A method for optimizing trajectories of multiple unmanned aerial vehicles in post-disaster emergency communication and related equipment

Abstract

The application relates to the technical field of unmanned aerial vehicle trajectory planning, and provides a multi-unmanned aerial vehicle trajectory optimization method in post-disaster emergency communication and related equipment, which comprises the following steps: modeling the movement state of each ground user equipment based on the current position of all ground user equipments, obtaining a user social force model of each ground user equipment, and constructing a reward function according to the user social force model; constructing an unmanned aerial vehicle trajectory constraint condition according to the user social force model of all ground user equipments and the takeoff airport position of all unmanned aerial vehicles; optimizing the trajectory of the unmanned aerial vehicle based on the reward function under the constraint of the unmanned aerial vehicle trajectory constraint condition, obtaining the running trajectory of the unmanned aerial vehicle in a communication time period; and controlling the movement of the unmanned aerial vehicle according to the running trajectory of the unmanned aerial vehicle, so that the emergency communication of the ground user equipment is realized. The method can improve the rationality of the unmanned aerial vehicle trajectory in post-disaster emergency communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle trajectory optimization, and particularly relates to a multi-unmanned aerial vehicle trajectory optimization method in post-disaster emergency communication and related equipment. BACKGROUND

[0002] Unmanned aerial vehicles (UAVs) are playing an increasingly important role as air-based communication platforms in mobile edge computing (MEC) systems, especially in complex environments such as post-disaster rescue, maritime communication, and urban emergency. Compared with traditional ground MEC systems, MEC architectures assisted by UAVs have stronger deployment flexibility and coverage capabilities, and have become an important part of non-terrestrial networks (NTNs). To address issues such as dynamic user distribution, unstable channel state, and resource constraints, designing resource allocation strategies is crucial for system performance optimization. The academic community has made some progress in key dimensions such as computation offloading, spectrum management, trajectory planning, energy consumption control, and service fairness. From the perspectives of system latency minimization, system energy consumption minimization, and system performance optimization, this paper reviews representative research achievements in the scenario of UAVs as air-based communication platforms.

[0003] System latency minimization research:

[0004] In multi-task coordination scenarios such as task offloading, cache scheduling, and trajectory planning, system latency is one of the key indicators of MEC performance. To address the challenges posed by concurrent and dynamic communication demands in edge computing, researchers have been exploring joint optimization solutions for UAV path control, task scheduling, and spectrum management.

[0005] Some researchers have proposed a MEC framework that integrates device-to-device (D2D) communication and multi-UAV cooperation, which reduces the average system latency by jointly optimizing content caching, task offloading, UAV trajectory, and computing resource allocation. Some researchers have focused on maritime search and rescue scenarios and optimized the deployment and computing association strategy of multiple UAVs, achieving minimization of task latency. Some researchers have proposed a UAV relay-assisted emergency communication network based on cognitive radio and rate-splitting multiple access, which minimizes transmission delay by optimizing bandwidth allocation, transmission power, and UAV altitude.

[0006] System energy consumption minimization research:

[0007] In the context of limited UAV platform resources, reducing system energy consumption is a core goal for achieving sustainable communication services. Recent research has focused on joint optimization of UAV deployment, task scheduling, and spectrum power to maximize energy efficiency.

[0008] In the context of UAVs combined with intelligent reflecting surfaces, researchers have proposed resource allocation methods based on robust deep reinforcement learning, effectively improving energy harvesting efficiency and communication performance. Researchers have studied the application of UAVs equipped with intelligent reflecting surfaces in communication systems, achieving joint optimization of ground user offloading power, UAV trajectory, and IRS phase matrix, minimizing total energy consumption of ground devices. Researchers and others have focused on air-ground collaborative MEC scenarios, proposing energy consumption control schemes that jointly optimize UAV deployment, task scheduling, and resource allocation, reducing energy consumption of UAVs and ground devices. Researchers and others have used multi-agent deep reinforcement learning algorithms to optimize resource scheduling and computing task collaboration among UAVs in a distributed manner, minimizing total energy consumption of users. Researchers have proposed a trajectory knowledge-based UAV swarm optimization routing protocol that effectively improves data transmission efficiency and reduces end-to-end delay and energy consumption of UAV swarms in dynamic network environments through time-dependent graph modeling and an improved dynamic weighted Dijkstra algorithm.

[0009] System performance optimization research:

[0010] System performance optimization not only focuses on delay and energy consumption, but also involves the coordinated improvement of multiple dimensions such as system throughput, Quality of Experience (QoE), Age of Information (AoI), and task allocation efficiency.

[0011] Researchers have proposed an online joint optimization method that maximizes user Quality of Experience (QoE) under energy consumption constraints through real-time optimization of task offloading, resource allocation, and UAV trajectory. Researchers have proposed a UAV-assisted mobile crowdsourcing perception framework based on multi-agent deep reinforcement learning and Transformer, which optimizes UAV trajectory planning to ensure data freshness within a certain threshold and maximizes data collection. Researchers have proposed a two-time scale joint optimization method for computing resource allocation, computing offloading, and trajectory control in UAV-assisted mobile edge computing systems, which maximizes system utility through a short-time scale price incentive method and a long-time scale convex optimization method.

[0012] Some researchers have studied the problem of deploying heterogeneous UAV networks in disaster areas to maximize network throughput, and proposed an approximation algorithm and an improved heuristic algorithm to improve the number of served users. Some researchers have proposed a multi-UAV path learning scheme based on deep reinforcement learning for the optimization of information freshness and energy consumption in the Internet of Things (IoT). By optimizing the UAV trajectory, the information freshness (AoI) and device energy consumption are minimized. At the same time, considering the battery life of the UAV and the flight time of the charging station, the data collection problem in large-scale IoT networks is solved.

