UAV self-organizing network routing method, device, UAV and readable storage medium

Abstract

The present disclosure relates to the field of intelligent communication technology, and specifically to a drone self-organizing network routing method, device, drone, and readable storage medium. The drone self-organizing network routing method includes: a drone node receiving a test packet sent by an adjacent drone node; determining the predicted location information of the adjacent drone node; obtaining the current location information of the drone node; updating the network topology of the drone node; when the drone node receives a data packet after sending a data packet, or when the drone node determines that the data packet transmission fails after sending the data packet, the drone node sends the data packet to a second adjacent drone node. The present disclosure can predict the network topology at the current moment based on the test packet information at the previous moment, reduce the occurrence of drone self-organizing network routing errors without increasing the test packet transmission frequency, and improve routing speed.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent communication technology, and in particular to a method and device for routing a UAV ad hoc network, a UAV, and a readable storage medium. Background Art

[0002] In recent years, with the development of drone and communication technologies, a new communication method has emerged: drone wireless ad hoc networking. As nodes in communication networks, drones possess self-organizing, self-configuring, and self-healing properties, enabling them to rapidly establish wireless communication networks and provide wireless transmission services for multimedia services such as voice, data, and video. Furthermore, due to their high mobility and ease of deployment, drones can be deployed more quickly than mobile communication vehicles, making them widely used in a variety of scenarios, including emergency assistance, post-disaster reconstruction, and outdoor exploration.

[0003] However, due to the high speed and wide range of drone nodes in UAV ad hoc networks, the network topology changes frequently and is easily affected by factors such as weather conditions, external signal interference, and obstacles. This increases the difficulty of establishing and maintaining communication links, reduces the stability and reliability of the links, and affects data transmission speed. Therefore, a method for quickly and stably establishing routing in UAV ad hoc networks is needed. Summary of the Invention

[0004] In order to solve the problems in the related art, the embodiments of the present disclosure provide a drone ad hoc network routing method, device, drone and readable storage medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for routing in a drone ad hoc network, comprising:

[0006] The drone node sends the first test packet;

[0007] The UAV node receives a second test packet sent by an adjacent UAV node; the second test packet includes the actual speed, actual acceleration, and actual position information of the adjacent UAV node at the time the second test packet is sent, as well as the first historical position information and historical RSSI (received signal strength) signal value of the adjacent UAV node at the time the first test packet is received; the historical RSSI signal value is the signal strength of the adjacent UAV node when it receives the first test packet sent by the UAV node;

[0008] When the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet, wherein when the drone node receives multiple test packets from the adjacent drone node, the most recently received test packet is selected and used as the second test packet;

[0009] Obtaining the current location information of the drone node;

[0010] Update the network topology of the drone node based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and a topographic map of the area where the drone node is located, wherein the network topology of the drone node includes the adjacent drone nodes predicted to be currently capable of establishing a connection with the drone node;

[0011] The drone node sends a data packet to a first adjacent drone node in the network topology of the drone node;

[0012] When the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet has failed to be sent after sending the data packet, the drone node sends the data packet to a second adjacent drone node in the network topology of the drone node.

[0013] According to an embodiment of the present disclosure, updating the network topology of the drone node based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet in the second test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located includes:

[0014] Calculate the predicted straight-line distance between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node;

[0015] Calculate the predicted RSSI (received signal strength) signal values ​​of the drone node and the adjacent drone node based on the first historical location information and historical RSSI signal value of the adjacent drone node at the time of receiving the first test packet in the second test packet, the second historical location information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node;

[0016] The predicted RSSI signal value is compared with the minimum RSSI (received signal strength) signal value to determine the predicted connection status of the drone node and the adjacent drone node, and the network topology of the drone node is updated according to the predicted connection status.

[0017] According to an embodiment of the present disclosure, the calculating of the predicted RSSI signal values ​​of the drone node and the adjacent drone node includes:

[0018] Calculate the environmental attenuation parameter based on the historical RSSI signal value, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the second historical location information of the drone node at the time of sending the first test packet, and the signal strength per unit distance between the drone node and the adjacent drone node;

[0019] According to the environmental attenuation parameter, the predicted straight-line distance, and the signal strength per unit distance between the drone node and the adjacent drone node, the predicted RSSI signal values ​​of the drone node and the adjacent drone node are calculated; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

[0020] According to an embodiment of the present disclosure, updating the network topology of the drone node based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet in the second test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located, further includes:

[0021] Obtaining a topographic map of the area where the drone node is located;

[0022] According to the topographic map, obtaining the terrain elevation between the UAV node and the adjacent UAV node;

[0023] Calculating the elevation of the line between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node;

[0024] An area where the connection line elevation is less than the terrain elevation is set as an obstacle area. When the connection line between the drone node and the adjacent drone node passes through the obstacle area, the predicted connection state between the drone node and the adjacent drone node is determined to be a disconnected state.

[0025] According to an embodiment of the present disclosure, the adjacent drone node sends the second test packet at a preset time interval; and / or

[0026] When the position of the adjacent UAV node changes, the second test packet is sent at the start time point and the end time point of the position change respectively; and / or

[0027] When the speed of the adjacent UAV node changes, sending the second test packet at the start time point of the speed change and the end time point of the speed change respectively; and / or

[0028] When the acceleration of the adjacent UAV node changes, sending the second test packet at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or

[0029] When the adjacent drone node is a newly added drone node, the second test packet is sent.

[0030] According to an embodiment of the present disclosure, the first adjacent drone node and / or the second adjacent drone node is determined based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

[0031] According to an embodiment of the present disclosure, the second test package further includes the network topology of the adjacent drone nodes, and the method further includes:

[0032] Determining a global network topology based on the network topology of the adjacent drone nodes;

[0033] Determining the number of nodes in a routing line of the data packet according to a target node of the data packet and the global network topology;

[0034] The first routing priority of the adjacent drone node is determined according to the number of nodes in the routing line of the data packet.

[0035] According to an embodiment of the present disclosure, the second test packet further includes a traffic load value of the adjacent drone node, and the method further includes:

[0036] Determine a second routing priority of the adjacent drone node based on the traffic load value of the adjacent drone node.

[0037] According to an embodiment of the present disclosure, the method further includes:

[0038] Determine a third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node.

[0039] According to an embodiment of the present disclosure, it further includes:

[0040] When the drone node fails to send a data packet to any adjacent drone node, the drone node determines the connection area between the drone node and any adjacent drone node as a suspected obstacle area.

[0041] According to an embodiment of the present disclosure, the second test package further includes the network topology of the adjacent drone nodes and the distribution information of suspected obstacle areas maintained by the adjacent drone nodes. The method further includes:

[0042] Determining a global network topology based on the network topology of the adjacent drone nodes;

[0043] Determine global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent UAV nodes;

[0044] Determining the number of suspected obstacle areas on the routing line of the data packet according to the target node of the data packet, the global network topology and the global suspected obstacle area distribution information;

[0045] Determine the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

[0046] According to an embodiment of the present disclosure, it further includes:

[0047] When the adjacent drone node fails to send a data packet to any other drone node, the adjacent drone node determines the connection area between the adjacent drone node and any other drone node as a suspected obstacle area.

[0048] According to an embodiment of the present disclosure, the method is executed by each drone node in the drone ad hoc network.

[0049] In a second aspect, an embodiment of the present disclosure provides a UAV self-organizing network routing device, comprising:

[0050] a first sending module, configured to send a first test packet to the UAV node;

[0051] The second sending module is configured to receive, by the UAV node, a second test packet sent by an adjacent UAV node; the second test packet includes: the actual speed, actual acceleration, and actual position information of the adjacent UAV node at the time of sending the second test packet, as well as the first historical position information and historical RSSI (received signal strength) signal value of the adjacent UAV node at the time of receiving the first test packet; the historical RSSI signal value is the signal strength when the adjacent UAV node receives the first test packet sent by the UAV node;

[0052] a first determination module configured to, when the drone node sends a data packet, determine, by the drone node, the predicted location information of the adjacent drone node based on a time difference between a current moment and a moment when the second test packet is sent, and actual speed, actual acceleration, and actual location information of the adjacent drone node in the second test packet, wherein, when the drone node receives multiple test packets from the adjacent drone node, select and use the most recently received test packet as the second test packet;

[0053] An acquisition module is configured to obtain the current location information of the drone node;

[0054] an updating module configured to update a network topology of the drone node based on the predicted location information of the adjacent drone node, the current location information of the drone node, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical location information of the drone node at the time of sending the first test packet, and a topographic map of the area where the drone node is located, wherein the network topology of the drone node includes adjacent drone nodes predicted to be currently capable of establishing a connection with the drone node;

[0055] a third sending module, configured for the drone node to send a data packet to a first adjacent drone node in the network topology of the drone node;

[0056] The fourth sending module is configured to send the data packet to a second adjacent drone node in the network topology of the drone node when the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet sending fails after sending the data packet.

[0057] According to an embodiment of the present disclosure, the update module includes:

[0058] A first calculation submodule is configured to calculate a predicted straight-line distance between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node;

[0059] The second calculation submodule is configured to calculate the predicted RSSI signal value of the drone node and the adjacent drone node based on the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node;

[0060] The first determination submodule is configured to compare the predicted RSSI signal value with the minimum RSSI signal value, determine the predicted connection status of the drone node and the adjacent drone node, and update the network topology of the drone node according to the predicted connection status.

