Implementation method and system of multi-hop network based on unmanned platform, electronic equipment, storage medium and program product

Abstract

The invention relates to an implementation method and system of a multi-hop network based on an unmanned platform, electronic equipment, a storage medium and a program product. The multi-hop network based on the unmanned platform comprises a controller and a plurality of actuators based on the unmanned platform, the different actuators are connected with one another through wireless network links, and the method comprises the following steps: the controller deploys the multi-hop network based on the unmanned platform according to deployment scene information of the multi-hop network based on the unmanned platform, generating a network topology adjustment instruction and / or an antenna radiation mode adjustment instruction; and the controller sends the network topology adjustment instruction and / or the antenna radiation mode adjustment instruction to the actuator so as to control the actuator to adjust the network topology and / or the antenna radiation mode. Through real-time control and adjustment of the controller, movement of the actuators and antenna pointing are guided, and joint optimization of the network topology and the antenna radiation mode is realized, so that interference between the actuators is minimized, the network capacity and stability are improved, and the communication performance of the network is enhanced.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless networking technology, and in particular to a method for implementing a multi-hop network based on an unmanned platform, a multi-hop network system based on an unmanned platform, an electronic device, a non-volatile computer-readable storage medium, and a computer program product. Background Art

[0002] With the rapid development of wireless communication technology, unmanned platforms (such as drones, unmanned vehicles, underwater robots, etc.) are increasingly used in communication networking. With their flexibility and mobility, unmanned platforms can quickly deploy communication networks in complex terrain or emergency scenarios to provide users with temporary or emergency communication services. However, related technologies still have many deficiencies in network deployment and optimization of unmanned platforms, making it difficult to meet the requirements of high efficiency, low interference and rapid networking. Summary of the invention

[0003] In view of this, the present disclosure provides a multi-hop network technology solution based on an unmanned platform.

[0004] According to one aspect of the present disclosure, a method for implementing a multi-hop network based on an unmanned platform is provided, wherein the multi-hop network based on the unmanned platform includes a controller and a plurality of actuators based on the unmanned platform, and different actuators are interconnected via wireless network links, and the method includes:

[0005] The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform;

[0006] The controller sends the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator to control the actuator to adjust the network topology and / or the antenna radiation pattern.

[0007] In a possible implementation, the deployment scenario information includes at least the following:

[0008] External interference information, terrain feature information, traffic information, and coverage area information.

[0009] In a possible implementation, the controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, including:

[0010] The controller constructs a channel model corresponding to the unmanned platform-based multi-hop network according to the deployment scenario information of the unmanned platform-based multi-hop network;

[0011] The controller constructs an interference power function corresponding to the multi-hop network based on the unmanned platform according to the channel model and the deployment scenario information;

[0012] The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction by minimizing the interference power function.

[0013] In a possible implementation manner, the method further includes:

[0014] The actuator reports the position information of the actuator and / or the link performance parameters of the actuator to the controller at a preset frequency.

[0015] In a possible implementation manner, the method further includes:

[0016] The controller updates the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction according to the position information of the actuator and / or the link performance parameters of the actuator.

[0017] In a possible implementation manner, the method further includes:

[0018] The controller monitors the outage probability of the multi-hop network based on the unmanned platform;

[0019] The controller alternately updates the network topology adjustment instruction and the antenna radiation pattern adjustment instruction in response to the outage probability being greater than or equal to a preset outage probability threshold.

[0020] In a possible implementation manner, the network topology adjustment instruction includes a position adjustment instruction and / or a link parameter configuration instruction;

[0021] The position adjustment instruction is used to control the actuator to adjust its position, and the link parameter configuration instruction is used to control the actuator to configure link parameters.

[0022] In a possible implementation manner, the antenna radiation pattern adjustment instruction is used to control the actuator to adjust the directivity and / or beam pattern of the antenna.

[0023] In a possible implementation manner, the method further includes:

[0024] The actuator adjusts the network topology of the actuator in response to the network topology adjustment instruction;

[0025] and / or,

[0026] The actuator adjusts the antenna radiation pattern of the actuator in response to the antenna radiation pattern adjustment instruction.

[0027] In a possible implementation, the method further includes:

[0028] The controller sets the position information of the virtual fence according to the geographical position information of the preset sensitive area;

[0029] The controller sends the position information of the virtual fence to the actuator to control the actuator to avoid the area defined by the virtual fence.

[0030] In a possible implementation, the controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, including:

[0031] The controller adopts a bent chain network structure in response to the multi-hop network based on the unmanned platform being deployed on flat terrain or in the air;

[0032] The controller generates a network topology adjustment instruction and an antenna radiation pattern adjustment instruction according to the deployment scenario information, where the network topology adjustment instruction is used to control the link angle of the actuator, and the antenna radiation pattern adjustment instruction is used to configure the main lobe width of the antenna of the actuator.

[0033] In a possible implementation, the actuator includes at least one of the following types: unmanned aerial vehicle, unmanned vehicle, underwater robot.

[0034] According to another aspect of the present disclosure, a multi-hop network system based on an unmanned platform is provided. The multi-hop network system based on the unmanned platform includes a controller and multiple actuators based on the unmanned platform, and different actuators are interconnected through a wireless network link;

[0035] The controller is configured to generate a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, and send the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator;

[0036] The actuator is configured to adjust the network topology and / or the antenna radiation pattern of the actuator in response to the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction.

[0037] In a possible implementation, the deployment scenario information includes at least some of the following:

[0038] External interference information, terrain feature information, traffic information, coverage area information.

[0039] In a possible implementation, the controller is specifically configured to:

[0040] Construct a channel model corresponding to the multi-hop network based on the unmanned platform according to the deployment scenario information of the multi-hop network based on the unmanned platform;

[0041] Construct an interference power function corresponding to the multi-hop network based on the unmanned platform according to the channel model and the deployment scenario information;

[0042] Generate a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction by minimizing the interference power function.

[0043] In a possible implementation, the actuator is further configured to:

[0044] Report the position information of the actuator and / or the link performance parameters of the actuator to the controller at a preset frequency.

[0045] In a possible implementation, the controller is further configured to:

[0046] Update the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction according to the position information of the actuator and / or the link performance parameters of the actuator.

[0047] In a possible implementation, the controller is further configured to:

[0048] Monitor the outage probability of the multi-hop network based on the unmanned platform;

[0049] In response to the outage probability being greater than or equal to a preset outage probability threshold, alternately update the network topology adjustment instruction and the antenna radiation pattern adjustment instruction.

[0050] In a possible implementation, the network topology adjustment instruction includes a position adjustment instruction and / or a link parameter configuration instruction;

[0051] The position adjustment instruction is used to control the actuator to adjust its position, and the link parameter configuration instruction is used to control the actuator to configure link parameters.

[0052] In a possible implementation, the antenna radiation pattern adjustment instruction is used to control the actuator to adjust the directivity and / or beam pattern of the antenna.

[0053] In a possible implementation, the controller is further configured to:

[0054] Set the position information of the virtual fence according to the geographical location information of the preset sensitive area;

[0055] Send the position information of the virtual fence to the actuator to control the actuator to avoid the area defined by the virtual fence.

[0056] In a possible implementation, the controller is specifically configured to:

[0057] The controller, in response to the multi-hop network based on the unmanned platform being deployed on flat terrain or in the air, adopts a bent chain network structure;

[0058] The controller generates a network topology adjustment instruction and an antenna radiation pattern adjustment instruction according to the deployment scenario information, wherein the network topology adjustment instruction is used to control the link angle of the actuator, and the antenna radiation pattern adjustment instruction is used to configure the main lobe width of the antenna of the actuator.

[0059] In a possible implementation, the actuator includes at least one of the following types: unmanned aerial vehicle, unmanned vehicle, underwater robot.

[0060] According to another aspect of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the above method.

[0061] According to another aspect of the present disclosure, there is provided a non-volatile computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the above method.

