Unmanned aerial vehicle access control optimization method based on ground user emergencies

Abstract

The invention discloses an unmanned aerial vehicle access control optimization method based on ground user emergencies, and relates to the technical field of wireless communication, and the method comprises the steps: building a space-air-ground integrated network model facing urban emergency communication based on ground users, an unmanned aerial vehicle and a satellite; according to the constructed urban emergency communication system model, utilizing a Shannon formula to calculate uplink channel capacity of two sections of communication in the air-space-ground integrated network; aiming at the constraint condition characteristics of the uplink channel capacity of the two sections of communication, the access emergency degree of the ground user and the access state of the ground user, constructing an optimization problem; solving an optimization problem by using a heuristic design algorithm based on branch and bound; through examples, the branching and delimiting processes of the heuristic design algorithm for branching and delimiting are explained. According to the invention, by constructing the accurate channel model, designing the efficient optimization algorithm and verifying the performance of the channel model, the optimal allocation of resources in the urban emergency communication scene is realized.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to an unmanned aerial vehicle access control optimization method based on the urgency of ground users. Background Art

[0002] By integrating aerial platforms into satellite and terrestrial networks, the Space-Air-Ground Integrated Network (SAGIN) shows great potential and broad prospects for the upcoming sixth-generation (6G) mobile communication system. Specifically, highly maneuverable unmanned aerial vehicles (UAVs) in aerial networks can achieve rapid emergency communications and play a vital role in post-disaster reconstruction or enhancing services in areas with high telecommunication traffic density.

[0003] In recent years, the research on emergency communication using drones has attracted widespread attention. When the local network is interrupted by natural disasters and loses the ability to communicate with the remote control center, the flexibility of drones enables the ground-to-ground network to receive signals sent by a large number of audiences. However, due to the limitation of drone coverage, drone-assisted relay communication for ground users cannot support large-area transmission, especially for control centers far away from the disaster area. Therefore, SAGIN uses satellite networks to enhance long-distance transmission capabilities.

[0004] Past experience has shown that ground communication infrastructure is vulnerable to natural disasters such as earthquakes, floods and hurricanes, resulting in communication equipment failure. Combining satellites and drones with ground communications provides a flexible and powerful solution to ensure the risk resilience of ground networks, which is particularly important for dense urban areas. In SAGIN, drones often act as relay stations to bridge space and ground networks, using their mobility and flexibility as temporary base stations and monitoring real-time situations. In addition to reliability, satellite networks also provide large coverage to support seamless access in remote or rural areas. Therefore, the collaborative nature of SAGIN forms a comprehensive and resilient communication system that provides strong protection for urban emergency communications.

[0005] In UAV-assisted SAGIN, most of the existing research focuses on optimizing continuous variables such as UAV flight trajectory, hovering height and power control, while ignoring the ground user access selection problem in the resource-constrained uplink SAGIN. To address this shortcoming, the present invention proposes an effective solution to the 0-1 access problem in the uplink SAGIN. In order to improve the throughput of urban emergency communications, the present invention formulates the priority access problem of the ground-to-air segment in the SAGIN uplink, and is limited by the capacity of the air-to-sky segment. It should also be noted that most of the research on the ground user access problem only focuses on the uplink channel capacity from ground users to UAVs, while ignoring the urgency of ground users accessing UAVs in urban emergency communication scenarios. In actual situations, it is usually necessary to ensure priority access for high-urgency users, which is a key consideration when performing access optimization. Summary of the invention

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a UAV access control optimization method based on the urgency of ground users to solve the problems of low efficiency of ground user access control and inability to effectively guarantee emergency needs in an air-ground integrated network.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for optimizing UAV access control based on the urgency of ground users, which includes constructing an air-ground-integrated network model for urban emergency communications based on ground users, UAVs and satellites; calculating the uplink channel capacity of two-segment communications in the air-ground-integrated network using the Shannon formula according to the constructed urban emergency communication system model; constructing an optimization problem based on the constraint characteristics of the uplink channel capacity of the two-segment communications, the access urgency of ground users and the access status of ground users; solving the optimization problem using a heuristic design algorithm based on branch and bound; and illustrating the branching and delimiting process of the heuristic design algorithm of branch and bound through examples.