[0061] According to an embodiment of the present disclosure, the update module further includes:

[0062] A first acquisition submodule is configured to acquire a topographic map of the area where the drone node is located;

[0063] A second acquisition submodule is configured to acquire the terrain elevation between the UAV node and the adjacent UAV node according to the terrain map;

[0064] a third calculation submodule, configured to calculate a line elevation of a line between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node;

[0065] The determination submodule is configured to set an area where the connection elevation is less than the terrain elevation as an obstacle area, and when the connection line between the drone node and the adjacent drone node passes through the obstacle area, determine that the predicted connection state between the drone node and the adjacent drone node is a disconnected state.

[0066] According to an embodiment of the present disclosure, the adjacent drone node sends the second test packet at a preset time interval; and / or

[0067] When the position of the adjacent UAV node changes, the second test packet is sent at the start time point and the end time point of the position change respectively; and / or

[0068] When the speed of the adjacent UAV node changes, sending the second test packet at the start time point of the speed change and the end time point of the speed change respectively; and / or

[0069] When the acceleration of the adjacent UAV node changes, sending the second test packet at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or

[0070] When the adjacent drone node is a newly added drone node, the second test packet is sent.

[0071] According to an embodiment of the present disclosure, the first adjacent drone node and / or the second adjacent drone node is determined based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

[0072] According to an embodiment of the present disclosure, the second test package further includes the network topology of the adjacent drone nodes, and the apparatus further includes:

[0073] A second determining module is configured to determine a global network topology based on the network topology of the adjacent drone nodes;

[0074] A third determining module is configured to determine the number of nodes of the routing line of the data packet according to the target node of the data packet and the global network topology;

[0075] The fourth determination module is configured to determine the first routing priority of the adjacent drone node based on the number of nodes in the routing line of the data packet.

[0076] According to an embodiment of the present disclosure, the test packet further includes a traffic load value of the adjacent drone node, and the apparatus further includes:

[0077] The fifth determination module is configured to determine the second routing priority of the adjacent drone node based on the traffic load value of the adjacent drone node.

[0078] According to an embodiment of the present disclosure, the device further includes:

[0079] The sixth determination module is configured to determine the third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node.

[0080] According to an embodiment of the present disclosure, the device further includes:

[0081] The first judgment module is configured to, when the drone node fails to send a data packet to any adjacent drone node, judge the connection area between the drone node and any adjacent drone node as a suspected obstacle area.

[0082] According to an embodiment of the present disclosure, the second test package further includes the network topology of the adjacent drone nodes and the distribution information of suspected obstacle areas maintained by the adjacent drone nodes, and the apparatus further includes:

[0083] a seventh determination module, configured to determine a global network topology based on the network topology of the adjacent drone nodes;

[0084] an eighth determining module, configured to determine global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent UAV node;

[0085] a ninth determining module configured to determine the number of suspected obstacle areas on the routing line of the data packet according to the target node of the data packet, the global network topology, and the global suspected obstacle area distribution information;

[0086] The tenth determination module is configured to determine the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

[0087] According to an embodiment of the present disclosure, the device further includes:

[0088] The second judgment module is configured to judge the connection area between the adjacent drone node and any other drone node as a suspected obstacle area when the adjacent drone node fails to send a data packet to any other drone node.

[0089] In a third aspect, an embodiment of the present disclosure provides a drone, comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement the method as described in any one of the first aspects.

[0090] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the method described in the first aspect is implemented.

[0091] According to the technical solution provided by the embodiment of the present disclosure, a method for routing in a drone ad hoc network is disclosed, comprising:

[0092] The drone node sends a first test packet; the drone node receives a second test packet sent by an adjacent drone node; the second test packet includes the actual speed, actual acceleration, actual position information of the adjacent drone node at the time the second test packet is sent, as well as the first historical position information and historical RSSI signal value of the adjacent drone node at the time the first test packet is received; the historical RSSI signal value is the signal strength of the adjacent drone node when it receives the first test packet sent by the drone node; when the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet, wherein, when the drone node receives multiple test packets from the adjacent drone node, it selects to use the most recently received test packet as the second test packet; and obtains the current position information of the drone node. According to the predicted location information of the adjacent drone node, the current location information of the drone node, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical location information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located, the network topology of the drone node is updated, wherein the network topology of the drone node includes the adjacent drone nodes predicted to be able to establish a connection with the drone node; the drone node sends a data packet to the first adjacent drone node in the network topology of the drone node; when the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet transmission fails after sending the data packet, the drone node sends the data packet to the second adjacent drone node in the network topology of the drone node, and no longer sends data packets to the first adjacent drone node. The technical solution disclosed in the present invention can predict the network topology at the current moment based on the test packet information at the previous moment, reduce the occurrence of drone self-organizing network routing errors without increasing the test packet transmission frequency, and improve routing speed.

[0093] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:

[0095] Figure 1A schematic diagram illustrating an application scenario of a drone ad hoc network routing method according to an embodiment of the present disclosure is shown.

[0096] Figure 2 A flowchart of a drone ad hoc network routing method according to an embodiment of the present disclosure is shown.

[0097] Figure 3 A flowchart of step S105 in the drone ad hoc network routing method according to an embodiment of the present disclosure is shown.

[0098] Figure 4 A flowchart of step S105 in a drone ad hoc network routing method according to another embodiment of the present disclosure is shown.

[0099] Figure 5 A schematic diagram of a scenario in which an obstacle zone is formed in a drone self-organizing network routing method according to an embodiment of the present disclosure is shown.

[0100] Figure 6 A schematic diagram of a scenario in which the drone network coverage is automatically expanded or contracted in a drone self-organizing network routing method according to an embodiment of the present disclosure is shown.

[0101] Figure 7-Figure 8 A schematic diagram of a scenario in which a suspected obstacle area is formed in a drone self-organizing network routing method according to an embodiment of the present disclosure is shown.

[0102] Figure 9 A structural block diagram of a drone ad hoc network routing device according to an embodiment of the present disclosure is shown.

[0103] Figure 10 A structural block diagram of an update module in a drone ad hoc network routing device according to an embodiment of the present disclosure is shown.

[0104] Figure 11 A structural block diagram of an update module in a drone ad hoc network routing device according to another embodiment of the present disclosure is shown.

[0105] Figure 12 A structural block diagram of a drone according to an embodiment of the present disclosure is shown.

[0106] Figure 13 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0107] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0108] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, actions, components, parts, or combinations thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof exist or are added.

[0109] It should also be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0110] In recent years, with the development of drone and communication technologies, a new communication method has emerged: drone wireless ad hoc networking. As nodes in communication networks, drones possess self-organizing, self-configuring, and self-healing properties, enabling them to rapidly establish wireless communication networks and provide wireless transmission services for multimedia services such as voice, data, and video. Furthermore, due to their high mobility and ease of deployment, drones can be deployed more quickly than mobile communication vehicles, making them widely used in a variety of scenarios, including emergency assistance, post-disaster reconstruction, and outdoor exploration.

[0111] However, due to the high speed and wide range of drone nodes in UAV ad hoc networks, the network topology changes frequently and is easily affected by factors such as weather conditions, external signal interference, and obstacles. This increases the difficulty of establishing and maintaining communication links, reduces the stability and reliability of the links, and affects data transmission speed. Therefore, a method for quickly and stably establishing routing in UAV ad hoc networks is needed.

[0112] In order to solve the above technical problems, the present invention discloses a drone self-organizing network routing method, including: a drone node sends a first test packet; the drone node receives a second test packet sent by an adjacent drone node; the second test packet includes the adjacent drone node's actual speed, actual acceleration, actual position information at the time the second test packet is sent, as well as the first historical position information and historical RSSI signal value of the adjacent drone node at the time the first test packet is received; the historical RSSI signal value is the signal strength of the adjacent drone node when it receives the first test packet sent by the drone node; when the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet, wherein, when the drone node receives multiple test packets from the adjacent drone node, the most recently received test packet is selected as the second test packet ; obtain the current location information of the drone node; update the network topology of the drone node based on the predicted location information of the adjacent drone node, the current location information of the drone node, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical location information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located, wherein the network topology of the drone node includes the adjacent drone nodes predicted to be able to establish a connection with the drone node; the drone node sends a data packet to the first adjacent drone node in the network topology of the drone node; when the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet transmission fails after sending the data packet, the drone node sends the data packet to the second adjacent drone node in the network topology of the drone node and no longer sends data packets to the first adjacent drone node. The technical solution disclosed in the present invention can predict the network topology at the current moment based on the test packet information at the previous moment, reduce the occurrence of drone self-organizing network routing errors without increasing the test packet transmission frequency, and improve routing speed.

[0113] Figure 1 A schematic diagram illustrating an application scenario of a drone ad hoc network routing method according to an embodiment of the present disclosure is shown.

[0114] like Figure 1As shown, the drone network includes multiple drone nodes W1-W12, each of which can be implemented by a drone. The drone nodes in the drone network act as relay nodes for wireless communications, enabling wireless communications between different communication terminals. For example, drone node W1 can receive a data packet from communication terminal A and transmit it to communication terminal B via drone nodes W3 and W5, thereby enabling communication between communication terminals A and B.