[0062] According to another aspect of the present disclosure, there is provided a computer program product, including a computer program, or a non-volatile computer-readable storage medium carrying the computer program, and the computer program, when executed by a processor, implements the steps of the above method.

[0063] In the embodiments of the present disclosure, the multi-hop network based on an unmanned platform includes a controller and multiple actuators based on the unmanned platform. Different actuators are interconnected through a wireless network link. The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform. The controller sends the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator to control the actuator to adjust the network topology and / or the antenna radiation pattern. Thus, through the real-time control and adjustment of the controller, the movement of the actuator and the antenna pointing are guided to achieve the joint optimization of the network topology and the antenna radiation pattern, so as to minimize the interference between nodes (i.e., actuators), improve the network capacity and stability, and enhance the communication performance of the network. The embodiments of the present disclosure have important application prospects in the field of rapid network formation. In scenarios such as complex terrains or post-disaster recovery, by dynamically adjusting the antenna attitude and network topology, the capacity and stability of the wireless communication network can be effectively improved, ensuring the rapid and reliable transmission of information. In addition, the embodiments of the present disclosure can also be widely applied to fields such as unmanned aerial vehicle networks, autonomous driving fleets, and remote monitoring systems to optimize network resource allocation, reduce signal interference, and thus improve the coverage range and data throughput of the wireless network.

[0064] Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The drawings included in and constituting a part of this specification illustrate exemplary embodiments, features, and aspects of the present disclosure and are used to explain the principles of the present disclosure together with the specification.

[0066] Figure 1 The flowchart showing the implementation method of the multi-hop network based on the unmanned platform provided by the embodiment of the present disclosure.

[0067] Figure 2 The schematic diagram showing the multi-hop network based on the unmanned platform provided by the embodiment of the present disclosure.

[0068] Figure 3 The schematic diagram showing the implementation method of the multi-hop network based on the unmanned platform provided by the embodiment of the present disclosure.

[0069] Figure 4 The schematic diagram showing the alternating dynamic adjustment scheme in the implementation method of the multi-hop network based on the unmanned platform provided by the embodiment of the present disclosure.

[0070] Figure 5 The schematic diagram showing the bent chain-like network structure in the implementation method of the multi-hop network based on the unmanned platform provided by the embodiment of the present disclosure.

[0071] Figure 6A schematic diagram showing the radiation pattern of an ideal planar sector directional antenna in the implementation method of a multi-hop network based on an unmanned platform provided by an embodiment of the present disclosure.

[0072] Figure 7 A schematic diagram showing the verification of the design method of a bent chain network based on the minimum interference angle using MATLAB.

[0073] Figure 8 A block diagram showing a multi-hop network system based on an unmanned platform provided by an embodiment of the present disclosure.

[0074] Figure 9 A block diagram of an electronic device 1900 shown according to an exemplary embodiment. Detailed implementation manners

[0075] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0076] As used herein, the terms "including", "comprising", "having", or variations thereof are open-ended and include one or more stated features, wholes, elements, steps, components, or functions, but do not exclude the existence or addition of one or more other features, wholes, elements, steps, components, functions, or groups thereof.

[0077] When an element is referred to as being "connected", "coupled", "responsive" or variations thereof to another element, it can be directly connected, coupled, or responsive to the other element, or intervening elements may be present.

[0078] Although the terms first, second, third, etc. may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Thus, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0079] The term "exemplary" as used herein means "serving as an example, embodiment, or illustration". Any embodiment illustrated herein as "exemplary" should not necessarily be construed as being superior to or better than other embodiments.

[0080] In addition, for a better illustration of the present disclosure, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can be implemented without some of these specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0081] Related technologies in the network deployment and optimization of unmanned platforms involve base station deployment technologies based on unmanned platforms, integrated access and backhaul technologies, interference minimization technologies, etc. The following are the current situations and existing problems of these technologies:

[0082] 1. Base station deployment technology based on unmanned platforms

[0083] Unmanned platforms are mainly used in scenarios such as emergency disaster relief and field scientific research in the deployment of communication base stations. By carrying base station equipment, they build a temporary access network and transmit data back to the ground core network. However, related technologies mostly adopt static deployment methods and lack the ability to dynamically adjust network topology and antenna directivity, making it difficult to meet the dynamic networking requirements in complex environments.

[0084] 2. Integrated access and backhaul technology

[0085] In 5G (5th Generation Mobile Networks) and next-generation communication networks, the integrated access and backhaul (IAB) technology realizes data backhaul between base stations through wireless relays, reducing the dependence on wired backhaul. However, related technologies mainly focus on the routing of logical channels, do not fully consider the physical location deployment of relay nodes and the antenna directivity design, and lack the collaborative optimization of node location and antenna directivity, resulting in limited network performance.

[0086] 3. Interference minimization technology

[0087] In large-scale wireless networking, interference between nodes is a key factor restricting network performance. Related technologies mainly use means such as power control and multiple access technologies to reduce interference, but in high-density and large-scale multi-hop networks, it is difficult to achieve global unified interference control, and antenna directivity is not fully combined for optimization. In addition, the energy consumption is too high in multi-user scenarios, restricting the battery life of unmanned platforms and the network lifetime.

[0088] In summary, the related technologies have the following deficiencies in the network deployment and optimization of unmanned platforms:

[0089] First, the efficiency of node deployment and network topology design is low. In node deployment and network topology design, related technologies usually use energy consumption or bit error rate as optimization indicators and adopt iterative methods or heuristic algorithms to determine the final structure. Although these methods can optimize certain performance indicators, the calculation time is long and they are not applicable to scenarios that require rapid network construction.

[0090] Second, there is a lack of collaborative design. When using relay nodes to construct an integrated access and backhaul network, the related technologies do not fully utilize the role of directional antennas in interference cancellation. Even if some technologies adopt directional antennas, they rarely consider the collaborative design of the radiation pattern of node antennas and the network topology at the same time, ignoring the mutual influence between the two.

[0091] Third, the ability to control spatial interference is insufficient. In high-density and large-scale multi-hop chain networks, the related technologies mainly rely on means such as power control and multiple access technologies to manage interference. However, these means are difficult to achieve global unified management and resource optimization allocation, and cannot effectively cope with the spatial interference problems in complex environments.

[0092] To solve the technical problems similar to those described above, the embodiments of the present disclosure provide a method for implementing a multi-hop network based on an unmanned platform. The multi-hop network based on the unmanned platform includes a controller and multiple actuators based on the unmanned platform. Different actuators are interconnected through a wireless network link. The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform. The controller sends the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator to control the actuator to adjust the network topology and / or the antenna radiation pattern. Thus, through the real-time control and adjustment of the controller, the movement of the actuator and the antenna pointing are guided to realize the joint optimization of the network topology and the antenna radiation pattern, so as to minimize the interference between nodes (i.e., actuators), improve the network capacity and stability, and enhance the communication performance of the network.

[0093] The embodiments of the present disclosure have important application prospects in the field of rapid network formation. In scenarios such as complex terrains or post-disaster recovery, by dynamically adjusting the antenna attitude and network topology, the capacity and stability of the wireless communication network can be effectively improved, ensuring the fast and reliable transmission of information. In addition, the embodiments of the present disclosure can also be widely applied to fields such as unmanned aerial vehicle networks, autonomous driving fleets, and remote monitoring systems to optimize network resource allocation, reduce signal interference, and thus improve the coverage range and data throughput of the wireless network.

[0094] The following will describe in detail the method for implementing a multi-hop network based on an unmanned platform provided by the embodiments of the present disclosure with reference to the accompanying drawings.

[0095] Figure 1 The flowchart showing the method for implementing a multi-hop network based on an unmanned platform provided by the embodiments of the present disclosure is shown. In some possible implementation manners, the method for implementing a multi-hop network based on an unmanned platform can be implemented by a processor calling computer-readable instructions stored in a memory. As Figure 1 shown, the method for implementing a multi-hop network based on an unmanned platform includes steps S11 to step S12.

