Unmanned aerial vehicle enhanced emergency communication and rescue system

Abstract

The invention relates to an unmanned aerial vehicle enhanced emergency communication and rescue system, and belongs to the field of unmanned aerial vehicle auxiliary communication. Comprising an unmanned aerial vehicle module which is deployed on an unmanned aerial vehicle body and is used for executing a multi-antenna unmanned aerial vehicle auxiliary ground rescue team member communication method, performing accurate communication with a ground rescue team member module, obtaining rescue information sent by the rescue team member module and sending the rescue information of multiple rescue team members to a rescue center module; the rescue team member module is carried by rescue team members on a rescue site and used for communicating with the unmanned aerial vehicle, receiving instructions and information of the unmanned aerial vehicle and sending support request information to the unmanned aerial vehicle when necessary; the rescue center module is deployed in a fixed rescue command center and is used for dispatching rescue team members and distributing rescue materials based on the rescue information acquired by the unmanned aerial vehicle module; wherein the rescue information comprises one or more of disaster area positions, disaster victims, personnel injury conditions, and types and quantity of required materials. And efficient recovery of rescue field communication is realized.

Description

Technical Field

[0001] The present invention relates to the field of emergency communication and rescue technology, and in particular to an unmanned aerial vehicle enhanced emergency communication and rescue system. Background Art

[0002] In disasters or special situations, such as natural disasters, emergency rescue, etc., ground communication infrastructure (base stations, etc.) is often damaged or cannot be restored in time, resulting in communication interruption. In these situations, timely and effective communication is crucial for the coordination of rescue operations, the safety of personnel, and the rational allocation of rescue resources.

[0003] When ground communication infrastructure is damaged, satellite communication can be used as an alternative, but satellite communication has problems such as high latency, limited bandwidth and high cost, and may not provide sufficient coverage in some cases; Temporary ground base stations can be quickly deployed in the disaster area to restore communication, but this solution has logistics and time limitations, especially in disaster areas with obstructed traffic or complex terrain; Radio communication uses radio equipment for communication, but this communication method is easily interfered by the terrain and buildings in the disaster area, and the communication distance and quality are limited; Mobile communication vehicles deploy mobile communication vehicles to provide temporary communication services. Although this method has certain flexibility, it is still limited by the ground traffic conditions and terrain in the disaster area, and the coverage is limited. The deployment of temporary ground base stations and mobile communication vehicles takes time. Due to the complex terrain and possible disconnection in the disaster area, it is impossible to deploy temporary ground base stations and deploy mobile communication vehicles in time, and it is impossible to meet the needs of rapid response to the disaster. In the complex electromagnetic environment of the disaster area, radio communications are easily interfered, resulting in unstable communications. The deployment cost of satellite communications and temporary ground base stations is high and the maintenance is complex. Summary of the invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a drone-enhanced emergency communication and rescue system to solve the technical problems of existing methods such as slow communication recovery speed, limited coverage, poor stability, high cost and susceptibility to terrain and electromagnetic environment at disaster or emergency rescue sites.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides an unmanned aerial vehicle enhanced emergency communication and rescue system, comprising the following modules:

[0007] The drone module is deployed on the drone body and is used to execute the multi-antenna drone-assisted ground rescue team member communication method, accurately communicate with the ground rescue team member module, obtain the rescue information sent by the rescue team member module, and send the rescue information of multiple rescue team members to the rescue center module;

[0008] Rescue team member module: carried by rescue team members at the rescue site, used to communicate with the drone, receive instructions and information from the drone, and send support request information to the drone when necessary;

[0009] Rescue center module: deployed in a fixed rescue command center, used to dispatch rescue team members and distribute rescue materials based on the rescue information obtained by the drone module;

[0010] The rescue information includes one or more of the location of the disaster area, the number of victims, injuries, and the types and quantities of materials required.

[0011] Furthermore, the system further comprises:

[0012] Warehouse module: deployed at the logistics base or designated warehouse location to store and manage relief supplies, and quickly allocate and prepare the required relief supplies according to the instructions of the rescue center;

[0013] Relief material distribution module: deployed on ground transport vehicles or temporary distribution centers, used to receive relief materials provided by the warehouse module, and quickly transport the materials to the disaster area or designated location according to the instructions of the rescue center.

[0014] Further, the drone module includes a uniform planar antenna array, and performs circular flight over the disaster area according to a predetermined flight path based on the flight mission planning of the rescue center module;

[0015] The rescue team member module includes a single antenna device, which is a mobile phone or a mobile terminal. When the drone passes by, the rescue team members establish a wireless communication connection with the drone. Each rescue team member module receives a directional beam signal sent from the drone module within a certain time slot, and then responds and sends the rescue information.

[0016] Furthermore, the method for the multi-antenna UAV to assist ground rescue team members in communication includes:

[0017] Step S1, calculating the steering vector from the drone to each rescue team member; calculating the channel gain from the drone to each rescue team member module based on the steering vector; calculating the communication rate between the drone and each rescue team member module based on the channel gain;

[0018] Step S2, constructing a UAV-assisted rescue team member communication optimization model that takes maximizing the average communication rate between the UAV and the multi-rescue team member module as the optimization objective function, while satisfying the rescue team member scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

[0019] Step S3, solving the communication optimization model of the UAV-assisted rescue team members to obtain the optimal communication solution between the UAV and multiple rescue team members; using the optimal communication solution to communicate accurately with the ground rescue team member module.

[0020] Furthermore, the UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas and the distance between antenna units of the uniform planar antenna array are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system to obtain the total number of antenna arrays M=M x ×M y and the distance d between the antennas along the coordinate system axis x =d y =λ / 2;

[0021] Among them, M x 、M y are the number of antennas arranged on the x-axis and y-axis respectively; d x d y are the spacing of the antennas along the x-axis and y-axis respectively; λ is the carrier wavelength;

[0022] Based on M x 、M y and d x d y Calculate the steering vector α(l(n),l from the UAV to the kth rescuer module k ), calculated as follows:

[0023]

[0024] Where k = 1, 2, ..., K, K is the number of rescue team members; l(n) is the position of the drone in the nth time slot, l k is the position of the kth rescuer, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth rescue team member.