[0115] In scenarios such as emergency assistance, post-disaster reconstruction, and field exploration, drone nodes in a drone network often move according to actual needs, causing changes in the connections between drone nodes and increasing the difficulty of determining communication routes. To address this difficulty in practical applications, this disclosure provides a drone ad hoc network routing method that effectively adapts to the frequent and unpredictable changes in drone node positions and connections in a drone ad hoc network, achieving fast and stable data routing.

[0116] Figure 2 FIG. 1 is a flow chart showing a method for routing a UAV ad hoc network according to an embodiment of the present disclosure. Figure 2 As shown, the UAV self-organizing network routing method includes the following steps S101-S106:

[0117] In step S101, the drone node sends a first test packet.

[0118] In step S102, a drone node receives a second test packet sent by a neighboring drone node. The second test packet includes the neighboring drone node's actual speed, actual acceleration, and actual location information at the time the second test packet was sent, as well as the neighboring drone node's first historical location information and historical RSSI (received signal strength) signal value at the time the first test packet was received. The historical RSSI signal value represents the signal strength at which the neighboring drone node receives the first test packet sent by the drone node. The second test packet is used to locate the neighboring drone node's position at the time the second test packet was sent and to predict its future location. The historical RSSI signal value represents the signal strength at which the neighboring drone node receives the first test packet sent by the drone node. The second test packet sent by the neighboring drone node includes the RSSI signal values ​​of all first test packets sent by the neighboring drone node and the corresponding identity codes of all drone nodes. Based on the second test packet sent by the neighboring drone node and the drone node's local identity code, the drone node obtains the corresponding RSSI signal value of the first test packet as the historical RSSI signal value.

[0119] In a drone network, each drone node sends a first test packet to an adjacent drone node and receives a second test packet sent by the adjacent drone node to obtain the connection status with the adjacent drone node, thereby determining its own network topology. The network topology of the drone node includes the adjacent drone nodes that are predicted to be able to establish a connection with the drone node. Figure 1 As shown, assuming that drone W1 predicts that it can establish connections with drones W2, W3, and W4, the network topology of drone W1 includes drones W2, W3, and W4.

[0120] According to an embodiment of the present disclosure, the second test packet also includes the network topology of the adjacent drone node, such as a drone node that is predicted to be currently able to establish a connection with the adjacent drone node. After receiving the second test packet, the drone node can update its own network topology based on the network topology of the adjacent drone node that sent the second test packet, and send its own network topology to the adjacent drone node when sending the first test packet. Each drone node in the drone network sends its own network topology to the adjacent drone node, so that each drone node can obtain the global network topology of the drone network. It should be noted that the global network topology obtained by different drone nodes may be different, and the global network topology obtained by each drone node only includes those drone nodes that are predicted to be able to establish a connection with itself directly or indirectly through other drone nodes.

[0121] like Figure 1 As shown, if the drone node W12 flies too far away from other drone nodes and loses connection with all other drone nodes, then the global network topology obtained by the drone node W12 does not include any other drone nodes, and the distance between drone nodes W11 and W8 is close enough to be connected, but the distance between W12 and other drone nodes is far enough to establish a connection, then the global network topology obtained by the drone node W11 only includes the drone node W8, and the global network topology obtained by the drone node W8 only includes the drone node W11.

[0122] According to an embodiment of the present disclosure, the adjacent drone node sends the second test packet at a preset time interval; and / or, when the position of the adjacent drone node changes, the second test packet is sent at the start time point of the position change and the end time point of the position change respectively; and / or, when the speed of the adjacent drone node changes, the second test packet is sent at the start time point of the speed change and the end time point of the speed change respectively; and / or, when the acceleration of the adjacent drone node changes, the second test packet is sent at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or, when the adjacent drone node is a newly joined drone node, the second test packet is sent.

[0123] Although test packages can enable drone nodes to report their current status to each other and improve the network topology, sending test packages too frequently will affect the communication effect between drone nodes and increase energy consumption. Sending test packages too infrequently will lead to untimely updates of the network topology and affect communication stability. Therefore, the timing of sending test packages needs to be set according to actual conditions. In particular, when the situation of the drone node itself changes, such as changes in the position, speed, and acceleration of the drone node, it is necessary to send test packages in time to notify adjacent drone nodes to ensure that the network topology of the drone nodes can be updated in time.

[0124] In step S103, when the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment when the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet. When the drone node receives multiple test packets from the adjacent drone node, the most recently received test packet is selected as the second test packet.

[0125] When a drone node wants to send a data packet to an adjacent drone node, there is a gap between the time it receives the second test packet and the current time. During this gap, the relative position change between the drone node and the adjacent drone node may cause the connection status to change. For example, the adjacent drone node that could have established a connection with it cannot establish a connection due to flying too far, but the drone node does not detect the connection interruption, resulting in data transmission failure. Therefore, it is necessary to predict the position of the adjacent drone node and update the drone's network topology and routing selection based on the prediction results. Based on the actual position, actual speed, actual acceleration of the adjacent drone node received, and the time difference between the current time and the time when the second test packet was sent, the drone node can calculate the predicted position information of the adjacent drone node at the current time. This method of predicting the position of adjacent drone nodes can improve the stability and reliability of the link between drone nodes without increasing the frequency of drone nodes sending test packets, thereby improving the data packet transmission success rate and data packet transmission speed.

[0126] In step S104, the current location information of the drone node is obtained.

[0127] In step S105, the network topology of the drone node is updated based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value in the second test packet, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located. The network topology of the drone node includes the adjacent drone nodes predicted to be able to establish a connection with the drone node at present.

[0128] Figure 3 FIG. 5 is a flow chart showing step S105 in the UAV self-organizing network routing method according to an embodiment of the present disclosure. Figure 3 As shown, the step S105 includes steps S1051-S1053:

[0129] In step S1051, the predicted straight-line distance between the drone node and the adjacent drone node is calculated based on the predicted location information of the adjacent drone node and the current location information of the drone node. The predicted location information of the adjacent drone node includes the predicted longitude and latitude information and the predicted altitude information of the adjacent drone node; the current location information of the drone node includes the longitude and latitude information and the altitude information of the current drone node.

[0130] In step S1052, the predicted RSSI (received signal strength) signal value of the drone node and the adjacent drone node is calculated based on the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node in the second test packet.

[0131] According to an embodiment of the present disclosure, the calculation of the predicted RSSI signal values ​​of the drone node and the adjacent drone node includes: calculating the environmental attenuation parameter based on the historical RSSI signal value, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the second historical position information of the drone node at the time of sending the first test packet, and the signal strength per unit distance between the drone node and the adjacent drone node; calculating the predicted RSSI signal value of the drone node and the adjacent drone node based on the environmental attenuation parameter, the predicted straight-line distance, and the signal strength per unit distance between the drone node and the adjacent drone node; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

[0132] Specifically, based on the first historical location information and the second historical location information, the historical straight-line distance between the drone node and the adjacent drone node at the time of receiving the first test packet can be determined. The environmental attenuation parameter is determined based on the historical straight-line distance, the historical RSSI signal value, and the signal strength per unit distance between the drone node and the adjacent drone node. Then, the predicted RSSI signal value is determined based on the environmental attenuation parameter, the signal strength per unit distance between the drone node and the adjacent drone node, and the predicted straight-line distance. The specific calculation method is as follows:

[0133] N=(R′-A) / (10*log 10 (d′);

[0134] R=10*N*log 10 (d)+A;

[0135] Among them, d is the predicted straight-line distance; d′ is the historical straight-line distance; R is the predicted RSSI signal value; R′ is the historical RSSI signal value; N is the environmental attenuation parameter; A is the signal strength per unit distance between the drone node and the adjacent drone node, which is related to the signal transmission power of the drone node and is a preset value.

[0136] In step S1053, the predicted RSSI signal value is compared with the minimum RSSI (received signal strength) signal value to determine the predicted connection status between the drone node and the adjacent drone node, and the network topology of the drone node is updated according to the predicted connection status. Specifically, the minimum RSSI signal value is the RSSI signal value that ensures normal communication between drone nodes. When the predicted RSSI signal value is greater than the minimum RSSI signal value, the drone node is considered to be in a connected state with the adjacent drone node. When the predicted RSSI signal value is less than the minimum RSSI signal value, the drone node is considered to be in a disconnected state with the adjacent drone node.

[0137] Figure 4 FIG. 5 is a flow chart showing step S105 in a UAV self-organizing network routing method according to another embodiment of the present disclosure. Figure 4 As shown, the step S105 further includes steps S1054-S1057:

[0138] In step S1054, a topographic map of the area where the drone node is located is obtained. A topographic map refers to a projection of the surface undulations, geographic location, and shape on a horizontal plane, including height data of the surface undulations.

[0139] In step S1055, the terrain elevation between the UAV node and the adjacent UAV node is obtained based on the terrain map. The terrain elevation between the UAV node and the adjacent UAV node is continuous surface height data in the vertical plane where the line connecting the UAV node and the adjacent UAV node is located. The terrain elevation is used to represent the surface height of the UAV node and the adjacent UAV node in the signal transmission direction.

[0140] In step S1056, the line elevation of the connection between the drone node and the adjacent drone node is calculated based on the predicted position information of the adjacent drone node and the current position information of the drone node. The line elevation is continuous height data of the connection between the drone node and the adjacent drone node, which is used to represent the highest effective height of each position on the signal transmission path between the drone node and the adjacent drone node. If the height of an obstacle exceeds the line elevation, it indicates that the signal transmission is blocked and the stability of signal transmission cannot be guaranteed.