[0009] As a preferred solution of the method for optimizing the access control of unmanned aerial vehicles based on the urgency of ground users described in the present invention, a space-ground integrated network model for urban emergency communications is constructed based on ground users, unmanned aerial vehicles and satellites, including the following steps: Setting terrestrial users, express; The channel model for the link between the ground user and the UAV includes both line-of-sight and non-line-of-sight components; Based on the impact of LoS probability on path loss, the probability of LoS contribution to path loss in the connection between the drone and the ground node is expressed as: ; ; in, Indicates the minimum angle allowed between the drone and any ground user. It refers to the elevation angle between the ground user and the drone. Indicates the hovering height of the drone. Indicates The distance between the ground user and the drone, and are all constants; The probability of NLoS is expressed as: ; Use what you get , from Channel gain from ground user to UAV It is expressed as, ; in, is the average path loss, and They represent the additional path loss under LoS and NLoS conditions in various environments respectively; Channel gain from drone to satellite , expressed as, ; in, , , and They represent the transmitting antenna gain of the drone, the receiving antenna gain of the satellite, the speed of light and the carrier frequency respectively. is the altitude of the satellite.

[0010] As a preferred solution of the UAV access control optimization method based on ground user urgency of the present invention, the average path loss is expressed as: ; in, Indicates the reference distance The channel power gain at Represents the path loss exponent.

[0011] As a preferred solution of the method for optimizing the access control of unmanned aerial vehicles based on the urgency of ground users described in the present invention, the uplink channel capacity of two-segment communications in the air-ground integrated network is calculated using the Shannon formula according to the constructed urban emergency communication system model, including the following steps: when When the ground signals are sent to the UAV at the same time, Channel capacity from ground users to UAVs for, ; in, For the The allocated bandwidth for each terrestrial user is For the The transmission power of a ground user is Indicates The channel gain from ground users to UAVs is Represents the noise power spectral density of the drone.

[0012] Similarly, the channel capacity on the channel from the drone to the satellite for, ; in, and are the allocated bandwidth and transmission power of the UAV respectively, represents the noise power spectral density of the satellite, Indicates the transmitting power of the drone.

[0013] As a preferred solution of the UAV access control optimization method based on the urgency of ground users described in the present invention, an optimization problem is constructed according to the constraint characteristics of the uplink channel capacity of the two-segment communication, the access urgency of the ground users and the access status of the ground users, including the following steps: Based on the multiple types of users served by drones at the same time, the drones use normalized weight values To evaluate the The information urgency of each user, and the total urgency follows the constraint: ; also, Indicates the access status. Indicates that the drone agrees to access Uplink information of ground users, otherwise 0, then the access selection process is expressed as, ; SAGIN forwards the information of ground users to the satellite through the relay drone using the decode-and-forward (DF) protocol. The expression for calculating the channel capacity of the SAGIN uplink is: ; ; in, represents the total channel capacity from ground users to UAVs; Defined by bandwidth and power constraints, the UAV can successfully communicate with the satellite by limiting the number of ground users that can access it. ; Based on the key role of drones in connecting the sky and the ground, high-urgency information is prioritized and the communication rate of two-level uplink transmission is evaluated; Formulate an optimization strategy to maximize the uplink transmission rate and access selection of SAGIN, expressed as, ; ; .

[0014] As a preferred solution of the UAV access control optimization method based on ground user urgency described in the present invention, the optimization problem is solved by using a heuristic design algorithm based on branch and bound, including the following steps: The variable The 0-1 integer constraint for selective access is relaxed to a linear condition between 0 and 1; Through the relaxation method, the optimization problem is transformed into a linear programming problem, which is expressed as: ; ; ; Define the tag as The root node of Sort all ground users waiting to access the drone in descending order of urgency; Drones visit high-urgency ground users until limits are reached; When the last visited ground user exceeds the information capacity, based on the defined linear programming problem, the node can only be partially visited by the UAV. It was determined to be ; in, No. Channel capacity for terrestrial users; For the first terrestrial users for further integration; The drone uses a heuristic sorting strategy to find the fraction in the current solution of the linear programming problem Start branching when The drone uses an urgency-first approach to calculate access capacity and compares it with Compare and determine Is it 0 or 1? Abandon branches whose maximum accommodating capacity of branch nodes is less than the maximum accommodating capacity of nodes with integer solutions; When multiple integer solutions are obtained from different branches, the optimal access scheme is determined by selecting the branch with the maximum transmission rate.