[0025] Furthermore, based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth rescuer module k,com (l(n),l k ), calculated as follows:

[0026]

[0027] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G bis the antenna gain received by the rescuer module, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the UAV and the kth rescuer.

[0028] Furthermore, under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the UAV and the kth rescue team member module The calculation is as follows:

[0029]

[0030] Among them, c k (n) is the rescue team dispatch of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that follows Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable;

[0031] When the UAV transmits a communication beam in time slot n to perform the communication task for the kth rescue team member, c k (n)=1, otherwise c k (n)=0.

[0032] Furthermore, the communication optimization model of the UAV-assisted rescue team members is as follows:

[0033]

[0034] in, is the communication rate between the UAV and the kth rescuer; K is the number of rescuers; n=1, 2, ... N, N is the number of time slots in the UAV flight cycle; C, W, L are the rescuer scheduling to be optimized, the beamforming vector emitted by the UAV, and the UAV trajectory variable, respectively; C1 and C2 represent the rescue team scheduling constraints, C3 represents the UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the drone.

[0035] Furthermore, the step S3 comprises:

[0036] The UAV-assisted rescue team communication optimization model includes three nonlinear coupling variables: rescue team scheduling, UAV-launched beamforming vectors, and UAV trajectories. The model is solved as a non-convex optimization problem.

[0037] Decomposing the non-convex optimization problem into three convex optimization sub-problems of rescue team member scheduling, beamforming vectors emitted by the UAV, and UAV trajectory;

[0038] Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution;

[0039] When the objective function converges, the global optimal solution is the optimal solution for communication between the UAV and multiple rescue team members; wherein the global optimal solution includes the dispatch of rescue team members, the beamforming vector emitted by the UAV, and the trajectory of the UAV;

[0040] The non-convex optimization problem includes the rescue team scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

[0041] Furthermore, the three convex optimization sub-problems of rescue team scheduling, UAV launch beamforming vectors, and UAV trajectory are alternately optimized to gradually approach the global optimal solution, including:

[0042] In the mth iteration, when solving the rescue team scheduling, given the preset UAV-launched beamforming vector and the initial UAV trajectory, the UAV-assisted rescue team communication optimization model is solved to obtain the rescue team scheduling C (m) ;

[0043] When solving the beamforming vector emitted by the UAV, based on C (m) , the initial UAV trajectory, solve the UAV-assisted rescue team communication optimization model to obtain the beamforming vector W (m) ;

[0044] When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted rescue team communication optimization model to obtain L (m) ;

[0045] The solution is solved in an alternating cycle until the optimization objective function converges, and the dispatch of rescue team members, the beamforming vector launched by the UAV, and the trajectory of the UAV in the current round are the global optimal solutions.

[0046] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0047] 1. The UAV-enhanced emergency communication and rescue system of the present invention can be quickly deployed to the disaster area without being restricted by ground transportation and terrain, achieving rapid response to the disaster area, filling the communication gap caused by the damage of ground communication infrastructure, and improving the rapid response capability after the disaster;

[0048] 2. The present invention uses multi-antenna technology and beamforming technology to enable the UAV to communicate accurately with ground rescue team members, thereby improving communication quality, reducing signal interference, ensuring accurate transmission of information, and improving communication quality in disaster areas;

[0049] 3. The present invention constructs a multi-antenna UAV-assisted multi-rescue team member communication system and a human-machine-assisted rescue team member communication optimization model, thereby realizing the optimization of rescue team member scheduling, UAV beamforming and flight trajectory, and effectively improving the utilization efficiency of rescue resources and the coordination of rescue operations;

[0050] 4. Compared with satellite communications and temporary ground base stations, drone systems have lower deployment and maintenance costs, while simplifying the complexity of communication recovery, providing a cost-effective emergency communication solution;

[0051] 5. The UAV system can flexibly and dynamically adjust the flight path and communication strategy in real time according to the actual situation in the disaster area, adapt to the ever-changing rescue needs and environmental conditions, and provide higher flexibility and adaptability.

[0052] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0054] Figure 1 A schematic diagram of a UAV enhanced emergency communication and rescue system module in an embodiment of the present invention;

[0055] Figure 2 This is a flow chart of a method for multi-antenna UAV to assist rescue team members in communication in an embodiment of the present invention;

[0056] Figure 3 Schematic diagram of a multi-antenna UAV-assisted rescue team communication system in an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of trajectory optimization of a multi-antenna UAV in an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of the dispatching of rescue team members by a multi-antenna UAV in an embodiment of the present invention;

[0059] Figure 6 A schematic diagram of a plane beam of the signal-to-noise ratio at the receiving location of the rescue team members when the drone is in a randomly selected time slot in an embodiment of the present invention;

[0060] Figure 7 A three-dimensional beam schematic diagram of the signal-to-noise ratio at the receiving point of the rescue team members at a randomly selected time slot of the UAV in an embodiment of the present invention;

[0061] Figure 8 It is a schematic diagram comparing the average communication rate of the system of the present invention and the preset trajectory solution under different maximum transmission powers of the UAV in the embodiment of the present invention. DETAILED DESCRIPTION

[0062] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0063] The UAV-enhanced emergency communication and rescue system proposed in this technical solution is intended to address the defects of the above-mentioned existing technologies. By using UAVs as aerial communication platforms, this system can be quickly deployed, provide flexible and stable communication services, and effectively cover a wide range of disaster areas. The multi-antenna technology of UAVs improves the directionality and quality of communications, while the optimization model ensures the efficient use of communication resources. In addition, the aerial advantage of UAVs enables them to avoid ground obstacles, quickly respond to dynamic changes in disaster areas, and provide critical communication support for rescue operations.