[0013] It can be seen that the current research on UAV-assisted post-disaster emergency communication mainly focuses on improving the response speed, energy use efficiency and overall communication service quality of the system in a sudden environment. Most of the research is still based on some idealized assumptions, and has not fully considered factors such as the strong randomness of user distribution, the dynamic change of channel state and the suddenness of tasks in post-disaster scenarios. This leads to the problem of poor rationality of the UAV operating trajectory in post-disaster emergency communication. SUMMARY

[0014] The embodiments of the present application provide a multi-UAV trajectory optimization method in post-disaster emergency communication and related equipment, which can solve the problem of poor rationality of the UAV operating trajectory in post-disaster emergency communication.

[0015] In a first aspect, the embodiments of the present application provide a multi-UAV trajectory optimization method in post-disaster emergency communication, which comprises:

[0016] Obtain the current positions of a plurality of ground user equipment in the disaster area and the takeoff airport positions of a plurality of UAVs;

[0017] Model the movement status of each ground user equipment based on the current positions of all ground user equipment, obtain a user social force model of each ground user equipment, and construct a reward function according to the user social force model; the user social force model is used to describe the movement status of the ground user equipment, and the reward function is used to describe the rationality of the UAV movement;

[0018] Construct a UAV trajectory constraint condition according to the user social force model of all ground user equipment and the takeoff airport positions of all UAVs; the UAV trajectory constraint condition is used to describe the constraints of the UAV operation;

[0019] Under the constraints of the UAV trajectory constraint condition, optimize the trajectory of each UAV based on the reward function to obtain the operating trajectory of each UAV in the communication time period; the communication time period is the time period from the current time to the time when the plurality of UAVs provide emergency communication services for the ground user equipment;

[0020] According to the operation track of each unmanned aerial vehicle, the unmanned aerial vehicle is controlled to move, and emergency communication of the ground user equipment is realized.

[0021] Optionally, the user social force model is:

[0022] ;

[0023] ;

[0024] ;

[0025] wherein, denotes the speed of the i-th ground user equipment at the j-th time point, denotes the speed of the i-th ground user equipment at the j-th time point, denotes the position of the i-th ground user equipment at the j-th time point, denotes the position of the i-th ground user equipment at the j-th time point, when denotes the current position of the i-th ground user equipment, , denotes a set of ground user equipment numbers, , denotes the number of time points in the communication time period, denotes the time difference between the i-th time point and the j-th time point, denotes the force acting result of the i-th ground user equipment, denotes the differential of the speed of the i-th ground user equipment, denotes the differential of the time point, denotes the expected speed of the i-th ground user equipment, denotes the unit vector of the i-th ground user equipment, denotes the current speed of the i-th ground user equipment, denotes the response time, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, denotes the obstacle encounter parameter corresponding to the i-th ground user equipment, Repulsive force between ground user equipment:

[0026] ;

[0027] in, Indicates the intensity of adjustment. Indicates the first The ground user equipment and the first Preset safe distance between ground user equipment Indicates the first The ground user equipment and the first Current distance between ground user equipment Indicates by the first The ground user equipment points to the first A unit vector for each ground user equipment.

[0028] Optionally, the reward function is:

[0029] ;

[0030] in, Indicates the first The drone in the first The reward value at each moment. , Indicates the number of drones, These are all weighted adjustment parameters. Indicates the first At the [time]th moment The drone and the first The relationship between ground user equipment. The balance parameters representing the power of the drone, A parameter representing the balance of communication power. This indicates the penalty for drone collisions. Indicates the first At the [time]th moment The speed of the drone Indicates the first At the [time]th moment The transmit power of each drone, Indicates the first At the [time]th moment The drone and the first Communication rate between ground user equipment Indicates that the heavy workload overrides the penalty item:

[0031] ;

[0032] ;

[0033] ;

[0034] wherein, denotes the channel bandwidth of the i-th UAV, denotes the noise power, denotes the communication channel gain between the i-th UAV and the j-th ground user equipment at the k-th time instant, denotes the interference term of the j-th ground user equipment, denotes the communication channel gain at the reference distance, denotes the position of the i-th UAV at the k-th time instant, denotes the path loss factor.

[0035] Optionally, the UAV trajectory constraint condition is:

[0036] ;

[0037] wherein, denotes the energy constraint in the UAV trajectory constraint condition, denotes the collision constraint in the UAV trajectory constraint condition, denotes the user association constraint in the UAV trajectory constraint condition, denotes the movement constraint in the UAV trajectory constraint condition, denotes the user movement constraint in the UAV trajectory constraint condition, denotes the number of time instants within the communication time period, denotes the maximum energy of the i-th UAV, denotes the universal quantifier symbol, denotes the position of the i-th UAV at the k-th time instant, denotes the minimum preset distance between the UAVs, denotes the starting position of the i-th UAV, denotes the position of the j-th ground user equipment, denotes the take-off airport position of the i-th UAV. Optionally, under the constraint of the UAV trajectory constraint condition, the trajectory optimization is performed on each UAV based on the reward function, to obtain the running trajectory of each UAV within the communication time period, including:

[0038] Optionally, under the constraint of the UAV trajectory constraint condition, the trajectory optimization is performed on each UAV based on the reward function, to obtain the running trajectory of each UAV within the communication time period, including:

[0039] ​​​​​​​​​​​​Construct the state input for each drone;

[0040] Under the constraints of the UAV trajectory, based on the reward function and the state input of each UAV, the action of each UAV at each moment during the communication period is calculated; the action includes the UAV's position, transmission power, and association with ground user equipment.

