing temporary communication services in a communication interruption area.

[0005] In a first aspect, the present application provides an unmanned aerial vehicle distribution scheduling method applied to a communication interruption area, the method comprising:

[0006] determining unmanned aerial vehicle information for the communication interruption area, and air networking all unmanned aerial vehicles according to the unmanned aerial vehicle information;

[0007] after successful networking, defining a coordinate set of all unmanned aerial vehicles participating in networking, constructing a logarithmic path loss model according to the coordinate set, and obtaining signal propagation intensity of any position point in the communication interruption area according to the path loss model;

[0008] constructing a signal coverage rate objective function according to the signal propagation intensity, constructing a network average link quality objective function and a node energy consumption balance objective function, and constructing a comprehensive objective function according to the signal coverage rate objective function, the network average link quality objective function and the node energy consumption balance objective function;

[0009] taking the maximization of the comprehensive objective function as the goal, and using an improved particle swarm optimization algorithm to solve, to obtain the deployment position of all unmanned aerial vehicles in the communication interruption area.

[0010] In a second aspect, the present application provides a UAV distribution scheduling system applied to a communication interruption area, the system comprising:

[0011] a networking module configured to determine UAV information for the communication interruption area and to perform aerial networking for all UAVs according to the UAV information;

[0012] a model construction module configured to define a coordinate set of all UAVs participating in the networking after the networking is successful, to construct a logarithmic path loss model according to the coordinate set, and to obtain signal propagation intensity of any position point in the communication interruption area according to the path loss model;

[0013] a target function construction module configured to construct a signal coverage rate target function according to the signal propagation intensity, to construct a network average link quality target function and a node energy consumption balance target function, and to construct a comprehensive target function according to the signal coverage rate target function, the network average link quality target function and the node energy consumption balance target function;

[0014] a deployment position solving module configured to maximize the comprehensive target function and to solve the maximum value by using an improved particle swarm optimization algorithm to obtain deployment positions of all UAVs in the communication interruption area.

[0015] In a third aspect, the present application provides a storage medium storing one or more programs, which are executed by a processor to implement the UAV distribution scheduling method applied to a communication interruption area.

[0016] In a fourth aspect, the present application provides an electronic device comprising a memory and a processor, wherein:

[0017] the memory is configured to store a computer program;

[0018] the processor is configured to execute the computer program stored in the memory to implement the UAV distribution scheduling method applied to a communication interruption area.

[0019] Compared with the prior art, the present application has the following advantages:

[0020] This invention accurately determines the information of UAVs participating in communication tasks and completes aerial networking, which can fully adapt to the complex environment of communication interruption areas, such as terrain undulations and obstacle distribution, making the networking more scientific and reasonable, and laying a solid foundation for subsequent work. After networking, a logarithmic path loss model is constructed, which can accurately calculate the signal propagation intensity at any location in the area, clearly present the distribution of signal coverage strength, quickly locate signal blind spots, and provide accurate data support for optimized scheduling. The subsequent construction of signal coverage, average network link quality, node energy consumption balance objective functions, and comprehensive objective functions comprehensively considers the key elements of communication recovery. It ensures that the signal can cover a wide area, allowing more areas to restore communication; it also focuses on link quality to ensure stable and reliable data transmission; and it also takes into account the energy consumption balance of UAV nodes to prevent some UAVs from prematurely exiting due to excessive energy consumption, thus prolonging the working time of the entire communication network. The subsequent construction of signal coverage, average network link quality, node energy consumption balance objective functions, and comprehensive objective functions comprehensively considers the key elements of communication recovery. It ensures wide signal coverage, allowing more areas to resume communication; it also emphasizes link quality to ensure stable and reliable data transmission; and it takes into account the balanced energy consumption of drone nodes to prevent some drones from prematurely exiting due to excessive energy consumption, thus extending the working time of the entire communication network. Attached Figure Description

[0021] Figure 1 This is a flowchart of a UAV distributed scheduling method for communication interruption areas proposed in an embodiment of the present invention;

[0022] Figure 2 This is a network topology diagram illustrating an example of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of a distributed scheduling system for unmanned aerial vehicles (UAVs) applied to areas with communication disruptions, as proposed in an embodiment of the present invention.

[0024] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0025] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application. Unless otherwise defined, the technical terms or scientific terms used herein should be understood as the common meanings thereof by those skilled in the art. The similar words such as "comprise" used herein mean that the elements or objects before the words cover the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects.