[0096] In step S11, the controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform.

[0097] In step S12, the controller sends the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator to control the actuator to adjust the network topology and / or the antenna radiation pattern.

[0098] An unmanned platform can refer to a device or system that can autonomously or remotely control to complete specific tasks without direct human operation. Unmanned platforms can include unmanned aerial vehicles, unmanned vehicles, underwater robots, etc. They are usually equipped with advanced sensors, communication modules, and autonomous navigation systems, capable of autonomous flight, driving, or sailing, and performing tasks such as data collection, monitoring, transportation, or communication relay in complex environments. Due to their high flexibility, strong adaptability, and remote deployability, unmanned platforms have been widely used in fields such as emergency rescue, environmental monitoring, logistics transportation, military reconnaissance, and wireless communication.

[0099] In the embodiment of the present disclosure, the multi-hop network based on the unmanned platform can include a controller and multiple actuators based on the unmanned platform. The controller can also be referred to as a central controller, a central control unit, etc., which is not limited herein. The actuator can also be referred to as a network element actuator, a dynamic network element actuator, etc., which is not limited herein.

[0100] Among them, the controller is the core decision-making unit of the multi-hop network based on the unmanned platform, that is, the "brain" of the multi-hop network based on the unmanned platform, and can be responsible for the optimal design and dynamic adjustment of the network topology and the antenna radiation pattern. The controller can be flexibly deployed at different positions according to the task requirements and the deployment environment. For example, the controller can be deployed on the ground (such as a ground control center) to perform centralized control using ground computing resources; it can also be deployed in the air (such as a medium-high orbit satellite) to achieve wide-area coverage and remote control; or a hybrid architecture (such as ground computing and air publishing) can be adopted to combine the advantages of the ground and the air to achieve distributed computing and real-time instruction publishing. This flexible deployment method can adapt to different application scenarios and ensure the efficient operation and stable control of the network.

[0101] In a possible implementation, the controller may include a computing unit, a control unit, and a monitoring unit. As the core processor, the computing unit may be responsible for performing channel modeling and topology optimization based on real-time network data (such as terrain, interference, etc.), and generating network topology adjustment instructions and antenna radiation pattern adjustment instructions; the control unit may send the network topology adjustment instructions and antenna radiation pattern adjustment instructions to the actuator via a wireless link (such as satellite / 5G); the monitoring unit may continuously collect link status, device location, and environmental data to form a closed-loop control loop to achieve network dynamic optimization (such as triggering topology reconfiguration when the signal-to-noise ratio deteriorates).

[0102] In the embodiments of the present disclosure, the actuators may be connected wirelessly to form a wireless access network and a multi-hop wireless bearer network to achieve the functions of wireless access and wireless backhaul. The access network may be responsible for directly communicating with the terminal device to achieve wireless access to data. The bearer network may transmit the accessed data from the actuator to the core network. The core network is the central part of the network, responsible for centralized processing, routing, and management of data, and finally transmitting the data to the destination. In the embodiments of the present disclosure, a wireless bearer network is constructed by the actuator to achieve data backhaul, enabling the mobile base stations to be directly connected in a multi-hop manner, avoiding satellite forwarding, and reducing the backhaul delay.

[0103] In a possible implementation, the actuator may include a network topology adjustment unit, an antenna radiation pattern adjustment unit, and a location reporting unit. Among them, the network topology adjustment unit may receive the network topology adjustment instructions sent by the controller and drive the platform (such as an unmanned aerial vehicle / unmanned vehicle) to accurately move to the target coordinates; the antenna radiation pattern adjustment unit may real-time adjust the antenna directivity and beam pattern (such as narrowing the main lobe width to 30°); the location reporting unit may periodically transmit high-precision positioning data.

[0104] In another possible implementation, the actuator may include a location reporting unit and an adjustment unit. Among them, the adjustment unit may have the functions of the above-mentioned network topology adjustment unit and antenna radiation pattern adjustment unit.

[0105] In a possible implementation, the actuator includes at least one of the following types: unmanned aerial vehicle, unmanned vehicle, underwater robot.

[0106] As an aerial mobile node, an Unmanned Aerial Vehicle (UAV) has the advantages of rapid deployment and three-dimensional maneuverability, and is suitable for large-scale communication coverage and networking in complex terrains. The communication equipment carried by it can achieve wireless relay over a large distance, and is particularly suitable for scenarios that require rapid establishment of communication links, such as emergency communication and temporary event guarantee.

[0107] An unmanned ground vehicle (UGV) is a ground mobile node with strong terrain adaptability and long-term working ability. It can operate stably in complex environments such as cities and the wild, and achieve precise networking through high-precision positioning. It is often used in scenarios that require long-term stable communication, such as intelligent mining areas and border patrols.

[0108] Autonomous underwater vehicles (AUVs / Unmanned Underwater Vehicles, UUVs) are designed specifically for underwater communication. They use acoustic communication technology to break through water area limitations and can work at depths of hundreds of meters. They are mainly used in special environments such as ocean scientific research and underwater monitoring, and cooperate with surface and air nodes to build an air-ground-water integrated communication network.

[0109] In a possible implementation, the controller can maintain a real-time connection with the actuator through a wireless link. For areas with fewer terrain obstacles, low-frequency ground control can be used. Taking advantage of the strong diffraction ability of the low-frequency band, the control area of the controller can be increased, and only control information such as position and direction is transmitted. For wide-area remote areas such as mountains, oceans, and deserts, a satellite controller can be used. By using technologies such as Beidou short message and Tiantong satellite direct connection, control information is sent to the actuator. The actuator can use high-frequency band communication to improve the transmission capacity. Through the multi-hop network design and dynamic position adjustment between actuators, the disadvantages of weak penetration and small coverage of high-frequency communication are overcome.

[0110] Figure 2 The figure shows a schematic diagram of a multi-hop network based on an unmanned platform provided by an embodiment of the present disclosure. In Figure 2 the example shown, the controller can include a satellite controller and a ground controller. Among them, the satellite controller can be deployed on a satellite, can be applicable to wide-area remote areas (such as mountains, oceans, deserts, etc.), and can interact with the actuator through satellite communication. The ground controller can be deployed in a ground control center, can be applicable to areas with fewer terrain obstacles, and can maintain a connection with the actuator through ground wireless communication. The satellite controller and the ground controller can form a management and control network. In Figure 2 the example shown, the actuator can include an unmanned aerial vehicle and an unmanned ground vehicle. The unmanned aerial vehicle can perform tasks in the air, can move quickly and adjust its position, and is applicable to complex terrains or scenarios that require rapid deployment. The unmanned ground vehicle can perform tasks on the ground and is applicable to scenarios with relatively flat terrains or scenarios that require long-term operation. The actuators can be connected through wireless communication to form a multi-hop wireless bearer network. Each actuator can act as an access node to communicate with terminal devices, or as a relay node to transmit data to other actuators or controllers, and finally transmit the data back to the core network.

[0111] In the embodiments of the present disclosure, one or two controllers can be configured according to actual application requirements (for example, a dual - controller hot - standby mode can be adopted in a wide - area complex scenario). Of course, the number of controllers can also be more. For example, in Figure 2 the example shown, at least one satellite controller and two ground controllers can be configured. The number of actuators can vary dynamically according to the network scale. For example, dozens to hundreds of mobile nodes such as unmanned aerial vehicles, unmanned vehicles, or underwater robots can be deployed.