[0141] In step S1057, the area where the connection line elevation is less than the terrain elevation is set as an obstacle zone. When the connection line between the UAV node and the adjacent UAV node passes through the obstacle zone, the predicted connection state between the UAV node and the adjacent UAV node is determined to be disconnected. An obstacle zone indicates that there is a high obstacle or terrain, such as a building or hill, between the UAV node and the adjacent UAV node, which blocks the direct signal transmission between the UAV node and the adjacent UAV node, potentially resulting in reduced signal transmission stability.

[0142] Figure 5 Schematic diagram of the scene of forming an obstacle area in the UAV self-organizing network routing method according to an embodiment of the present disclosure. Figure 5 As shown, drones W1 and W2 are separated by a mountain peak, and the mountain's elevation is higher than the elevation of the line connecting the drone node and its adjacent drone node at the peak, forming an obstruction zone. This indicates that communication between drones W1 and W2 will be interrupted by the mountain. Therefore, to ensure signal transmission speed, the obstructed area is classified as an obstacle zone by calculating the terrain elevation and the line elevation based on the predicted location information of the adjacent drone nodes and the current location information of the drone node. Disconnection between the drone node and its adjacent drone nodes is also determined in advance, reducing routing errors and improving routing speed.

[0143] The technical solution disclosed in the present invention is particularly suitable for application scenarios of natural disasters and post-disaster reconstruction. After a natural disaster occurs, normal ground communications are often interrupted and unusable. In addition, natural disasters such as earthquakes, mountain torrents, mudslides, etc. that cause great damage to ground facilities often occur in mountainous or hilly areas. The complex terrain blocks the communication between drone nodes, which has a great impact on the communication effect. Therefore, when predicting the connection status between drone nodes, adding the influence of terrain factors can improve the accuracy of the prediction results.

[0144] In step S106, the drone node sends a data packet to a first adjacent drone node in the network topology of the drone node.

[0145] In step S107, when the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet sending fails after sending the data packet, the drone node sends the data packet to the second adjacent drone node in the network topology of the drone node, and no longer sends the data packet to the first adjacent drone node.

[0146] According to an embodiment of the present disclosure, when a drone node sends a data packet to a first adjacent drone node and then receives the same data packet, it indicates that the first adjacent drone node cannot connect to the target node of the data packet, the data packet is returned along the original route, or there is a loop path through the route of the first adjacent drone node, and the data packet cannot be transmitted to the target node. Then, other adjacent drone nodes are selected as the second adjacent drone nodes to send the data packet.

[0147] According to an embodiment of the present disclosure, the first adjacent drone node and / or the second adjacent drone node is determined based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

[0148] After receiving a data packet sent by a communication terminal or other drone nodes, the drone node transmits the data packet to the next drone node in the route. The first adjacent drone node is the adjacent drone node with the highest priority selected from the network topology of the drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority, as the next drone node for data packet transmission. The second adjacent drone node is the adjacent drone node with the highest priority other than the first adjacent drone node selected from the network topology of the drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority, as the next drone node for data packet transmission when the first adjacent drone node fails to send a data packet. The first routing priority, the second routing priority, the third routing priority, and the fourth routing priority are used to prioritize adjacent drone nodes from different angles to improve the rationality of route selection and signal transmission speed.

[0149] According to an embodiment of the present disclosure, the UAV self-organizing network routing method further includes:

[0150] The global network topology is determined based on the network topologies of the neighboring drone nodes. As described above, the global network topology obtained by each drone node only includes those drone nodes that are predicted to be able to establish a connection with it directly or indirectly through other drone nodes. If a drone node flies too far away from other drone nodes and loses connection with all other drone nodes, the global network topology obtained by the drone node does not include any other drone nodes.

[0151] According to an embodiment of the present disclosure, in order to avoid a drone node losing connection with all other drone nodes, before any drone node flies to a designated destination, the connection status of the arbitrary drone node with the adjacent drone node when the arbitrary drone node arrives at the designated destination can be predicted based on the designated destination, the time when the arbitrary drone node arrives at the designated destination, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet sent by the adjacent drone node of the arbitrary drone node. If the connection between the arbitrary drone node and all adjacent drone nodes is disconnected when the arbitrary drone node arrives at the designated destination, then before any drone node flies to the designated destination, at least one adjacent drone node is notified to fly to a position where a connection can be established with the arbitrary drone node at the designated destination.

[0152] Figure 6 A schematic diagram of a scenario in which the drone network coverage is automatically expanded or contracted in the drone self-organizing network routing method according to an embodiment of the present disclosure is shown. Figure 6As shown, drone node W11 needs to fly to a designated destination W11, which is farther away and exceeds the maximum connection distance with drone node W9, in order to connect to communication terminal B. Before flying to W11's designated destination, drone node W11 notifies neighboring drone node W9 to move to a location where it can connect to both drone nodes W11 and W4. Furthermore, after receiving W11's movement request, drone node W9 determines that W9's designated destination exceeds the maximum communication distance between drone node W9 and drone node W4. Therefore, before proceeding to W9's designated destination, drone node W9 notifies drone node W4 to move to W4's designated destination, which ensures communication between drone nodes W9 and W4. In this way, the drone network's coverage can be automatically expanded and contracted while ensuring connectivity.

[0153] According to an embodiment of the present disclosure, the number of nodes in the routing line of the data packet is determined based on the target node of the data packet and the global network topology; and the first routing priority of the adjacent drone node is determined based on the number of nodes in the routing line of the data packet.

[0154] In drone ad hoc network communications, the more nodes there are in the routing path, the worse the data transmission speed and stability may be. Therefore, according to the global network topology and the target node of the data packet, the routing lines with the number of nodes arranged from the fewest to the most are sorted, and then the first routing priority of the adjacent drone nodes is determined according to the routing line sorting. The adjacent drone nodes on the routing line with the fewest nodes are preferentially selected as the first adjacent drone nodes and / or the second adjacent drone nodes, thereby shortening the routing length and improving the transmission stability and speed.

[0155] According to an embodiment of the present disclosure, the second test packet further includes a traffic load value of the adjacent drone node, and the method further includes:

[0156] The second routing priority of the adjacent drone node is determined based on the traffic load value of the adjacent drone node. For example, in a disaster relief application scenario, a certain area may have a high traffic load value for the drone node responsible for communication due to the concentration of people. Therefore, the traffic load of the drone nodes in this area should not be increased to prevent network congestion. The second routing priority sorts the adjacent drone nodes from low to high according to their traffic load values, and prioritizes the adjacent drone nodes with low traffic load values ​​as the first adjacent drone node and / or the second adjacent drone node.

[0157] According to an embodiment of the present disclosure, the method further includes: determining the third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node. A suspected obstacle area is an area that is not reflected in the topographic map but affects the communication between drone nodes. Furthermore, when the drone node fails to send a data packet to any adjacent drone node, the drone node determines the connecting area between the drone node and any adjacent drone node as a suspected obstacle area. For example, in the application scenario of emergency rescue in mountainous areas, there may be a forest area with very tall, dense and lush trees that are not marked on the topographic map, which will have a great impact on the communication of drone nodes on both sides of the forest area. When a drone node fails to send a data packet to any other drone node, the connecting area between the drone node and the adjacent drone node is determined to be a suspected obstacle area. The more suspected obstacle areas the connection between a drone node and an adjacent drone node passes through, the worse the communication quality may be. Therefore, the third routing priority is to sort the adjacent drone nodes according to the number of suspected obstacle areas the connection between the drone node and the adjacent drone node passes through, and prioritize the adjacent drone nodes with the fewest suspected obstacle areas passed through as the first adjacent drone node and / or the second adjacent drone node.

[0158] Figure 7-Figure 8 A schematic diagram of a scenario in which a suspected obstacle zone is formed in a UAV self-organizing network routing method according to an embodiment of the present disclosure is shown. Figure 7 As shown in FIG, in the network topology of drone node W4, the communication between drone node W4 and adjacent drone nodes W10 and W6 fails, then the area connecting drone nodes W4 and W10 is set as the first suspected obstacle area, and the area connecting drone nodes W4 and W6 is set as the second suspected obstacle area. Figure 8 As shown, the line connecting drone nodes W3 and W9 passes through the second suspected obstacle area, and the line connecting drone nodes W9 and W10 passes through the first suspected obstacle area. There is no suspected obstacle area between drone node W3 and adjacent drone nodes W6 and W1. In the network topology of drone node W3, the third routing priority of adjacent drone node W9 is the lowest.