[0026] As shown in Figure 1 An embodiment of the present application provides a UAV distribution scheduling method applied to a communication interruption area, which comprises steps S101 to S104, wherein:

[0027] S101: determining UAV information for the communication interruption area, and performing air networking on all UAVs according to the UAV information;

[0028] It should be noted that the UAV in the embodiment is equipped with a gripper structure. First, the position of the wire is detected by a wire sensor, and the UAV autonomously flies close to and clamps the high-voltage transmission line. The current transformer senses the secondary current from the primary high-voltage bus based on the electromagnetic induction principle, and continuously supplies power to the UAV battery through power conversion and energy storage circuit. This power supply mode breaks through the battery endurance limit and realizes long-term suspension and continuous work of the UAV.

[0029] Secondly, after the UAV is in place on the cable, the initialization of the communication module and the loading of the communication driver are first completed, supporting multiple communication protocols such as LoRa and 5G, to ensure flexible communication capability in different environments. After starting, the UAV periodically broadcasts its own state information, including unique ID, current position coordinates, remaining power, received signal strength (RSSI), signal-to-noise ratio (SNR), timestamp and available routing information, etc. These information provides real-time communication quality and resource status reference for neighboring UAVs.

[0030] After each UAV receives the state information of the neighbor nodes, the relevant data is stored in the local neighbor table and updated regularly. By counting the signal strength and link stability of the neighbor nodes, the sliding window algorithm is used to calculate the mean and stability of the signal, and short-term fluctuation interference is filtered out to ensure the accuracy and robustness of the link quality evaluation.

[0031] Based on the link quality data recorded by the neighbor table, the dynamic routing protocol is run between the unmanned aerial vehicle nodes, and the end-to-end routing path is intelligently established in combination with the least hop number and the optimal link quality strategy.

[0032] Exemplarily, it is assumed that there are currently three unmanned aerial vehicles with cable power taking and air networking capabilities. After arriving at the task site, the three unmanned aerial vehicles clamp the power taking wire on the cable. At this time, each unmanned aerial vehicle will start state broadcasting. The three unmanned aerial vehicles will send their own state information to the surrounding unmanned aerial vehicles, including: ID, coordinates, remaining power, received signal strength (RSSI), signal-to-noise ratio (SNR), timestamp, and available routing information. For example, unmanned aerial vehicle A will receive the state information of unmanned aerial vehicles B and C, and for the same reason, unmanned aerial vehicle B will receive the state information of unmanned aerial vehicles A and C. The received state information will be used to establish a neighbor table, which will record all the nearby unmanned aerial vehicle state information received by the unmanned aerial vehicle. Then the link line is established, and the three unmanned aerial vehicles will calculate the optimal routing path according to the neighbor table running the dynamic routing protocol.

[0033] For example: the link quality between A and B is good; the link quality between B and C is good; the link quality between A and C is poor; the link quality between A and D is poor; the link quality between B and D is good; the link quality between C and D is poor. As shown in the following figure, the routing protocol will select the route with the best link quality to establish a connection, such as A to B to D, as shown in detail. Figure 2

[0034] Step S102: After the networking is successful, the coordinate set of all unmanned aerial vehicles participating in the networking is defined, and a logarithmic path loss model is constructed according to the coordinate set, and the signal propagation strength of any position point in the communication interruption area is obtained according to the path loss model;

[0035] It should be pointed out that in this step, the number of unmanned aerial vehicles participating in networking is first defined as N, and the coordinate set is: , wherein, is the spatial coordinates of the i-th unmanned aerial vehicle, are the x-coordinate, y-coordinate and height of the i-th unmanned aerial vehicle respectively, and T is the coordinate set.

[0036] Then the logarithmic path loss model is constructed according to the following formula:

[0037] ;

[0038] , wherein, is the signal propagation strength of the i-th unmanned aerial vehicle at the spatial position , and are the transmitting and receiving gains respectively, is the reference path loss, and n is the environmental loss factor; is the transmitting power of the i-th unmanned aerial vehicle, ​Reference distance, which is a unit distance for power attenuation calculation in the logarithmic path loss model, usually 10m in long-distance communication, Gaussian random variable is simulated for multipath fading.