[0064] A specific embodiment of the present invention, as Figure 1 As shown, a drone-enhanced emergency communication and rescue system is disclosed, including the following modules:

[0065] The drone module is deployed on the drone body and is used to execute the multi-antenna drone-assisted ground rescue team member communication method, accurately communicate with the ground rescue team member module, obtain the rescue information sent by the rescue team member module, and send the rescue information of multiple rescue team members to the rescue center module;

[0066] Rescue team member module: carried by rescue team members at the rescue site, used to communicate with the drone, receive instructions and information from the drone, and send support request information to the drone when necessary;

[0067] Rescue center module: deployed in a fixed rescue command center, used to dispatch rescue team members and distribute rescue materials based on the rescue information obtained by the drone module;

[0068] The rescue information includes one or more of the location of the disaster area, the number of victims, injuries, and the types and quantities of materials required.

[0069] The multi-antenna UAV-assisted multi-rescue team member communication system includes a UAV and multiple rescue team members distributed in different locations in the disaster area. The UAV is equipped with a uniform planar antenna array (UPA) and serves as an aerial information collection and communication platform.

[0070] As an aerial communication platform, the drone module communicates accurately with ground rescuers through its multi-antenna system to ensure the accuracy and efficiency of information transmission. It collects rescue information sent by each rescuer module, including key information such as the location of the disaster area, the number of victims, injuries, and the types and quantities of materials required. It transmits the collected rescue information to the rescue center module in real time to provide real-time data support for rescue decisions. According to the instructions of the rescue center module, it sends corresponding instructions and information to the rescue team members to assist in the coordination of rescue operations.

[0071] The rescuer module is deployed on the rescuer body at the rescue site. The rescuer carries it with him as a direct interface for communication between the rescuer and the drone. It receives instructions and information sent by the drone, enabling the rescuer to act according to the guidance of the command center. When necessary, it sends a request for support to the drone, including requesting additional supplies, medical assistance or other emergency support.

[0072] The rescue center module is the nerve center of the entire rescue operation and is located in a fixed rescue command center. It receives rescue information transmitted by the drone module and makes analysis and decisions based on this information. It is responsible for dispatching rescue team members to ensure that rescue forces are effectively allocated to the places where they are most needed. It is responsible for the distribution of rescue materials, quickly deploying and preparing the required rescue materials according to the needs of the disaster area, and guiding the rescue material distribution module to transport the materials to the disaster area.

[0073] Overall, these three modules together form an efficient emergency communication and rescue system that can respond quickly when a disaster occurs, provide real-time communication support, ensure the rapid transmission of rescue information, and the rational allocation and effective use of rescue resources. This integrated solution can significantly improve the efficiency and effectiveness of emergency rescue operations.

[0074] The system further comprises:

[0075] Warehouse module: deployed at the logistics base or designated warehouse location to store and manage relief supplies, and quickly allocate and prepare the required relief supplies according to the instructions of the rescue center;

[0076] Relief material distribution module: deployed on ground transport vehicles or temporary distribution centers, used to receive relief materials provided by the warehouse module, and quickly transport the materials to the disaster area or designated location according to the instructions of the rescue center.

[0077] The warehouse module is responsible for storing and managing relief supplies. According to the instructions of the rescue center, it quickly allocates and prepares the required relief supplies to respond to emergency needs. The relief material distribution module is responsible for receiving relief supplies from the warehouse and distributing them according to the instructions of the rescue center. It quickly transports relief supplies to the disaster area or designated locations to ensure material support for the rescue operation.

[0078] The drone module includes a uniform planar antenna array and performs circular flight over the disaster area according to a predetermined flight path based on the flight mission planning of the rescue center module;

[0079] The rescue team member module includes a single antenna device, which is a mobile phone or a mobile terminal. When the drone passes by, the rescue team members establish a wireless communication connection with the drone. Each rescue team member module receives a directional beam signal sent from the drone module within a certain time slot, and then responds and sends the rescue information.

[0080] The emergence of beamforming technology provides an effective way to solve the above technical problems. Beamforming technology is based on antenna arrays. By accurately adjusting the phase and amplitude of the signal transmitted by each antenna unit, the transmitted electromagnetic waves can form a specific radiation pattern in space, and concentrate the signal energy on the target rescue team. In the communication scenario of drones assisting rescue team members, beamforming technology can significantly enhance the performance of the communication link between drones and rescue team members. By accurately aiming a narrower communication beam at the target rescue team member, the diffusion and waste of the signal in space is effectively reduced, and the strength of the signal reaching the rescue team end is increased, thereby improving the signal-to-noise ratio and reliability, solving the problems of communication quality and low efficiency of the above-mentioned single antenna of the drone, and providing a better solution for drone-assisted rescue team communications.

[0081] The present invention builds a UAV-assisted communication network based on the rescue team's growing demand for communication quality and rate, equips the UAV with a multi-antenna array, and uses beamforming technology to enhance signal strength and anti-interference. By jointly optimizing the dispatch of rescue team members, the beamforming vectors emitted by the UAV, and the flight trajectory of the UAV, the precise coverage of the communication network is achieved, thereby effectively improving the average communication rate to meet the rescue team's expectations for communication quality in various complex scenarios, and provide strong support for the in-depth application of UAV communication technology.

[0082] like Figure 2 As shown, the method for the multi-antenna UAV to assist ground rescue team members in communication includes:

[0083] Step S1, calculating the steering vector from the drone to each rescue team member; calculating the channel gain from the drone to each rescue team member module based on the steering vector; calculating the communication rate between the drone and each rescue team member module based on the channel gain;

[0084] Step S2, constructing a UAV-assisted rescue team member communication optimization model that takes maximizing the average communication rate between the UAV and the multi-rescue team member module as the optimization objective function, while satisfying the rescue team member scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

[0085] Step S3, solving the communication optimization model of the UAV-assisted rescue team members to obtain the optimal communication solution between the UAV and multiple rescue team members; using the optimal communication solution to communicate accurately with the ground rescue team member module.