[0112] In a possible implementation, both the controller and the actuator can adopt a backup design. For example, the controller can support a primary - standby dual - machine hot - switch mechanism. When the primary controller fails, the standby controller can seamlessly take over the network management work within milliseconds. Each actuator can also have hardware redundancy capabilities. The key communication module and the power system can adopt a dual - backup configuration, and the basic operation can still be maintained when a single module fails. This dual - redundancy architecture not only supports independent upgrade and maintenance of individual components during system operation (such as updating the antenna control algorithm or replacing the navigation module), but also can quickly replace the faulty unit when the device is damaged without interrupting the overall network operation, thus significantly improving the long - term working reliability of the system in complex environments and the flexibility of architecture expansion, and is particularly suitable for application scenarios such as emergency communication that have strict requirements for system robustness.

[0113] In a possible implementation, the control signaling between the controller and the actuator can be transmitted along with the service data. In this way, the control signaling between the controller and the actuator can share the same communication link with the service data. The control signaling can be embedded in the transmission process of the service data and transmitted using the existing data transmission channel. This method saves additional channel resources and is applicable to scenarios where channel resources are limited or the real - time requirement for control signaling is not high. However, since the control signaling and the service data share the link, it may be affected by the service data traffic, resulting in delays or reduced reliability of the signaling transmission.

[0114] In another possible implementation, the control signaling between the controller and the actuator can be transmitted through a separate control channel. In this way, the control signaling between the controller and the actuator can be transmitted through an independent control channel, separated from the transmission link of the service data. This method ensures the real - time performance and reliability of the control signaling and avoids interference from service data traffic to the signaling transmission. It is applicable to scenarios with high real - time requirements for control signaling or complex network environments. Although it requires additional channel resources, it can significantly improve the stability and response speed of network control.

[0115] In a possible implementation, the deployment scenario information includes at least some of the following: external interference information, terrain feature information, traffic volume information, coverage area information.

[0116] Deployment scenario information is an important basis for network optimization. In this implementation, the deployment scenario information may include at least some of external interference information, terrain feature information, traffic information, and coverage area information. As an example of this implementation, the deployment scenario information may include: external interference information, terrain feature information, traffic information, and coverage area information.

[0117] The external interference information may relate to the results of electromagnetic environment monitoring. For example, it may include parameters such as the location, intensity, and frequency band of known interference sources. The external interference information can help the controller identify spectrum congestion areas, so as to actively avoid high-interference areas during topology planning. For example, when deploying in an urban area, it can scan and record the transmission frequency bands of surrounding base stations and automatically select the communication channel with the least interference.

[0118] The terrain feature information may include three-dimensional geographical data of the actuator deployment area. The terrain feature information can include not only surface elevation information, but also features such as building distribution and vegetation density that affect wireless propagation. The controller can use the terrain feature information to construct a three-dimensional channel model and predict the signal propagation loss between different positions. For example, in a mountainous scenario, special attention can be paid to the mountain occlusion effect, and the line-of-sight link can be established by adjusting the height of the UAV node; while in an urban environment, non-line-of-sight relay can be achieved by using building reflections.

[0119] The traffic information can reflect the actual load demand of the network and may include user density, traffic volume, and service priority in each region, etc. By analyzing the traffic information, the controller can dynamically adjust the resource allocation strategy. For example, in an emergency command scenario, the network capacity of the area around the command center can be prioritized, and the node density in this area can be appropriately increased; while for a monitoring area with a small amount of data, an energy-saving mode can be adopted. This differential deployment based on service requirements significantly improves the resource utilization efficiency.

[0120] The coverage area information can define the spatial range that the network needs to serve and its boundary conditions. In one example, the coverage area information can include both the geographical range of the target coverage and the coordinates of sensitive areas that need to be avoided. For example, in the application of forest fire prevention monitoring, the boundary coordinates of the monitoring area can be accurately defined, and the positions of no-fly zones and ecological protection areas can be marked at the same time, ensuring that the actuator can meet the coverage requirements during topology optimization without entering sensitive areas.

[0121] In a possible implementation, the controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, including: the controller constructs a channel model corresponding to the multi-hop network based on the unmanned platform according to the deployment scenario information of the multi-hop network based on the unmanned platform; the controller constructs an interference power function corresponding to the multi-hop network based on the unmanned platform according to the channel model and the deployment scenario information; the controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction by minimizing the interference power function.

[0122] In this implementation, the controller can construct an accurate channel model based on the deployment scenario information. The deployment scenario information can include multi-dimensional data such as terrain features, external interference source distribution, traffic demand, etc. These information can help the controller accurately characterize the propagation characteristics of wireless signals in a specific environment. For example, in an urban environment, the model can consider the multipath effect caused by building occlusion; in an open area, the free space propagation model can be adopted. By integrating these environmental parameters, the controller can establish a channel model reflecting the actual communication conditions, providing a theoretical basis for subsequent optimization.

[0123] Based on the channel model, the controller can further construct an interference power function to quantify the interference situation in the network. The interference power function can comprehensively consider the influence of key parameters such as node location, antenna radiation pattern, and transmit power on interference, and accurately calculate the interference intensity caused by each node to other nodes through a mathematical expression.

[0124] The controller can aim to minimize the interference power function and generate specific network topology adjustment instructions and antenna radiation pattern adjustment instructions through an optimization algorithm. On the premise of maintaining network connectivity and energy constraints, the controller can adjust the physical positions of nodes to increase the distance between interference sources, or optimize the antenna beam direction to avoid main lobe overlap. The adjustment instructions can be either position changes at the network topology level, beam optimizations at the antenna parameter level, or coordinated adjustments of both. Through this systematic method based on the channel model and the interference power function, the controller can effectively reduce the overall interference level of the network and improve communication quality and network capacity.

[0125] In a possible implementation, the method further includes: the actuator reports the position information of the actuator and / or the link performance parameters of the actuator to the controller at a preset frequency.

[0126] In this implementation, the actuator can periodically send its own status information to the controller at preset time intervals. This periodic reporting mechanism can ensure that the controller always has the latest information about each node in the network, providing data support for network optimization decisions. As an example of this implementation, the status information reported by the actuator can include location information, such as the current latitude and longitude coordinates, altitude, and motion state. As an example of this implementation, the status information reported by the actuator can include link performance parameters, such as key metrics like Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), path loss, interference level, network latency, and throughput. These data can be transmitted back to the controller in real time via a wireless communication link to form a dynamic database of network operation.

[0127] The controller can comprehensively analyze the operation status of the entire network based on the received reporting information. When it detects significant changes in the positions of certain nodes or a decline in link quality, the controller can immediately initiate an optimization algorithm to recalculate the optimal network topology and antenna parameter configuration. For example, when a drone deviates from its predetermined position due to wind influence, the controller can quickly determine the impact of this change on the entire network through the reporting data of other nodes and generate corresponding adjustment instructions. This closed-loop control mechanism based on periodic status feedback enables the entire network to have an adaptive ability and maintain stable operation in a complex environment.

[0128] As an example of this implementation, the reporting frequency can be dynamically adjusted according to network requirements. During network initialization or a stage of drastic topological changes, a higher reporting frequency (such as once per second) can be adopted to achieve a quick response; during the stable operation of the network, the frequency can be reduced (such as once every 5 seconds) to save communication resources.

[0129] As an example of this implementation, an event-triggered reporting mechanism can also be supported. When a sudden change in link quality or a position deviation exceeding a threshold is detected, the actuator can immediately report actively without waiting for the next preset reporting moment, which further enhances the system's ability to handle emergencies.

[0130] In a possible implementation, the method further includes: the controller updates the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction according to the position information of the actuator and / or the link performance parameters of the actuator.

[0131] Figure 3 Schematic diagram showing the implementation method of a multi-hop network based on an unmanned platform provided by an embodiment of the present disclosure. In Figure 3In the example shown, the implementation method of the multi-hop network based on the unmanned platform may include four steps.

[0132] The first step is external interference and terrain analysis. In this step, the controller can analyze the current electromagnetic wave transmission environment and external interference according to different deployment terrains (such as flat terrain, mountains, cities, forests, waters, etc.) and the collected position information of the actuators, providing basic information for channel modeling. Among them, the terrain data can come from remote sensing, radar, or high-precision geographic information systems, etc.

