A data transmission method and related equipment for air-space-ground integrated network

Abstract

The present application is applicable to the technical field of network resource management, and provides a data transmission method and related equipment for an air-space-ground integrated network, including clustering sensing nodes to obtain multiple aggregation nodes; constructing a first data transmission model between aggregation nodes and sensing nodes; constructing a second data transmission model and a third data transmission model between multiple aggregation nodes and multiple drones, and between multiple drones and low-orbit satellites; constructing a first optimization model based on the first and second data transmission models; constructing a second optimization model for reducing drone data backlog based on the third data transmission model; constructing a third optimization model based on the first and second optimization models, and obtaining a data transmission strategy for configuring an air-space-ground integrated network through alternating iterations of the first and second optimization models; and realizing data transmission of the air-space-ground integrated network based on the data transmission strategy. The present application can ensure the timeliness of end-to-end data transmission in the air-space-ground integrated network.

Description

Technical Field

[0001] The present application belongs to the technical field of network resource management, and in particular, relates to a data transmission method and related equipment of an air-ground integrated network. Background Art

[0002] With the advent of the information age, people have a growing demand for seamless network coverage and continuous communication services. Although many advanced ground wireless technologies are used to improve the performance of wireless networks, there are still many blind spots that are difficult to be covered by ground wireless networks. Satellite communication systems include geostationary orbit (GEO), middle earth orbit (MEO) and low earth orbit (LEO) satellites, which have been widely used in ecological environment monitoring, emergency rescue, navigation and positioning and other fields. In addition, in the research of the sixth generation (6G) mobile communication system, LEO satellites have received extensive attention. In particular, extreme areas (such as alpine areas, Gobi deserts, islands and remote areas) still face challenges in accessing the Internet due to the high cost of ground infrastructure construction and maintenance. Therefore, it is very worthy of attention to promote broadband LEO satellite networks to realize various remote Internet of Things (IoRT) applications. At present, ground wireless terminals access satellite networks heavily relying on satellite ground stations. However, there are huge cost challenges in establishing many satellite ground stations in extreme areas. UAVs can not only play multiple aerial roles (such as aerial base stations and data collectors), but can also be flexibly deployed as mobile satellite-ground stations. The ground network is an important part of the integrated air-ground-space network, and drone-assisted communications improve the performance of the ground network.

[0003] After all, using a large number of dedicated drones for communication tasks alone is also costly. In recent years, the opportunistic use of idle resources of drones to complete various tasks (such as regional patrols, target detection, video shooting, cargo delivery, etc.) to assist in improving the performance of ground networks has attracted the attention of researchers. In these tasks, only a few drones provide dedicated communication services, and most drones hardly need to communicate with other drones or provide only small feedback. However, in the application scenario of ecological environment monitoring in extreme areas, it is far from enough to make full use of these available resources to improve the performance of ground networks. In order to ensure the timeliness of data collection, the corresponding drones should forward the collected data from ground nodes (such as aggregation nodes) to remote data centers in a timely manner, which is impossible for drones performing specific tasks. In this case, it is very necessary to make full use of the communication resources of drones and satellites.

[0004] Through development in recent years, the growth rate of LEO satellite communication resources is much faster than that of GEO and MEO. However, for a low-orbit satellite, the length of the time window in which it covers a specific ground is limited. If the drone wants to maintain continuous air-to-space communication, it needs to constantly switch between different low-orbit satellites. On the other hand, even if a spot beam is used to send / receive signals, a LEO satellite has a large ground coverage area. In this case, when considering data collection through LEO satellites, it is recommended to use the random access (RA, Random Access) protocol, because the use of the dedicated access (DA, Dedicated Access) protocol will cause a large air-to-space communication delay. Therefore, making full use of the above-mentioned air-to-space resources faces challenging problems such as the uncertainty of the probability of conflict when drones randomly access LEO satellites.

[0005] For ground sensing nodes in extreme areas, due to the severe path loss of the ground-to-space channel and the lack of high-gain receiving / transmitting antennas, it is impossible to successfully send signals directly to the satellite network. Therefore, the sensed data can be collected to several satellite-to-ground stations, which are then connected to remote data centers through a satellite network to achieve end-to-end data transmission in a timely manner. However, a large number of ground sensing nodes are distributed in remote and vast areas, making the small number of satellite-to-ground stations a bottleneck for end-to-end data transmission. How to overcome the challenging issues of ground and space resource utilization and provide more end-to-end data transmission paths for a large number of ground sensing nodes is of great practical significance. Summary of the invention

[0006] The present application provides a data transmission method and related equipment for an air-space-ground integrated network, which can ensure the timeliness of end-to-end data transmission in the air-space-ground integrated network.

[0007] In a first aspect, the present application provides a data transmission method of an air-space-ground integrated network, the air-space-ground integrated network including a low-orbit satellite, multiple drones, and multiple sensing nodes, the data transmission method including:

[0008] Clustering multiple sensing nodes to obtain multiple clusters, and determining the cluster head of each cluster as a sink node to obtain multiple sink nodes; the multiple sink nodes correspond to the multiple clusters one by one, and each cluster includes at least one sensing node;

[0009] For each cluster, a first data transmission model between the sink node and each sensing node in the cluster is constructed; the first data transmission model is used to describe the process of data transmission between the sink node and each sensing node in each cluster;

[0010] A second data transmission model and a third data transmission model are respectively constructed between multiple aggregation nodes and multiple UAVs, and between multiple UAVs and low-orbit satellites; the second data transmission model is used to describe the process of data transmission between UAVs and aggregation nodes, and the third data transmission model is used to describe the process of data transmission between low-orbit satellites and UAVs;

[0011] Constructing a first optimization model for reducing data backlog between a plurality of aggregation nodes and a plurality of drones according to the first data transmission model and the second data transmission model;

[0012] Based on the third data transmission model, a second optimization model is constructed to reduce the data backlog of low-orbit satellites and drones;

[0013] Constructing a third optimization model based on the first optimization model and the second optimization model, and obtaining a data transmission strategy for configuring an air-ground integrated network by alternately iterating the first optimization model and the second optimization model;

[0014] According to the data transmission strategy, data transmission of the integrated air-space-ground network is realized.