[0041] For each drone, the corresponding actions of the drone are integrated to obtain the drone's running trajectory during the communication period.

[0042] Optional, the status input is:

[0043] ;

[0044] in, Indicates the first The drone in the first The state input at each moment Indicates the first The drone in the first The state of oneself at any given moment. Indicates the first The drone in the first User-related status at any given moment Indicates the first The drone in the first The status of the neighbor's drone at any given moment:

[0045] ;

[0046] ;

[0047] ;

[0048] in, Indicates the first At the [time]th moment The location of the drone. Indicates the first At the [time]th moment The speed of the drone Indicates the first The drone in the first The remaining energy at any given moment. Indicates the first The ground user equipment in the first The position at that moment Indicates the farthest communication distance. A set representing the IDs of ground user equipment. This represents a multilayer perceptron. Indicates the first The drone in the first The state at any given moment Indicates the first The drone is the first The level of attention given to individual drones:

[0049] ;

[0050] in, Represents an exponential function. This represents the similarity operation. Indicates the first The query vector for each drone. Indicates the first The key vector of a drone, Indicates the first The key vectors of each drone:

[0051] ;

[0052] ;

[0053] ;

[0054] in, , Indicates the first At the [time]th moment The set of IDs of neighboring drones of a given drone. Indicates the query vector weight. Represents the key vector weights. Indicates the first The drone in the first The state at any given moment Indicates the first The drone in the first The state at a given moment.

[0055] Optionally, the actions of the drone can be integrated to obtain the drone's trajectory during the communication period, including:

[0056] Based on the sequence of all moments within the communication period from morning to night, the corresponding actions of the drone are integrated into a single data set to obtain the drone's trajectory within the communication period.

[0057] Secondly, embodiments of this application provide a multi-UAV trajectory optimization device for post-disaster emergency communication, comprising:

[0058] The acquisition module is used to acquire the current location of multiple ground user devices in the disaster area, as well as the take-off airport location of multiple drones;

[0059] The modeling module is configured to model a movement state of each ground user equipment based on current positions of all ground user equipments, to obtain a user social force model of each ground user equipment, and to construct a reward function according to the user social force model; the user social force model is configured to describe the movement state of the ground user equipment, and the reward function is configured to describe a reasonable degree of movement of the unmanned aerial vehicle;

[0060] The constructing module is configured to construct an unmanned aerial vehicle trajectory constraint condition according to the user social force model of all ground user equipments and takeoff airport positions of all unmanned aerial vehicles; the unmanned aerial vehicle trajectory constraint condition is configured to describe a constraint of operation of the unmanned aerial vehicle;

[0061] The trajectory optimizing module is configured to optimize a trajectory of each unmanned aerial vehicle based on the reward function under a constraint of the unmanned aerial vehicle trajectory constraint condition, to obtain an operation trajectory of each unmanned aerial vehicle in a communication time period; the communication time period is a time period from a current time to a time when the unmanned aerial vehicles provide emergency communication services for the ground user equipments;

[0062] The control module is configured to control movement of the unmanned aerial vehicles according to the operation trajectory of each unmanned aerial vehicle, to realize emergency communication of the ground user equipments.

[0063] In a third aspect, an embodiment of the present application provides a terminal device, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the post-disaster emergency communication multi-unmanned aerial vehicle trajectory optimization method when executing the computer program.

[0064] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the post-disaster emergency communication multi-unmanned aerial vehicle trajectory optimization method.

[0065] The above-mentioned scheme of the present application has the following advantages:

[0066] In the embodiment of the present application, the current positions of a plurality of ground user equipments in the disaster area and the takeoff airport positions of a plurality of unmanned aerial vehicles are acquired, then the movement status of each ground user equipment is modeled based on the current positions of all ground user equipments, the user social force model of each ground user equipment is obtained, the reward function is constructed according to the user social force model, the unmanned aerial vehicle trajectory constraint condition is constructed according to the user social force model of all ground user equipments and the takeoff airport positions of all unmanned aerial vehicles, then the trajectory optimization of each unmanned aerial vehicle is performed based on the reward function under the constraint of the unmanned aerial vehicle trajectory constraint condition, the running trajectory of each unmanned aerial vehicle in the communication time period is obtained, finally the unmanned aerial vehicles are controlled to move according to the running trajectory of each unmanned aerial vehicle, and the emergency communication of the ground user equipments is realized. In the embodiment of the present application, the unmanned aerial vehicle trajectory constraint condition is constructed based on the user social force model of the ground user equipment and the takeoff airport position of the unmanned aerial vehicle, the movement status of the ground user equipment and the constraint of the takeoff position of the unmanned aerial vehicle are considered, the rationality and information richness of the unmanned aerial vehicle trajectory constraint condition are improved, the reward function is constructed according to the movement status of the ground user equipment, the movement of the ground user equipment is considered, the accuracy of the rationality degree of the unmanned aerial vehicle trajectory represented by the reward function is improved, and the trajectory optimization of the unmanned aerial vehicle is performed based on the reward function under the constraint of the unmanned aerial vehicle trajectory constraint condition, so that the rationality degree of the unmanned aerial vehicle trajectory is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0068] Figure 1 The flowchart of the multi-unmanned aerial vehicle trajectory optimization method in post-disaster emergency communication provided by an embodiment of the present application;

[0069] Figure 2 The simulation schematic diagram of the disaster area provided by an embodiment of the present application;

[0070] Figure 3 The structural schematic diagram of the multi-unmanned aerial vehicle trajectory optimization device in post-disaster emergency communication provided by an embodiment of the present application;