[0086] Step S1, specifically.

[0087] (1) Construct a channel model for multi-antenna UAV-assisted rescue team communication and a UAV flight motion model.

[0088] Based on the needs of actual application scenarios, a multi-antenna UAV-assisted communication system for rescue team members is constructed, covering the channel model of the communication link and the UAV flight motion model; by adjusting the radiation direction of the antenna array, the signal energy can be focused and transmitted directionally to the target rescue team members, and combined with the optimized design of the UAV flight trajectory, it is ensured that rescue team members in different geographical locations can receive stable communication signals.

[0089] When performing a circular flight mission, the UAV transmits a communication beam to the single-antenna rescue team members on the ground at a constant altitude, thereby collecting and transmitting the rescue team members' information. To ensure that the position of the UAV in each time slot can be regarded as relatively static, its flight cycle is divided into N equal-length time slots.

[0090] The communication channel is specifically as follows: the UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna units are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system, and the total number of antenna arrays M = M x ×M y and the distance d between the antennas along the coordinate system axis x =d y =λ / 2;

[0091] Among them, M x 、M y are the number of antennas arranged on the x-axis and y-axis respectively; d x d y are the spacing of the antennas along the x-axis and y-axis respectively; λ is the carrier wavelength;

[0092] (2) Calculate the steering vector α(l(n),l k ).

[0093] Based on M x 、M y and d x d y Calculate the steering vector α(l(n),l from the UAV to the kth rescuer module k ), calculated as follows:

[0094]

[0095] Where k = 1, 2, ..., K, K is the number of rescue team members; l(n) is the position of the drone in the nth time slot, l k is the position of the kth rescuer, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth rescue team member.

[0096] j is the imaginary unit; T is the transpose of the matrix; θ(l(n),l k ) is the Angle of Departure (AoD) of the UAV’s transmission signal when it reaches the kth rescue team member, reflecting the change of the signal in the vertical direction relative to the UAV’s transmission angle; Φ(l(n),l k ) is the AoD azimuth of the UAV’s transmission signal to the kth rescuer, describing the transmission pointing angle of the signal in the horizontal direction relative to the UAV.

[0097] (3) Calculate the channel gain h from the drone to each rescue team member based on the steering vector k,com (l(n),lk ).

[0098] Considering that the downlink communication link between the UAV and the rescue team members is a line-of-sight channel, the free space fading model is used to simulate the line-of-sight channel gain.

[0099] Based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth rescuer module k,com (l(n),l k ), calculated as follows:

[0100]

[0101] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is the antenna gain received by the rescuer module, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the UAV and the kth rescuer.

[0102] (4) Calculating the communication rate between the UAV and each rescue team member based on the channel gain

[0103] Under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the UAV and the kth rescue team member module The calculation is as follows:

[0104]

[0105] Among them, c k (n) is the rescue team dispatch of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable;

[0106] When the UAV transmits a communication beam in time slot n to perform the communication task for the kth rescue team member, c k (n)=1, otherwise c k (n)=0.

[0107] c k (n) is a binary discrete variable used to describe the dispatch of UAVs to rescue team members.

[0108] The noise that follows Gaussian random distribution is used. The noise in the communication rate is the noise at the receiving point of the rescue team. This noise is a noise that follows a mean of 0 and a variance of Gaussian white noise, describes the power distribution of the noise signal.

[0109] The motion model of the drone includes kinematic constraints and flight position constraints. Considering the physical characteristics of the drone and the flight safety requirements, the flight speed of the drone will always be controlled at the preset maximum speed v during its flight. max the following.

[0110] For example, the preset maximum speed v max It is 40m / s.

[0111] The drone has the following distance constraints in each time slot:

[0112]

[0113] Among them, l(n) is the coordinate of the UAV at the nth time slot, l(n-1) is the coordinate of the UAV at the n-1th time slot, and D max =v max δ T is the maximum flight distance of the UAV in each time slot, δ T is the length of each time slot in seconds;

[0114] When the drone performs rescue communication services, in order to ensure that its flight trajectory in a specific area has clear start and end settings, the following flight constraints must be followed:

[0115] l(1)=l(N)=l I Formula (5)

[0116] Among them, l I represents the starting position of the UAV, and N is the total number of time slots in the UAV flight cycle.

[0117] The physical meaning of formula (5) is that the UAV flies back to the starting position at the end of the flight mission.

[0118] The power constraint of the UAV is as follows: P(n) is defined as the average power transmitted by the UAV in the nth time slot, then:

[0119]

[0120] in, is a set containing all time slot numbers.

[0121] Since the average transmission power of the drone is limited by the maximum power, the following constraints are met:

[0122] P(n)≤P max Formula (7)

[0123] Among them, P max Indicates the maximum transmission power of the drone, which is the parameter of the drone itself.

[0124] The function of step S1 is to calculate the channel model of the UAV-assisted rescue team communication and the UAV flight motion model, and calculate the key parameters of the communication link, including the steering vector α(l(n),l between the UAV and each of the rescue team members k ), channel gain h k,com (l(n),l k ) and communication rate Provide basic support for subsequent communication optimization.

[0125] Step S2, specifically.