[0015] Optionally, the expression of the first data transmission model is as follows:

[0016]

[0017] in, represents the data transmission volume of sink node n in time slot t, K g represents the maximum number of sensor nodes that transmit data with each sink node, τ represents the pre-divided data transmission duration, r represents the rth sensor node in the cluster where the sink node n is located, R represents the total number of sensor nodes in the cluster where the sink node n is located, Represents the sensing node S r,n A binary variable indicating S r,n Whether to transmit data in time slot t, Represents the sensing node s r,n The instantaneous achievable rate of the link to the sink node n in time slot t, W GG represents the bandwidth between any sensing node and the sink node it is connected to, Represents the sensing node s r,n The transmission power to the sink node n, represents the power spectral density of the additive white Gaussian noise at all receivers.

[0018] Optionally, the expression of the second data transmission model is as follows:

[0019]

[0020]

[0021] in, represents the path loss between the sink node n and the drone m in time slot t, η LoS ,η NLoS ,a,b represent constants related to the network environment, c represents the speed of light, Indicates the center frequency of the ground network carrier, represents the distance between the sink node n and the drone m, represents the elevation angle from the sink node n to the drone m at time slot t, (x n ,y n ) represents the coordinates of the sink node n, represents the coordinates of UAV m at time slot t.

[0022] Optionally, the expression of the third data transmission model is as follows:

[0023]

[0024] in, represents the channel gain between UAV m and LEO satellite l in time slot t, g m,l represents the large-scale fading factor of the channel between UAV m and LEO satellite l, λ m,l represents the small-scale fading factor of the channel between UAV m and LEO satellite l, represents the LOS component of the channel between UAV m and LEO satellite l, represents the NLOS component of the channel between UAV m and LEO satellite l, represents the first-order Bessel function, represents the third-order Bessel function, d s represents the diameter of the circular antenna array on the low-orbit satellite l, represents the center frequency of the satellite network carrier, θ m,l represents the off-axis distance of the low-orbit satellite l from the UAV m, represents the free space loss number, d0 represents the orbital height of the low-orbit satellite l, G m,l represents the transmitting antenna gain from UAV m to LEO satellite l, κ represents the Boltzman constant, W AS represents the carrier bandwidth from UAV m to LEO satellite l, T N represents the noise temperature at the receiving end, r m,l It represents the rain attenuation coefficient between UAV m and LEO satellite l, and its power gain dB follows the log-normal random distribution.

[0025] Optionally, the expression of the first optimization model is as follows:

[0026]

[0027] in, and Indicates the upper limit of the battery capacity of the sink node and the maximum energy harvested by the sink node, and represents the remaining battery capacity and current energy harvested when the sink node m transmits data to the drone m. Constraints C1 and C2 ensure that each drone can serve up to K s Each sink node can be served by at most one drone in a time slot. C3 guarantees that each drone has K s The sum of the plane angles of the projections of the non-intersecting beams on the vertical plane is no greater than π. C4 and C5 indicate that the data transmission power is limited by the battery capacity of the sink node. C6 ensures that the energy harvested by the sink node is not infinite. represents the total number of time slots, N represents the total number of aggregation nodes, It indicates whether the kth beam of the UAV serves the aggregation node n in time slot t, and M represents the total number of UAVs.

[0028] Optionally, the expression of the second optimization model is as follows:

[0029]

[0030] in, and Indicates the upper limit of the drone battery capacity and the maximum energy harvested. and It indicates the remaining battery capacity and the current amount of energy harvested when the drone m forwards data to the low-orbit satellite l. represents a positive number, which is used to control the intensity of the penalty for collision access behavior. C7 and C8 indicate that the upload power is limited by the battery capacity of the drone, and C9 ensures that the amount of energy harvested by the drone cannot be infinite.

[0031] Optionally, obtaining a data transmission strategy for configuring the air-ground integrated network according to the first optimization model and the second optimization model includes:

[0032] According to the first optimization model and the second optimization model, a third optimization model for optimizing the access resource utilization of low-orbit satellites is constructed; the expression of the third optimization model is:

[0033]

[0034] in, A positive number indicating the restriction on the use of low-orbit satellite access resources. The larger the value, the greater the degree of restriction.

[0035] The first optimization model, the second optimization model and the third optimization model are solved respectively to obtain the data transmission strategy.

[0036] In a second aspect, the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned data transmission method when executing the computer program.

[0037] In a third aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned data transmission method is implemented.

[0038] The above solution of the present application has the following beneficial effects:

[0039] The data transmission method of the air-space-ground integrated network provided in the present application respectively constructs data transmission models between low-orbit satellites, drones and perception nodes in the air-space-ground integrated network, and constructs a data transmission optimization model between low-orbit satellites, drones and perception nodes based on the data transmission model, obtains data transmission strategies, and realizes the phased reduction of data backlogs in the data transmission process, which is beneficial to ensure the timeliness of end-to-end data transmission in the air-space-ground integrated network.