[0015] As a preferred solution of the method for optimizing UAV access control based on the urgency of ground users described in the present invention, the UAV uses an urgency priority method to calculate the access capacity and compares it with the Comparisons include, when When the drone does not visit users, updated according to the linear programming problem , currently, the upper limit of its child nodes is updated to ; when When the drone visits users and update according to the linear programming problem , the maximum amount of data from the drone is reduced to , and update the upper limit of the current child node .

[0016] As a preferred solution of the method for optimizing the access control of unmanned aerial vehicles based on the urgency of ground users described in the present invention, the score , expressed as, .

[0017] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the drone access control optimization method based on ground user urgency as described in the first aspect of the present invention is implemented.

[0018] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the drone access control optimization method based on ground user urgency as described in the first aspect of the present invention.

[0019] The beneficial effects of the present invention are as follows: by constructing an air-ground integrated network model for urban emergency communications, the overall architecture is clarified, covering the communication links between ground users, drones and satellites, ensuring that subsequent analysis can be based on a complete network topology structure; the Shannon formula is used to calculate the uplink channel capacity of two-segment communications, the theoretical formula is combined with the actual communication environment, the calculation method of the channel capacity is concretized, and the influence of factors such as path loss, transmission power and noise are considered, ensuring that the constraints of the optimization problem have a scientific basis, and at the same time providing a reliable performance reference for the formulation of subsequent access control strategies; the construction of the optimization problem enables high-emergency users to be accessed preferentially while meeting the capacity constraints, significantly improving the emergency response capability and resource allocation efficiency; the integer programming problem is converted into a linear programming problem using a relaxation method, and the global optimal solution is gradually approached in combination with the branch and bound technique, while unnecessary calculations are reduced through pruning operations, thereby achieving efficient solution to the integer optimization problem, thereby avoiding the computational complexity problem brought about by the traditional method, and ultimately achieving the effect of reducing the search complexity and ensuring the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 This is a flow chart of the drone access control optimization method based on the urgency of ground users in Example 1.

[0022] Figure 2 This is a schematic diagram of the UAV access control optimization method based on the urgency of ground users in Example 1.

[0023] Figure 3 This is an example diagram of the algorithm solution of the drone access control optimization method based on the urgency of ground users in Example 1.

[0024] Figure 4 This is a schematic diagram of the uplink throughput under different numbers of ground users of the UAV access control optimization method based on the urgency of ground users in Example 1.

[0025] Figure 5 Schematic diagram of the relationship between the uplink throughput and the hovering height of the drone in the drone access control optimization method based on the urgency of ground users in Example 1. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0029] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for optimizing UAV access control based on the urgency of ground users, comprising the following steps: S1. Based on ground users, drones and satellites, an integrated air-ground network model for urban emergency communications is constructed, including the following steps: In the scenario of urban emergency communication in Space-Air-Ground Integrated Network (SAGIN), a power-constrained unmanned aerial vehicle (UAV) wants to access some ground users of the satellite in order to get in touch with the remote control center. terrestrial users, express; Channel from ground to drone: The channel model for the link between ground users and drones includes both line-of-sight (LoS) and non-line-of-sight (NLoS) components, which accurately assesses the shadowing and scattering effects caused by various ground infrastructures.

[0030] Based on the impact of LoS probability on path loss, the probability of LoS contribution to path loss in the connection between the drone and the ground node is expressed as: ; ; in, 15° is a given reference angle, indicating the minimum angle allowed between the drone and any ground user. It refers to the elevation angle between the ground user and the drone. Indicates the hovering height of the drone. Indicates The distance between the ground user and the drone, and are all constants, which vary with the communication environment; Correspondingly, the probability of NLoS is expressed as: ; Use what you get , from Channel gain from ground user to UAV It is expressed as, ; in, is the average path loss, Indicates the reference distance = Channel power gain at 1m, represents the path loss exponent, and They represent the additional path loss under LoS and NLoS conditions in various environments respectively; Specifically, an empirical formula is used to estimate the additional path loss. For example, in an urban environment, a commonly used additional path loss model is: ; in: and is a constant that depends on the specific environment. For urban environments, typical values ​​are: ≈1 ≈1 (dB) ≈15a ≈15 (dB) Channel from drone to satellite: The horizontal distance between drone and satellite is negligible compared to the vertical distance. Therefore, the channel gain from drone to satellite is , expressed as, ; in, is the transmitting antenna gain of the drone, which indicates the ability of the drone antenna to concentrate power in a specific direction. is the satellite’s receiving antenna gain, which indicates the satellite antenna’s ability to capture signals from a specific direction. is the speed of light, Indicates the carrier frequency, is the altitude of the satellite.