[0071] Figure 4 The structural schematic diagram of the terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0072] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0073] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0074] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0075] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0076] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0077] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0078] To solve the problem of poor rationality of the existing UAV operation trajectory for post-disaster emergency communication, the embodiment of the present application provides a multi-UAV trajectory optimization method in post-disaster emergency communication. The method comprises the following steps: obtaining the current positions of a plurality of ground user equipment in a disaster area and the takeoff airport positions of a plurality of UAVs; modeling the movement status of each ground user equipment based on the current positions of all ground user equipment, obtaining a user social force model of each ground user equipment, and constructing a reward function according to the user social force model; constructing a UAV trajectory constraint condition according to the user social force model of all ground user equipment and the takeoff airport positions of all UAVs; performing trajectory optimization on each UAV based on the reward function under the constraint of the UAV trajectory constraint condition, obtaining the operation trajectory of each UAV in a communication time period; and controlling the movement of the UAV according to the operation trajectory of each UAV to realize emergency communication of the ground user equipment. In the method, the UAV trajectory constraint condition is constructed based on the user social force model of the ground user equipment and the takeoff airport positions of the UAVs, and the movement status of the ground user equipment and the constraint of the takeoff point position of the UAVs are considered, so that the rationality and information richness of the UAV trajectory constraint condition are improved. The reward function is constructed according to the movement status of the ground user equipment, and the movement of the ground user equipment is considered, so that the accuracy of the representation of the rationality of the UAV trajectory by the reward function is improved. The trajectory optimization is performed on the UAV based on the reward function under the constraint of the UAV trajectory constraint condition, so that the rationality of the UAV trajectory is effectively improved.

[0079] Next, the multi-UAV trajectory optimization method in post-disaster emergency communication provided by the present application is exemplarily described.

[0080] As shown in Figure 1 , the multi-UAV trajectory optimization method in post-disaster emergency communication provided by the present application comprises the following steps:

[0081] Step 11, obtaining the current positions of a plurality of ground user equipment in a disaster area and the takeoff airport positions of a plurality of UAVs.

[0082] The disaster area is an area where a disaster occurs, such as an area where a flood, earthquake, or other disaster occurs. The ground user equipment is a device (such as a terminal device such as a mobile phone) of a person in the disaster area who needs emergency communication. The UAV is a UAV carrying an edge computing device such as a computer server for communication. The plurality of UAVs are UAVs belonging to the same formation. The takeoff airport is a facility specially providing services such as takeoff, landing, parking, and charging for UAVs.

[0083] In some embodiments of the present application, the current positions of the ground user equipment and the takeoff airport positions of the UAVs can be obtained by using a global positioning system.

[0084] It should be noted that the current location of the aforementioned ground user equipment and the location of the takeoff airport are in the same coordinate system, which can be the Earth coordinate system, etc.

[0085] For example, a simulation diagram of the disaster-stricken area is shown below. Figure 2 As shown, the horizontal axis X and the vertical axis Y are coordinate axes in a two-dimensional coordinate system. This two-dimensional coordinate system can be a coordinate system with the ground of the disaster-stricken area as the plane and points on the ground of the disaster-stricken area as the origin. Figure 2 The horizontal axis corresponds to the x-coordinate of the position, and the vertical axis corresponds to the y-coordinate of the position. The unit is meters. The two types of points in the figure represent ground user equipment and targets (i.e., refuge locations), respectively. The ellipse represents the range of obstacles.

[0086] Step 12: Model the movement status of each ground user equipment based on the current location of all ground user equipment to obtain the user social force model of each ground user equipment, and construct a reward function based on the user social force model.

[0087] The aforementioned user social force model is used to describe the movement status of ground user equipment, and the reward function is used to describe the rationality of the drone's movement.

[0088] Specifically, the user social power model is as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] in, Indicates the first The ground user equipment in the first The speed at that moment Indicates the first The ground user equipment in the first The speed at that moment Indicates the first The ground user equipment in the first The position at that moment Indicates the first The ground user equipment in the first The position at that moment, when hour, Indicates the first The current location of each ground user equipment , A set representing the IDs of ground user equipment. , Indicates the number of moments within a communication period. Indicates the first The moment and the The time difference between each moment Indicates the first The force results of each ground user equipment Indicates the first The derivative of the speed of each ground user equipment The derivative of time. Indicates the first The expected speed of a ground user equipment Indicates the first Unit vector of a ground user equipment Indicates the first The current speed of each ground user equipment Indicates response time. Indicates the first The obstacle encounter parameters for each ground user equipment (UAE) describe the user's reaction to an obstacle, and are expressed as follows: , Indicates the repulsive force strength coefficient. Indicates the range of the repulsive force. Indicates the first The actual distance from a ground user device to the surface of an obstacle. Represents the exponential decay coefficient. Indicates the direction from the obstacle to the first The unit direction vector of each ground user equipment Indicates the first The ground user equipment and the first Repulsive force between ground user equipment:

[0093] ;

[0094] in, Indicates the intensity of adjustment. Indicates the first The ground user equipment and the first Preset safe distance between ground user equipment Indicates the first The ground user equipment and the first Current distance between ground user equipment Indicates by the first The ground user equipment points to the first A unit vector for each ground user equipment.

[0095] The reward function is:

[0096] ;

[0097] in, Indicates the first The drone in the first The reward value at each moment. , Indicates the number of drones, These are all weighted adjustment parameters. Indicates the first At the [time]th moment The drone and the first The relationship between ground user equipment. The balance parameters representing the power of the drone, A parameter representing the balance of communication power. This indicates the penalty for drone collisions. Indicates the first At the [time]th moment The speed of the drone Indicates the first At the [time]th moment The transmit power of each drone, Indicates the first At the [time]th moment The drone and the first Communication rate between ground user equipment Indicates that the heavy workload overrides the penalty item:

[0098] ;

[0099] ;

[0100] ;

[0101] in, Indicates the first Channel bandwidth of a drone Indicates noise power. Indicates the first At the [time]th moment The drone and the first Communication channel gain between ground user equipment Indicates the first Interference items for ground user equipment This represents the communication channel gain at the reference distance. Indicates the first At the [time]th moment The location of the drone (including the drone's three-dimensional coordinates, which can be coordinates in a coordinate system such as the Earth coordinate system, such as longitude, latitude, and altitude). This represents the path loss factor.

