CN119652392B - Unmanned aerial vehicle enhanced emergency communication and rescue system - Google Patents

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. The unmanned aerial vehicle-assisted ground rescue team member communication system comprises an unmanned aerial vehicle module, a rescue team member module, a rescue center module and a fixed rescue command center, wherein the unmanned aerial vehicle module is used for executing a multi-antenna unmanned aerial vehicle-assisted ground rescue team member communication method, performing accurate communication with the ground rescue team member module, acquiring rescue information sent by the rescue team member module, and sending the rescue information of a plurality of rescue team members to the rescue center module, the rescue team member module is carried by the rescue team member on a rescue scene and used for communicating with the unmanned aerial vehicle, receiving instructions and information of the unmanned aerial vehicle and sending request support information to the unmanned aerial vehicle when necessary, and the rescue center module is deployed in the fixed rescue command center and used for scheduling the rescue team members and distributing the 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 injuries, required material types and required material numbers. And the high-efficiency recovery rescue site communication is realized.

Description

A drone-enhanced emergency communication and rescue system

Technical Field

The present invention relates to the field of emergency communication and rescue technology, and in particular to an unmanned aerial vehicle (UAV) enhanced emergency communication and rescue system.

Background Art

In disasters or special circumstances, such as natural disasters and emergency rescues, ground communication infrastructure (base stations, etc.) is often damaged or cannot be restored in time, resulting in communication interruptions. 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.

When ground communication infrastructure is damaged, satellite communications can serve as an alternative. However, satellite communications suffer from high latency, limited bandwidth, and high costs, and in some cases, may not provide adequate coverage. Temporary ground base stations can be quickly deployed in disaster areas to restore communications, but this solution is limited by logistics and time constraints, especially in areas with limited access or complex terrain. Radio communications use radio equipment for communication, but this method is susceptible to interference from the terrain and buildings in the disaster area, and communication range and quality are limited. Mobile communication vehicles can be deployed to provide temporary communication services. Although this method offers some flexibility, it is still limited by ground transportation conditions and terrain in the disaster area, and its coverage is limited. The deployment of temporary ground base stations and mobile communication vehicles takes time. Due to the complex terrain and potential road outages in the disaster area, temporary ground base stations and mobile communication vehicles cannot be deployed in a timely manner, making them unable to meet the needs of rapid disaster response. In the complex electromagnetic environment of the disaster area, radio communications are susceptible to interference, resulting in unstable communications. Satellite communications and temporary ground base stations are expensive to deploy and complex to maintain.

Summary of the Invention

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.

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

The present invention provides a drone-enhanced emergency communication and rescue system, comprising the following modules:

The drone module is deployed on the drone body and is used to implement 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 supplies based on the rescue information obtained by the drone module;

The rescue information includes one or more of the following: the location of the disaster area, the number of people affected, the injuries of the people, and the types and quantities of materials required.

Furthermore, the system further comprises:

Warehouse module: Deployed at a logistics base or designated warehouse location, it is used to store and manage relief supplies and quickly deploy 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.

Furthermore, the drone module includes a uniform planar antenna array and performs a 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 mobile terminal. When the drone passes by, the rescue team member establishes a wireless communication connection with the drone. Each rescue team member module receives the directional beam signal sent from the drone module within a certain time slot, and then responds and sends the rescue information.

Furthermore, 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: Solve 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; use the optimal communication solution to communicate accurately with the ground rescue team member module.

Furthermore, 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 elements are divided along the x-axis and y-axis of a three-dimensional Cartesian coordinate system, respectively obtaining the total number of antenna arrays M = M _x × _My and the spacing of antennas along the coordinate system axis d _x = _dy = λ/2;

Where M _x and _My are the number of antennas arranged on the x-axis and y-axis respectively; d _x and _dy are the spacing between antennas along the x-axis and y-axis respectively; λ is the carrier wavelength;

Based on M _x , _My and d _x , _{dy ,} the steering vector α(l(n), l _k ) from the drone to the k-th rescuer module is 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 rescue team member, θ(l(n), l _k ) and 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, respectively.

Furthermore, the channel gain h _k,com (l(n), l _k ) from the UAV to the k-th rescuer module is calculated based on α(l(n), l _k ), which is calculated as follows:

Where n = 1, 2, ... N, where N is the number of time slots in the UAV flight cycle; _Gt is the antenna gain of the UAV transmitter, _Gb is the antenna gain of the rescuer module receiving antenna, and d(l( _n ),lk) = ||l(n) _-lk || represents the Euclidean distance between the UAV and the kth rescuer.

Furthermore, under unit bandwidth, the communication rate between the UAV and the kth rescuer module is calculated based on h _k,com (l(n),l _k ) The calculation is as follows:

Among them, c _k (n) is the rescue team member scheduling of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise obeying Gaussian random distribution, _H is the conjugate transpose of the channel gain hk _,com (l(n), _lk ); _ck (n) is a binary discrete variable;

When the UAV transmits a communication beam in time slot n to perform a communication mission to the kth rescue team member, c _k (n) = 1, otherwise c _k (n) = 0.