[0031] Furthermore, the transmitting antenna gain of the drone and the receiving antenna gain of the satellite are obtained by consulting the technical manuals of the drone and the satellite to obtain the nominal value of the antenna gain; the speed of light is a physical constant, and its value is =3×108 m / s, the carrier frequency is determined by the operating frequency band of the communication system, and the altitude of the satellite is determined by its orbit type: low earth orbit (LEO): about 780 km, medium earth orbit (MEO): about 20,000 km, geosynchronous orbit (GEO): about 35,786 km. The altitude is selected according to the actual deployment of the satellite.

[0032] It should be noted that due to the transmission capacity limitation between the UAV and the satellite during the coherence period, the UAV cannot meet all these continuous accesses at the same time, so it is necessary to establish channels from the ground to the UAV and from the UAV to the satellite separately.

[0033] S2. Based on the constructed urban emergency communication system model, the Shannon formula is used to calculate the uplink channel capacity of two-segment communications in the air-ground integrated network, including the following steps: when When the ground signals are sent to the UAV at the same time, Channel capacity from ground users to UAVs for, ; in, For the The allocated bandwidth for each terrestrial user is For the The transmission power of a ground user is Indicates The channel gain from ground users to UAVs is Represents the noise power spectral density of the drone.

[0034] Similarly, the channel capacity on the channel from the drone to the satellite for, ; in, and are the allocated bandwidth and transmission power of the UAV respectively, represents the noise power spectral density of the satellite, Indicates the transmitting power of the drone.

[0035] S3. Construct an optimization problem based on the constraint characteristics of the uplink channel capacity of the two-segment communication, the access urgency of the ground user, and the access status of the ground user, including the following steps: In general, it is assumed that the coherence time of uplink transmission is much shorter than the orbital flight time of the satellite, so the topology of the three-layer SAGIN can be approximated as static. In addition, the drones hovering in the air act as relays connecting ground users and satellites.

[0036] Since drones serve multiple types of users at the same time, the urgency of their access to the drone is different, and the drones are assigned a normalized weight value. To evaluate the The information urgency of each user, and the total urgency follows the constraint: ; This means that the sum of the urgency of all users is 1, and the urgency of each user is relative. In the optimization problem, Used to weight the channel capacity of users , to reflect the priorities of different users.

[0037] also, Indicates the access status. Indicates that the drone agrees to access Uplink information of ground users, otherwise 0, then the access selection process is expressed as, ; SAGIN uses the decode-and-forward (DF) protocol to forward ground user information to the satellite via a relay drone. The channel capacity of the SAGIN uplink is calculated. The expression of is, ; ; in, represents the total channel capacity from ground users to UAVs; Defined by bandwidth and power constraints, the UAV can successfully communicate with the satellite by limiting the number of ground users that can access it. ; Due to the key role of UAVs in connecting the ground-to-sky network, they not only need to prioritize high-urgency information, but also need to evaluate the communication rate of two-level uplink transmission. Therefore, UAVs need to make a trade-off to develop an optimization strategy that maximizes the uplink transmission rate and access selection of SAGIN, which is expressed as, ; ; .

[0038] It should be noted that the objective function reflects the UAV's consideration of channel capacity and the priority of potential access users. The integer optimization problem determined in the optimization strategy considers the transmission capacity of the link from the UAV to the satellite when accessing ground users with different needs. Since the causal relationship in the two-stage uplink transmission in SAGIN is considered, it is more valuable to study this scheduling problem than to maximize the uplink throughput alone. However, it is challenging to effectively solve such integer optimization problems because the search complexity increases exponentially with the number of ground users, making it a non-deterministic polynomial time difficult (NP-hard) problem. Therefore, the goal of the present invention is to develop a low search complexity method for a large number of ground users waiting to access the SAGIN uplink.