[0102] It should be noted that the interference term is used to describe the interference generated by other signals to the ground user equipment in addition to the expected signal, and the expression is:

[0103]

[0104] wherein, represents the communication channel gain between the i-th unmanned aerial vehicle and the j-th ground user equipment at the k-th time point, represents the position of the i-th unmanned aerial vehicle at the k-th time point, represents the communication channel gain between the i-th unmanned aerial vehicle and the j-th ground user equipment at the k-th time point.

[0105] The flight height of the unmanned aerial vehicle is higher than the terrain elevation, and , represents the horizontal coordinate of the unmanned aerial vehicle, represents the vertical coordinate of the unmanned aerial vehicle, represents the minimum height, represents the elevation function, and the maximum flight speed of the unmanned aerial vehicle is limited to , the communication time period is the time period during which the plurality of unmanned aerial vehicles provide emergency communication services for the ground user equipment starting from the current time, and the value of the weight adjustment parameter is preset. The association relationship between the i-th unmanned aerial vehicle and the j-th ground user equipment at the k-th time point , when , it is considered that there is an association relationship between the i-th unmanned aerial vehicle and the j-th ground user equipment at the k-th time point, that is, the j-th ground user equipment can perform emergency communication through the i-th unmanned aerial vehicle at the k-th time point, when , it is considered that there is no association relationship between the i-th unmanned aerial vehicle and the j-th ground user equipment at the k-th time point. In the present application, first, the cumulative total transmission rate of the ground user equipment in the communication time period is maximized as the target (i.e. the first term of the reward function), and the expression is:

[0106] ;​​​​​​​​​​​​​​​​​​​​​​

[0107] Step 13: Construct UAV trajectory constraints based on the user social force model of all ground user equipment and the takeoff airport locations of all UAVs.

[0108] The above-mentioned drone trajectory constraints are used to describe the constraints on drone operation.

[0109] Specifically, the drone trajectory constraints are as follows:

[0110] ;

[0111] in, This represents the energy constraint in the trajectory constraints of the drone. This represents the collision constraint in the drone trajectory constraint conditions. This indicates the user association constraint in the drone trajectory constraint (i.e., at any given time, a ground user equipment can only connect to one drone). This indicates the movement constraint in the drone trajectory constraint conditions (i.e., the drone's takeoff starting point must be at the takeoff airport). This indicates the user movement constraint in the drone trajectory constraint conditions (i.e., the location of the ground user equipment needs to conform to the user social force model). Indicates the number of moments within a communication period. Indicates the first The maximum energy of a drone The symbol for universal quantifiers, Indicates the first At the [time]th moment The location of the drone. This indicates the minimum preset distance between drones. Indicates the first The starting position of each drone Indicates the first The location of each ground user equipment. Indicates the first Location of the takeoff airport for each drone.

[0112] Step 14: Under the constraints of the UAV trajectory, optimize the trajectory of each UAV based on the reward function to obtain the running trajectory of each UAV during the communication time period.

[0113] The aforementioned communication period refers to the time period from the current moment during which multiple drones provide emergency communication services to ground user equipment.

[0114] In some embodiments of this application, the steps of optimizing the trajectory of each UAV based on a reward function under the constraints of UAV trajectory constraints to obtain the operating trajectory of each UAV during the communication time period include:

[0115] The first step is to build the status input for each drone.

[0116] Specifically, the status input is:

[0117] ;

[0118] in, Indicates the first The drone in the first The state input at each moment Indicates the first The drone in the first The state of oneself at any given moment. Indicates the first The drone in the first User-related status at any given moment Indicates the first The drone in the first The status of the neighbor's drone at any given moment:

[0119] ;

[0120] ;

[0121] ;

[0122] in, Indicates the first At the [time]th moment The location of the drone. Indicates the first At the [time]th moment The speed of the drone Indicates the first The drone in the first The remaining energy at any given moment. Indicates the first The ground user equipment in the first The position at that moment Indicates the farthest communication distance. A set representing the IDs of ground user equipment. This represents a multilayer perceptron. Indicates the first The drone in the first The state at any given moment Indicates the first The drone is the first The level of attention given to individual drones:

[0123] ;

[0124] wherein, denotes an exponential function, denotes a similarity operation, denotes a query vector of the th UAV, denotes a key vector of the th UAV, denotes a key vector of the th UAV:

[0125] ;

[0126] ;

[0127] ;

[0128] wherein, , denotes a set of numbers of neighbor UAVs of the th UAV at the th time, denotes a query vector weight, denotes a key vector weight, denotes a state of the th UAV at the th time, denotes a state of the th UAV at the th time.

[0129] It should be noted that at each time, the neighbor UAVs of the UAV are the UAVs within a certain range of the UAV, i.e., the UAV and the neighbor UAVs satisfy , denotes an attention action radius. The neighbor UAV state includes the distance between each neighbor UAV and the UAV, the speed and the remaining energy of the neighbor UAV after being weighted by the attention degree.

[0130] Secondly, under the constraint of the UAV trajectory constraint condition, based on the reward function and the state input of each UAV, the action of each UAV at each time within the communication time period is calculated.