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

in, is the communication rate between the UAV and the kth rescuer; K is the number of rescuers; n = 1, 2, ... N, where N is the number of time slots in the UAV flight cycle; C, W, and L are the rescuer scheduling to be optimized, the UAV’s transmitted beamforming vector, and the UAV’s 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, P _max is the maximum transmission power of the UAV, D _max is the maximum flight distance of the UAV in each time slot, and l _I is the starting position of the UAV.

Furthermore, the step S3 includes:

The UAV-assisted rescue team communication optimization model includes three nonlinear coupling variables: rescue team scheduling, UAV emission beamforming vector and UAV trajectory. The model is solved as a non-convex optimization problem.

The non-convex optimization problem is decomposed into three convex optimization sub-problems: rescue team scheduling, UAV launch beamforming vector and UAV trajectory;

Alternately iteratively optimize three convex optimization sub-problems and 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. The global optimal solution includes the rescue team member scheduling, the beamforming vectors emitted by the UAV, and the UAV trajectory.

Among them, the non-convex optimization problem includes rescue team scheduling Beamforming vectors transmitted by the drone Drone tracks Three nonlinear coupled variables.

Furthermore, the three convex optimization subproblems of alternately optimizing rescue team scheduling, UAV launch beamforming vectors, and UAV trajectory are gradually approached to the global optimal solution, including:

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

When solving the beamforming vector transmitted by the UAV, based on C ^(m) and the initial UAV trajectory, the UAV-assisted rescue team member communication optimization model is solved to obtain the beamforming vector W ^(m) ;

When solving the UAV trajectory, based on W ^(m) and C ^(m) , solve the UAV-assisted rescue team member communication optimization model to obtain L ^(m) ;

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

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

1. The UAV-enhanced emergency communication and rescue system of the present invention can be rapidly deployed to disaster areas without being restricted by ground transportation and terrain, enabling rapid response to disaster areas. This fills the communication gap caused by damage to ground communication infrastructure and improves the ability to respond quickly after a disaster.

2. This invention uses multi-antenna technology and beamforming technology to enable drones to communicate accurately with ground rescue teams, improving communication quality, reducing signal interference, ensuring accurate information transmission, and improving communication quality in disaster areas.

3. This invention builds a multi-antenna UAV-assisted multi-rescue team communication system and a human-machine-assisted rescue team communication optimization model, which achieves the optimization of rescue team scheduling, UAV beamforming, and flight trajectory, effectively improving the utilization efficiency of rescue resources and the coordination of rescue operations;

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

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.

In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying 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 parts throughout the drawings.

FIG1 is a schematic diagram of a module of a drone-enhanced emergency communication and rescue system according to an embodiment of the present invention;

FIG2 is a flow chart of a method for multi-antenna UAV-assisted rescue team communication according to an embodiment of the present invention;

FIG3 is a schematic diagram of a multi-antenna UAV-assisted rescue team communication system according to an embodiment of the present invention;

FIG4 is a schematic diagram of trajectory optimization of a multi-antenna UAV according to an embodiment of the present invention;

FIG5 is a schematic diagram of a multi-antenna UAV dispatching rescue team members according to an embodiment of the present invention;

FIG6 is a plane beam diagram of the signal-to-noise ratio at the receiving location of the rescue team members at a randomly selected time slot of the UAV according to an embodiment of the present invention;

FIG7 is a schematic diagram of a three-dimensional beamforming signal-to-noise ratio at a rescue team member's receiving location during a randomly selected time slot of a UAV according to an embodiment of the present invention;

FIG8 is a schematic diagram showing a comparison of the average communication rates of the system under different maximum transmission powers of the UAVs according to an embodiment of the present invention and a solution with a preset trajectory.

DETAILED DESCRIPTION

The preferred embodiments of the present invention will be 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, and are not used to limit the scope of the present invention.

The drone-enhanced emergency communication and rescue system proposed in this technical solution aims to address the shortcomings of the aforementioned existing technologies. By using drones as aerial communication platforms, this system can be rapidly deployed, providing flexible and stable communication services and effectively covering a wide range of disaster-affected areas. The drone's multi-antenna technology improves the directionality and quality of communication, while the optimization model ensures the efficient use of communication resources. Furthermore, the drone's aerial advantage enables it to avoid ground obstacles and quickly respond to dynamic changes in the disaster area, providing critical communication support for rescue operations.

A specific embodiment of the present invention, as shown in FIG1 , discloses a drone-enhanced emergency communication and rescue system, including the following modules:

The drone module is deployed on the drone body and is used to implement 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 supplies based on the rescue information obtained by the drone module;

The rescue information includes one or more of the following: the location of the disaster area, the number of people affected, the injuries of the people, and the types and quantities of materials required.