[0040] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A flow chart of a data transmission method for an air-ground integrated network provided in an embodiment of the present application;

[0043] Figure 2 A comparison chart of the number of access resources required to achieve an absolute backlog of drone data below 50% provided in an embodiment of the present application;

[0044] Figure 3 A comparison chart of the number of access resources required to achieve an absolute backlog of drone data below 40% provided in an embodiment of the present application;

[0045] Figure 4 A comparison chart of the number of access resources required to achieve an absolute backlog of drone data below 30% provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0047] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0048] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0049] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0050] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0051] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0052] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0053] In response to the problem of low data transmission efficiency in the traditional high-altitude ground-ground integrated network data transmission process, the present application provides a data transmission method and related equipment for an air-space-ground integrated network. The method constructs data transmission models between low-orbit satellites, drones and perception nodes in the air-space-ground integrated network respectively, and constructs a data transmission optimization model between low-orbit satellites, drones and perception nodes based on the data transmission model, obtains data transmission strategies, and realizes the phased reduction of data backlogs in the data transmission process, which is conducive to ensuring the timeliness of end-to-end data transmission in the air-space-ground integrated network.

[0054] To facilitate understanding, the air-ground integrated network structure in the embodiment of the present application is first described.

[0055] Specifically, the integrated air-space-ground network includes low-orbit satellites, multiple drones and multiple sensing nodes, and a drone-assisted data collection scenario in the integrated air-space-ground network, in which the sensing nodes are randomly deployed on the ground (such as mobile base stations, sensors, etc.), drones opportunistically fly above the sensing node deployment area, and LEO satellites periodically appear above the sensing node deployment area.

[0056] This communication scenario usually occurs in the ecological environment monitoring of desert Gobi. The construction of ground communication infrastructure is difficult, and at most only a small number of satellite-ground stations can be built to facilitate the execution of field scientific research tasks. These satellite-ground stations are mainly responsible for connecting the application terminals of field researchers to the Internet and transmitting the data collected by nearby sensing nodes to remote data centers, but they cannot transmit the data collected by sensing nodes in a timely manner over a large range. Therefore, it is necessary to establish other communication paths for data transmission in a timely manner. Drones can act as mobile data collectors when performing specific tasks. Then, once these drones are connected to the LEO satellite network, the collected data can be forwarded in a timely manner.

[0057] The data transmission method of the air-space-ground integrated network provided by the present application is described in detail below.

[0058] like Figure 1 As shown, the data transmission method specifically includes the following steps:

[0059] Step 11, clustering the multiple sensing nodes to obtain multiple clusters, and determining the cluster head of each cluster as a sink node to obtain multiple sink nodes.

[0060] The multiple sink nodes correspond one-to-one to the multiple clusters, and each cluster includes at least one sensing node.

[0061] Specifically, after the sensing nodes are clustered according to the distance, the set of clusters is represented by For any cluster, its sink node is also identified by n, so the corresponding coordinates are represented by (xn ,y n ) is represented by . Each cluster has only one sink node but can have multiple sensor nodes, and at least the sink node can remain active throughout the cycle. Each sink node has the necessary information of its sensor nodes (such as model, operation cycle, etc.).

[0062] The set of sensing nodes associated with the sink node n is Indicates that, s r,n represents a sensing node of the sink node, Sensing node s in time slot t r,n The channel gain between the sink node n is expressed as follows: in, It conforms to the Rayleigh distribution, which is randomly generated in time slot t; d r,n Represents the sensing node s r,n The distance to the sink node n, β is the path loss exponent. Sensing node s r,n The probability of generating a data packet is recorded as It is also called active probability. r,n When it is active and plans to send data at time slot t, the corresponding binary variable Set it to 1. Otherwise, set it to 0.

[0063] In each time slot, each active sensing node is scheduled to send data to its corresponding sink node at most in a fixed time slot (i.e., one sub-time slot), and the sink node receives at most g Each sink node in the cluster collects data from its active sensing nodes using time division multiple access (TDMA).

[0064] Then, the sink node aggregates all received data into one data packet for transmission in the next time slot.

[0065] The aggregation node has the same limited hardware resources as the sensing node, and the sensing node is generally equipped with only an RF transceiver device with an omnidirectional antenna. Therefore, if each scheduled aggregation node collects data from the sensing node it serves in the current time slot, it will be forwarded to its associated (data transmission) drone in the next time slot. Since drones usually have sufficient hardware resources, this application assumes that it has RF transceivers with non-overlapping beams. Therefore, when communicating with a LEO satellite in the aerospace satellite frequency band (such as the Ka band) in the same time slot, data from up to k aggregation nodes in the same ground-to-space frequency band can be received simultaneously. When each scheduled opportunistic drone receives data in the current time slot, it can forward the data aggregated in the previous time slot to its associated (data transmission) LEO satellite. In the current time slot, the data aggregated by the opportunistic drone will be transmitted in the next time slot.

[0066] It should be noted that each sink node collects data in a time slot in a time division multiplexing manner and then forwards the data to the drone in another time slot. It should also be noted that each available drone can use the same frequency band (i.e., spatial diversity mode) to simultaneously receive data from up to k sink nodes in one time slot. For available opportunistic drones, they will be matched with the sink nodes of the corresponding cluster according to the given strategy.

[0067] In particular, when the cluster has K g When the above active sensing nodes are used, a proportional fair scheduling method is used to select service objects.

[0068] It should be noted that in the embodiments of the present application, in order to simplify the problem-solving process and improve data transmission efficiency, time slot alignment will also be performed on the starting times of data transmission tasks performed by low-orbit satellites, multiple drones, and multiple aggregation nodes.

[0069] Step 12: For each cluster, construct a first data transmission model between the sink node and each sensing node in the cluster.

[0070] The first data transmission model is used to describe the process of data transmission between the sink node and each sensing node in each cluster.