[0131] The above action includes the position of the UAV, the transmission power, and the association relationship with the ground user equipment (i.e., whether the ground user equipment performs emergency communication through the UAV).

[0132] Exemplarily, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm can be used to calculate the action of the UAV at each time. Specifically, the state input of all UAVs is input as the state space of the MADDPG, the action of all UAVs is input as the action space of the MADDPG, the reward function is input as the reward function of the MADDPG, the decision of the MADDPG follows the Markov game, the MADDPG makes decisions under the constraint of the UAV trajectory constraint condition, and the action of the UAV, the state input of the UAV at the next time, and the value of the reward function corresponding to the action are output. The experience replay mechanism is used to train the decision-making process of the MADDPG to improve the decision-making accuracy, and finally the action of each UAV at each time is output.

[0133] In the third step, the action corresponding to each UAV is integrated to obtain the running trajectory of the UAV in the communication time period.

[0134] Specifically, according to the order of all time periods from early to late in the communication time period, the action corresponding to the UAV is integrated in sequence to obtain the running trajectory of the UAV in the communication time period.

[0135] In step 15, the UAV is controlled to move according to the running trajectory of each UAV to realize the emergency communication of the ground user equipment.

[0136] Specifically, each UAV is controlled to move according to the corresponding running trajectory, and communicates with the ground user equipment having a correlation relationship at each time to realize the emergency communication of the ground user equipment.

[0137] Exemplarily, after the disaster occurs, the traditional communication network is damaged, and the UAV is used as a relay node to provide a temporary communication network. The ground user terminal can be connected to the network provided by the edge computing device such as the computer server carried by the UAV through WIFI, Bluetooth and other wireless communication to realize emergency communication. The UAV moves to the position corresponding to the time according to the trajectory at each time in the communication time period, and provides emergency communication service for the ground user equipment having a correlation relationship at the time according to the transmission power at the time. In the environment where the user distribution is random and the channel state changes dynamically, high coverage and high service quality guarantee of the communication in the disaster area are realized.

[0138] It is worth mentioning that the UAV trajectory constraint condition is constructed based on the user social force model of the ground user equipment and the take-off airport position of the UAV, the movement condition of the ground user equipment and the constraint of the take-off position of the UAV are considered, the rationality and information richness of the UAV trajectory constraint condition are improved, the reward function is constructed according to the movement condition of the ground user equipment, the movement of the ground user equipment is considered, the accuracy of the reward function in representing the rationality of the UAV trajectory is improved, and the rationality of the UAV trajectory is effectively improved based on the reward function under the constraint of the UAV trajectory constraint condition.

[0139] In addition, current research on UAV-aided post-disaster emergency communication mainly focuses on improving the reaction speed, energy use efficiency and comprehensive communication service quality of the system in a sudden environment. Most researches are still based on some idealized assumptions, and have not fully considered the strong randomness of user distribution, the dynamic change of channel state and the suddenness of tasks in post-disaster scenarios. In view of the damage of ground communication facilities in complex post-disaster environment, this application starts from the actual post-disaster emergency communication scene, considers the multi-UAV system in the environment with random user distribution and dynamic channel state, and realizes high coverage rate and high service quality guarantee of disaster area communication by studying the cooperative trajectory planning and spectrum power resource joint allocation of UAV base stations, while solving the scheduling problems caused by resource conflicts and task bursts. Under the premise of guaranteeing the quality of user communication service, the maximum coverage and minimum resource conflict are realized through cooperative trajectory planning and spectrum power joint scheduling. The social force model is introduced to simulate personnel flow, and a deep reinforcement learning algorithm with multi-attention mechanism is used to solve complex non-convex problems, and an efficient scheduling framework with emergency response capability is constructed.

[0140] The post-disaster emergency communication multi-UAV trajectory optimization device provided by the application will be described below.

[0141] As shown in Figure 3 The post-disaster emergency communication multi-UAV trajectory optimization device 300 provided by the embodiments of the application comprises:

[0142] The acquisition module 301 is configured to acquire the current positions of a plurality of ground user equipments in a disaster area and the take-off airport positions of a plurality of UAVs.

[0143] The modeling module 302 is configured to model the movement condition of each ground user equipment based on the current positions of all ground user equipments, obtain a user social force model of each ground user equipment, and construct a reward function according to the user social force model; the user social force model is used to describe the movement condition of the ground user equipment, and the reward function is used to describe the rationality of the movement of the UAV;

[0144] Module 303 is used to construct UAV trajectory constraints based on the user social force model of all ground user equipment and the takeoff airport locations of all UAVs; the UAV trajectory constraints are used to describe the constraints of UAV operation.

[0145] The trajectory optimization module 304 is used to optimize the trajectory of each UAV based on the reward function under the constraints of the UAV trajectory constraints, so as to obtain the running trajectory of each UAV during the communication time period; the communication time period is the period from the current moment when multiple UAVs provide emergency communication services to ground user equipment.

[0146] The control module 305 is used to control the movement of each UAV according to its operating trajectory, so as to realize emergency communication for ground user equipment.