[0039] It should be noted that the conventional deployment method usually ignores complex factors such as terrain obstruction and building reflection, and only based on a simplified distance model or an ideal coverage radius, resulting in frequent deployment blind area and communication dead angle. Based on this, the above path loss model is based on the principle of logarithmic path loss, and combines Gaussian fading to model the signal attenuation process in the real environment, and is used to evaluate the received signal strength at any point. The model accurately describes the propagation loss through the environmental loss factor and the fading term, improves the simulation capability and prediction accuracy of the complex disaster area wireless channel.

[0040] Step S103: constructing a signal coverage rate objective function according to the signal propagation intensity, constructing a network average link quality objective function and a node energy consumption balance objective function, and constructing a comprehensive objective function according to the signal coverage rate objective function, the network average link quality objective function and the node energy consumption balance objective function;

[0041] It should be noted that in some embodiments, the communication interruption area is assumed to be meshed into M spatial points, and if the signal propagation intensity of the jth spatial point is greater than a first threshold value, a signal coverage rate objective function is constructed according to the following formula:

[0042]

[0043] Wherein, is the signal coverage rate objective function, if the spatial position is covered by the ith unmanned aerial vehicle, then is 1, otherwise 0; if the spatial position is covered by at least one unmanned aerial vehicle, then is 1, otherwise 0;

[0044] If the signal propagation intensity of the ith unmanned aerial vehicle at the spatial position is greater than or equal to a preset intensity threshold, it is determined that the spatial position is covered by the ith unmanned aerial vehicle;

[0045] If the signal propagation intensity of the ith unmanned aerial vehicle at the spatial position is less than the preset intensity threshold, it is determined that the spatial position is not covered by the ith unmanned aerial vehicle.

[0046] ​It should be noted that the traditional method usually uses a regular grid or an empirical point distribution method, which is difficult to adapt to the actual terrain and task target distribution, and the coverage is limited. Therefore, a signal coverage objective function is constructed to maximize the proportion of effective communication points in the region, ensuring that the relay platform achieves maximum signal penetration rate in the disaster area.

[0047] In addition, in some embodiments, a network average link quality objective function is constructed according to the following formula:

[0048] ;

[0049] A node energy consumption balancing objective function is constructed according to the following formula:

[0050] ;

[0051] The traditional scheme does not consider the difference between the load and the energy supply capacity of the nodes, which may cause some nodes to run for a long time under high load, off-network in advance, and cause network splitting or paralysis. Therefore, by constructing a node energy consumption balancing objective function, the energy consumption and available energy are bound, and load balancing is achieved through global coordination deployment.

[0052] A comprehensive objective function is constructed according to the following formula:

[0053] ;

[0054] Wherein, 、 、 network average link quality objective function, node energy consumption balancing objective function, comprehensive objective function, is a link set, is the inter-chain link signal-to-noise ratio between the i-th unmanned aerial vehicle and the j-th unmanned aerial vehicle, is the online time of the i-th unmanned aerial vehicle, is the data forwarding load of the i-th unmanned aerial vehicle, is the maximum output power of the mutual inductor, are adjustable weight factors.

[0055] In summary, the comprehensive objective function integrates multiple key indicators such as signal coverage, network average link quality, and node energy consumption balancing. Signal coverage ensures that as many locations as possible in the communication interruption area can receive effective signals, which is a basic requirement for communication recovery; network average link quality focuses on the stability and reliability of communication links to ensure accurate data transmission; node energy consumption balancing focuses on the endurance of unmanned aerial vehicles to avoid premature work of some unmanned aerial vehicles due to excessive energy consumption. By integrating these indicators, the pros and cons of the unmanned aerial vehicle distribution and scheduling scheme can be comprehensively evaluated, avoiding the one-sidedness caused by single indicator evaluation.

[0056] Step S104: taking the maximization of the comprehensive objective function as the goal, and using the improved particle swarm optimization algorithm to solve, to obtain the deployment positions of all unmanned aerial vehicles in the communication interruption area.