[0126] Design an optimization problem to maximize the average communication rate of the UAV while satisfying the rescue team scheduling constraints, transmission power constraints, the minimum communication rate constraints of the rescue team members, and the kinematic constraints of the UAV flight;

[0127] In order to maximize the average communication rate of the UAV, the UAV-assisted rescue team communication optimization model is as follows:

[0128]

[0129] in, is the communication rate between the UAV and the kth rescuer; K is the number of rescuers; n=1, 2, ... N, N is the number of time slots in the UAV flight cycle; C, W, L are the rescuer scheduling to be optimized, the beamforming vector emitted by the UAV, and the UAV trajectory variable, respectively;

[0130]

[0131] C1 and C2 represent the rescue team scheduling constraints, C3 represents the UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the drone.

[0132] C1 and C2 are rescuer scheduling constraints, which means that the UAV is dedicated to providing services to a single rescuer in each time slot. 0 means that the rescuer does not establish a communication connection with the UAV, and 1 means that the rescuer establishes a communication connection with the UAV. Only one rescuer module can communicate at the same time. C3 is the UAV communication performance constraint, which requires that the communication rate between the UAV and the base station must exceed the preset threshold. Ensure the quality of signal data transmission; C4 UAV transmission power constraint requires that the UAV's transmission power does not exceed the upper limit of the transmission power to ensure equipment safety; C5 and C6 UAV flight trajectory constraints require the starting point and the end point to coincide to ensure the safety and stability of the UAV's flight.

[0133] Exemplarily, the preset communication rate threshold Set to 16bps / Hz.

[0134] The purpose of step S2 is to design an optimization problem, which aims to maximize the average communication rate between the UAV and the rescue team members by adjusting the rescue team member scheduling, beamforming vector and UAV trajectory variables, while ensuring that the rescue team member scheduling, communication performance, transmission power and flight trajectory constraints are met to achieve efficient, stable and safe UAV-assisted communication.

[0135] The step S3 comprises:

[0136] The UAV-assisted rescue team communication optimization model includes three nonlinear coupling variables: rescue team scheduling, UAV-launched beamforming vectors, and UAV trajectories. The model is solved as a non-convex optimization problem.

[0137] Decomposing the non-convex optimization problem into three convex optimization sub-problems of rescue team member scheduling, beamforming vectors emitted by the UAV, and UAV trajectory;

[0138] Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution;

[0139] When the objective function converges, the global optimal solution is the optimal solution for communication between the UAV and multiple rescue team members; wherein the global optimal solution includes the dispatch of rescue team members, the beamforming vector emitted by the UAV, and the trajectory of the UAV;

[0140] This optimization problem is a mixed integer non-convex optimization problem and is difficult to solve directly.

[0141] The non-convex optimization problem includes the rescue team scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

[0142] The non-convex optimization problem is decomposed into three sub-problems, namely, the rescue team scheduling optimization problem, the beamforming vector optimization problem of UAV launch and the UAV trajectory optimization problem. In the process of solving each sub-problem, the Successive Convex Approximation (SCA) method and the Semidefinite Relaxation (SDR) technique are used to transform the non-convex problem into a convex problem.

[0143] The details are as follows:

[0144] (1) For the rescue team scheduling optimization subproblem, the binary scheduling variables are relaxed into continuous variables with a value range between 0 and 1, so that the problem is transformed into a convex optimization problem. Then, the CVX toolbox in Matlab can be used to directly solve the transformed convex problem.

[0145] (2) For the subproblem of optimizing the beamforming vector emitted by the UAV, the SDR technology is used to transform the beamforming vector into a semi-positive definite matrix of rank 1. At this time, the problem is initially transformed into a convex problem. Then, the relaxed rank 1 constraint with strong non-convexity is added to the objective function as a penalty factor, so that it can be directly solved using the CVX toolbox;

[0146] (3) For the UAV trajectory optimization subproblem, the SCA method is used to perform Taylor first-order expansion on the non-convex parts of the objective function and constraints, and the problem is converted into a convex problem that can be directly solved by CVX.

[0147] After solving each sub-problem separately, an alternating optimization strategy is adopted to switch optimization between different sub-problems through cyclic iteration, so that the solutions of the three sub-problems can influence and evolve co-evolved with each other, gradually approaching the global optimal solution, thereby achieving a joint optimization solution to the entire complex optimization problem.

[0148] The three convex optimization sub-problems of alternately optimizing the dispatch of rescue team members, the beamforming vector of the UAV launch, and the trajectory of the UAV gradually approach the global optimal solution, including:

[0149] In the mth iteration, when solving the rescue team scheduling, given the preset UAV-launched beamforming vector and the initial UAV trajectory, the UAV-assisted rescue team communication optimization model is solved to obtain the rescue team scheduling C (m) ;

[0150] When solving the beamforming vector emitted by the UAV, based on C (m) , the initial UAV trajectory, solve the UAV-assisted rescue team communication optimization model to obtain the beamforming vector W (m) ;

[0151] When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted rescue team communication optimization model to obtain L (m) ;

[0152] The solution is solved in an alternating cycle until the optimization objective function converges, and the dispatch of rescue team members, the beamforming vector launched by the UAV, and the trajectory of the UAV in the current round are the global optimal solutions.

[0153] Among them, the mth round is the iteration round, m≥1 round, and it is the iteration round before the objective function converges.

[0154] The optimization steps are explained in detail as follows:

[0155] There are three nonlinear coupling variables in the original optimization problem (rescue team scheduling Beamforming vectors transmitted by drones Drone tracks Therefore, the optimization problem is a non-convex problem. To solve this problem, the original problem is decomposed into three sub-problems, and each sub-problem is solved iteratively until the objective function value converges;

[0156] Method for determining convergence of objective function: When the objective function in the current number of iterations minus the objective function in the previous number of iterations is less than a given threshold or reaches the set maximum number of iterations, it can be considered that the objective value remains basically unchanged and has reached the optimal value. At this time, the solution (C, W, L) is an approximate global optimal solution.

[0157] Exemplarily, the maximum number of iterations is set to 20 times.