[0147] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0148] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0149] like Figure 4 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 4 The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0150] Specifically, the processor D100 executes the computer program D102 to obtain the current positions of the plurality of ground user devices in the disaster area and the takeoff airport positions of the plurality of unmanned aerial vehicles, model the movement status of each ground user device based on the current positions of all the ground user devices, obtain a user social force model of each ground user device, construct a reward function according to the user social force model, construct an unmanned aerial vehicle trajectory constraint condition according to the user social force models of all the ground user devices and the takeoff airport positions of all the unmanned aerial vehicles, perform trajectory optimization on each unmanned aerial vehicle based on the reward function under the constraint of the unmanned aerial vehicle trajectory constraint condition, obtain the running trajectory of each unmanned aerial vehicle in the communication time period, and finally control the unmanned aerial vehicles to move according to the running trajectory of each unmanned aerial vehicle, thereby realizing emergency communication of the ground user devices. The unmanned aerial vehicle trajectory constraint condition is constructed based on the user social force model of the ground user device and the takeoff airport position of the unmanned aerial vehicle, and the movement status of the ground user device and the constraint of the takeoff position of the unmanned aerial vehicle are considered, thereby improving the rationality and information richness of the unmanned aerial vehicle trajectory constraint condition. The reward function is constructed according to the movement status of the ground user device, and the movement of the ground user device is considered, thereby improving the accuracy of the reward function in representing the rationality of the unmanned aerial vehicle trajectory. The trajectory optimization is performed on the unmanned aerial vehicle based on the reward function under the constraint of the unmanned aerial vehicle trajectory constraint condition, thereby effectively improving the rationality of the unmanned aerial vehicle trajectory.

[0151] The processor D100 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0152] The storage D101 can be an internal storage unit of the terminal device D10 in some embodiments, such as a hard disk or a memory of the terminal device D10. The storage D101 can also be an external storage device of the terminal device D10 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device D10. Further, the storage D101 can include both the internal storage unit and the external storage device of the terminal device D10. The storage D101 is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of the computer program, etc. The storage D101 can also be used to temporarily store data that has been output or will be output.

[0153] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in the above-mentioned various method embodiments.

[0154] The embodiments of the present application provide a computer program product. When the computer program product is run on a terminal device, the terminal device is caused to implement the steps in the above-mentioned various method embodiments.

[0155] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the embodiments of the present application can implement all or part of the processes in the above-mentioned method embodiments by a computer program to instruct related hardware to complete, and the computer program can be stored in a computer readable storage medium. The computer program is executed by a processor to implement the steps in the above-mentioned various method embodiments. The computer program includes computer program codes, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program codes to the multi-unmanned aerial vehicle trajectory optimization method and device / terminal device in post-disaster emergency communication, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc.

[0156] In the above embodiments, the description of each embodiment is focused on, and the part not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0157] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0158] The above is the preferred embodiment of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method for multi-UAV trajectory optimization in post-disaster emergency communication, characterized in that, The method comprises the following steps: obtaining the current positions of a plurality of ground user equipment in a disaster area and the takeoff airport positions of a plurality of unmanned aerial vehicles; modeling the movement status of each ground user equipment based on the current positions of all ground user equipment, obtaining a user social force model of each ground user equipment, and constructing a reward function according to the user social force model; the user social force model is used to describe the movement status of the ground user equipment, and the reward function is used to describe the rationality of the movement of the unmanned aerial vehicle; constructing an unmanned aerial vehicle trajectory constraint condition according to the user social force model of all ground user equipment and the takeoff airport positions of all unmanned aerial vehicles; the unmanned aerial vehicle trajectory constraint condition is used to describe the constraint of the operation of the unmanned aerial vehicle; under the constraint of the unmanned aerial vehicle trajectory constraint condition, trajectory optimization is performed on each unmanned aerial vehicle based on the reward function, and the operation trajectory of each unmanned aerial vehicle in a communication time period is obtained; the communication time period is a time period from the current time to the time when the plurality of unmanned aerial vehicles provide emergency communication services for the ground user equipment; controlling the movement of the unmanned aerial vehicle according to the operation trajectory of each unmanned aerial vehicle to realize the emergency communication of the ground user equipment; wherein the user social force model is: ； ； ； wherein, denotes the velocity of the th ground user equipment at the th time instant, denotes the velocity of the th ground user equipment at the th time instant, denotes the position of the th ground user equipment at the th time instant, denotes the position of the th ground user equipment at the th time instant, when , denotes the current position of the th ground user equipment, , denotes a set of ground user equipment numbers, , denotes the number of time instants within a communication time period, denotes the time difference between the th time instant and the th time instant, denotes the force acting result of the th ground user equipment, denotes the differential of the velocity of the th ground user equipment, denotes the differential of the time instant, denotes the desired velocity of the th ground user equipment, denotes the unit vector of the th ground user equipment, denotes the current velocity of the th ground user equipment, denotes the response time, denotes the obstacle encounter parameter corresponding to the th ground user equipment, denotes the repulsive force between the th ground user equipment and the th ground user equipment: ； wherein, denotes the adjustment strength, denotes the preset safety distance between the first ground user equipment and the second ground user equipment, denotes the current distance between the first ground user equipment and the second ground user equipment, denotes the unit vector pointing from the first ground user equipment to the second ground user equipment.

2. The method of claim 1, wherein, the reward function is: ； wherein, represents the reward value of the i-th UAV at the t-th time instant, , represents the number of UAVs, are weight adjustment parameters, represents the association relationship between the i-th UAV and the j-th ground user equipment at the t-th time instant, represents the balance parameter of the UAV power, represents the balance parameter of the communication power, represents the collision penalty of the UAV, represents the speed of the i-th UAV at the t-th time instant, represents the transmission power of the i-th UAV at the t-th time instant, represents the communication rate between the i-th UAV and the j-th ground user equipment at the t-th time instant, represents the heavy negative coverage penalty term:​​​​​​​​​​​​ ； ； ； wherein, denotes the channel bandwidth of the th UAV, denotes the noise power, denotes the communication channel gain between the th UAV and the th ground user equipment at the th time instant, denotes the interference term of the th ground user equipment, denotes the communication channel gain at the reference distance, denotes the position of the th UAV at the th time instant, denotes the path loss factor.