[0057] It should be pointed out that in the specific solving process, first, K particles are randomly generated, each particle is a set of deployment schemes of all unmanned aerial vehicles, and each set of deployment schemes includes three-dimensional space deployment coordinates of N unmanned aerial vehicles;

[0058] After t iterations, the d-dimensional velocity update formula of the i-th unmanned aerial vehicle in the k-th particle is:

[0059] ;

[0060] Wherein, , are the d-dimensional velocities of the i-th unmanned aerial vehicle in the k-th particle in the t+1 iteration and the t iteration respectively, is the inertia weight, , are learning factors for adjusting individual and global learning weights, , are random factors, is the historical optimal unmanned aerial vehicle coordinate of the i-th unmanned aerial vehicle in the k-th particle in the t iteration, is the global particle historical optimal solution in the d-dimensional unmanned aerial vehicle coordinate;

[0061] According to the d-dimensional velocity, the unmanned aerial vehicle position is updated:

[0062] ;

[0063] Wherein, , are the three-dimensional space deployment coordinates of the i-th unmanned aerial vehicle in the k-th particle in the t iteration and the t+1 iteration respectively;

[0064] According to the unmanned aerial vehicle position, a position matrix is constructed, and the global optimal position is determined based on the position matrix:

[0065] ;

[0066] Wherein, is the value of the comprehensive objective function in the t iteration, , , is the signal coverage rate objective function value, the network average link quality objective function value, and the node energy consumption balance objective function value in the t iteration;

[0067] When the number of iterations reaches a second threshold or the comprehensive objective function converges to a third threshold, the algorithm terminates and outputs the global optimal position layout.

[0068] It should be noted that traditional optimization methods such as greedy search or grid exhaustive search are prone to local optimization or high computational cost in complex high-dimensional scenarios. Based on this, an improved particle swarm optimization algorithm is designed to simulate group behavior and gradually tend towards optimal deployment, thereby achieving low-cost, high-precision adaptive deployment, improving deployment efficiency and network quality, and being suitable for real-time disaster area communication support and other tasks. In addition, in the position update of the unmanned aerial vehicle, the new speed is calculated by using inertia, individual optimal guidance and global optimal guidance, the new position of the unmanned aerial vehicle is obtained by "current coordinates + new speed", the speed here is equivalent to a displacement, and each coordinate dimension (such as x, y, h) is updated independently, thereby completing the next step of the whole group of unmanned aerial vehicles.

[0069] In addition, in some embodiments, the position matrix is constructed with x coordinates, y coordinates and z coordinates as column names, and with unmanned aerial vehicle numbers in the kth particle as row names:

[0070] ;

[0071] wherein, is the position matrix corresponding to the kth particle in the tth iteration, , , are the x coordinate, y coordinate and z coordinate of the first unmanned aerial vehicle in the kth particle in the tth iteration, , , are the x coordinate, y coordinate and z coordinate of the second unmanned aerial vehicle in the kth particle in the tth iteration, , , are the x coordinate, y coordinate and z coordinate of the Nth unmanned aerial vehicle in the kth particle in the tth iteration.

[0072] In summary, by randomly generating K particles containing three-dimensional space deployment coordinates of the unmanned aerial vehicle as a deployment scheme, a plurality of starting points are provided for the search. In the iteration process, in the velocity update formula of the ith unmanned aerial vehicle in the dth dimension, the inertia weight dynamically adjusts the influence of the original velocity of the particle, and the learning factor and the random factor cooperate to enable the particle to learn from the historical optimal and the global historical optimal, thereby ensuring the global search capability to explore a more optimal solution region and avoiding falling into a local optimal dilemma, and having a local development capability to speed up the convergence process and reduce the iteration number and the consumption of computing resources. The position of the unmanned aerial vehicle is updated according to the velocity update, and a position matrix is constructed to determine the global optimal position. The comprehensive objective function (covering the signal coverage rate, the network average link quality, and the node energy consumption balance objective function value) is evaluated, and the optimal deployment layout of the unmanned aerial vehicle meeting the multi-objective requirements is finally obtained.

[0073] As shown in Figure 3 An embodiment of the present application provides a unmanned aerial vehicle distribution scheduling system applied to a communication interruption area, the system comprises:

[0074] A networking module 10 is configured to determine unmanned aerial vehicle information for the communication interruption area, and perform air networking on all unmanned aerial vehicles according to the unmanned aerial vehicle information.

[0075] A model construction module 20 is configured to define a coordinate set of all unmanned aerial vehicles participating in networking after successful networking, and construct a logarithmic path loss model according to the coordinate set, and obtain signal propagation intensity of any position point in the communication interruption area according to the path loss model.

[0076] A target function construction module 30 is configured to construct a signal coverage rate target function according to the signal propagation intensity, construct a network average link quality target function and a node energy consumption balance target function, and construct a comprehensive target function according to the signal coverage rate target function, the network average link quality target function and the node energy consumption balance target function.