[0158] It should be noted that in each round of solving, each sub-problem is solved in sequence, including the rescue team scheduling problem, the beamforming vector problem of the drone launch, and the drone trajectory optimization problem, and the current approximate optimal solution of the variables in each sub-problem is found. This solution should be used in solving the next sub-problem, and finally the purpose of joint solution is achieved.

[0159] For example, in the mth iteration, when solving the rescue team member scheduling C, W and L are first given. The given here means that the W value of all time slots is pre-set during the simulation. L is the position where the circle is divided into N time slots on the initial circular trajectory with a radius r set to r = 250m, which is the initial trajectory of the drone (given trajectory L). The C obtained after solving (m) ;

[0160] Similarly, when solving the beamforming vector W emitted by the drone, given C (m) , L, where C is replaced by the solution C obtained above (m), L is still the initial trajectory (because the trajectory subproblem has not been solved yet, it will not be replaced yet), and the solved W (m) ;

[0161] When solving the UAV trajectory L, given W (m) , C (m) Solve and finally get C (m) , W (m) , L (m) , these three values ​​are the solutions of the current round, and then the next round begins, still following the solution order of C, W, and L until the maximum iteration round is reached and finally the global optimal solution is approached.

[0162] In step S3, the original problem is first divided into the non-convex problem of rescue team scheduling optimization problem, the beamforming vector optimization problem of UAV launch and the UAV trajectory optimization problem, and the non-convex problem is converted into a convex problem that can be directly solved by the CVX toolbox. Finally, the global optimal solution of the original optimization problem is approached through an alternating iterative optimization algorithm.

[0163] The optimal solution consists of the following three decision variables:

[0164] (1) Rescue team scheduling: which rescue team member should be selected for communication in each time slot;

[0165] (2) Beamforming vector of UAV transmission: The beamforming vector of UAV transmission is designed for each time slot to ensure that the corresponding rescue team can receive the strongest signal;

[0166] (3) Drone trajectory: The flight path of the drone in each time slot to ensure the optimal communication rate.

[0167] These three decision variables are iteratively optimized under the constraints to maximize the average communication rate of the drone. That is, after multiple iterations and adjustments, the average communication rate between the drone and the rescue team is maximized under all the constraints.

[0168] The purpose of step S3 is to decompose the complex non-convex optimization problem into three solvable convex optimization sub-problems, and adopt an alternating iterative optimization strategy to gradually approach the global optimal solution, and finally determine the rescue team scheduling of the UAV, the beamforming vector launched by the UAV, and the flight trajectory of the UAV, so as to maximize the average communication rate between the UAV and the rescue team while satisfying all constraints.

[0169] Figure 3 In the network model shown, G is set t =15dBi, G b =10dBi,σ k 2 =-110dBm, M x =M y =4, K=10, H=90m, T=75s, δ t =0.5s, P max =10w,v max =40m / s.

[0170] σ k 2 = -110dBm converted to watts is 10 -14 W is the noise power value.

[0171] The center of the area where the drone performs the mission (0, 0) is used as the center of the drone's circular initial trajectory, and the radius is set to r = 250m. In this way, when the drone starts the communication mission, its initial trajectory can contact the surrounding target rescue team members in a relatively balanced manner. In the actual flight process, the drone further dynamically adjusts its flight trajectory according to the real-time channel state information and the communication needs of the rescue team members, completing the task of effectively assisting the rescue team members in communication.

[0172] Figure 4 The optimized trajectory of the drone presented in Figure 1 clearly demonstrates this adaptive adjustment process. Compared with the initial trajectory, the optimized trajectory shows that the drone actively moves closer to the rescue team. Because under the goal of maximizing the communication rate, the shortened distance between the drone and the target rescue team means that the path loss during signal propagation will be significantly reduced, so that the target rescue team can obtain higher signal strength at their receiving end.

[0173] The optimization goal is to maximize the average communication rate between the UAV and the rescue team members, that is, the maximum sum of the communication rates of all time slots. According to formula (2) and formula (3), the closer the distance between the UAV and the rescue team members, the greater the channel gain h. k,com (l(n),l k ) is larger, so the communication rate So after the algorithm optimization, the drone will serve as close to the rescue team as possible. When the set flight time and speed are sufficient, the drone will serve right above each rescue team member, and then continue to fly forward while serving. Figure 3 It is a two-dimensional plane diagram of the UAV's flight trajectory. From the two-dimensional plane, the UAV's flight trajectory is connected to multiple rescue team members. In three-dimensional space, the UAV itself will have a certain height, and it is actually flying above the rescue team members with such a trajectory.

[0174] Figure 5The figure shows the dispatch of rescue team members by drones in different time slots. Starting from the starting position, the drone gives priority to serving the rescue team members who are closest to it, while ensuring that every rescue team member is served. From the perspective of channel quality, the drone gives priority to rescue team members with better channel conditions for communication dispatch. This is because in a good channel environment, limited communication resources can be used more efficiently and the data transmission rate per unit bandwidth can be improved. From the perspective of ensuring the quality of service for rescue team members, the scheduling algorithm avoids the situation where excessive service to some rescue team members leads to a lack of communication resources for other rescue team members. Through the tightly coupled scheduling and trajectory optimization mechanism, the drone can make the most reasonable rescue team scheduling decision in each time slot in a complex and changeable communication environment, thereby effectively improving the communication efficiency of the entire drone-assisted communication network and providing rescue team members with continuous, stable and efficient communication services. Figure 4 The horizontal axis refers to the number of time slots N in the UAV flight cycle, and the vertical axis refers to the serial numbers of the target rescue team members from 1 to 10. Figure 4 This indicates that the UAV dispatches the corresponding rescue team members at the corresponding time slot of the flight.