The multi-antenna UAV-assisted multi-rescue team communication system consists of a UAV and multiple rescue team members distributed across the disaster area. The UAV is equipped with a uniform planar array (UPA) antenna, serving as an aerial information collection and communication platform.

The drone module serves as an aerial communication platform, communicating precisely with ground rescue teams through its multi-antenna system to ensure accurate and efficient information transmission. It collects rescue information sent by each rescue team module, including key information such as the disaster area location, number of affected people, injuries, and the types and quantities of required supplies. This information is transmitted to the rescue center module in real time, providing real-time data support for rescue decision-making. Based on the instructions from the rescue center module, the module sends corresponding instructions and information to rescue teams to assist in the coordination of rescue operations.

The rescuer module is deployed on the rescuer's body at the rescue site. Carried by the rescuer, it serves as a direct communication interface between the rescuer and the drone. It receives commands and information from the drone, enabling the rescuer to act according to the command center's guidance. When necessary, it sends a request for support to the drone, including requests for additional supplies, medical assistance, or other emergency support.

The Rescue Center module, located in a fixed rescue command center, serves as the nerve center of the entire rescue operation. It receives rescue information transmitted by the drone module and makes analysis and decisions based on this information. It is responsible for dispatching rescue teams, ensuring that rescue forces are effectively deployed to where they are most needed. It is also responsible for distributing relief supplies, quickly allocating and preparing the necessary relief supplies based on the needs of the disaster area, and directing the relief supply distribution module to transport the supplies to the disaster area.

Overall, these three modules form a highly efficient emergency communications and rescue system that can respond quickly to disasters, provide real-time communication support, ensure the rapid transmission of rescue information, and rationally allocate and effectively utilize rescue resources. This integrated solution can significantly improve the efficiency and effectiveness of emergency rescue operations.

The system further comprises:

Warehouse module: Deployed at a logistics base or designated warehouse location, it is used to store and manage relief supplies and quickly deploy 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.

The warehouse module is responsible for storing and managing relief supplies. Following instructions from the rescue center, it rapidly allocates and prepares the necessary relief supplies to respond to emergency needs. The relief supply distribution module is responsible for receiving relief supplies from the warehouse and distributing them according to instructions from the rescue center. It rapidly transports relief supplies to the disaster area or designated locations to ensure material support for rescue operations.

The drone module includes a uniform planar antenna array and performs a 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 mobile terminal. When the drone passes by, the rescue team member establishes a wireless communication connection with the drone. Each rescue team member module receives the directional beam signal sent from the drone module within a certain time slot, and then responds and sends the rescue information.

The emergence of beamforming technology provides an effective way to solve the above technical problems. Beamforming technology is based on antenna arrays. By precisely 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, concentrating the signal energy on the target rescue team. In the UAV-assisted rescue team communication scenario, beamforming technology can significantly enhance the performance of the communication link between the UAV and the rescue team. By accurately aiming the narrower communication beam at the target rescue team, the diffusion and waste of the signal in space is effectively reduced, and the strength of the signal reaching the rescue team is increased, thereby improving the signal-to-noise ratio and reliability, solving the problems of low communication quality and efficiency of the above-mentioned single antenna of the UAV, and providing a better solution for UAV-assisted rescue team communication.

This invention addresses the increasing demand for communication quality and speed among rescuers, building a drone-assisted communication network. This system equips drones with multi-antenna arrays and utilizes beamforming technology to enhance signal strength and interference resistance. By jointly optimizing rescuer scheduling, the beamforming vectors emitted by drones, and drone flight paths, the network achieves precise coverage and effectively improves average communication speeds, meeting rescuers' expectations for communication quality in a variety of complex scenarios and providing strong support for the in-depth application of drone communication technology.

As shown in FIG2 , the method for using a 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: Solve 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; use the optimal communication solution to communicate accurately with the ground rescue team member module.

Step S1, specifically.

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

Based on the needs of actual application scenarios, a multi-antenna drone-assisted communication system for rescue team members is constructed, covering the channel model of the communication link and the drone 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. At the same time, combined with the optimized design of the drone flight trajectory, it is ensured that rescue team members in different geographical locations can receive stable communication signals.

During a circular flight, the drone transmits a communication beam at a constant altitude toward a single-antenna rescuer on the ground, thereby collecting and transmitting information about the rescuer. To ensure that the drone's position within each time slot can be considered relatively stationary, its flight cycle is divided into N equal-length time slots.

Specifically, the communication channel is 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 elements are divided along the x-axis and y-axis of a three-dimensional Cartesian coordinate system, and the total number of antenna arrays M = M _x × _My and the spacing of antennas along the coordinate system axis d _x = _dy = λ/2 are obtained respectively;

Where M _x and _My are the number of antennas arranged on the x-axis and y-axis respectively; d _x and _dy are the spacing between antennas along the x-axis and y-axis respectively; λ is the carrier wavelength;

(2) Calculate the steering vector α(l(n), l _k ) from the UAV to each of the rescue team members.