[0077] A deployment position solving module 40 is configured to maximize the comprehensive target function, and solve the deployment position of all unmanned aerial vehicles in the communication interruption area by using an improved particle swarm optimization algorithm.

[0078] The present application also provides a storage medium having one or more programs stored thereon, which, when executed by a processor, implement the above-mentioned unmanned aerial vehicle distribution scheduling method applied to a communication interruption area.

[0079] The present application also provides an electronic device comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to implement the above-mentioned unmanned aerial vehicle distribution scheduling method applied to a communication interruption area.

[0080] Those skilled in the art will appreciate that the logic and / or steps represented in the flow diagrams, or otherwise described herein, for example, can be thought of as a list of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0081] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0082] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), or the like.

[0083] While the embodiments of the application have been illustrated and described in detail, it will be clear to those skilled in the art that various modifications and changes can be made to the embodiments without departing from the scope and spirit of the application as set forth in the claims. Moreover, the application described herein can have other embodiments and be practiced or carried out in various ways.

Claims

1. A method for distributed scheduling of unmanned aerial vehicles (UAVs) in areas with communication disruptions, characterized in that, The method includes: Identify drone information for areas with communication disruptions, and establish an aerial network for all drones based on this information; After the network is successfully established, the coordinate set of all UAVs participating in the network is defined, and a logarithmic path loss model is constructed based on the coordinate set. The signal propagation strength at any point in the communication interruption area is obtained based on the path loss model. The number of drones participating in the network is defined as N, and the coordinate set is: ,in, Let i be the spatial coordinates of the i-th UAV. Let x, y, and altitude be the x-coordinate, y-coordinate, and altitude of the i-th drone, respectively, and T be the coordinate set; Construct a logarithmic path loss model based on the following formula: ； in, Let i be the spatial position of the i-th drone. The signal propagation strength, These are the transmit and receive gains, respectively. The reference path loss is n, where n is the environmental loss factor. Let be the transmission power of the i-th drone. For reference distance, Simulate multipath fading for Gaussian random variables; Based on the signal propagation strength, construct a signal coverage objective function, a network average link quality objective function, and a node energy consumption balance objective function, and then construct a comprehensive objective function based on the signal coverage objective function, the network average link quality objective function, and the node energy consumption balance objective function. Assuming the communication interruption area is gridded into M spatial points, if the spatial location of the j-th spatial point... If the signal propagation strength is greater than the first threshold, then the signal coverage objective function is constructed according to the following formula: ； in, Let the objective function be the signal coverage rate, and if the spatial location... Whether it is covered by the i-th drone, then =1, otherwise =0; if spatial position If it is covered by at least one drone, then =1, otherwise =0; If the i-th drone is in spatial location If the signal propagation strength is greater than or equal to a preset strength threshold, then the spatial location is determined. Covered by the i-th drone; If the i-th drone is in spatial location If the signal propagation strength is less than a preset strength threshold, then the spatial location is determined. Not covered by the i-th drone; The deployment locations of all UAVs in the communication interruption area were obtained by maximizing the comprehensive objective function and solving it using an improved particle swarm optimization algorithm.

2. The UAV distribution scheduling method applied to areas with communication disruptions according to claim 1, characterized in that, The steps of constructing the network average link quality objective function and the node energy consumption balance objective function, and constructing a comprehensive objective function based on the signal coverage objective function, the network average link quality objective function, and the node energy consumption balance objective function include: Construct the network average link quality objective function based on the following formula: ； Construct the node energy consumption balance objective function according to the following formula: ； Construct the comprehensive objective function based on the following formula: ； in, , , These are the network average link quality objective function, the node energy consumption balance objective function, and the comprehensive objective function, respectively. For a set of links, Let be the signal-to-noise ratio of the inter-link between the i-th drone and the j-th drone. Let i be the online time of the i-th drone. For the data forwarding payload of the i-th drone, This is the maximum output power of the current transformer. All are adjustable weighting factors.