[0133] The second step is channel modeling. In this step, the controller can adopt targeted channel modeling methods for different terrains. For example, for flat terrain, the channel model can be mainly based on free-space propagation; for mountainous terrain, occlusion and reflection can be considered; for urban terrain, multi-path propagation and shadow fading effects can be introduced; for water terrain, non-electromagnetic communication methods such as underwater acoustic communication can be adopted.

[0134] The third step is the joint design of network topology and antenna radiation pattern. In this step, the controller can generate the initial deployment positions based on the terrain feature information according to the constructed large-scale channel model, the traffic volume information and the coverage area information of the task, and can adopt models such as regular polygon tiling and random distribution as the initial topology structure. The actuator can move to the specified position by itself according to the instructions issued by the controller. Among them, the deployment positions guided by the controller can always be on the ground surface or on buildings, that is, the actuator does not need to hover in the air, thus reducing the hovering energy consumption. Subsequently, with the goal of minimizing interference, the node positions and directions can be further adjusted through optimization algorithms. Mathematically, let the position of node i be d i , the transmission power be P i , the gain of the antenna pointing in the direction of vector d mn be G i (d mn ), and the gain of the channel model on vector d mn be h(d mn ). Then the effective power received by node j from node i is P ij =P i G i (d j -d i )h(d j -d i ), and the interference power received by node j from other nodes is I j =∑ k≠i P kj . This optimization problem is to optimize d i and G i (d mn ) while maintaining network connection and energy constraints, so that ∑ j Ij Minimum.

[0135] Step 4: Link state monitoring and dynamic adjustment. In this step, the actuator can periodically feedback its own position and link performance parameters to the controller, such as signal strength, signal-to-noise ratio, path loss, interference level, network delay, and throughput. Based on the real-time data, the controller determines whether topology optimization is required according to a preset threshold, especially whether to adjust the position or directivity of the actuator. If it is found that the signal quality of some links deteriorates or the interference increases, the controller can instruct the actuator to adjust its position to avoid the interference area, or adjust the antenna directivity to optimize signal coverage and reduce interference.

[0136] In a possible implementation, the method further includes: the controller monitors the outage probability of the multi-hop network based on the unmanned platform; the controller alternately updates the network topology adjustment instruction and the antenna radiation pattern adjustment instruction in response to the outage probability being greater than or equal to a preset outage probability threshold.

[0137] Due to the influence of factors such as environmental interference and node movement, it is difficult for each node in the network to always maintain an optimal position configuration. Therefore, in this implementation, by establishing a dynamic adjustment mechanism, the system can always approach the optimal performance. In this implementation, when it is monitored that the outage probability of the multi-hop network based on the unmanned platform exceeds the preset outage probability threshold, the controller can immediately trigger the optimization process to improve the network performance by alternately adjusting the network topology and the antenna radiation pattern.

[0138] As an example of this implementation, in a scenario with stable channel conditions, a mapping relationship between the outage probability and network parameters can be established based on theoretical models and simulation data. For example, for a scenario with a stable channel, deterministic algorithms such as the Newton descent method can be used to quickly converge to the optimal solution.

[0139] As an example of this implementation, in the actual operating environment, the change trend of the outage probability can be predicted by statistical analysis of historical communication data. That is, in the face of a complex time-varying environment, a robust optimization direction can be found based on historical statistical data.

[0140] Figure 4 Schematic diagram showing the alternating dynamic adjustment scheme in the implementation method of the multi-hop network based on the unmanned platform provided by the embodiments of the present disclosure. As Figure 4As shown, the controller can monitor the outage probability of the multi-hop network based on the unmanned platform in real time. When the outage probability of the multi-hop network based on the unmanned platform exceeds a preset threshold, two adjustment phases can be executed in sequence: First, update the network topology adjustment instruction to optimize the node position distribution to improve the basic connection quality; Subsequently, generate a new antenna radiation pattern adjustment instruction to further reduce interference by precisely controlling the beam directivity. After each adjustment phase is completed, the controller can re-evaluate the outage probability until it drops below the threshold.

[0141] This implementation method effectively improves the stability and adaptability of the network in a dynamic environment through an alternating optimization mechanism triggered by the outage probability. When the outage probability exceeds the threshold, the controller can first optimize the network topology to adjust the node positions, and then optimize the antenna radiation pattern to improve the link quality. This phased processing avoids parameter conflicts and makes the optimization process smoother and more efficient. Through closed-loop monitoring and alternating adjustment, the system can adapt to environmental changes, reduce unnecessary adjustment energy consumption while ensuring communication reliability, and is particularly suitable for the networking requirements of unmanned platforms in complex terrains or mobile scenarios.

[0142] In a possible implementation method, the network topology adjustment instruction includes a position adjustment instruction and / or a link parameter configuration instruction; the position adjustment instruction is used to control the actuator to adjust the position, and the link parameter configuration instruction is used to control the actuator to configure the link parameters.

[0143] Among them, the position adjustment instruction can precisely guide the physical displacement of the actuator through three-dimensional coordinates (longitude, latitude, altitude). For example, it can control the unmanned aerial vehicle to move from coordinate point A(x1, y1, z1) to point B(x2, y2, z2). This optimization of spatial position can fundamentally change the interference coupling relationship between nodes.

[0144] The link parameter configuration instruction can focus on the optimization at the communication level. For example, it can include but is not limited to: dynamic adjustment of the transmission power, switching of the operating frequency band, resetting of multiple access parameters, etc. The collaborative configuration of these parameters can significantly improve the link quality through the optimization of the communication protocol without changing the physical position of the nodes.

[0145] The synergistic effect of the two types of instructions forms a multi-dimensional optimization space: Position adjustment can solve large-scale interference problems (such as reducing interference by increasing the node spacing), while link parameter optimization can handle small-scale performance improvement (such as balancing the link quality through power control). The controller can intelligently select the instruction combination according to the scenario requirements. For example, in rapid deployment scenarios such as emergency communication, position adjustment is preferred, and in energy-constrained environments, link parameter optimization is emphasized.

[0146] In a possible implementation, the antenna radiation pattern adjustment instruction is used to control the actuator to adjust the directivity and / or beam pattern of the antenna.

[0147] In this implementation, the antenna radiation pattern adjustment instruction can optimize the wireless link performance by precisely controlling the spatial radiation characteristics of electromagnetic waves. In one example, the directivity adjustment instruction can drive the smart antenna system (such as a phased array or a mechanically steered antenna) carried by the actuator to change the main lobe radiation direction. For example, the beam center can be adjusted from an azimuth angle of 120° to 95°. In one example, the beam pattern adjustment instruction can dynamically configure antenna parameters, including the main lobe width (such as narrowing from 60° to 30° to enhance the directional gain), the side lobe suppression ratio (such as increasing to -20 dB to reduce interference), and the beam null depth (such as forming a -30 dB signal depression in the interference direction).

[0148] In this implementation, a high-quality communication link is established through beam directivity optimization, and the balance between coverage and interference suppression is achieved by cooperating with the intelligent shaping of the beam pattern (such as adaptive widening / narrowing).

[0149] In a possible implementation, the method further includes: the actuator adjusts the network topology of the actuator in response to the network topology adjustment instruction; and / or, the actuator adjusts the antenna radiation pattern of the actuator in response to the antenna radiation pattern adjustment instruction.

[0150] As an example of this implementation, the network topology adjustment unit in the actuator can adjust the network topology of the actuator in response to the network topology adjustment instruction.

[0151] As an example of this implementation, the line radiation pattern adjustment unit in the actuator can adjust the antenna radiation pattern of the actuator according to the antenna radiation pattern adjustment instruction.

[0152] In this implementation, the actuator can adjust the network topology and the antenna radiation pattern in response to the network topology adjustment instruction and the antenna radiation pattern adjustment instruction issued by the controller.