Based on M _x , _My and d _x , _{dy ,} the steering vector α(l(n), l _k ) from the drone to the k-th rescuer module is 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 rescue team member, θ(l(n), l _k ) and 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, respectively.

j is the imaginary unit; T is the transpose of the matrix; θ(l(n), l _k ) is the Angle of Departure (AoD) elevation angle of the UAV's transmitted signal when it reaches the kth rescuer, reflecting the change in the signal's transmission angle in the vertical direction relative to the UAV; Φ(l(n), l _k ) is the AoD azimuth angle of the UAV's transmitted signal to the kth rescuer, describing the signal's transmission pointing angle in the horizontal direction relative to the UAV.

(3) Calculate the channel gain h _k,com (l(n),l _k ) from the UAV to each rescue team member based on the steering vector.

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

Based on α(l(n),l _k ), the channel gain h _k,com (l(n),l _k ) from the UAV to the k-th rescuer module is calculated as follows:

Where n = 1, 2, ... N, where N is the number of time slots in the UAV flight cycle; _Gt is the antenna gain of the UAV transmitter, _Gb is the antenna gain of the rescuer module receiving antenna, and d(l( _n ),lk) = ||l(n) _-lk || represents the Euclidean distance between the UAV and the kth rescuer.

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

Under unit bandwidth, the communication rate between the UAV and the kth rescuer module is calculated based on h _k,com (l(n),l _k ) The calculation is as follows:

Among them, c _k (n) is the rescue team member scheduling of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise obeying Gaussian random distribution, H is the conjugate transpose of the channel gain hk _,com (l(n), _lk ); _ck (n) is a binary discrete variable;

When the UAV transmits a communication beam in time slot n to perform a communication mission to the kth rescue team member, c _k (n) = 1, otherwise c _k (n) = 0.

c _k (n) is a binary discrete variable used to describe the dispatching of UAVs to rescue team members.

The noise obeys Gaussian random distribution. The noise in the communication rate is the noise at the receiving point of the rescue team. The noise is a noise with a mean of 0 and a variance of Gaussian white noise, is the power distribution of the noise signal.

The UAV's motion model includes kinematic constraints and flight position constraints. Considering the UAV's physical characteristics and flight safety requirements, the flight speed will always be controlled below the preset maximum speed _vmax during the flight of the UAV.

Exemplarily, the preset maximum speed v _max is 40 m/s.

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

Where 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, D _max = v _max δ _T is the maximum flight distance of the UAV in each time slot, and δ _T is the length of each time slot in seconds;

When a drone performs communication services for rescuers, the following flight constraints must be adhered to to ensure that its flight trajectory within a specific area has clear starting and ending points:

l(1)＝l(N)＝l _I Formula (5)

Where, l _I represents the starting position of the UAV, and N is the total number of time slots in the UAV flight cycle.

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

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

in, is a set containing all time slot numbers.

Since the average transmission power of the UAV is limited by the maximum power, the following constraints are met:

P(n)≤P _max Formula (7)

Among them, P _max represents the maximum transmission power of the UAV, which is the parameter of the UAV itself.

The function of step S1 is to build 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 _k ), the channel gain h _k,com (l(n),l _k ) and the communication rate between the UAV and each of the rescue team members. Provide basic support for subsequent communication optimization.

Step S2: Specifically.

Design an optimization problem with the goal of maximizing 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.

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

in, is the communication rate between the UAV and the kth rescuer; K is the number of rescuers; n = 1, 2, ... N, where N is the number of time slots in the UAV flight cycle; C, W, and L are the rescuer scheduling to be optimized, the UAV’s transmitted beamforming vector, and the UAV’s 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, P _max is the maximum transmission power of the UAV, D _max is the maximum flight distance of the UAV in each time slot, and l _I is the starting position of the UAV.

C1 and C2 are rescuer scheduling constraints, indicating 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 a time. C3 is a UAV communication performance constraint, requiring that the communication rate between the UAV and the base station must exceed a 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 end point to coincide, ensuring the safety and stability of the UAV's flight.

Exemplarily, the communication rate threshold is preset Set to 16bps/Hz.

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.

The step S3 includes:

The UAV-assisted rescue team communication optimization model includes three nonlinear coupling variables: rescue team scheduling, UAV emission beamforming vector and UAV trajectory. The model is solved as a non-convex optimization problem.

The non-convex optimization problem is decomposed into three convex optimization sub-problems: rescue team scheduling, UAV launch beamforming vector and UAV trajectory;

Alternately iteratively optimize three convex optimization sub-problems and 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. The global optimal solution includes the rescue team member scheduling, the beamforming vectors emitted by the UAV, and the UAV trajectory.