[0159] According to an embodiment of the present disclosure, the second test package also includes the network topology of the adjacent drone node and the suspected obstacle area distribution information maintained by the adjacent drone node. The method also includes: determining the global network topology based on the network topology of the adjacent drone node; determining the global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent drone node; determining the number of suspected obstacle areas on the routing line of the data packet based on the target node of the data packet, the global network topology and the global suspected obstacle area distribution information; determining the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

[0160] As mentioned above, the suspected obstacle area has an impact on the communication between the two drone nodes. The more suspected obstacle areas the entire routing line passes through, the worse the stability and transmission speed of the routing line. Therefore, it is necessary to summarize the suspected obstacle areas of the discovered global network topology and avoid the suspected obstacle areas as much as possible when selecting routing lines. By adding the network topology of the adjacent drone nodes and the suspected obstacle area distribution information maintained by the adjacent drone nodes to the second test package, the global network topology and the global suspected obstacle area can be integrated. Combined with the actual position information of the drone nodes and the predicted position information of the adjacent drone nodes, the positional relationship between the drone nodes and the suspected obstacle areas can be predicted, and the routing line that is least affected by the suspected obstacle areas can be selected. The fourth routing priority is to prioritize the adjacent drone nodes on the routing line with a small number of suspected obstacle areas as the first adjacent drone nodes and / or the second adjacent drone nodes based on the number of suspected obstacle areas on the routing line of the data packet. As Figure 8 As shown, the drone node W3 obtains the global network topology and the distribution information of the first suspected obstacle area and the second suspected obstacle area maintained by the drone node W4. When the data packet needs to be transmitted from the drone node W3 to W10, the routing route W3-W9-W10 passes through two suspected obstacle areas, the routing route W3-W9-W4-W10 passes through one suspected obstacle area, and the other routing routes do not pass through the suspected obstacle area. Then, the fourth routing priority of the adjacent drone node W9 in the network topology of the drone node W3 is the lowest. The drone node W3 prefers to choose the routing route W3-W1-W2-W10 or W3-W1-W4-W10 that does not pass through the obstacle area to communicate with the drone node W10 to avoid the suspected obstacle area from affecting the communication.

[0161] According to an embodiment of the present disclosure, the method further includes: when the adjacent drone node fails to send a data packet to any other drone node, the adjacent drone node will judge the connection area between itself and any other drone node as a suspected obstacle area.

[0162] According to an embodiment of the present disclosure, the method is performed by each drone node in the drone ad hoc network. The drone node, adjacent drone node, first adjacent drone node, and second adjacent drone node in the method are applicable to any drone node in the global network topology of the drone ad hoc network.

[0163] Figure 9 The following is a block diagram of a UAV ad hoc network routing device according to an embodiment of the present disclosure. The device can be implemented as part or all of an electronic device through software, hardware, or a combination of both.

[0164] like Figure 9 As shown, the drone ad hoc network routing device 500 includes a first sending module 510, a second sending module 520, a first determining module 530, an acquiring module 540, an updating module 550, a third sending module 560 and a fourth sending module 570.

[0165] The first sending module 510 is configured to send a first test packet to the drone node.

[0166] The second sending module 520 is configured to cause the drone node to receive a second test packet sent by a neighboring drone node. The second test packet includes the neighboring drone node's actual speed, actual acceleration, and actual location information at the time the second test packet was sent, as well as the neighboring drone node's first historical location information and historical RSSI signal value at the time the first test packet was received. The historical RSSI signal value represents the signal strength when the neighboring drone node receives the first test packet sent by the drone node. The second test packet is used to locate the neighboring drone node's location at the time the second test packet was sent and predict its location at future times. The historical RSSI signal value represents the signal strength when the neighboring drone node receives the first test packet sent by the drone node. The second test packet sent by the neighboring drone node includes the RSSI signal values ​​of all first test packets sent by the neighboring drone node and the corresponding identity codes of all drone nodes. Based on the second test packet sent by the neighboring drone node and the drone node's local identity code, the drone node obtains the corresponding RSSI signal value of the first test packet as the historical RSSI signal value.

[0167] In a drone network, each drone node sends a first test packet to an adjacent drone node and receives a second test packet sent by the adjacent drone node to obtain the connection status with the adjacent drone node, thereby determining its own network topology. The network topology of the drone node includes the adjacent drone nodes that are predicted to be able to establish a connection with the drone node. For example, Figure 1In the example, assuming that drone W1 predicts that it can establish connections with drones W2, W3, and W4, the network topology of drone W1 includes drones W2, W3, and W4.

[0168] According to an embodiment of the present disclosure, the second test packet also includes the network topology of the adjacent drone node, such as a drone node that is predicted to be currently able to establish a connection with the adjacent drone node. After receiving the second test packet, the drone node can update its own network topology based on the network topology of the adjacent drone node that sent the second test packet, and send its own network topology to the adjacent drone node when sending the first test packet. Each drone node in the drone network sends its own network topology to the adjacent drone node, so that each drone node can obtain the global network topology of the drone network. It should be noted that the global network topology obtained by different drone nodes may be different, and the global network topology obtained by each drone node only includes those drone nodes that are predicted to be able to establish a connection with itself directly or indirectly through other drone nodes.

[0169] like Figure 1 As shown, if the drone node W12 flies too far away from other drone nodes and loses connection with all other drone nodes, then the global network topology obtained by the drone node W12 does not include any other drone nodes, and the distance between drone nodes W11 and W8 is close enough to be connected, but the distance between W12 and other drone nodes is far enough to establish a connection, then the global network topology obtained by the drone node W11 only includes the drone node W8, and the global network topology obtained by the drone node W8 only includes the drone node W11.

[0170] According to an embodiment of the present disclosure, the adjacent drone node sends the second test packet at a preset time interval; and / or, when the position of the adjacent drone node changes, the second test packet is sent at the start time point of the position change and the end time point of the position change respectively; and / or, when the speed of the adjacent drone node changes, the second test packet is sent at the start time point of the speed change and the end time point of the speed change respectively; and / or, when the acceleration of the adjacent drone node changes, the second test packet is sent at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or, when the adjacent drone node is a newly joined drone node, the second test packet is sent.

[0171] Although test packages can enable drone nodes to report their current status to each other and improve the network topology, sending test packages too frequently will affect the communication effect between drone nodes and increase energy consumption. Sending test packages too infrequently will lead to untimely updates of the network topology and affect communication stability. Therefore, the timing of sending test packages needs to be set according to actual conditions. In particular, when the situation of the drone node itself changes, such as changes in the position, speed, and acceleration of the drone node, it is necessary to send test packages in time to notify adjacent drone nodes to ensure that the network topology of the drone nodes can be updated in time.

[0172] The first determination module 530 is configured to determine the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment when the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet when the drone node sends a data packet, wherein when the drone node receives multiple test packets from the adjacent drone node, the most recently received test packet is selected as the second test packet.

[0173] When a drone node wants to send a data packet to an adjacent drone node, there is a gap between the time it receives the second test packet and the current time. During this gap, the relative position change between the drone node and the adjacent drone node may cause the connection status to change. For example, the adjacent drone node that could have established a connection with it cannot establish a connection due to flying too far, but the drone node does not detect the connection interruption, resulting in data transmission failure. Therefore, it is necessary to predict the position of the adjacent drone node and update the drone's network topology and routing selection based on the prediction results. Based on the actual position, actual speed, actual acceleration of the adjacent drone node received, and the time difference between the current time and the time when the second test packet was sent, the drone node can calculate the predicted position information of the adjacent drone node at the current time. This method of predicting the position of adjacent drone nodes can improve the stability and reliability of the link between drone nodes without increasing the frequency of drone nodes sending test packets, thereby improving the data packet transmission success rate and data packet transmission speed.

[0174] The acquisition module 540 is configured to obtain the current location information of the drone node.

[0175] The update module 550 is configured to update the network topology of the drone node based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value in the second test packet, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located. The network topology of the drone node includes the adjacent drone nodes predicted to be able to establish a connection with the drone node at present.

[0176] Figure 10 FIG. 5 shows a structural block diagram of the update module 550 in the UAV ad hoc network routing device according to an embodiment of the present disclosure. Figure 10 As shown, the updating module 550 includes a first calculation submodule 551, a second calculation submodule 552, and a first determination submodule 553:

[0177] The first calculation submodule 551 is configured to calculate the predicted straight-line distance between the drone node and the adjacent drone node based on the predicted location information of the adjacent drone node and the current location information of the drone node. The predicted location information of the adjacent drone node includes the latitude, longitude, and altitude information of the adjacent drone node; the current location information of the drone node includes the latitude, longitude, and altitude information of the current drone node.

[0178] The second calculation submodule 552 is configured to calculate the predicted RSSI signal values ​​of the drone node and the adjacent drone node based on the first historical position information of the adjacent drone node at the time of receiving the first test packet in the second test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node. According to an embodiment of the present disclosure, the calculation of the predicted RSSI signal values ​​of the drone node and the adjacent drone node includes: calculating an environmental attenuation parameter based on the historical RSSI signal value, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the second historical position information of the drone node at the time of sending the first test packet, and the signal strength per unit distance between the drone node and the adjacent drone node; calculating the predicted RSSI signal values ​​of the drone node and the adjacent drone node based on the environmental attenuation parameter, the predicted straight-line distance, and the signal strength per unit distance between the drone node and the adjacent drone node; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

[0179] Specifically, based on the first historical location information and the second historical location information, the historical straight-line distance between the drone node and the adjacent drone node at the time of receiving the first test packet can be determined. The environmental attenuation parameter is determined based on the historical straight-line distance, the historical RSSI signal value, and the signal strength per unit distance between the drone node and the adjacent drone node. Then, the predicted RSSI signal value is determined based on the environmental attenuation parameter, the signal strength per unit distance between the drone node and the adjacent drone node, and the predicted straight-line distance. The specific calculation method is as follows:

[0180] N=(R′-A) / (10*log 10 (d′);

[0181] R=10*N*log 10 (d)+A;

[0182] Among them, d is the predicted straight-line distance; d′ is the historical straight-line distance; R is the predicted RSSI signal value; R′ is the historical RSSI signal value; N is the environmental attenuation parameter; A is the signal strength per unit distance between the drone node and the adjacent drone node, which is related to the signal transmission power of the drone node and is a preset value.