[0071] The expression of the first data transmission model is as follows:

[0072]

[0073] in, represents the data transmission volume of sink node n in time slot t, K grepresents the maximum number of sensor nodes that transmit data with each sink node, τ represents the pre-divided data transmission duration, r represents the rth sensor node in the cluster where the sink node n is located, R represents the total number of sensor nodes in the cluster where the sink node n is located, Represents the sensing node S r,n A binary variable indicating S r,n Whether to transmit data in time slot t, Represents the sensing node s r,n The instantaneous achievable rate of the link to the sink node n in time slot t, W GG represents the bandwidth between any sensing node and the sink node it is connected to, Represents the sensing node s r,n The transmission power to the sink node n, represents the power spectral density of the additive white Gaussian noise at all receivers.

[0074] Step 13, respectively constructing a second data transmission model and a third data transmission model between multiple aggregation nodes and multiple drones, and between multiple drones and low-orbit satellites.

[0075] The second data transmission model is used to describe the process of data transmission between the UAV and the aggregation node, and the third data transmission model is used to describe the process of data transmission between the low-orbit satellite and the UAV.

[0076] In an embodiment of the present application, one of the most widely used ground-to-air (G2A) channel models is adopted from the aggregation point to the drone, which takes into account the probabilities of line-of-sight (LOS) and non-line-of-sight (NLOS) links.

[0077] In addition, when the drone receives data from the sink node it serves, it will be interfered by other sink nodes because the frequency band of the sink node-drone link is shared. In order to achieve efficient data transmission, the real-time upload power of the sink node needs to be dynamically adjusted. After receiving the data in the time slot, the drone forwards the data to the LEO satellite in the time slot and receives new data at the same time. This LEO satellite can temporarily store the received data and then eventually forward it to the remote data center on the ground. Since the delay of drone-satellite signal propagation cannot be ignored, the starting point of the LEO satellite cycle needs to be offset backward relative to the starting point of the sink node and the drone. The offset time is approximately the sum of the two time slots determined in a synchronization cycle of the sink node-drone-low-orbit satellite and the one-way propagation delay.

[0078] Specifically, the expression of the second data transmission model is as follows:

[0079]

[0080] in, represents the path loss between the sink node n and the drone m in time slot t, η LoS ,η NLoS ,a,b represent constants related to the network environment, c represents the speed of light, Indicates the center frequency of the ground network carrier, represents the distance between the sink node n and the drone m, represents the elevation angle from the sink node n to the drone m at time slot t, (x n ,y n ) represents the coordinates of the sink node n, represents the coordinates of UAV m at time slot t.

[0081] The expression of the third data transmission model is as follows:

[0082]

[0083]

[0084] in, represents the channel gain between UAV m and LEO satellite l in time slot t, g m,l represents the large-scale fading factor of the channel between UAV m and LEO satellite l, λ m,l represents the small-scale fading factor of the channel between UAV m and LEO satellite l, represents the LOS component of the channel between UAV m and LEO satellite l, represents the NLOS component of the channel between UAV m and LEO satellite l, represents the first-order Bessel function, represents the third-order Bessel function, d s represents the diameter of the circular antenna array on the low-orbit satellite l, represents the center frequency of the satellite network carrier, θ m,l represents the off-axis distance of the low-orbit satellite l from the UAV m, represents the free space loss number, d0 represents the orbital height of the low-orbit satellite l, G m,l represents the transmitting antenna gain from UAV m to LEO satellite l, κ represents the Boltzman constant, W AS represents the carrier bandwidth from UAV m to LEO satellite l, T N represents the noise temperature at the receiving end, r m,l It represents the rain attenuation coefficient between UAV m and LEO satellite l, and its power gain dB follows the log-normal random distribution.

[0085] Assume that the z coordinate value of the sink node is relative to the flight altitude of the time slot drone (i.e. ) can be ignored. Therefore, the channel gain corresponding to the time slot can be estimated by the following formula:

[0086]

[0087] In the formula, represents the ground-to-air channel gain from sink node n to UAV m in time slot. Since each sink node has omnidirectional transmission characteristics, its transmission gain and transmission interference gain are approximately 1. However, due to the directional reception characteristics of each UAV, the reception gain of the kth receiving beam of UAV m to sink node n in time slot t can be expressed by the following formula:

[0088]

[0089] In the above formula, is the beam width of the kth receiving beam of UAV m, It represents the angle between the beam centerline of the kth receiving beam of UAV m and the beam direction from transmitter n to receiver m in time slot t. ∈ is the sidelobe gain, and the corresponding value range is ∈<1. Therefore, the instantaneous reachable rate from the sink node n to the kth receiving beam of UAV m in time slot t can be expressed as:

[0090]

[0091]

[0092] In the above formula, W GA It represents the bandwidth from any sink node n to any drone m. is the signal to interference plus noise ratio (SINR) of the kth receiving beam from the sink node n to the drone m in time slot t. is the transmission power from the sink node to the drone, and is the transmission power from the sink node to the UAV. is the interference channel gain of the sink-drone link n→m generated by the sink n` in time slot t, and is the receiving interference gain of the kth receiving beam of UAV m caused by the sink node n` in time slot t.

[0093] Step 14: construct a first optimization model for reducing data backlog between multiple aggregation nodes and multiple drones based on the first data transmission model and the second data transmission model.

[0094] Specifically, the expression of the first optimization model is as follows:

[0095]

[0096] in, and Indicates the upper limit of the battery capacity of the sink node and the maximum energy harvested by the sink node, and represents the remaining battery capacity and current energy harvested when the sink node n transmits data to the drone m. Constraints C1 and C2 ensure that each drone can serve up to K drones at the same time. s Each sink node can be served by at most one drone in a time slot. C3 guarantees that each drone has K s The sum of the plane angles of the projections of the non-intersecting beams on the vertical plane is no greater than π. C4 and C5 indicate that the data transmission power is limited by the battery capacity of the sink node. C6 ensures that the energy harvested by the sink node is not infinite. represents the total number of time slots, N represents the total number of aggregation nodes, It indicates whether the kth beam of the UAV serves the aggregation node n in time slot t, and M represents the total number of UAVs.