[0039] S4. Use a branch-and-bound based heuristic design algorithm to solve the optimization problem, including the following steps: The variable The 0-1 integer constraint for selective access is relaxed to a linear condition between 0 and 1; Through the relaxation method, the optimization problem is transformed into a linear programming problem, which is expressed as: ; ; ; It should be noted that this relaxation method for solving integer optimization is usually used to jointly optimize the trajectory and transmission power of UAVs. The solution of relaxing the 0-1 integer constraint of the variable selective access in the equation to a linear condition between 0 and 1 can only obtain a heuristic solution, which is actually subordinate to the greedy algorithm. In order to further improve the efficiency, it is necessary to explore the branch and bound algorithm to identify the branch nodes and establish the upper bound of the child nodes, so as to speed up the solution process.

[0040] Specifically, define the mark The root node of Sort all ground users waiting to access the drone in descending order of urgency, i.e. ; Drones visit high-urgent ground users until the limit is reached, ; When the last visited ground user exceeds the information capacity, based on the defined linear programming problem, the node can only be partially visited by the UAV. It was determined to be ; in, No. Channel capacity for terrestrial users; For the first terrestrial users for further integration; UAV adopts a heuristic sorting strategy to effectively manage the branching process before branching. Specifically, the fraction appears in the current solution of the linear programming problem. When the branch starts, the score It is expressed as, ; in, No. The transmission capacity (or channel capacity) of a ground user reflects the maximum transmission capability of the user under the current channel conditions. It is calculated based on the Shannon formula. Channel capacity from ground users to UAVs; It should be noted that when the drone attempts to access the If the remaining capacity is insufficient to fully support , then it is necessary to partially access the user. The value is between 0 and 1, indicating the proportion of partial access.

[0041] The drone uses an urgency-first approach to calculate access capacity and compares it with For comparison, It is a score, the drone needs to be further determined Is it 0 or 1? when When the drone does not visit users, updated according to the linear programming problem , currently, the upper limit of its child nodes is updated to ; when When the drone visits users and update according to the linear programming problem , the maximum amount of data from the drone is reduced to , and update the upper limit of the current child node .

[0042] A branch whose maximum accommodating capacity of a branch node is less than the maximum accommodating capacity of a node with an integer solution is abandoned because subsequent access selection of the abandoned branch node will not produce an optimal solution.

[0043] When multiple integer solutions are obtained from different branches, the optimal access scheme is determined by selecting the branch with the maximum transmission rate.

[0044] S5. By using examples, the branching and bounding process of the heuristic design algorithm of branch and bound is explained, including the following steps: definition , among which Elements Indicates The transmission capacity of terrestrial users. , Indicates The capacity limit of the air-to-sky segment is set to The key branch and bound process can be described as follows: According to the linear programming problem, users with high urgency are given priority access as long as their combined capacity is less than In the scenario, we first determine that users 2 and 3 are to be connected, so and Next, consider the fifth user, since it has the highest urgency after the second and third users. However, the fifth user can only be partially accessed by the drone, and the calculation formula is . Subsequently, the upper limit of the transmission capacity at the root node can be defined as .

[0045] Then use branching to further determine Is 0 or 1. If , then according to the access status , and the upper bound of the child node is updated to The heuristic search continues until it encounters one of the following two situations: Case 1: When the determined access makes When , or the solution obtained is an integer; Case 2: The upper bound of the newly obtained solution is smaller than the existing solution The upper bound of .

[0046] This method enables the exploration process to gradually approach the global optimal solution, thus ensuring the efficiency of branch search.