[0183] The first determination submodule 553 is configured to compare the predicted RSSI signal value with the minimum RSSI signal value to determine the predicted connection status between the drone node and the adjacent drone node, and update the network topology of the drone node based on the predicted connection status. Specifically, the minimum RSSI signal value is the RSSI signal value that ensures normal communication between drone nodes. When the predicted RSSI signal value is greater than the minimum RSSI signal value, the drone node is considered to be in a connected state with the adjacent drone node. When the predicted RSSI signal value is less than the minimum RSSI signal value, the drone node is considered to be in a disconnected state with the adjacent drone node.

[0184] Figure 11 FIG. 5 is a block diagram showing a structure of an update module 550 in a drone ad hoc network routing device according to another embodiment of the present disclosure. Figure 11 As shown, the updating module 550 further includes a first obtaining submodule 554, a second obtaining submodule 555, a third calculating submodule 556, and a determining submodule 557:

[0185] The first acquisition submodule 554 is configured to acquire a topographic map of the area where the drone node is located. A topographic map refers to a projection of the surface undulations, geographic location, and shape on a horizontal plane, including height data of the surface undulations.

[0186] The second acquisition submodule 555 is configured to acquire the terrain elevation between the UAV node and the adjacent UAV node based on the terrain map. The terrain elevation between the UAV node and the adjacent UAV node is continuous surface height data in the vertical plane where the line connecting the UAV node and the adjacent UAV node lies. The terrain elevation is used to represent the surface height between the UAV node and the adjacent UAV node in the signal transmission direction.

[0187] The third calculation submodule 556 is configured to calculate the line elevation of the connection between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node. The line elevation is continuous height data of the connection between the drone node and the adjacent drone node, representing the highest effective height of each position on the signal transmission path between the drone node and the adjacent drone node. If the height of an obstacle exceeds the line elevation, it indicates that signal transmission is blocked and the stability of signal transmission cannot be guaranteed.

[0188] The determination submodule 557 is configured to define an area where the elevation of the connection line is less than the elevation of the terrain as an obstacle zone. When the connection line between the drone node and the adjacent drone node passes through the obstacle zone, the predicted connection state between the drone node and the adjacent drone node is determined to be disconnected. An obstacle zone indicates that there is a high obstacle or terrain, such as a building or hill, between the drone node and the adjacent drone node, which blocks the direct signal transmission between the drone node and the adjacent drone node, potentially reducing the stability of signal transmission.

[0189] The technical solution disclosed in the present invention is particularly suitable for application scenarios of natural disasters and post-disaster reconstruction. After a natural disaster occurs, normal ground communications are often interrupted and unusable. In addition, natural disasters such as earthquakes, mountain torrents, mudslides, etc. that cause great damage to ground facilities often occur in mountainous or hilly areas. The complex terrain blocks the communication between drone nodes, which has a great impact on the communication effect. Therefore, when predicting the connection status between drone nodes, adding the influence of terrain factors can improve the accuracy of the prediction results.

[0190] The third sending module 560 is configured to enable the drone node to send a data packet to a first adjacent drone node in the network topology of the drone node.

[0191] The fourth sending module 570 is configured to send the data packet to the second adjacent drone node in the network topology of the drone node when the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet sending fails after sending the data packet, and no longer send the data packet to the first adjacent drone node.

[0192] According to an embodiment of the present disclosure, when a drone node sends a data packet to a first adjacent drone node and receives the same data packet, it indicates that the first adjacent drone node cannot connect to the target node of the data packet or there is a loop path in the route through the first adjacent drone node, and the data packet cannot be transmitted to the target node. Then, other adjacent drone nodes are selected as the second adjacent drone nodes to send the data packet.

[0193] According to an embodiment of the present disclosure, the first adjacent drone node and / or the second adjacent drone node is determined based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

[0194] After receiving a data packet sent by a communication terminal or other drone nodes, the drone node transmits the data packet to the next drone node in the route. The first adjacent drone node is the adjacent drone node with the highest priority selected from the network topology of the drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority, as the next drone node for data packet transmission. The second adjacent drone node is the adjacent drone node with the highest priority other than the first adjacent drone node selected from the network topology of the drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority, as the next drone node for data packet transmission when the first adjacent drone node fails to send a data packet. The first routing priority, the second routing priority, the third routing priority, and the fourth routing priority are used to prioritize adjacent drone nodes from different angles to improve the rationality of route selection and signal transmission speed.

[0195] According to an embodiment of the present disclosure, the UAV self-organizing network routing device further includes a second determination module, a third determination module, and a fourth determination module:

[0196] The second determination module is configured to determine a global network topology based on the network topologies of the neighboring drone nodes. As described above, the global network topology obtained by each drone node only includes those drone nodes that are predicted to be able to establish a connection with it directly or indirectly through other drone nodes. If a drone node flies too far away from other drone nodes and loses connection with all other drone nodes, the global network topology obtained by the drone node does not include any other drone nodes.

[0197] According to an embodiment of the present disclosure, in order to avoid a drone node losing connection with all other drone nodes, before any drone node flies to a designated destination, the connection status of the arbitrary drone node with the adjacent drone node when the arbitrary drone node arrives at the designated destination can be predicted based on the designated destination, the time when the arbitrary drone node arrives at the designated destination, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet sent by the adjacent drone node of the arbitrary drone node. If the connection between the arbitrary drone node and all adjacent drone nodes is disconnected when the arbitrary drone node arrives at the designated destination, then before any drone node flies to the designated destination, at least one adjacent drone node is notified to fly to a position where a connection can be established with the arbitrary drone node at the designated destination.

[0198] The third determining module is configured to determine the number of nodes in the routing line of the data packet according to the target node of the data packet and the global network topology.

[0199] The fourth determination module is configured to determine the first routing priority of the adjacent drone node based on the number of nodes in the routing line of the data packet.

[0200] In drone ad hoc network communications, the more nodes there are in the routing path, the worse the data transmission speed and stability may be. Therefore, according to the global network topology and the target node of the data packet, the routing lines with the number of nodes arranged from the fewest to the most are sorted, and then the first routing priority of the adjacent drone nodes is determined according to the routing line sorting. The adjacent drone nodes on the routing line with the fewest nodes are preferentially selected as the first adjacent drone nodes and / or the second adjacent drone nodes, thereby shortening the routing length and improving the transmission stability and speed.

[0201] According to an embodiment of the present disclosure, the second test packet further includes a traffic load value of the adjacent drone node, and the apparatus further includes:

[0202] The fifth determination module is configured to determine a second routing priority for the adjacent drone node based on the traffic load value of the adjacent drone node. For example, in a disaster relief application scenario, a certain area may have a high traffic load value for the drone node responsible for communications due to a large concentration of people. Therefore, the traffic load of the drone nodes in this area should not be increased to prevent network congestion. The second routing priority ranks adjacent drone nodes from low to high according to their traffic load values, prioritizing adjacent drone nodes with low traffic load values ​​as the first and / or second adjacent drone nodes.

[0203] According to an embodiment of the present disclosure, the device further includes:

[0204] The sixth determining module is configured to determine a third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node. The suspected obstacle area is an area that is not shown in the topographic map but affects communication between the drone nodes.

[0205] The first judgment module is configured to, when the drone node fails to send a data packet to any adjacent drone node, determine that the area connecting the drone node and the adjacent drone node is a suspected obstacle area. For example, in a mountain disaster relief application scenario, there may be a forest area with tall, dense trees that is not marked on the topographic map, which will significantly affect the communication of drone nodes on both sides of the forest area. When the drone node fails to send a data packet to any other drone node, the area connecting the drone node and the adjacent drone node is determined to be a suspected obstacle area. The more suspected obstacle areas the connection between the drone node and the adjacent drone node passes through, the worse the communication quality may be. Therefore, the third routing priority is to sort the adjacent drone nodes according to the number of suspected obstacle areas the connection between the drone node and the adjacent drone node passes through, and prioritize the adjacent drone nodes with the fewest suspected obstacle areas as the first and / or second adjacent drone nodes.

[0206] According to an embodiment of the present disclosure, the test package also includes the network topology of the adjacent drone nodes and the suspected obstacle area distribution information maintained by the adjacent drone nodes. The device also includes: a seventh determination module, configured to determine the global network topology based on the network topology of the adjacent drone nodes; an eighth determination module, configured to determine the global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent drone nodes; a ninth determination module, configured to determine the number of suspected obstacle areas on the routing line of the data packet based on the target node of the data packet, the global network topology and the global suspected obstacle area distribution information; a prefecture-level city determination module, configured to determine the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

[0207] As mentioned above, if the suspected obstacle area has an impact on the communication between the two drone nodes, the more suspected obstacle areas the entire routing line passes through, the worse the stability and transmission speed of the routing line. Therefore, it is necessary to summarize the suspected obstacle areas of the discovered global network topology and avoid the suspected obstacle areas as much as possible when selecting routing lines. By adding the network topology of adjacent drone nodes and the distribution information of suspected obstacle areas maintained by adjacent drone nodes to the test package, the global network topology and the global suspected obstacle areas can be integrated. Combined with the actual location information of the drone nodes and the predicted location information of the adjacent drone nodes, the positional relationship between the drone nodes and the suspected obstacle areas can be predicted, and the routing line that is least affected by the suspected obstacle areas can be selected. The fourth routing priority is to prioritize the adjacent drone nodes on the routing line with a small number of suspected obstacle areas as the first adjacent drone nodes and / or the second adjacent drone nodes based on the number of suspected obstacle areas on the routing line of the data packet.