[0175] Figure 6 and Figure 7 The plane beam schematic diagram and three-dimensional beam schematic diagram of the signal-to-noise ratio at the receiving end of the rescue team in a randomly selected time slot are shown respectively. The narrower communication beam is concentrated at the rescue team scheduled in the current time slot, indicating that by optimizing the communication beamforming vector of the drone, the uniform planar antenna array equipped with it can accurately control the phase and amplitude of the transmitted signal. In the communication process for a specific rescue team member in the current time slot, according to the location information of the rescue team member and the channel characteristics, the transmitted signal energy is highly concentrated in the direction of the rescue team member, which is reflected in the significant improvement of the signal-to-noise ratio of the rescue team member. When beamforming improves the signal-to-noise ratio, according to Shannon's theorem, the communication rate will be significantly increased under unit bandwidth.

[0176] Figure 6 In the simulation, an area of ​​1000 meters * 1000 meters is simulated, and 10 target rescue team members are arranged in this area. The innovation of the present invention is that the UAV is combined with beamforming technology to assist communication, so the advantages of beamforming technology need to be reflected through simulation results. The present invention optimizes the beamforming vector emitted by the UAV, which is a complex matrix containing amplitude and phase. By optimizing (adjusting the amplitude and phase of the matrix), the signal emitted by the antenna array on the UAV is directed to a rescue team member, so combined with formula (3), the communication rate is enhanced to meet the optimization goal. In each time slot, the UAV will only provide services to one rescue team member. Therefore, in each time slot, there will be a beam concentrated at a corresponding rescue team member, which means that only this position has a communication rate. Therefore, a time slot is randomly selected for observation ( Figure 6 and Figure 7 The corresponding time slot is 55, combined with Figure 4 It can be seen that the drone is serving the fifth rescuer at this time), so the narrow beam is concentrated on the position of the fifth rescuer in the figure. A narrow beam means high energy. The height value of the beam is the size of the signal-to-noise ratio.

[0177] Figure 7 For a two-dimensional graph Figure 6 The three-dimensional perspective of the simulation area is shown in Figure 3, where the x and y axes represent the simulation area (meters). The communication rate in formula (3) is In order to display the beam diagram linearly (if the communication rate is used as the ordinate, the image will produce multiple beams due to the existence of log, and it is not possible to intuitively see which rescue team member has the highest communication rate in this time slot), the signal-to-noise ratio is used as the ordinate, and the larger the signal-to-noise ratio, the larger the communication rate. In general, using the signal-to-noise ratio as the ordinate makes the image more intuitive and also satisfies the optimization problem.

[0178] Figure 8 The comparison of the communication rate between the preset trajectory scheme and the optimized trajectory scheme is shown. As the maximum transmission power of the drone increases, the communication rates of both schemes are improved, and the communication rate of the optimized trajectory scheme is always higher than that of the preset trajectory scheme. This is because the optimized trajectory scheme does not rely solely on the increase in transmission power to improve the communication conditions. The drone will dynamically adjust the flight path based on multiple factors such as the distribution of rescue team members and channel status, so that it can be closer to rescue team members with better signal transmission quality, thereby reducing the path loss and interference during signal propagation. Under the same transmission power, the optimized trajectory scheme can effectively transmit more signal energy to the receiving end, improving the communication quality and rate. However, due to the lack of such flexibility, the preset trajectory scheme is relatively limited in terms of communication rate improvement. In addition, it can be observed from the figure that the number of antenna units also has an impact on the communication rate of the system. The more antenna units there are, the more significantly the gain of beamforming will be improved, and the higher the communication rate that the system can achieve.

[0179] The preset trajectory plan refers to the fixed drone along Figure 4 Obviously, compared with the scheme of optimizing the trajectory in the present invention (adjusting the flight path to approach the rescue team), the flight trajectory of the unoptimized drone is farther away from the rescue team. Therefore, under the same transmission power, the communication rate is low. With the increase of the maximum transmission power of the drone, the communication rate of both schemes is improved. Combined with formulas (6) and (7), this is because the drone’s beamforming vector w c(n) can have a larger amplitude at a higher transmission power, thus increasing the communication rate between the UAV and the rescue team members from formula (3). Similarly, the more antennas there are, the stronger the enhanced signal can be generated at a specific rescue team member, and the higher the communication rate between the UAV and the rescue team members.

[0180] The rescue team receives the directional beam signal transmitted by the drone, converts the received analog signal into a digital signal, and extracts the transmitted data from it. The received data is decoded to restore the original information. According to the actual needs, the rescue team sends a confirmation signal or feedback information to the drone, as well as a request to achieve two-way communication. Through this two-way communication mechanism, the drone can achieve effective data exchange with the rescue team, supporting various applications such as remote monitoring, emergency rescue, data collection and distribution, etc.

[0181] In summary, the UAV enhanced emergency communication and rescue system according to the embodiment of the present invention has the following beneficial effects:

[0182] 1. The UAV-enhanced emergency communication and rescue system of the present invention can be quickly deployed to the disaster area without being restricted by ground transportation and terrain, achieving rapid response to the disaster area, filling the communication gap caused by the damage of ground communication infrastructure, and improving the rapid response capability after the disaster;

[0183] 2. The present invention uses multi-antenna technology and beamforming technology to enable the UAV to communicate accurately with ground rescue team members, thereby improving communication quality, reducing signal interference, ensuring accurate transmission of information, and improving communication quality in disaster areas;

[0184] 3. The present invention constructs a multi-antenna UAV-assisted multi-rescue team member communication system and a human-machine-assisted rescue team member communication optimization model, thereby realizing the optimization of rescue team member scheduling, UAV beamforming and flight trajectory, and effectively improving the utilization efficiency of rescue resources and the coordination of rescue operations;

[0185] 4. Compared with satellite communications and temporary ground base stations, drone systems have lower deployment and maintenance costs, while simplifying the complexity of communication recovery, providing a cost-effective emergency communication solution;

[0186] 5. The UAV system can flexibly and dynamically adjust the flight path and communication strategy in real time according to the actual situation in the disaster area, adapt to the ever-changing rescue needs and environmental conditions, and provide higher flexibility and adaptability.