3. The UAV distribution and scheduling method applied to areas with communication disruptions according to claim 2, characterized in that, The step of obtaining the deployment locations of all UAVs in the communication interruption area by maximizing the comprehensive objective function and solving it using an improved particle swarm optimization algorithm includes: K particles are randomly generated, each particle representing a set of deployment schemes for all drones. Each set of deployment schemes includes the three-dimensional spatial deployment coordinates of N drones. After t iterations, the formula for updating the d-dimensional velocity of the i-th UAV in the k-th particle is: ； in, , Let be the d-dimensional velocity of the i-th drone in the k-th particle of the (t+1)-th iteration and the t-th iteration, respectively. For inertial weights, , These are all learning factors used to adjust individual and global learning weights. , All are random factors. The historical best drone coordinates are for the i-th drone in the k-th particle of the t-th iteration. The global particle history optimal solution is represented in the d-dimensional UAV coordinates. Update the drone's position based on the d-th dimension velocity: ； in, , These are the three-dimensional spatial deployment coordinates of the i-th UAV in the k-th particle of the t-th and t+1-th iterations, respectively. A position matrix is ​​constructed based on the drone's location, and the globally optimal position is determined based on the position matrix: ； in, Let be the value of the comprehensive objective function in the t-th iteration. , , Let be the objective function values ​​for signal coverage, average network link quality, and node energy consumption balance in the t-th iteration; The algorithm terminates when the number of iterations reaches the second threshold or the comprehensive objective function converges to the third threshold, and outputs the globally optimal position layout.

4. The UAV distribution and scheduling method applied to areas with communication disruptions according to claim 3, characterized in that, The step of constructing a position matrix based on the location of the UAV includes: Using the x, y, and z coordinates as column names and the drone number in the k-th particle as the row name, a position matrix is ​​constructed: ； in, This is the position matrix corresponding to the k-th particle in the t-th iteration. , , Let x, y, and z be the x, y, and z coordinates of the first UAV in the k-th particle of the t-th iteration, respectively. , , Let x, y, and z be the x, y, and z coordinates of the second UAV in the k-th particle of the t-th iteration, respectively. , , These are the x, y, and z coordinates of the Nth UAV in the kth particle of the t-th iteration.

5. A distributed dispatch system for unmanned aerial vehicles (UAVs) applied in areas with communication disruptions, characterized in that, The system includes: The networking module is used to determine the drone information for areas with communication interruptions, and to form an aerial network for all drones based on the drone information. The model building module is used to define the coordinate set of all UAVs participating in the network after successful network formation, construct a logarithmic path loss model based on the coordinate set, and obtain the signal propagation strength at any location in the communication interruption area based on the path loss model. The number of drones participating in the network is defined as N, and the coordinate set is: ,in, Let i be the spatial coordinates of the i-th UAV. Let x, y, and altitude be the x-coordinate, y-coordinate, and altitude of the i-th drone, respectively, and T be the coordinate set; Construct a logarithmic path loss model based on the following formula: ； in, Let i be the spatial position of the i-th drone. The signal propagation strength, These are the transmit and receive gains, respectively. The reference path loss is n, where n is the environmental loss factor. Let be the transmission power of the i-th drone. For reference distance, Simulate multipath fading for Gaussian random variables; The objective function construction module is used to construct a signal coverage objective function based on the signal propagation strength, and to construct a network average link quality objective function and a node energy consumption balance objective function, and to construct a comprehensive objective function based on the signal coverage objective function, the network average link quality objective function, and the node energy consumption balance objective function; Assuming the communication interruption area is gridded into M spatial points, if the spatial location of the j-th spatial point... If the signal propagation strength is greater than the first threshold, then the signal coverage objective function is constructed according to the following formula: ； in, Let the objective function be the signal coverage rate, and if the spatial location... Whether it is covered by the i-th drone, then =1, otherwise =0; if spatial position If it is covered by at least one drone, then =1, otherwise =0; If the i-th drone is in spatial location If the signal propagation strength is greater than or equal to a preset strength threshold, then the spatial location is determined. Covered by the i-th drone; If the i-th drone is in spatial location If the signal propagation strength is less than a preset strength threshold, then the spatial location is determined. Not covered by the i-th drone; The deployment location solution module is used to maximize the comprehensive objective function and solve it using an improved particle swarm optimization algorithm to obtain the deployment locations of all UAVs in the communication interruption area.

6. A storage medium, characterized in that, The storage medium stores one or more programs that, when executed by a processor, implement the UAV distributed scheduling method for communication-disrupted areas as described in any one of claims 1-4.

7. An electronic device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the UAV distribution scheduling method for communication interruption areas as described in any one of claims 1-4.

Images