[0153] For example, when receiving a network topology adjustment instruction, the actuator can immediately activate the position adjustment mechanism and quickly and accurately move to the target coordinate position through a high-precision positioning system and a path planning algorithm. Taking an unmanned aerial vehicle as an example, the Micro-Electro-Mechanical Systems (MEMS) gyroscope equipped on it can sense the attitude change in real time and maintain an angle adjustment accuracy of the order of 0.1° at an angular velocity of 10° / s, ensuring the stability during the movement. Meanwhile, the actuator can combine satellite navigation signals and ground base station attitude measurement data to periodically correct the cumulative error of the gyroscope and avoid positioning deviation caused by long-term operation.

[0154] For another example, when performing antenna radiation pattern adjustment, the actuator can precisely control the directivity and beam pattern of the antenna according to the parameters in the antenna radiation pattern adjustment instruction. This process can also be realized based on a high-precision attitude sensing system. Through the fast attitude feedback provided by the gyroscope and the reference direction correction of the navigation system, it is ensured that the main lobe of the antenna can accurately align with the target node. For example, when it is necessary to narrow the main lobe width of the antenna to 30° to enhance the directional transmission, the actuator can monitor its own attitude change in real time and finely adjust the antenna angle.

[0155] In a possible implementation manner, the controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, including: the controller responds to the multi-hop network based on the unmanned platform being deployed on flat terrain or in the air and adopts a bent chain network structure; the controller generates a network topology adjustment instruction and an antenna radiation pattern adjustment instruction according to the deployment scenario information, where the network topology adjustment instruction is used to control the link angle of the actuator, and the antenna radiation pattern adjustment instruction is used to configure the main lobe width of the antenna of the actuator.

[0156] Figure 5 Schematic diagram showing the bent chain network structure in the implementation method of the multi-hop network based on the unmanned platform provided by the embodiments of the present disclosure. As Figure 5 shown, on flat terrain or in the air, a bent chain network structure can be adopted. The actuator can form a bent chain network under the guidance of the controller, and each link can form a bent link with an included angle of 2θ. The network can include service nodes that generate traffic and relay nodes that are only responsible for forwarding. When each node transmits data to the east and west ends, different frequencies, different spatial distributions or different times can be adopted to reduce the self-interference of the nodes. However, when all nodes transmit data in the same direction, in order to save resources and achieve the scalability of the network, the same transmission resources can be adopted. Therefore, a mode in which all nodes transmit data to the west direction can be selected for research. The same directional antenna is used for both the receiving and transmitting of the nodes.

[0157] Figure 6 Schematic diagram showing the radiation pattern of an ideal planar sector directional antenna in the implementation method of a multi-hop network based on an unmanned platform provided by an embodiment of the present disclosure. Considering an ideal planar sector directional antenna, its radiation model is as Figure 6 shown. Using g(θ) to represent its radiation pattern, the planar energy conservation needs to be satisfied, that is Corresponding to specific parameters, assuming its main lobe width is θ M , the main lobe gain is G0, and the side lobe gain is G c , then it is necessary to satisfy θ M ·G0+(2π - θ M )G c = 2π.

[0158] If it is an antenna in three-dimensional space, the spatial energy conservation needs to be satisfied, that is

[0159] As Figure 5 shown, assuming the receiving node number is 0, the transmitting node number is 1, and the rest of the nodes are interference nodes. Considering a data stream in one direction, that is, all nodes transmit from right to left; different frequencies can be used in the other direction, so the two directions are independent of each other. Since the main lobe width of the directional antenna is not infinitely narrow, when the bending angle is too large or too small, it may cause the receiving node to fall within the main lobe range of other nodes. The interference angle δ of the bending structure is introduced to analyze the structure. This angle represents the angle at which the receiving node deviates from the main direction of the interference node, and its relationship with the node number is:

[0160]

[0161] Furthermore, the signal-to-interference-plus-noise ratio (SINR) of the receiving node is:

[0162]

[0163] Among them, P t is the transmit power, N0 is the additive white Gaussian noise, and γ is the path loss factor. It can be seen that the interference gain h n (θ) and the path loss f n (θ) jointly determine the magnitude of the interference. For a fixed receiving node, there is an optimal bending angle to minimize the interference gain; it shows that for different antenna main lobe widths, there is an optimal network structure corresponding to it.

[0164] Considering that the largest interference in the structure is the main lobe interference, to avoid it, the structure needs to be adjusted so that the minimum interference angle is exactly equal to the half main lobe width of the beam, that is, the originally strongest interference becomes the side lobe interference. Specifically, for the interference angle, the interference from the interference nodes on the left side of the receiving node is all side lobe interference; while for the interference nodes on the right side of the receiving node that are not on the same straight line (i.e., the nodes with n being a positive odd number), the farther away from the receiving node, the larger the interference angle and the lower the interference gain. Therefore, we need to find the nearest interference node whose interference angle is exactly equal to the half main lobe width of the beam. For the interference angle of the fixed node, δ n (θ) reaches the maximum value at , so the nearest interference node should satisfy:

[0165]

[0166] This step is to ensure finding the smallest n such that for this node, adjusting the network structure makes the interference angle possibly greater than the half main lobe width. Subsequently, the network structure is adjusted so that the interference angle of this node is exactly equal to the half main lobe width, that is

[0167]

[0168] In this way, the optimal chain bending angle under the fixed main lobe width is found.

[0169] It should be noted that the above-mentioned method for designing the bent chain network based on the minimum interference angle should ensure that the system operates in the interference-limited region. Generally speaking, that is, the interference is 10 times the noise. Therefore, the node spacing and transmission power need to be determined according to the noise.

[0170] Figure 7 The figure shows a schematic diagram for verifying the method of designing the bent chain network based on the minimum interference angle using MATLAB. As Figure 7 shown, the red cross is the optimal structure solved by the above-mentioned method for designing the bent chain network based on the minimum interference angle, which can match the experimental values represented by the black dashed line when the main lobe width is relatively narrow.

[0171] For the dynamic adjustment of this chain network, its network topology is determined by the link angle θ, and the antenna radiation pattern is determined by the antenna main lobe width θ M , so the optimization objective is the joint optimization of the link angle θ and the antenna main lobe width θ M , and the alternating dynamic adjustment scheme based on the outage probability as shown in Figure 4 can still be adopted. In actual operation, if the channel conforms to the Rayleigh channel, its outage probability can be obtained through theoretical derivation.

[0172] In other different terrains, path loss prediction methods such as digital terrain modeling and ray tracing can be further used, combined with existing multi-objective optimization algorithms to achieve the joint design of network topology and antenna radiation pattern.

[0173] In a possible implementation, the method further includes: the controller sets the location information of the virtual fence according to the geographic location information of a preset sensitive area; the controller sends the location information of the virtual fence to the actuator to control the actuator to avoid the area defined by the virtual fence.

[0174] In this implementation, the virtual fence technology can be used to achieve intelligent avoidance of sensitive areas. The controller can pre-load the geographic location information of sensitive areas, where sensitive areas can include no-fly zones, military restricted areas, or ecological protection areas, and then automatically generate the location information of the virtual fence based on the geographic location information of the sensitive areas. The location information of the virtual fence can be sent to each actuator in real time through a wireless communication link to ensure that drones, unmanned vehicles and other equipment always maintain a safe distance from sensitive areas during movement.

[0175] After receiving the location information of the virtual fence, the actuator can write the location information of the virtual fence into the adjustment unit, and can build the location information of the virtual fence into the navigation system for real-time comparison. When the positioning system of the actuator detects that it is approaching the boundary of the virtual fence, it can automatically trigger the avoidance algorithm, adjust the route or hover on standby.