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

Among them, the non-convex optimization problem includes rescue team scheduling Beamforming vectors transmitted by the drone Drone tracks Three nonlinear coupled variables.

The non-convex optimization problem is decomposed into three sub-problems, namely, the rescue team scheduling optimization problem, the UAV launch beamforming vector optimization problem, 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.

The details are as follows:

(1) For the rescue team scheduling optimization subproblem, the binary scheduling variables are relaxed into continuous variables with values ranging from 0 to 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.

(2) For the subproblem of optimizing the beamforming vector emitted by the UAV, the SDR technique is used to transform the beamforming vector into a rank-1 positive semidefinite matrix. This initially transforms the problem into a convex problem. The relaxed rank-1 constraint with strong non-convexity is then added as a penalty factor to the objective function, allowing it to be directly solved using the CVX toolbox.

(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 form that can be directly solved by CVX.

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 each other and evolve co-evolvingly, gradually approaching the global optimal solution, thereby achieving a joint optimization solution to the entire complex optimization problem.

The three convex optimization subproblems of alternately optimizing rescue team scheduling, UAV launch beamforming vectors, and UAV trajectory gradually approach the global optimal solution, including:

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

When solving the beamforming vector transmitted by the UAV, based on C ^(m) and the initial UAV trajectory, the UAV-assisted rescue team member communication optimization model is solved to obtain the beamforming vector W ^(m) ;

When solving the UAV trajectory, based on W ^(m) and C ^(m) , solve the UAV-assisted rescue team member communication optimization model to obtain L ^(m) ;

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

Among them, the mth round is the iteration round, and m≥1 rounds are the iteration rounds before the objective function converges.

The optimization steps are explained in detail as follows:

There are three nonlinear coupling variables in the original optimization problem (rescue team scheduling Beamforming vectors transmitted by the drone 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;

Method for determining convergence of objective function: If the objective function in the current iteration minus the objective function in the previous iteration 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.

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

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 will be used in solving the next sub-problem, ultimately achieving the goal of joint solution.

For example, in the mth iteration, when solving the rescue team member scheduling C, first give W and L. Here, given means that the W value of all time slots is pre-set during the simulation. L is the position of the circle 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). After solving, C ^(m) is obtained;

Similarly, when solving the beamforming vector W emitted by the drone, given C ^(m) and L, C is replaced with the solution C ^(m) obtained above, and L is still the initial trajectory (because the trajectory subproblem has not been solved yet, it is not replaced yet). The solved W ^(m) ;

When solving the UAV trajectory L, W ^(m) and C ^(m) are given for solution, and finally C ^(m) , W ^(m) , and L ^(m) are obtained. These three values are the solutions of the current round. Then the next round begins, and the solution order of C, W, and L is still followed until the maximum number of iterations is reached, and finally the global optimal solution is approached.

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. The non-convex problem is converted into a convex problem that can be directly solved by the CVX toolbox, and finally the global optimal solution of the original optimization problem is approached through the alternating iterative optimization algorithm.

The optimal solution consists of the following three decision variables:

(1) Rescue team member scheduling: Which rescue team member should be selected for communication in each time slot?

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

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

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, taking all the constraints into account.

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 UAV rescue team scheduling, the UAV launch beamforming vector and the UAV flight trajectory, so as to maximize the average communication rate between the UAV and the rescue team while satisfying all constraints.

In the network model shown in FIG3 , it is assumed that G _t = 15 dBi, G _b = 10 dBi, σ _k ² = -110 dBm, M _x =M _y =4, K =10, H =90m, T =75s, δ _t =0.5s, P _max =10w, v _max =40m/s.

σ _k ² = -110 dBm, which is converted to 10 ^-14 W in watts, is the noise power value.

The center of the drone's mission area (0, 0) is used as the center of the drone's initial circular trajectory, with a radius of r = 250m. This allows the drone to maintain a relatively balanced initial trajectory when initiating a communication mission, connecting with the surrounding target rescuers. During actual flight, the drone further dynamically adjusts its flight trajectory based on real-time channel state information and the rescuers' communication needs, effectively assisting the rescuers in their communications.

The optimized trajectory of the drone, shown in Figure 4, clearly demonstrates this adaptive adjustment process. Compared to the initial trajectory, the optimized trajectory shows that the drone is actively moving closer to the rescuer. This is because, with the goal of maximizing communication rate, the shortened distance between the drone and the target rescuer significantly reduces path loss during signal propagation, resulting in higher signal strength for the target rescuer at their receiving end.

The optimization goal is to maximize the average communication rate between the UAV and the rescue team members, that is, to maximize the sum of the communication rates of all time slots. According to formulas (2) and (3), the closer the distance between the UAV and the rescue team members, the greater the channel gain hk _,com (l(n), _lk ), so the communication rate The larger the distance, the closer the drone is to the rescue team. Therefore, after algorithmic optimization, the drone will serve as close as possible to the rescue team members. When the flight time and speed are sufficient, the drone will fly directly above each rescue team member, continuing to fly forward while providing services. Figure 3 shows a two-dimensional diagram of the drone's flight trajectory. From a two-dimensional perspective, the drone's flight path connects multiple rescue team members. In three-dimensional space, the drone itself will have a certain height, and in fact, it is flying above the rescue team members in this trajectory.