[0047] In order to demonstrate the correctness and effectiveness of the above derivation results, the advantages of the proposed heuristic design algorithm based on branch and bound are further verified through simulation. The specific simulation parameters are set as follows: The satellite is at an altitude of The combined antenna gain , carrier frequency The minimum safe flight altitude for drones is , the maximum transmit power is , the noise power spectral density of all receivers is set to , , In urban environments, the level of urgency The ground users are randomly distributed in an area, and the distance from the ground users to the drones In addition, the transmit power is in the range of 0.1W to 0.5W. The total bandwidth is evenly distributed to each ground user, and for the drone-to-satellite segment, the bandwidth is allocated MHz. The performance of the greedy algorithm, random matching algorithm and heuristic design algorithm based on branch and bound are also compared. Figure 4 The results show the uplink throughput of the considered algorithms under different numbers of ground users. It is obvious that the proposed BBH algorithm significantly outperforms the traditional greedy and random algorithms in terms of achievable throughput. The results show the necessity of the proposed throughput enhancement scheme. From the perspective of search complexity, the available throughput performance of the random scheme is not satisfactory, although its complexity is negligible.

[0048] Figure 5 It also proves the effectiveness of the algorithm. Figure 5 The uplink throughput of the BBH algorithm is evaluated as the hovering altitude of the UAV increases. Clearly, the results show that the uplink throughput decreases as the hovering altitude of the UAV increases, mainly due to the amplified path loss. The increase in altitude prolongs the signal propagation distance, and the throughput performance of the BBH algorithm confirms its effectiveness in achieving global throughput. The synchronization results show that the BBH algorithm has good robustness within the allowed hovering altitude range, while the greedy matching algorithm and the random matching algorithm cannot completely suppress the increase in path loss at higher hovering altitudes.

[0049] This embodiment also provides a computer device, which is applicable to the case of a drone access control optimization method based on ground user urgency, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the drone access control optimization method based on ground user urgency proposed in the above embodiment. The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0050] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the access control of unmanned aerial vehicles based on the urgency of ground users proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0051] In summary, the present invention achieves an accurate description of complex communication scenarios by: constructing an urban emergency communication system model including ground users, drones and satellites, thereby laying the foundation for the construction of subsequent channel capacity calculation and optimization problems, and ensuring that subsequent analysis can be based on a complete network topology; by introducing the Shannon formula to calculate the uplink channel capacity from the ground to the drone and from the drone to the satellite, a quantitative evaluation of the communication link performance is achieved, thereby providing key parameters for the construction of the optimization problem, and ultimately achieving the purpose of improving resource utilization; by comprehensively considering the uplink channel capacity constraints of the two-stage communication, the urgency of the ground users and the access status, an optimization problem with the goal of maximizing the uplink transmission rate is constructed, thereby achieving priority protection of the communication needs of high-urgency users under limited resource conditions; by adopting a heuristic design algorithm based on branch and bound, an efficient solution to the integer optimization problem is achieved, thereby avoiding the computational complexity problem brought by the traditional method, and ultimately achieving the effect of reducing the search complexity and ensuring the global optimal solution. Compared with the traditional method, the branch and bound algorithm significantly reduces the search complexity while retaining the ability of the global optimal solution, and is particularly suitable for optimization problems in large-scale ground user access scenarios.

[0052] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing UAV access control based on the urgency of ground users, characterized by: include, Based on ground users, drones and satellites, an integrated air-ground network model for urban emergency communications is constructed; Based on the constructed urban emergency communication system model, the uplink channel capacity of two-segment communication in the air-ground integrated network is calculated using the Shannon formula. According to the constraint characteristics of the uplink channel capacity of the two-segment communication, the access urgency of ground users, and the access status of ground users, an optimization problem is constructed; Use branch-and-bound based heuristic design algorithms to solve optimization problems; The branching and bounding process of the branch-and-bound heuristic design algorithm is explained through examples.

2. The method for optimizing UAV access control based on ground user urgency as claimed in claim 1, characterized in that: Based on ground users, drones and satellites, an integrated air-ground network model for urban emergency communications is constructed, including the following steps: Setting terrestrial users, express; The channel model for the link between the ground user and the UAV includes both line-of-sight and non-line-of-sight components; Based on the impact of LoS probability on path loss, the probability of LoS contributing to path loss in the connection between the drone and the ground node It is expressed as: ； ； in, Indicates the minimum angle allowed between the drone and any ground user. It refers to the elevation angle between the ground user and the drone. Indicates the hovering height of the drone. Indicates The distance between the ground user and the drone, and are all constants; Probability of NLoS It is expressed as: ； Use what you get , from Channel gain from ground user to UAV It is expressed as, ； in, is the average path loss, and They represent the additional path loss under LoS and NLoS conditions in various environments respectively; Channel gain from drone to satellite , expressed as, ； in, , , and They represent the transmitting antenna gain of the drone, the receiving antenna gain of the satellite, the speed of light and the carrier frequency respectively. is the altitude of the satellite.