[0176] This implementation deeply integrates the geo-fencing function into the dynamic topology management of the multi-hop network. When an actuator needs to change its position to avoid the virtual fence, the controller can recalculate the optimal network topology in real time and coordinate other nodes to adjust the antenna pointing and link parameters to ensure that the communication quality of the entire network is not affected. This design not only meets the regulatory requirements of airspace management and environmental protection, but also ensures the continuous stability of the communication network. It has important application value in scenarios such as emergency rescue and border patrol.

[0177] The following describes a method for implementing a multi-hop network based on an unmanned platform provided by an embodiment of the present disclosure through a specific application scenario. In this application scenario, the multi-hop network based on an unmanned platform may include a controller and multiple actuators based on the unmanned platform.

[0178] In this application scenario, the controller consists of a computing unit, a control unit, and a monitoring unit, which work together to achieve intelligent management and dynamic optimization of multi-hop networks based on unmanned platforms.

[0179] As the core processing module, the computing unit can perform tasks such as dynamic channel modeling, network performance prediction, and joint optimization of network topology (node positions, link parameters) and antenna radiation patterns (directivity, beam morphology) based on the real-time data provided by the monitoring unit (such as link status, terrain features, and external interference). Finally, it can generate network topology adjustment instructions and antenna radiation pattern adjustment instructions containing parameters such as position coordinates, antenna angles, and transmission powers.

[0180] The control unit is responsible for sending the network topology adjustment instructions and antenna radiation pattern adjustment instructions generated by the computing unit to each actuator via wireless communication links such as satellites, 5G, or dedicated frequency bands. The control unit supports two signaling transmission modes: the in-band transmission mode can save resources and achieve channel sharing of control signaling and service data; the independent control channel mode can ensure low latency and high reliability of signaling transmission to meet critical control requirements.

[0181] The monitoring unit can monitor the network operation status in real time and collect three types of key data: link performance parameters (signal-to-noise ratio, interference level, throughput, and latency), actuator status information (position, remaining energy, antenna attitude), and environmental data (terrain obstacles, electromagnetic interference sources, and meteorological conditions). These data can be fed back to the computing unit in real time to form a complete closed-loop control system. For example, when it is detected that the signal-to-noise ratio of a certain link deteriorates, the network topology re-optimization process can be automatically triggered. After the new instructions generated by the computing unit are sent down by the control unit, the actuator completes the corresponding adjustment, and the monitoring unit then verifies the effect and starts iterative optimization if necessary to ensure that the network always maintains the best performance state.

[0182] Each actuator can respectively include three core functional units: a network topology adjustment unit, an antenna radiation pattern adjustment unit, and a position reporting unit. These three units work together to ensure that the actuator can accurately respond to the network topology adjustment instructions and antenna radiation pattern adjustment instructions of the controller, realizing the dynamic optimization and stable operation of the network topology. This modular design enables the actuator to operate as an independent node and integrate into the overall network architecture to adapt to complex and changing communication environments.

[0183] The network topology adjustment unit is responsible for receiving and executing the network topology adjustment instructions sent by the controller. The network topology adjustment unit can obtain real-time position information through high-precision satellite positioning (such as GPS (Global Positioning System) / Beidou) and inertial navigation systems and drive the actuator to move to the specified coordinates. In the UAV actuator, the network topology adjustment unit can achieve centimeter-level hovering positioning; in the unmanned vehicle actuator, it can plan the optimal path to avoid obstacles. When the network needs to be reconstructed, the network topology adjustment unit can ensure that multiple actuators synchronously adjust their positions to maintain the optimal network topology structure.

[0184] The antenna radiation pattern adjustment unit can be responsible for optimizing the wireless link quality. The antenna radiation pattern adjustment unit can dynamically adjust parameters such as the beam pointing and main lobe width of the antenna according to the antenna radiation pattern adjustment instructions from the controller. The actuator using phased array technology can complete beam switching in milliseconds, while the mechanical steering antenna can achieve large-angle adjustments of ±90°. By real-time matching the network topology changes, the antenna radiation pattern adjustment unit can significantly improve the directional transmission efficiency and reduce the interference between adjacent nodes by more than 60%, which is particularly suitable for high-density node deployment scenarios.

[0185] The position reporting unit can continuously feedback the key status data of the actuator to the controller. The position reporting unit can report positioning information such as longitude, latitude, and altitude at a frequency of 1 Hz, and at the same time can collect device status such as remaining battery power and movement speed. Using the Beidou short message and 5G dual-channel transmission mechanism, the reliability of data backhaul can be ensured even in complex terrains. These real-time data provide an important basis for the decision-making optimization of the controller, forming a closed-loop control of "monitoring - decision-making - execution - feedback", enabling the entire network to have self-healing and self-adaptive capabilities.

[0186] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not elaborate further. Those skilled in the art can understand that in the above methods of the specific implementation manner, the specific execution order of each step should be determined according to its function and possible internal logic.

[0187] In addition, the present disclosure also provides a multi-hop network system based on an unmanned platform, a non-volatile computer-readable storage medium, and a computer program product, all of which can be used to implement any one of the multi-hop network methods provided by the present disclosure. The corresponding technical solutions and technical effects can be seen in the corresponding records in the method part, and will not be elaborated further.

[0188] Figure 8 The block diagram of the multi-hop network system based on an unmanned platform provided by the embodiment of the present disclosure is shown. As Figure 8 shown, the multi-hop network system based on an unmanned platform includes a controller and multiple actuators based on an unmanned platform, and different actuators are interconnected through a wireless network link; the controller is used to generate a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on an unmanned platform, and send the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator; the actuator is used to adjust the network topology and / or the antenna radiation pattern of the actuator in response to the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction.

[0189] In a possible implementation, the deployment scenario information includes at least some of the following:

[0190] External interference information, terrain feature information, traffic volume information, coverage area information.

[0191] In a possible implementation, the controller is specifically configured to:

[0192] Construct a channel model corresponding to the multi-hop network based on the unmanned platform according to the deployment scenario information of the multi-hop network based on the unmanned platform;

[0193] Construct an interference power function corresponding to the multi-hop network based on the unmanned platform according to the channel model and the deployment scenario information;

[0194] Generate a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction by minimizing the interference power function.

[0195] In a possible implementation, the actuator is further configured to:

[0196] Report the position information of the actuator and / or the link performance parameters of the actuator to the controller at a preset frequency.

[0197] In a possible implementation, the controller is further configured to:

[0198] Update the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction according to the position information of the actuator and / or the link performance parameters of the actuator.

[0199] In a possible implementation, the controller is further configured to:

[0200] Monitor the outage probability of the multi-hop network based on the unmanned platform;

[0201] In response to the outage probability being greater than or equal to a preset outage probability threshold, alternately update the network topology adjustment instruction and the antenna radiation pattern adjustment instruction.

[0202] In a possible implementation, the network topology adjustment instruction includes a position adjustment instruction and / or a link parameter configuration instruction;

[0203] The position adjustment instruction is used to control the actuator to adjust its position, and the link parameter configuration instruction is used to control the actuator to configure link parameters.

[0204] In a possible implementation, the antenna radiation pattern adjustment instruction is used to control the actuator to adjust the directivity and / or beam pattern of the antenna.

[0205] In a possible implementation, the controller is further configured to:

[0206] Set the location information of the virtual fence according to the geographical location information of the preset sensitive area;

[0207] Send the location information of the virtual fence to the actuator to control the actuator to avoid the area defined by the virtual fence.

[0208] In a possible implementation, the controller is specifically configured to:

[0209] The controller responds to the multi-hop network based on the unmanned platform being deployed on flat terrain or in the air, and adopts a bent chain network structure;

[0210] The controller generates a network topology adjustment instruction and an antenna radiation pattern adjustment instruction according to the deployment scenario information, where the network topology adjustment instruction is used to control the link angle of the actuator, and the antenna radiation pattern adjustment instruction is used to configure the main lobe width of the antenna of the actuator.

[0211] In a possible implementation, the actuator includes at least one of the following types: unmanned aerial vehicle, unmanned vehicle, underwater robot.