Figure 5 illustrates the drone's dispatching of rescuers at different time slots. Starting from its starting position, the drone prioritizes serving the closest rescuer, ensuring that every rescuer is served. In terms of channel quality, the drone prioritizes rescuers with favorable channel conditions for communication dispatch. This is because a favorable channel environment allows for more efficient utilization of limited communication resources, increasing the data transmission rate per unit bandwidth. To ensure service quality for rescuers, the scheduling algorithm avoids overserving some rescuers, resulting in a lack of communication resources for others. Through a tightly coupled scheduling and trajectory optimization mechanism, the drone is able to make the most appropriate rescuer dispatch decisions within each time slot in complex and changing communication environments, effectively improving the communication efficiency of the entire drone-assisted communication network and providing rescuers with continuous, stable, and efficient communication services. The horizontal axis of Figure 4 represents the number of time slots N in the drone's flight cycle, while the vertical axis represents the target rescuer's sequence number (1-10). Figure 4 illustrates the drone dispatching the corresponding rescuer at the corresponding time slot in flight.

Figures 6 and 7, respectively, show the planar beamforming and three-dimensional beamforming diagrams of the signal-to-noise ratio (SNR) at the receiving end of a rescuer in a randomly selected time slot. The narrow communication beam is concentrated at the rescuer scheduled in the current time slot, demonstrating that by optimizing the drone's communication beamforming vector, its uniform planar antenna array can precisely control the phase and amplitude of the transmitted signal. During communication with a specific rescuer in the current time slot, the transmitted signal energy is highly concentrated in the direction of the rescuer based on their location information and channel characteristics, resulting in a significant improvement in the rescuer's SNR. When beamforming improves the SNR, according to Shannon's theorem, the communication rate per unit bandwidth will increase significantly.

In Figure 6, a simulation is performed in an area of 1000 meters by 1000 meters, where 10 target rescue team members are located. The innovation of the present invention is that the UAV uses beamforming technology to assist in 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 one rescue team member, thereby enhancing the communication rate in accordance with formula (3), meeting 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 (Figures 6 and 7 correspond to time slot 55. Combined with Figure 4, it can be seen that the UAV is serving the fifth target rescue team member at this time). Therefore, the narrower beam is concentrated at the position of the fifth rescue team member as shown in the figure. A narrower beam means higher energy. The height value of the beam is the size of the signal-to-noise ratio.

Figure 7 is a three-dimensional view of the two-dimensional graph in Figure 6, where the x and y axes represent the simulation area (meters). The communication rate in formula (3) To display the beam pattern linearly (if the communication rate is plotted as the vertical axis, the logarithm will produce multiple beams, making it difficult to clearly identify which rescuer has the highest communication rate at that time slot), the signal-to-noise ratio is used as the vertical axis. A higher signal-to-noise ratio indicates a higher communication rate. In general, using the signal-to-noise ratio as the vertical axis provides a more intuitive graph and satisfies the optimization problem.

Figure 8 compares the communication rates of the preset and optimized trajectory schemes. As the drone's maximum transmit power increases, the communication rates of both schemes improve, and the optimized trajectory scheme consistently outperforms the preset trajectory scheme. This is because the optimized trajectory scheme doesn't rely solely on increasing transmit power to improve communication conditions. The drone dynamically adjusts its flight path based on multiple factors, such as the distribution of rescuers and channel conditions, allowing it to approach rescuers with better signal transmission quality, thereby reducing path loss and interference during signal propagation. At the same transmit power, the optimized trajectory scheme effectively transmits more signal energy to the receiver, improving communication quality and rate. However, the preset trajectory scheme, lacking this flexibility, is relatively limited in its ability to increase communication rates. Furthermore, the figure also shows that the number of antenna units affects the system's communication rate. A larger number of antenna units significantly increases beamforming gain, leading to higher achievable communication rates.

The preset trajectory scheme refers to a scheme in which the drone is fixed to fly along the initial trajectory of Figure 4, that is, the drone's flight trajectory is not optimized. Obviously, compared with the scheme of optimizing the trajectory in the present invention (adjusting the flight path to approach the rescue team), the drone's flight trajectory that is not optimized is farther away from the rescue team, so the communication rate is low at the same transmission power. With the increase in 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 _wc (n) can have a larger amplitude at a higher transmission power, which also increases the communication rate between the drone and the rescue team 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 drone and the rescue team member.