3. The method for optimizing UAV access control based on ground user urgency as claimed in claim 2, characterized in that: The average path loss is expressed as, ； in, Indicates the reference distance The channel power gain at Represents the path loss exponent.

4. The method for optimizing UAV access control based on ground user urgency as claimed in claim 3, characterized in that: According to the constructed urban emergency communication system model, the Shannon formula is used to calculate the uplink channel capacity of two-segment communication in the air-ground integrated network, including the following steps: when When the ground signals are sent to the UAV at the same time, Channel capacity from ground users to UAVs for, ； in, For the The allocated bandwidth for each terrestrial user is For the The transmission power of a ground user is Indicates The channel gain from ground users to UAVs is Represents the noise power spectral density of the drone. Similarly, the channel capacity on the channel from the drone to the satellite for, ； in, and are the allocated bandwidth and transmission power of the UAV respectively, represents the noise power spectral density of the satellite, Indicates the transmitting power of the drone.

5. The method for optimizing UAV access control based on ground user urgency as claimed in claim 4, characterized in that: According to the constraint characteristics of the uplink channel capacity of the two-segment communication, the access urgency of ground users and the access status of ground users, an optimization problem is constructed, including the following steps: Based on the multiple types of users served by drones at the same time, the drones use normalized weight values To evaluate the The information urgency of each user, and the total urgency follows the constraint: ； also, Indicates the access status. Indicates that the drone agrees to access Uplink information of ground users, otherwise 0, then the access selection process is expressed as, ； SAGIN forwards the information of ground users to the satellite through the relay drone using the decode-and-forward (DF) protocol. The expression for calculating the channel capacity of the SAGIN uplink is: ； ； in, represents the total channel capacity from ground users to UAVs; Defined by bandwidth and power constraints, the UAV can successfully communicate with the satellite by limiting the number of ground users that can access it. ; Based on the key role of drones in connecting the sky and the ground, high-urgency information is prioritized and the communication rate of two-level uplink transmission is evaluated; Formulate an optimization strategy to maximize the uplink transmission rate and access selection of SAGIN, expressed as, ； ； 。 6. The method for optimizing UAV access control based on ground user urgency as claimed in claim 5, characterized in that: Use the branch-and-bound based heuristic design algorithm to solve the optimization problem, including the following steps: The variable The 0-1 integer constraint for selective access is relaxed to a linear condition between 0 and 1; Through the relaxation method, the optimization problem is transformed into a linear programming problem, which is expressed as: ； ； ； Define the tag as The root node of Sort all ground users waiting to access the drone in descending order of urgency; Drones visit high-urgency ground users until limits are reached; When the last visited ground user exceeds the information capacity, based on the defined linear programming problem, the node can only be partially visited by the UAV. It was determined to be ； in, No. Channel capacity for terrestrial users; For the first terrestrial users for further integration; The drone uses a heuristic sorting strategy to find the fraction in the current solution of the linear programming problem Start branching when The drone uses an urgency-first approach to calculate access capacity and compares it with Compare and determine Is it 0 or 1? Abandon branches whose maximum accommodating capacity of branch nodes is less than the maximum accommodating capacity of nodes with integer solutions; When multiple integer solutions are obtained from different branches, the optimal access scheme is determined by selecting the branch with the maximum transmission rate.

7. The method for optimizing UAV access control based on ground user urgency as claimed in claim 6, characterized in that: The drone uses an urgency-first approach to calculate access capacity and compares it with For comparison, include, when When the drone does not visit users, updated according to the linear programming problem , currently, the upper limit of its child nodes is updated to ; when When the drone visits users and update according to the linear programming problem , the maximum amount of data from the drone is reduced to , and update the upper limit of the current child node .

8. The method for optimizing UAV access control based on ground user urgency as claimed in claim 7, characterized in that: The score , expressed as, 。 9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the drone access control optimization method based on ground user urgency as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the drone access control optimization method based on ground user urgency as described in any one of claims 1 to 8 are implemented.

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