[0212] The embodiments of the present disclosure provide a multi-hop network system based on an unmanned platform. The multi-hop network system based on an unmanned platform includes a controller and multiple actuators based on an unmanned platform. Different actuators are interconnected through wireless network links. The controller is configured to generate a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, and send the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator. The actuator is configured to adjust the network topology and / or the antenna radiation pattern of the actuator in response to the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction. Thus, through the real-time control and adjustment of the controller, the movement of the actuator and the antenna pointing are guided, and the joint optimization of the network topology and the antenna radiation pattern is realized to minimize the interference between nodes (i.e., actuators), improve the network capacity and stability, and enhance the communication performance of the network. The embodiments of the present disclosure have important application prospects in the field of rapid network formation. In scenarios such as complex terrain or post-disaster recovery, by dynamically adjusting the antenna attitude and network topology, the capacity and stability of the wireless communication network can be effectively improved, ensuring the fast and reliable transmission of information. In addition, the embodiments of the present disclosure can also be widely applied to fields such as unmanned aerial vehicle networks, autonomous driving fleets, and remote monitoring systems to optimize network resource allocation, reduce signal interference, and thus improve the coverage range and data throughput of the wireless network.

[0213] In some embodiments, the functions or modules included in the systems, controllers, and actuators provided by the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. Their specific implementations and technical effects can be referred to the descriptions of the above method embodiments. For the sake of brevity, they will not be elaborated here.

[0214] The embodiments of the present disclosure further provide a controller, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the above method.

[0215] The embodiments of the present disclosure further provide an actuator, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the above method.

[0216] The embodiments of the present disclosure further provide a non - volatile computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0217] The embodiments of the present disclosure further provide a computer program product, including a computer program, or a non - volatile computer - readable storage medium carrying the computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0218] Figure 9 is a block diagram of an electronic device 1900 shown according to an exemplary embodiment. For example, the electronic device 1900 can be provided as a controller or an actuator. Referring to Figure 9 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 can include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above - mentioned method.

[0219] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows Server TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM or the like.

[0220] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, and the computer program instructions can be executed by a processing component 1922 of the electronic device 1900 to complete the above method.

[0221] A computer-readable storage medium can be a tangible device that can hold and store programs / instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the above. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through an optical fiber cable), or electrical signals transmitted through wires.

[0222] The computer programs (or computer-readable program instructions) described herein can be downloaded from the computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0223] A computer program (or computer program instructions) for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or, alternatively, may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0224] Aspects of the present disclosure are described herein with reference to the flowchart and / or block diagram of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions.

[0225] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing apparatus, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which causes a computer, a programmable data processing apparatus, and / or other devices to operate in a specific manner, so that the computer-readable medium storing the instructions includes a manufacture, which includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0226] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0227] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0228] The computer program product may be implemented specifically in the form of hardware, software, or a combination thereof. In an alternative embodiment, the computer program product is specifically embodied as a computer storage medium. In another alternative embodiment, the computer program product is specifically embodied as a software product, such as a Software Development Kit (SDK), etc.

[0229] The above descriptions of the various embodiments tend to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to each other. For the sake of brevity, they will not be elaborated herein.

[0230] If the technical solution of an embodiment of the present disclosure involves personal information, before the product applying the technical solution of the embodiment of the present disclosure processes personal information, it has clearly informed the personal information processing rules and obtained the individual's independent consent. If the technical solution of an embodiment of the present disclosure involves sensitive personal information, before the product applying the technical solution of the embodiment of the present disclosure processes sensitive personal information, it has obtained the individual's separate consent and at the same time meets the requirements of "express consent". For example, at a personal information collection device such as a camera, a clear and prominent sign is set to inform that the personal information collection scope has been entered and personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed to have consented to the collection of their personal information; or on a personal information processing device, when the personal information processing rules are informed by obvious signs / information, personal authorization is obtained through pop-up messages or by asking the individual to upload their personal information by themselves; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0231] The embodiments of the present disclosure have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technologies in the market, or to enable other ordinary skill in the art in the technical field to understand the embodiments disclosed herein.

Claims

1. A method for implementing a multi-hop network based on an unmanned platform, characterized in that, The multi-hop network based on the unmanned platform includes a controller and multiple actuators based on the unmanned platform. Different actuators are interconnected through wireless network links. The method includes: The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform; The controller sends the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator to control the actuator to adjust the network topology and / or the antenna radiation pattern.

2. The method according to claim 1, wherein The deployment scenario information includes at least some of the following: External interference information, terrain feature information, traffic information, coverage area information.

3. The method according to claim 1 or 2, characterized in that, The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, including: The controller constructs a channel model corresponding to the multi-hop network based on the unmanned platform according to the deployment scenario information of the multi-hop network based on the unmanned platform; The controller constructs an interference power function corresponding to the multi-hop network based on the unmanned platform according to the channel model and the deployment scenario information; The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction by minimizing the interference power function.

4. The method according to claim 1 or 2, characterized in that, The method further includes: The actuator reports the position information of the actuator and / or the link performance parameters of the actuator to the controller at a preset frequency.

5. The method according to claim 4, characterized in that The method further includes: The controller updates the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction according to the position information of the actuator and / or the link performance parameters of the actuator.

6. The method according to claim 1 or 2, characterized in that, The method further includes: The controller monitors the outage probability of the multi-hop network based on the unmanned platform; In response to the outage probability being greater than or equal to a preset outage probability threshold, the controller alternately updates the network topology adjustment instruction and the antenna radiation pattern adjustment instruction.

7. The method according to claim 1 or 2, characterized in that, The network topology adjustment instruction includes a position adjustment instruction and / or a link parameter configuration instruction; The position adjustment instruction is used to control the actuator to adjust its position, and the link parameter configuration instruction is used to control the actuator to configure link parameters.

8. The method according to claim 1 or 2, characterized in that, The antenna radiation pattern adjustment instruction is used to control the actuator to adjust the directivity and / or beam pattern of the antenna.

9. The method according to claim 1 or 2, characterized in that, The method further includes: The actuator adjusts the network topology of the actuator in response to the network topology adjustment instruction; and / or, The actuator adjusts the antenna radiation pattern of the actuator in response to the antenna radiation pattern adjustment instruction.

10. The method according to claim 1 or 2, characterized in that, The method further includes: The controller sets the position information of the virtual fence according to the geographical location information of the preset sensitive area; The controller sends the position information of the virtual fence to the actuator to control the actuator to avoid the area defined by the virtual fence.

11. The method according to claim 1 or 2, characterized in that, The controller generates a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, including: In response to the multi-hop network based on the unmanned platform being deployed on flat terrain or in the air, the controller adopts a bent chain network structure; The controller generates a network topology adjustment instruction and an antenna radiation pattern adjustment instruction according to the deployment scenario information, wherein the network topology adjustment instruction is used to control the link angle of the actuator, and the antenna radiation pattern adjustment instruction is used to configure the main lobe width of the antenna of the actuator.

12. The method according to claim 1, characterized in that, The actuator includes at least one of the following types: unmanned aerial vehicle, unmanned vehicle, underwater robot.

13. A multi-hop network system based on an unmanned platform, characterized in that, The multi-hop network system based on the unmanned platform includes a controller and multiple actuators based on the unmanned platform, and different actuators are interconnected through wireless network links; The controller is configured to generate a network topology adjustment instruction and / or an antenna radiation pattern adjustment instruction according to the deployment scenario information of the multi-hop network based on the unmanned platform, and send the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction to the actuator; The actuator is configured to adjust the network topology and / or the antenna radiation pattern of the actuator in response to the network topology adjustment instruction and / or the antenna radiation pattern adjustment instruction.

14. An electronic device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 12.

15. A non-volatile computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 12 are implemented.

16. A computer program product, comprising a computer program, or a non-volatile computer-readable storage medium carrying the computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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