[0187] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. An unmanned aerial vehicle enhanced emergency communication and rescue system, characterized in that: include: The drone module is deployed on the drone body and is used to execute the multi-antenna drone-assisted ground rescue team member communication method, accurately communicate with the ground rescue team member module, obtain the rescue information sent by the rescue team member module, and send the rescue information of multiple rescue team members to the rescue center module; Rescue team member module: carried by rescue team members at the rescue site, used to communicate with the drone, receive instructions and information from the drone, and send support request information to the drone when necessary; Rescue center module: deployed in a fixed rescue command center, used to dispatch rescue team members and distribute rescue materials based on the rescue information obtained by the drone module; The rescue information includes one or more of the location of the disaster area, the number of victims, injuries, and the types and quantities of materials required.

2. The system according to claim 1, characterized in that: The system further comprises: Warehouse module: deployed at the logistics base or designated warehouse location to store and manage relief supplies, and quickly allocate and prepare the required relief supplies according to the instructions of the rescue center; Relief material distribution module: deployed on ground transport vehicles or temporary distribution centers, used to receive relief materials provided by the warehouse module, and quickly transport the materials to the disaster area or designated location according to the instructions of the rescue center.

3. The system according to claim 1, characterized in that: The drone module includes a uniform planar antenna array and performs circular flight over the disaster area according to a predetermined flight path based on the flight mission planning of the rescue center module; The rescue team member module includes a single antenna device, which is a mobile phone or a mobile terminal. When the drone passes by, the rescue team members establish a wireless communication connection with the drone. Each rescue team member module receives a directional beam signal sent from the drone module within a certain time slot, and then responds and sends the rescue information.

4. The system according to claim 1, characterized in that: The method for the multi-antenna UAV to assist ground rescue team members in communication includes: Step S1, calculating the steering vector from the drone to each rescue team member; calculating the channel gain from the drone to each rescue team member module based on the steering vector; calculating the communication rate between the drone and each rescue team member module based on the channel gain; Step S2, constructing a UAV-assisted rescue team member communication optimization model that takes maximizing the average communication rate between the UAV and the multi-rescue team member module as the optimization objective function, while satisfying the rescue team member scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints; Step S3, solving the communication optimization model of the UAV-assisted rescue team members to obtain the optimal communication solution between the UAV and multiple rescue team members; using the optimal communication solution to communicate accurately with the ground rescue team member module.

5. The method according to claim 4, characterized in that: The UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna units are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system to obtain the total number of antenna arrays M=M x ×M y and the distance d between the antennas along the coordinate system axis x =d y =λ / 2; Among them, M x 、M y are the number of antennas arranged on the x-axis and y-axis respectively; d x d y are the spacing of the antennas along the x-axis and y-axis respectively; λ is the carrier wavelength; Based on M x 、M y and d x d y Calculate the steering vector α(l(n),l from the UAV to the kth rescuer module k ), calculated as follows: Where k = 1, 2, ..., K, K is the number of rescue team members; l(n) is the position of the UAV in the nth time slot, l k is the position of the kth rescuer, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth rescue team member.

6. The method according to claim 5, characterized in that: Based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth rescuer module k,com (l(n),l k ), calculated as follows: Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is the antenna gain received by the rescuer module, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the UAV and the kth rescuer.

7. The method according to claim 6, characterized in that: Under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the UAV and the kth rescue team member module The calculation is as follows: Among them, c k (n) is the rescue team dispatch of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable; When the UAV transmits a communication beam in time slot n to perform the communication task for the kth rescue team member, c k (n)=1, otherwise c k (n)=0.

8. The method according to claim 4, characterized in that: The communication optimization model of the UAV-assisted rescue team members is as follows: C6:l(1)=l(N)=l I in, is the communication rate between the UAV and the kth rescuer; K is the number of rescuers; n=1, 2, ... N, N is the number of time slots in the UAV flight cycle; C, W, L are the rescuer scheduling to be optimized, the beamforming vector emitted by the UAV, and the UAV trajectory variable, respectively; C1 and C2 represent the rescue team scheduling constraints, C3 represents the UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the drone.

9. The method according to claim 8, characterized in that: The step S3 comprises: The UAV-assisted rescue team communication optimization model includes three nonlinear coupling variables: rescue team scheduling, UAV-launched beamforming vectors, and UAV trajectories. The model is solved as a non-convex optimization problem. Decomposing the non-convex optimization problem into three convex optimization sub-problems of rescue team member scheduling, beamforming vectors emitted by the UAV, and UAV trajectory; Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution; When the objective function converges, the global optimal solution is the optimal solution for communication between the UAV and multiple rescue team members; wherein the global optimal solution includes the dispatch of rescue team members, the beamforming vector emitted by the UAV, and the trajectory of the UAV; The non-convex optimization problem includes the rescue team scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

10. The method according to claim 9, characterized in that: The three convex optimization sub-problems of alternately optimizing the dispatch of rescue team members, the beamforming vector of the UAV launch, and the trajectory of the UAV gradually approach the global optimal solution, including: In the mth iteration, when solving the rescue team scheduling, given the preset UAV-launched beamforming vector and the initial UAV trajectory, the UAV-assisted rescue team communication optimization model is solved to obtain the rescue team scheduling C (m) ; When solving the beamforming vector emitted by the UAV, based on C (m) , the initial UAV trajectory, solve the UAV-assisted rescue team communication optimization model to obtain the beamforming vector W (m) ; When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted rescue team communication optimization model to obtain L (m) ; The solution is solved in an alternating cycle until the optimization objective function converges, and the dispatch of rescue team members, the beamforming vector launched by the UAV, and the trajectory of the UAV in the current round are the global optimal solutions.

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