[0097] It should be noted that the first optimization model only focuses on data transmission from the sink node to the UAV, maximizing the data transmission volume while minimizing the data backlog and energy waste of the sink node. However, it does not focus on the data backlog and power resources of the UAV, the total amount of data received by a LEO satellite, and the optimization of satellite access resources.

[0098] Step 15: construct a second optimization model for reducing the data backlog of low-orbit satellites and drones based on the third data transmission model.

[0099] Specifically, the expression of the second optimization model is as follows:

[0100]

[0101] in, and Indicates the upper limit of the drone battery capacity and the maximum energy harvested. and It indicates the remaining battery capacity and the current amount of energy harvested when the drone m forwards data to the low-orbit satellite l. represents a positive number, which is used to control the intensity of the penalty for collision access behavior. C7 and C8 indicate that the upload power is limited by the battery capacity of the drone, and C9 ensures that the amount of energy harvested by the drone cannot be infinite.

[0102] It should be noted that the solution of the second optimization model is based on fixed LEO satellite access resources and does not consider how to optimize the utilization of LEO satellite access resources under given constraints.

[0103] Step 16: construct a third optimization model based on the first optimization model and the second optimization model, and obtain a data transmission strategy for configuring the air-ground integrated network by alternately iterating the first optimization model and the second optimization model.

[0104] Specifically, it includes step 16.1 to step 16.2:

[0105] Step 16.1: construct a third optimization model for optimizing access resource utilization of low-orbit satellites based on the first optimization model and the second optimization model.

[0106] The expression of the third optimization model is:

[0107]

[0108] in, A positive number that indicates the restriction on the use of low-orbit satellite access resources. The larger the value, the greater the degree of restriction.

[0109] Step 16.2, respectively solving the first optimization model, the second optimization model and the third optimization model to obtain a data transmission strategy.

[0110] The solution process of each optimization model is exemplified below.

[0111] For the first optimization model Solving with DRL tools Set up an agent on the satellite station to be responsible for the sub-problem The centralized solution of is a non-convex MINLP problem, so it is formulated as a discrete-time Markov decision process (MDP) and can be represented by the tuple Based on the DRL framework, by defining the state, action, and reward for each time slot t in this tuple, Formalized as MDP.

[0112] 1) State Space

[0113]

[0114] When the agent obtains the battery power of the sink node n at the current time slot t Energy collection rate of sink node n The transmission task volume of sink node n Data backlog at sink node n Data backlog of drone m and the aggregation node-UAV channel gain Finally, it can choose an action for the entire network.

[0115] 2) Action Space

[0116]

[0117] The actions determined by the agent include the scheduling vector for each period and the sink node power vector When an action is performed, you will receive an immediate reward.

[0118] 3) Instant Rewards

[0119]

[0120] In the formula, Γ(x) is a piecewise function. If x is greater than 0, then Γ(x) = x, otherwise Γ(x) = 0. This immediate reward is to ensure the timeliness of data collection while maximizing the amount of data collection. At the same time, violations of constraints are punished, and the degree of punishment is positively correlated with the degree of violation. After performing an action, the agent will receive immediate feedback as a basis for subsequent decisions.

[0121] This application is designed using the PPO tool solution. PPO is a policy-based DRL algorithm that includes an actor neural network and a critic neural network. The actor network θ is responsible for outputting the probability of action based on the current state, while the critic network φ is responsible for evaluating the quality of the current strategy. The evaluation is performed by predicting future rewards and feeding them back to the actor network. Based on this feedback, the actor network adjusts the probability of selecting actions in the future. This process is repeated in each learning iteration to improve the strategy. In order to avoid large fluctuations during training and ensure the stability of the learning process, the neural network parameters of PPO are updated by the following formula:

[0122]

[0123] In the formula, represents the expected value of all time steps, is the ratio of the new and old strategies, is Taken under the state The new strategy probability of the action, is the old strategy probability, is the advantage function value of the action at time step t, which is used to measure the advantage of the action relative to the average action. And ∈ is a small positive number used to clip the ratio r in the objective function t (θ) to avoid excessive or too small policy updates. In order to achieve a balance between bias and variance in value learning, PPO uses a generalized advantage estimator (GAE) to calculate the advantage. The specific calculation formula is as follows:

[0124]

[0125] In the formula, γ is the discount factor used to reduce the weight of future returns, λ is a GAE hyperparameter that controls the trade-off between bias and variance, T is the total number of time steps, is the Bellman residual term, and V is an approximate value function. The total PPO loss combines the policy loss, value loss, and policy entropy regularization term through a weighted sum, and the calculation formula is as follows:

[0126]

[0127] In the formula, is the value function loss at time step t, which measures how close the value function is to the expected return, is the policy entropy at time step t, which promotes exploration by suppressing deterministic policies, and c1 and c2 are coefficients that control relative importance.

[0128] The action space includes the scheduling results of the drones and the transmission power of the aggregation nodes, so the combination of all possible actions will make the action space very large. Based on the idea of ​​hierarchical reinforcement learning, multi-discrete action space allows the agent to perform multiple discrete actions at each time step, which can alleviate the problem of combinatorial space explosion.

[0129] After executing the PPO algorithm, the network parameter V can be obtained. φ , π θ .