[0208] According to an embodiment of the present disclosure, the device also includes: a second judgment module, which is configured to judge the connecting area between the adjacent drone node and any other drone node as a suspected obstacle area when the adjacent drone node fails to send a data packet to any other drone node.

[0209] The present disclosure also discloses a drone, Figure 12 A structural block diagram of a drone according to an embodiment of the present disclosure is shown.

[0210] like Figure 12 As shown, the drone includes a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to an embodiment of the present disclosure.

[0211] A UAV self-organizing network routing method, comprising:

[0212] The drone node sends the first test packet;

[0213] The UAV node receives a second test packet sent by an adjacent UAV node; the second test packet includes the actual speed, actual acceleration, and actual location information of the adjacent UAV node at the time the second test packet is sent, as well as the first historical location information and historical RSSI signal value of the adjacent UAV node at the time the first test packet is received; the historical RSSI signal value is the signal strength of the adjacent UAV node when it receives the first test packet sent by the UAV node;

[0214] When the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet, wherein when the drone node receives multiple test packets from the adjacent drone node, the most recently received test packet is selected and used as the second test packet;

[0215] Obtaining the current location information of the drone node;

[0216] Update the network topology of the drone node based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and a topographic map of the area where the drone node is located, wherein the network topology of the drone node includes the adjacent drone nodes predicted to be currently capable of establishing a connection with the drone node;

[0217] The drone node sends a data packet to a first adjacent drone node in the network topology of the drone node;

[0218] When the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet has failed to be sent after sending the data packet, the drone node sends the data packet to the second adjacent drone node in the network topology of the drone node, and no longer sends the data packet to the first adjacent drone node.

[0219] The updating of the network topology of the drone node according to the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located includes:

[0220] Calculate the predicted straight-line distance between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node;

[0221] Calculate the predicted RSSI signal values ​​of the drone node and the adjacent drone node based on the first historical position information and historical RSSI signal value of the adjacent drone node at the time of receiving the first test packet in the second test packet, the second historical position information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node;

[0222] The predicted RSSI signal value is compared with the minimum RSSI signal value to determine the predicted connection status of the drone node and the adjacent drone node, and the network topology of the drone node is updated according to the predicted connection status.

[0223] The calculating the predicted RSSI signal values ​​of the drone node and the adjacent drone nodes includes:

[0224] Calculate the environmental attenuation parameter based on the historical RSSI signal value, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the second historical location information of the drone node at the time of sending the first test packet, and the signal strength per unit distance between the drone node and the adjacent drone node;

[0225] According to the environmental attenuation parameter, the predicted straight-line distance, and the signal strength per unit distance between the drone node and the adjacent drone node, the predicted RSSI signal values ​​of the drone node and the adjacent drone node are calculated; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

[0226] The updating of the network topology of the drone node according to the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located further includes:

[0227] Obtaining a topographic map of the area where the drone node is located;

[0228] According to the topographic map, obtaining the terrain elevation between the UAV node and the adjacent UAV node;

[0229] Calculating the elevation of the line between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node;

[0230] An area where the connection line elevation is less than the terrain elevation is set as an obstacle area. When the connection line between the drone node and the adjacent drone node passes through the obstacle area, the predicted connection state between the drone node and the adjacent drone node is determined to be a disconnected state.

[0231] The adjacent UAV node sends the second test packet at a preset time interval; and / or

[0232] When the position of the adjacent UAV node changes, the second test packet is sent at the start time point and the end time point of the position change respectively; and / or

[0233] When the speed of the adjacent UAV node changes, sending the second test packet at the start time point of the speed change and the end time point of the speed change respectively; and / or

[0234] When the acceleration of the adjacent UAV node changes, sending the second test packet at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or

[0235] When the adjacent drone node is a newly added drone node, the second test packet is sent.

[0236] Determine the first adjacent drone node and / or the second adjacent drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

[0237] The second test package also includes a network topology of the adjacent drone nodes, and the method further includes:

[0238] Determining a global network topology based on the network topology of the adjacent drone nodes;

[0239] Determining the number of nodes in a routing line of the data packet according to a target node of the data packet and the global network topology;

[0240] The first routing priority of the adjacent drone node is determined according to the number of nodes in the routing line of the data packet.

[0241] The second test packet also includes a traffic load value of the adjacent drone node, and the method further includes:

[0242] Determine a second routing priority of the adjacent drone node based on the traffic load value of the adjacent drone node.

[0243] The method further comprises:

[0244] Determine a third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node.

[0245] The method further comprises:

[0246] When the drone node fails to send a data packet to any adjacent drone node, the drone node determines the connection area between the drone node and any adjacent drone node as a suspected obstacle area.

[0247] The second test package also includes the network topology of the adjacent drone nodes and the distribution information of suspected obstacle areas maintained by the adjacent drone nodes. The method further includes:

[0248] Determining a global network topology based on the network topology of the adjacent drone nodes;

[0249] Determine global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent UAV nodes;

[0250] Determining the number of suspected obstacle areas on the routing line of the data packet according to the target node of the data packet, the global network topology and the global suspected obstacle area distribution information;

[0251] Determine the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

[0252] The method further comprises:

[0253] When the adjacent drone node fails to send a data packet to any other drone node, the adjacent drone node determines the connection area between the adjacent drone node and any other drone node as a suspected obstacle area.

[0254] The method is executed by each drone node in the drone ad hoc network.

[0255] Figure 13 A schematic diagram showing the structure of a computer system suitable for implementing the method according to an embodiment of the present disclosure is shown.

[0256] like Figure 13 As shown, the computer system includes a processing unit, which can execute the various methods in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). In the RAM, various programs and data required for the operation of the computer system are also stored. The processing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0257] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, etc.; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN card, a modem, etc. The communication part performs a communication process via a network such as the Internet. The drive is also connected to the I / O interface as needed. Removable media, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed. Among them, the processing unit can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0258] In particular, according to embodiments of the present disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for executing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication component and / or installed from a removable medium.

[0259] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0260] The units or modules involved in the embodiments described in this disclosure may be implemented by software or programmable hardware. The units or modules described may also be provided in a processor, and the names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0261] As another aspect, the present disclosure further provides a computer-readable storage medium. This computer-readable storage medium may be included in the drone or computer system described in the above embodiments, or may be a standalone computer-readable storage medium not incorporated into a device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the methods described in the present disclosure.

[0262] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims

1. A UAV self-organizing network routing method, characterized in that: include: The drone node sends the first test packet; The UAV node receives a second test packet sent by an adjacent UAV node; The second test packet includes: the actual speed, actual acceleration, and actual location information of the adjacent drone node at the time of sending the second test packet, as well as the first historical location information and historical received signal strength RSSI signal value of the adjacent drone node at the time of receiving the first test packet; the historical RSSI signal value is the signal strength of the first test packet sent by the drone node received by the adjacent drone node; When the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node based on the time difference between the current moment and the moment the second test packet is sent, and the actual speed, actual acceleration, and actual position information of the adjacent drone node in the second test packet, wherein when the drone node receives multiple test packets from the adjacent drone node, the most recently received test packet is selected and used as the second test packet; Obtaining the current location information of the drone node; Update the network topology of the drone node based on the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and a topographic map of the area where the drone node is located, wherein the network topology of the drone node includes the adjacent drone nodes predicted to be currently capable of establishing a connection with the drone node; The drone node sends a data packet to a first adjacent drone node in the network topology of the drone node; When the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet has failed to be sent after sending the data packet, the drone node sends the data packet to a second adjacent drone node in the network topology of the drone node.

2. The UAV self-organizing network routing method according to claim 1, characterized in that: The updating of the network topology of the drone node according to the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located includes: Calculate the predicted straight-line distance between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node; Calculate the predicted RSSI signal values ​​of the drone node and the adjacent drone node based on the first historical position information and historical RSSI signal value of the adjacent drone node at the time of receiving the first test packet in the second test packet, the second historical position information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node; The predicted RSSI signal value is compared with the minimum RSSI signal value to determine the predicted connection status of the drone node and the adjacent drone node, and the network topology of the drone node is updated according to the predicted connection status.