Rescue workers receive the directional beam signal transmitted by the drone, convert the received analog signal into a digital signal, and extract the transmitted data. The received data is decoded to restore the original information. Depending on the actual needs, rescue workers send confirmation signals or feedback information, as well as requests, to the drone to achieve two-way communication. This two-way communication mechanism enables efficient data exchange between the drone and rescue workers, supporting a variety of applications such as remote monitoring, emergency rescue, and data collection and distribution.

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

1. The UAV-enhanced emergency communication and rescue system of the present invention can be rapidly deployed to disaster areas without being restricted by ground transportation and terrain, enabling rapid response to disaster areas. This fills the communication gap caused by damage to ground communication infrastructure and improves the ability to respond quickly after a disaster.

2. This invention uses multi-antenna technology and beamforming technology to enable drones to communicate accurately with ground rescue teams, improving communication quality, reducing signal interference, ensuring accurate information transmission, and improving communication quality in disaster areas.

3. This invention builds a multi-antenna UAV-assisted multi-rescue team communication system and a human-machine-assisted rescue team communication optimization model, which achieves the optimization of rescue team scheduling, UAV beamforming, and flight trajectory, effectively improving the utilization efficiency of rescue resources and the coordination of rescue operations;

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

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.

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

Claims (9)

1. A method for multi-antenna unmanned aerial vehicle assisted ground rescue team member communication, comprising:

step S1, calculating steering vectors of the unmanned aerial vehicle to rescue team members in the unmanned aerial vehicle, calculating channel gains of the unmanned aerial vehicle to the rescue team member modules based on the steering vectors, and calculating communication rates between the unmanned aerial vehicle and the rescue team member modules based on the channel gains;

S2, constructing an unmanned aerial vehicle auxiliary rescue team member communication optimization model which takes the average communication rate of the maximized unmanned aerial vehicle and the multi-rescue team member module as an optimization objective function and meets rescue team member scheduling constraint, unmanned aerial vehicle communication performance constraint, unmanned aerial vehicle transmitting power constraint and unmanned aerial vehicle flight track constraint;

Step S3, solving the unmanned aerial vehicle auxiliary rescue team member communication optimization model to obtain an optimal communication solution of the unmanned aerial vehicle and multiple rescue team members;

The unmanned aerial vehicle assists rescue team member communication optimization model is as follows:

C6:l(1)=l(N)=l_I

Wherein, the The method comprises the steps of enabling communication speed between an unmanned aerial vehicle and a kth rescue team member to be high, enabling K to be the number of the rescue team member, enabling n=1, 2,..N, N to be the time slot number of the unmanned aerial vehicle flight cycle, enabling C, W, L to be the rescue team member scheduling to be optimized, a beam forming vector emitted by the unmanned aerial vehicle and an unmanned aerial vehicle track variable respectively; C1 and C2 represent rescue team member scheduling constraints, C3 represents unmanned aerial vehicle communication performance constraints, For a preset communication rate threshold value of the unmanned aerial vehicle and the base station, C4 represents unmanned aerial vehicle transmitting power constraint, C5 and C6 represent unmanned aerial vehicle flight track constraint, P _max is unmanned aerial vehicle maximum transmission power, D _max is unmanned aerial vehicle maximum flight distance in each time slot, and l _I is unmanned aerial vehicle starting position.

2. The method according to claim 1, characterized in that the drone is equipped with a uniform planar antenna array, deployed parallel to the ground, the total number of antennas and the distance between the antenna units of the uniform planar antenna array being divided along the x-axis and the y-axis of a three-dimensional cartesian coordinate system, resulting in a total number of antenna arrays M = M _x×M_y and a spacing of antennas along the coordinate system axis d _x＝d_y = λ/2, respectively;

Wherein M _x、M_y is the number of the antennas arranged on the x axis and the y axis respectively, d _x、d_y is the distance between the antennas along the x axis and the y axis respectively, and lambda is the carrier wavelength;

The steering vector α (l (n), l _k) of the drone to the kth rescue team member module is calculated based on M _x、M_y and d _x、d_y as follows:

Wherein k=1, 2..k, K is the number of rescue team members, l (n) is the position of the unmanned aerial vehicle in the nth time slot, l _k is the position of the kth rescue team member, θ (l (n), l _k)、Φ(l(n),l_k) are the AoD elevation angle and AoD azimuth angle respectively when the unmanned aerial vehicle transmits signals to the kth rescue team member.

3. The method of claim 2, wherein the channel gain h _k,com(l(n),l_k of the drone to the kth rescue team member module is calculated based on α (l (n), l _k), as follows:

Wherein n=1, 2,..n, N is the number of time slots of the unmanned aerial vehicle flight cycle, G _t is the antenna gain of the unmanned aerial vehicle transmitter, G _b is the antenna gain received by the rescue team member module, d (i (N), i _k)＝||l(n)-l_k i represents the euclidean distance between the unmanned aerial vehicle and the kth rescue team member.