[0130] For the second optimization model The goal of the solution is to maximize the total amount of data received by LEO satellites under the constraints of the actual conditions of the LEO satellite network (such as access resources, etc.), while minimizing the data backlog and access collision rate of drones. It independently decides whether to access the LEO satellite or take backoff action. If access is selected, a PRACH preamble is randomly selected. However, due to the time-varying network environment of opportunistic drones and LEO satellites, it is challenging to optimize the above operations. To this end, this application designs a multi-agent DRL algorithm to solve In order to apply the multi-agent DRL method, it is also necessary to design an MDP model, which includes the state space, action space and reward function in the network environment that this application focuses on.

[0131] The multi-agent DRL framework in the scenario includes multiple opportunistic drones, following the designed MDP model. Corresponding to each RA opportunity Each drone is the agent, which observes the state And take action according to the state-action strategy μ Here, μ represents the probability of selecting an action. The goal of multi-agent DRL is to obtain the optimal strategy μ * , maximizing the long-term average reward.

[0132] a) State Space

[0133] Based on the above MDP model, the state space of UAV m is:

[0134]

[0135] In the formula, t is the time slot, is the battery power of drone m, is the energy collection rate of drone m, is the data backlog of drone m, is the UAV-LEO satellite channel gain, is the throughput, c m [t] is a binary indicator function for RA collision events. and is the initial value randomly selected at the beginning of Algorithm 2 below. While sending the RA signal, the LEO satellite sends feedback to the drones so that each drone can obtain information about whether it has collided. The periodic orbital position map of each LEO satellite can be obtained through the global satellite navigation system.

[0136] b) Action Space

[0137] Based on the above network scenario, UAV m can access at most one LEO satellite in the same orbit. The action at time slot t Defined as:

[0138]

[0139] c) Instant Rewards

[0140] The goal of this reward function is to maximize the throughput of the UAV and minimize its data backlog, while penalizing any actions that lead to conflicting access and invalid power. Therefore, the reward function of the agent UAV m is defined as:

[0141]

[0142] In the formula, the coefficient ε is used to balance throughput and data backlog.

[0143] In the embodiment of the present application, after executing the VDN algorithm, network parameters θ and θ' are obtained.

[0144] For the third optimization model The solution process is as follows:

[0145] Step G1: Collecting the location information of ground sensing nodes;

[0146] Step G2: Call the K-means algorithm to obtain the aggregation node set (9);

[0147] Step G3: Construct a drone collection based on the collected drone information (4);

[0148] Step G4: Find a LEO satellite that currently covers itself and mark its successor as the first LEO satellite in the same orbital plane;

[0149] Step G5: Construct LEO satellite set based on satellite motion pattern (22);

[0150] Step G6: Set For each satellite in, perform the following steps:

[0151] Step G7: Estimate the time slot set of LEO satellite l based on the satellite motion pattern (about 2330);

[0152] Step G8: Calculate its initial position at the beginning of time slot t = 1, set it to

[0153] Step G9: Synchronize the aggregation node, drone and LEO satellite to ensure the aggregation node is complete. Drone collection The time slot of LEO satellite 1 is aligned, and the process goes to step G6;

[0154] Step G10: Run the solution Process (see above);

[0155] Step G11: Use Solve Process (see above);

[0156] Step G12: Get and

[0157] Step G13: Determination (such as 1Mb) and Are both true? If yes, go to step G14; otherwise, go to step G17;

[0158] Step G14: Reduce the access resource set using a preset step size size;

[0159] Step G15: Determination and Are both true? If yes, go to step G16; otherwise, go to step G17;

[0160] Step G16: Add a set according to the preset step size size;

[0161] Step G17: Determination or If so, terminate the algorithm, otherwise go to step G11.

[0162] Step 17, according to the data transmission strategy, realize the data transmission of the air-ground integrated network.

[0163] Specifically, according to the parameters of each data transmission subject (low-orbit satellite, UAV, aggregation node) in the air-space-ground integrated network obtained in step 16, the air-space-ground integrated network is configured to achieve efficient data transmission.

[0164] In order to further verify the effectiveness of the data transmission method of the air-ground integrated network provided by the present application, in one embodiment of the present application, the following simulation is performed:

[0165] The actor network of PPO is set up with three hidden layers and 1000 neurons in each layer. The critic network also has three hidden layers and 1000 neurons in each layer. All simulation parameters are shown in Table 1. In the simulation, 200 sensing nodes are randomly distributed in an area of ​​1km×1km and divided into 9 clusters. In addition, 4 drones perform area patrol and target detection tasks, performing these tasks according to pre-planned trajectories. These drones move from the initial position to the given direction at a speed of 30 meters per second. When they reach the target area, they can serve the coverage area. When they leave this area, the service ends.

[0166] exist In the training model, all agents share the same neural network in order to share knowledge and experience during execution. This approach can improve training efficiency and performance. The shared network has two multilayer perceptron (MLP) hidden layers and one recurrent neural network (RNN) hidden layer, with 64 neurons in each layer. All simulation parameters are listed in Table 2. There are 22 LEO satellites in the 550km orbital plane, the orbital track circumference is 43486km, and the inter-satellite distance is 1977km. The orbital velocity of each LEO satellite is 7.59km / s, so the orbital period is Since each RA opportunity is set to a time slot length of τ = 0.5s, the RA signaling duration and data transmission duration are set to τ respectively. s = 0.05s and τ d = 0.45s. Only when the access attempt is successful will the data be transmitted in τ d When the LEO satellite altitude is 550km, About 23°. About 233s, less than the interstellar time Mission cycle of LEO satellite Limited to Therefore, is 233s. The number of RA opportunities is This is also the number of time steps in each training epoch.