3. The method of claim 2, wherein, the unmanned aerial vehicle trajectory constraint condition is: ； wherein, denotes an energy constraint in the UAV trajectory constraint condition, denotes a collision constraint in the UAV trajectory constraint condition, denotes a user association constraint in the UAV trajectory constraint condition, denotes a movement constraint in the UAV trajectory constraint condition, denotes a user movement constraint in the UAV trajectory constraint condition, denotes a number of time instants within a communication time period, denotes a maximum energy of the th UAV, denotes a universal quantifier symbol, denotes a position of the th UAV at the th time instant, denotes a minimum preset distance between UAVs, denotes a start position of the th UAV, denotes a position of the th ground user equipment, denotes a take-off airport position of the th UAV.

4. The method of claim 1, wherein, under the constraint of the unmanned aerial vehicle trajectory constraint condition, trajectory optimization is performed on each unmanned aerial vehicle based on the reward function, and the operation trajectory of each unmanned aerial vehicle in a communication time period is obtained, which comprises: constructing the state input of each unmanned aerial vehicle; under the constraint of the unmanned aerial vehicle trajectory constraint condition, the action of each unmanned aerial vehicle at each time in the communication time period is calculated based on the reward function and the state input of each unmanned aerial vehicle; the action comprises the position, the transmission power and the association relationship with the ground user equipment of the unmanned aerial vehicle; for each unmanned aerial vehicle, the corresponding action of the unmanned aerial vehicle is integrated to obtain the operation trajectory of the unmanned aerial vehicle in the communication time period.

5. The method of claim 4, wherein, the state input is: ； wherein, denotes the state input of the th drone at the th time instant, denotes the own state of the th drone at the th time instant, denotes the user-related state of the th drone at the th time instant, denotes the neighbor drone state of the th drone at the th time instant, ； ； ； wherein, denotes the position of the -th UAV at the -th time instant, denotes the velocity of the -th UAV at the -th time instant, denotes the remaining energy of the -th UAV at the -th time instant, denotes the position of the -th ground user equipment at the -th time instant, denotes the farthest communication distance, denotes the set of ground user equipment numbers, denotes the multi-layer perception machine, denotes the state of the -th UAV at the -th time instant, denotes the attention of the -th UAV to the -th UAV: ； wherein, denotes an exponential function, denotes a similarity operation, denotes a query vector of the thdrone, denotes a key vector of the thdrone, denotes a key vector of the thdrone: ； ； ； wherein, , denotes a set of neighbor drone IDs of the th drone at the th time instant, denotes a query vector weight, denotes a key vector weight, denotes a state of the th drone at the th time instant, denotes a state of the th drone at the th time instant.

6. The method of claim 5, wherein, the corresponding action of the unmanned aerial vehicle is integrated to obtain the operation trajectory of the unmanned aerial vehicle in the communication time period, which comprises: according to the order of all time in the communication time period from early to late, the corresponding action of the unmanned aerial vehicle is integrated into a data in the order to obtain the operation trajectory of the unmanned aerial vehicle in the communication time period.

7. A device for multi-UAV trajectory optimization in post-disaster emergency communication, characterized in that, The method comprises the following steps: an acquisition module is configured to obtain the current positions of a plurality of ground user equipment in a disaster area and the takeoff airport positions of a plurality of unmanned aerial vehicles; a modeling module is configured to model the movement status of each ground user equipment based on the current positions of all ground user equipment, obtain a user social force model of each ground user equipment, and construct a reward function according to the user social force model; the user social force model is used to describe the movement status of the ground user equipment, and the reward function is used to describe the rationality of the movement of the unmanned aerial vehicle; a construction module is configured to construct an unmanned aerial vehicle trajectory constraint condition according to the user social force model of all ground user equipment and the takeoff airport positions of all unmanned aerial vehicles; the unmanned aerial vehicle trajectory constraint condition is used to describe the constraint of the operation of the unmanned aerial vehicle; a trajectory optimization module, configured to perform trajectory optimization on each of the UAVs based on the reward function under the constraints of the UAV trajectory constraint conditions, to obtain a running trajectory of each of the UAVs within a communication time period; the communication time period is a time period during which the UAVs provide emergency communication services for the ground user equipment starting from a current time; a control module, configured to control the UAVs to move according to the running trajectory of each of the UAVs, to realize emergency communication of the ground user equipment; wherein the user social force model is: ； ； ； wherein, denotes the velocity of the th ground user equipment at the th time instant, denotes the velocity of the th ground user equipment at the th time instant, denotes the position of the th ground user equipment at the th time instant, denotes the position of the th ground user equipment at the th time instant, when , denotes the current position of the th ground user equipment, , denotes a set of ground user equipment numbers, , denotes the number of time instants within a communication time period, denotes the time difference between the th time instant and the th time instant, denotes the force action result of the th ground user equipment, denotes the differential of the velocity of the th ground user equipment, denotes the differential of the time instant, denotes the desired velocity of the th ground user equipment, denotes the unit vector of the th ground user equipment, denotes the current velocity of the th ground user equipment, denotes the response time, denotes the obstacle encounter parameter corresponding to the th ground user equipment, denotes the repulsive force between the th ground user equipment and the th ground user equipment: ； wherein, denotes the adjustment strength, denotes a preset safety distance between the first ground user equipment and the second ground user equipment, denotes a current distance between the first ground user equipment and the second ground user equipment, denotes a unit vector pointing from the first ground user equipment to the second ground user equipment.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, the processor executes the computer program to realize the method for trajectory optimization of multiple UAVs in post-disaster emergency communication according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. the computer program is executed by the processor to realize the method for trajectory optimization of multiple UAVs in post-disaster emergency communication according to any one of claims 1 to 6.

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