4. A method according to claim 3, characterized in that the communication rate between the drone and the kth rescue team member module is calculated based on h _k,com(l(n),l_k) at a unit bandwidthThe calculation is as follows:

Wherein c _k (n) is the rescue team member schedule of the unmanned aerial vehicle in the nth time slot, The communication beam forming vector of the unmanned aerial vehicle in the nth time slot,For noise following Gaussian random distribution, H is the conjugate transpose of channel gain H _k,com(l(n),l_k), c _k (n) is a binary discrete variable;

When the drone transmits a communication beam at time slot n to perform a communication task for the kth rescue team member, c _k (n) =1, otherwise c _k (n) =0.

5. The method according to claim 1, wherein the step S3 comprises:

The unmanned aerial vehicle auxiliary rescue team member communication optimization model comprises three nonlinear coupling variables including rescue team member scheduling, beam forming vectors emitted by the unmanned aerial vehicle and unmanned aerial vehicle tracks, and the model is solved into a non-convex optimization problem;

Decomposing the non-convex optimization problem into three convex optimization sub-problems of rescue team member scheduling, beam forming vectors emitted by the unmanned aerial vehicle and unmanned aerial vehicle tracks;

alternately iterating and optimizing three convex optimization sub-problems to gradually approach a global optimal solution;

when the objective function is converged, the obtained global optimal solution is the communication optimal solution of the unmanned aerial vehicle and a plurality of rescue teammates, wherein the global optimal solution comprises rescue teammate scheduling, beam forming vectors emitted by the unmanned aerial vehicle and unmanned aerial vehicle tracks;

Wherein the non-convex optimization problem includes rescue team member scheduling Beamforming vector emitted by unmanned aerial vehicleUnmanned aerial vehicle trackThree non-linear coupling variables.

6. The method of claim 5, wherein alternately optimizing three convex optimization sub-problems of rescue team member schedule, beam forming vector emitted by the unmanned aerial vehicle, and unmanned aerial vehicle trajectory, successively approximates a global optimal solution, comprises:

in the iteration of the mth round, when solving the rescue team member scheduling, giving a preset beam forming vector emitted by the unmanned aerial vehicle and an initial unmanned aerial vehicle track, and solving an unmanned aerial vehicle auxiliary rescue team member communication optimization model to obtain a rescue team member scheduling C ^(m);

When the beamforming vector emitted by the unmanned aerial vehicle is solved, solving an unmanned aerial vehicle auxiliary rescue team member communication optimization model based on C ^(m) and the initial unmanned aerial vehicle track to obtain a beamforming vector W ^(m);

When solving the unmanned aerial vehicle track, solving the unmanned aerial vehicle auxiliary rescue team member communication optimization model based on W ^(m)、C^(m) to obtain L ^(m);

And (3) alternately and circularly solving until the optimization objective function converges, wherein the rescue team member scheduling of the current round, the beam forming vector emitted by the unmanned aerial vehicle and the unmanned aerial vehicle track are the global optimal solution.

7. An unmanned aerial vehicle enhanced emergency communication and rescue system for use in a method of assisting ground rescue team member communications as defined in any one of claims 1 to 6, comprising:

The unmanned aerial vehicle module is deployed on the unmanned aerial vehicle body and is used for executing a multi-antenna unmanned aerial vehicle auxiliary ground rescue team member communication method, carrying out accurate communication with the ground rescue team member module, acquiring rescue information sent by the rescue team member module and sending the rescue information of a plurality of rescue team members to the rescue center module;

the rescue team member module is carried by a rescue team member on the rescue site and is used for communicating with the unmanned aerial vehicle, receiving the instruction and the information of the unmanned aerial vehicle and sending request support information to the unmanned aerial vehicle when necessary;

the rescue center module is deployed in the fixed rescue command center and is used for scheduling rescue team members and distributing rescue materials based on the rescue information acquired by the unmanned aerial vehicle module;

The rescue information comprises one or more of disaster area positions, disaster victims, personal injuries, required material types and quantity.

8. The system of claim 7, wherein the system further comprises:

The warehouse module is deployed at a logistic base or a designated warehouse position and is used for storing and managing rescue materials and rapidly allocating and preparing the needed rescue materials according to instructions of a rescue center;

the rescue material distribution module is deployed on the ground transportation vehicle or the temporary distribution center and is used for receiving rescue materials provided by the warehouse module and rapidly transporting the materials to disaster areas or appointed places according to instructions of the rescue center.

9. The system of claim 7, wherein the unmanned aerial vehicle module comprises a uniform planar antenna array that performs a circumferential flight according to a predetermined flight path over a disaster area based on a mission plan of the rescue center module;

the rescue team member module comprises single antenna equipment, the single antenna equipment is a mobile terminal, when the rescue team member passes through the unmanned aerial vehicle, wireless communication connection is established between the single antenna equipment and the unmanned aerial vehicle, each rescue team member module receives a directional beam signal sent by the unmanned aerial vehicle module in a certain time slot, and then responds to and sends rescue information.