[0167] Table 1: Environmental parameters

[0168]

[0169] Table 1: Environmental parameters (continued)

[0170]

[0171] Table 2: and Environmental parameters

[0172]

[0173] Table 2: and Environmental parameters (continued)

[0174]

[0175] The present invention uses python+pytorch tools to implement the above scheme. By dividing the load of data acquisition tasks from low to high into 9 levels, namely A, B, C, D, E, F, G, H, and I, the ability of three integration schemes to cope with variable loads is compared. The three integration schemes are as follows:

[0176] (1) OPOS-RATG-VDN: This application provides a data transmission method for an air-ground integrated network.

[0177] (2) OPOS-Slotted-ALOHA: The difference between this scheme and the scheme provided in this application is that in this scheme all drones choose to try to access the satellite and obtain a random preamble code in each time slot, and the transmission power of the drone is fixed at 0.5W.

[0178] (3) OPOS-RACH: The only difference between this solution and the solution provided in the present application is that the former uses a backoff window to avoid collisions. When the UAV sends an access collision, it waits for a random window value (the present invention uses 0 or 1) time slots before trying to access.

[0179] For simplicity without loss of generality, it should be ensured that the total amount of data transmitted from the aggregation node to the UAV in each time slot is reduced equally from level I to level A. Among them, when all UAVs have access opportunities in each time slot and successfully transmit data at maximum power, the transmission capacity of the UAV to the LEO satellite determines the level I load. When the absolute value of the UAV data backlog percentage is less than 50%, 40% and 30% of the total amount of data received by the aggregation node in each time slot, this application evaluates the amount of LEO satellite access resources required under different load levels and obtains Figures 2 to 4 The results shown. Among them, Figure 2 This is a comparison chart of the number of access resources required to keep the absolute backlog of drone data below 50%. Figure 3 This is a comparison chart of the number of access resources required to keep the absolute backlog of drone data below 40%. Figure 4 Comparison chart of the number of access resources required to keep the absolute backlog of drone data below 30%.

[0180] In general, the performance of the OPOS-RATG-VDN integrated solution designed by the present invention is better than the other two traditional solutions, mainly for two reasons. First, when the load level is low, the access resource demand for matching the load is relatively small, so the collision rate is relatively high, making the collision rate a major factor affecting the transmission capacity. The OPOS-RATG-VDN solution can make reasonable decisions and strategically avoid collisions. Secondly, as the load level increases, the number of access resources matching the load also increases accordingly. At this time, the collision rate tends to decrease, and all solutions tend to satellite access, making energy reserves a major factor affecting transmission capacity. In this case, the OPOS-RATG-VDN solution can reasonably utilize energy by adjusting the power value. The above two points help to improve transmission performance and reduce resource requirements.

[0181] Then, by comparing the load levels, we can see that the demand for access resources gradually increases with the increase in load level, and increases exponentially after exceeding a certain threshold. This is because energy reserves and access resources can be stably adjusted as the load level increases. When each drone reaches the upper limit of the transmission power due to energy reserve limitations, the number of access resources is relatively large at this time. If additional resources are invested, the impact on reducing the collision rate is quite small.

[0182] Finally, through the comparison of the backlog percentage of drone data, it can be seen that at the same load level, the access resource demand of each solution increases as the backlog ratio decreases. This is because, under the constraint that the power has reached the rated value at high load levels, the only way to achieve a smaller backlog ratio is to increase access resources. At low load levels, the access resource demand of each solution is basically consistent at different backlog ratios. This is because, at a sufficiently small load level, only a small amount of access resources is needed to reduce the backlog ratio below the threshold, resulting in basically consistent access resource requirements in different scenarios.

[0183] It can be seen from the above steps that the data transmission method of the air-space-ground integrated network provided by the present application, by respectively constructing data transmission models between low-orbit satellites, drones and perception nodes in the air-space-ground integrated network, and constructing a data transmission optimization model between low-orbit satellites, drones and perception nodes based on the data transmission model, obtains the data transmission strategy, and realizes the phased reduction of data backlog in the data transmission process, which is conducive to ensuring the timeliness of end-to-end data transmission in the air-space-ground integrated network.

[0184] like Figure 5 As shown, an embodiment of the present application provides a terminal device, such as Figure 5 As shown, the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 5Only one processor is shown in the figure), 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 implements the steps of any of the above-mentioned method embodiments when executing the computer program D102.

[0185] Specifically, when the processor D100 executes the computer program D102, it clusters multiple sensing nodes to obtain multiple clusters, and determines the cluster head of each cluster as a sink node to obtain multiple sink nodes; for each cluster, constructs a first data transmission model between the sink node and each sensing node in the cluster; constructs a second data transmission model and a third data transmission model between multiple sink nodes and multiple drones, and between multiple drones and low-orbit satellites, respectively; constructs a first optimization model for reducing data backlogs between multiple sink nodes and multiple drones based on the first data transmission model and the second data transmission model; constructs a second optimization model for reducing data backlogs between low-orbit satellites and drones based on the third data transmission model; constructs a third optimization model based on the first optimization model and the second optimization model, and obtains a data transmission strategy for configuring an air-space-ground integrated network by alternately iterating the first optimization model and the second optimization model; and realizes data transmission of the air-space-ground integrated network based on the data transmission strategy. Among them, by respectively constructing data transmission models between low-orbit satellites, UAVs and perception nodes in the integrated air-space-ground network, and constructing data transmission optimization models between low-orbit satellites, UAVs and perception nodes based on the data transmission models, the data transmission strategy is obtained, and the data backlog in the data transmission process is reduced in stages, which is conducive to ensuring the timeliness of end-to-end data transmission in the integrated air-space-ground network.