3. The UAV self-organizing network routing method according to claim 2, characterized in that: The calculating the predicted RSSI signal values ​​of the drone node and the adjacent drone nodes includes: Calculate the environmental attenuation parameter based on the historical RSSI signal value, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the second historical location information of the drone node at the time of sending the first test packet, and the signal strength per unit distance between the drone node and the adjacent drone node; According to the environmental attenuation parameter, the predicted straight-line distance, and the signal strength per unit distance between the drone node and the adjacent drone node, the predicted RSSI signal values ​​of the drone node and the adjacent drone node are calculated; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

4. The UAV self-organizing network routing method according to claim 2, characterized in that: The updating of the network topology of the drone node according to the predicted position information of the adjacent drone node, the current position information of the drone node, the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the topographic map of the area where the drone node is located further includes: Obtaining a topographic map of the area where the drone node is located; According to the topographic map, obtaining the terrain elevation between the UAV node and the adjacent UAV node; Calculating the elevation of the line between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node; An area where the connection line elevation is less than the terrain elevation is set as an obstacle area. When the connection line between the drone node and the adjacent drone node passes through the obstacle area, the predicted connection state between the drone node and the adjacent drone node is determined to be a disconnected state.

5. The UAV ad hoc network routing method according to claim 1, characterized in that: The adjacent UAV node sends the second test packet at a preset time interval; and / or When the position of the adjacent UAV node changes, the second test packet is sent at the start time point and the end time point of the position change respectively; and / or When the speed of the adjacent UAV node changes, sending the second test packet at the start time point of the speed change and the end time point of the speed change respectively; and / or When the acceleration of the adjacent UAV node changes, sending the second test packet at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or When the adjacent drone node is a newly added drone node, the second test packet is sent.

6. The UAV self-organizing network routing method according to claim 1, characterized in that: Determine the first adjacent drone node and / or the second adjacent drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

7. The UAV self-organizing network routing method according to claim 6, characterized in that: The second test package also includes a network topology of the adjacent drone nodes, and the method further includes: Determining a global network topology based on the network topology of the adjacent drone nodes; Determining the number of nodes in a routing line of the data packet according to a target node of the data packet and the global network topology; The first routing priority of the adjacent drone node is determined according to the number of nodes in the routing line of the data packet.

8. The UAV self-organizing network routing method according to claim 6, characterized in that: The second test packet also includes a traffic load value of the adjacent drone node, and the method further includes: Determine a second routing priority of the adjacent drone node based on the traffic load value of the adjacent drone node.

9. The UAV self-organizing network routing method according to claim 6, characterized in that: The method further comprises: Determine a third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node.

10. The UAV self-organizing network routing method according to claim 9, characterized in that: Also includes: When the drone node fails to send a data packet to any adjacent drone node, the drone node determines the connection area between the drone node and any adjacent drone node as a suspected obstacle area.

11. The UAV self-organizing network routing method according to claim 10, characterized in that: The second test package also includes the network topology of the adjacent drone nodes and the distribution information of suspected obstacle areas maintained by the adjacent drone nodes. The method further includes: Determining a global network topology based on the network topology of the adjacent drone nodes; Determine global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent UAV nodes; Determining the number of suspected obstacle areas on the routing line of the data packet according to the target node of the data packet, the global network topology and the global suspected obstacle area distribution information; Determine the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

12. The UAV self-organizing network routing method according to claim 11, characterized in that: Also includes: When the adjacent drone node fails to send a data packet to any other drone node, the adjacent drone node determines the connection area between the adjacent drone node and any other drone node as a suspected obstacle area.

13. The UAV self-organizing network routing method according to claim 1, characterized in that: The method is executed by each drone node in the drone ad hoc network.

14. A UAV self-organizing network routing device, characterized in that: include: a first sending module, configured to send a first test packet to the UAV node; The second sending module is configured to receive, by the UAV node, a second test packet sent by an adjacent UAV node; the second test packet includes: the actual speed, actual acceleration, and actual position information of the adjacent UAV node at the time of sending the second test packet, as well as the first historical position information and historical RSSI signal value of the adjacent UAV node at the time of receiving the first test packet; the historical RSSI signal value is the signal strength of the adjacent UAV node when receiving the first test packet sent by the UAV node; a first determination module configured to, when the drone node sends a data packet, determine, by the drone node, the predicted location information of the adjacent drone node based on a time difference between a current moment and a moment when the second test packet is sent, and actual speed, actual acceleration, and actual location information of the adjacent drone node in the second test packet, wherein, when the drone node receives multiple test packets from the adjacent drone node, select and use the most recently received test packet as the second test packet; An acquisition module is configured to obtain the current location information of the drone node; an updating module configured to update a network topology of the drone node based on the predicted location information of the adjacent drone node, the current location information of the drone node, the first historical location information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical location information of the drone node at the time of sending the first test packet, and a topographic map of the area where the drone node is located, wherein the network topology of the drone node includes adjacent drone nodes predicted to be currently capable of establishing a connection with the drone node; a third sending module, configured for the drone node to send a data packet to a first adjacent drone node in the network topology of the drone node; The fourth sending module is configured to send the data packet to a second adjacent drone node in the network topology of the drone node when the drone node receives the data packet again after sending the data packet, or when the drone node determines that the data packet sending fails after sending the data packet.

15. The UAV ad hoc network routing device according to claim 14, characterized in that: The update module includes: A first calculation submodule is configured to calculate a predicted straight-line distance between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node; The second calculation submodule is configured to calculate the predicted RSSI signal value of the drone node and the adjacent drone node based on the first historical position information of the adjacent drone node at the time of receiving the first test packet, the historical RSSI signal value, the second historical position information of the drone node at the time of sending the first test packet, and the predicted straight-line distance between the drone node and the adjacent drone node in the second test packet; The first determination submodule is configured to compare the predicted RSSI signal value with the minimum RSSI signal value, determine the predicted connection status of the drone node and the adjacent drone node, and update the network topology of the drone node according to the predicted connection status.

16. The UAV ad hoc network routing device according to claim 15, characterized in that: The update module further includes: A first acquisition submodule is configured to acquire a topographic map of the area where the drone node is located; A second acquisition submodule is configured to acquire the terrain elevation between the UAV node and the adjacent UAV node according to the terrain map; a third calculation submodule, configured to calculate a line elevation of a line between the drone node and the adjacent drone node based on the predicted position information of the adjacent drone node and the current position information of the drone node; The determination submodule is configured to set an area where the connection elevation is less than the terrain elevation as an obstacle area, and when the connection line between the drone node and the adjacent drone node passes through the obstacle area, determine that the predicted connection state between the drone node and the adjacent drone node is a disconnected state.

17. The UAV ad hoc network routing device according to claim 14, characterized in that: The adjacent UAV node sends the second test packet at a preset time interval; and / or When the position of the adjacent UAV node changes, the second test packet is sent at the start time point and the end time point of the position change respectively; and / or When the speed of the adjacent UAV node changes, sending the second test packet at the start time point of the speed change and the end time point of the speed change respectively; and / or When the acceleration of the adjacent UAV node changes, sending the second test packet at the start time point of the acceleration change and the end time point of the acceleration change respectively; and / or When the adjacent drone node is a newly added drone node, the second test packet is sent.

18. The UAV ad hoc network routing device according to claim 14, characterized in that: Determine the first adjacent drone node and / or the second adjacent drone node based on one or more of the first routing priority, the second routing priority, the third routing priority, and the fourth routing priority of the adjacent drone node.

19. The UAV ad hoc network routing device according to claim 18, characterized in that: The second test package also includes a network topology of the adjacent drone nodes, and the apparatus further includes: A second determining module is configured to determine a global network topology based on the network topology of the adjacent drone nodes; A third determining module is configured to determine the number of nodes of the routing line of the data packet according to the target node of the data packet and the global network topology; The fourth determination module is configured to determine the first routing priority of the adjacent drone node based on the number of nodes in the routing line of the data packet.

20. The UAV ad hoc network routing device according to claim 18, characterized in that: The test packet also includes a traffic load value of the adjacent drone node, and the device further includes: The fifth determination module is configured to determine the second routing priority of the adjacent drone node based on the traffic load value of the adjacent drone node.

21. The UAV ad hoc network routing device according to claim 18, characterized in that: The device further comprises: The sixth determination module is configured to determine the third routing priority of the adjacent drone node based on the number of suspected obstacle areas between the drone node and the adjacent drone node.

22. The UAV ad hoc network routing device according to claim 20, characterized in that: The device further comprises: The first judgment module is configured to, when the drone node fails to send a data packet to any adjacent drone node, judge the connection area between the drone node and any adjacent drone node as a suspected obstacle area.

23. The UAV ad hoc network routing device according to claim 18, characterized in that: The second test package also includes the network topology of the adjacent drone nodes and the distribution information of suspected obstacle areas maintained by the adjacent drone nodes. The device also includes: a seventh determination module, configured to determine a global network topology based on the network topology of the adjacent drone nodes; an eighth determining module, configured to determine global suspected obstacle area distribution information based on the suspected obstacle area distribution information maintained by the adjacent UAV node; a ninth determining module configured to determine the number of suspected obstacle areas on the routing line of the data packet according to the target node of the data packet, the global network topology, and the global suspected obstacle area distribution information; The tenth determination module is configured to determine the fourth routing priority of the adjacent drone node based on the number of suspected obstacle areas on the routing line of the data packet.

24. The UAV ad hoc network routing device according to claim 23, characterized in that: The device further comprises: The second judgment module is configured to judge the connection area between the adjacent drone node and any other drone node as a suspected obstacle area when the adjacent drone node fails to send a data packet to any other drone node.

25. A drone, characterized in that: The method comprises a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps described in any one of claims 1 to 13.

26. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the method steps described in any one of claims 1 to 13 are implemented.

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