[0186] The processor D100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0187] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card (Flash Card), etc. equipped on the terminal device D10. Further, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data and other programs, such as the program code of the computer program, etc. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0188] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0189] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, RandomAccess Memory), an electric carrier signal, a telecommunication signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0191] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0192] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0193] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0194] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data transmission method for an air-ground integrated network, wherein the air-ground integrated network includes a low-orbit satellite, multiple drones, and multiple sensing nodes, characterized in that: The data transmission method comprises: Clustering the multiple sensing nodes to obtain multiple clusters, and determining the cluster head of each cluster as a sink node to obtain multiple sink nodes; the multiple sink nodes correspond to the multiple clusters one by one, and each cluster includes at least one sensing node; For each of the clusters, a first data transmission model between the sink node and each sensing node in the cluster is constructed; the first data transmission model is used to describe the process of data transmission between the sink node and each sensing node in each of the clusters; Constructing a second data transmission model and a third data transmission model between the multiple aggregation nodes and the multiple drones, and between the multiple drones and the low-orbit satellite, respectively; the second data transmission model is used to describe the process of data transmission between the drone and the aggregation node, and the third data transmission model is used to describe the process of data transmission between the low-orbit satellite and the drone; Constructing a first optimization model for optimizing data transmission between the plurality of sink nodes and the plurality of drones according to the first data transmission model and the second data transmission model; According to the third data transmission model, constructing a second optimization model for optimizing the data transmission between the low-orbit satellite and the UAV; Constructing a third optimization model based on the first optimization model and the second optimization model, and acquiring a data transmission strategy for configuring an air-ground integrated network by alternately iterating the first optimization model and the second optimization model; According to the data transmission strategy, data transmission of the air-ground integrated network is realized; The expression of the first data transmission model is as follows: in, represents the data transmission volume of sink node n in time slot t, K g represents the maximum number of sensor nodes that transmit data with each sink node, τ represents the pre-divided data transmission duration, r represents the rth sensor node in the cluster where the sink node n is located, R represents the total number of sensor nodes in the cluster where the sink node n is located, Represents the sensing node S r,n A binary variable indicating S r,n Whether to transmit data in time slot t, Represents the sensing node s r,n The instantaneous achievable rate of the link to the sink node n in time slot t, W GG represents the bandwidth between any sensing node and the sink node it is connected to, Represents the sensing node s r,n The transmission power to the sink node n, represents the power spectral density of additive white Gaussian noise at all receivers, represents the sensing node s in time slot t r,n The channel gain between the sink node n; The expression of the second data transmission model is as follows: in, represents the path loss between the sink node n and the drone m in time slot t, η LoS ,η NLoS ,a,b represent constants related to the network environment, c represents the speed of light, Indicates the center frequency of the ground network carrier, represents the distance between the sink node n and the drone m, represents the elevation angle from the sink node n to the drone m at time slot t, (x n ,y n ) represents the coordinates of the sink node n, represents the coordinates of UAV m at time slot t; The expression of the third data transmission model is as follows: in, represents the channel gain between UAV m and LEO satellite l in time slot t, g m,l represents the large-scale fading factor of the channel between UAV m and LEO satellite l, λ m,l represents the small-scale fading factor of the channel between UAV m and LEO satellite l, represents the LOS component of the channel between UAV m and LEO satellite l, represents the NLOS component of the channel between UAV m and LEO satellite l, represents the first-order Bessel function, represents the third-order Bessel function, d s represents the diameter of the circular antenna array on the low-orbit satellite l, represents the center frequency of the satellite network carrier, θ m,l represents the off-axis distance of the low-orbit satellite l from the UAV m, represents the free space loss number, d0 represents the orbital height of the low-orbit satellite l, G m,l represents the transmitting antenna gain from UAV m to LEO satellite l, κ represents the Boltzman constant, W AS represents the carrier bandwidth from UAV m to LEO satellite l, T N represents the noise temperature at the receiving end, r m,l represents the rain attenuation coefficient between UAV m and LEO satellite l, and its power gain dB follows the log-normal random distribution. represents the UAV-LEO satellite channel gain; The expression of the first optimization model is as follows: in, and Indicates the upper limit of the battery capacity of the sink node and the maximum energy harvested by the sink node, and represents the remaining battery capacity and current energy harvested when the sink node n transmits data to the drone m. Constraints C1 and C2 ensure that each drone can serve up to K drones at the same time. s Each sink node can be served by at most one drone in a time slot. C3 guarantees that each drone has K s The sum of the plane angles of the projections of the non-intersecting beams on the vertical plane is no greater than π. C4 and C5 indicate that the data transmission power is limited by the battery capacity of the sink node. C6 ensures that the energy harvested by the sink node is not infinite. represents the total number of time slots, N represents the total number of aggregation nodes, Indicates whether the kth beam of the drone serves the aggregation node n in time slot t, M represents the total number of drones, represents the transmission power from sink node n to drone m, represents the beam width of the kth receiving beam of UAV m, represents the data backlog at sink node n; The expression of the second optimization model is as follows: in, and Indicates the upper limit of the drone battery capacity and the maximum energy harvested. and It indicates the remaining battery capacity and the current amount of energy harvested when the drone m forwards data to the low-orbit satellite l. is a positive number used to control the intensity of the penalty for collision access behavior. C7 and C8 ensure that the upload power is limited by the battery capacity of the drone. C9 ensures that the amount of energy harvested by the drone cannot be infinite. Represents a collection of low-orbit satellites.

2. The data transmission method according to claim 1, characterized in that: The expression of the third optimization model is: in, A positive number that indicates the limit on the use of low-orbit satellite access resources. The larger the value, the greater the degree of restriction. Indicates the preamble code of each low-orbit satellite.

3. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data transmission method according to any one of claims 1 to 2 is implemented.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data transmission method according to any one of claims 1 to 2 is implemented.

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