Unmanned aerial vehicle ad hoc network routing method and device, unmanned aerial vehicle and readable storage medium

Abstract

The invention relates to the technical field of intelligent communication, in particular to an unmanned aerial vehicle ad hoc network routing method and device, an unmanned aerial vehicle and a readable storage medium, and the unmanned aerial vehicle ad hoc network routing method comprises the steps that an unmanned aerial vehicle node receives a test packet sent by an adjacent unmanned aerial vehicle node; determining predicted position information of the adjacent unmanned aerial vehicle nodes; acquiring current position information of the unmanned aerial vehicle node; updating the network topology of the unmanned aerial vehicle node; and when the unmanned aerial vehicle node receives the data packet after sending the data packet, or the unmanned aerial vehicle node judges that the data packet fails to be sent after sending the data packet, the unmanned aerial vehicle node sends the data packet to a second adjacent unmanned aerial vehicle node. According to the method and the device, the network topology at the current moment can be predicted according to the test packet information at the previous moment, so that the occurrence of routing error reporting of the unmanned aerial vehicle ad hoc network is reduced under the condition that the test packet sending frequency is not improved, and the routing speed is improved.

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 technology and communication technology, a new communication mode, drone wireless ad hoc network communication, has emerged. As a node in the communication network, drones have the characteristics of self-organization, self-configuration, and self-healing. They can quickly establish a wireless communication network and provide wireless transmission services for multimedia services such as voice, data, and video. At the same time, due to the characteristics of fast movement and easy deployment, drones can be deployed more quickly in the required area than mobile communication vehicles, and are widely used in various scenarios such as emergency assistance, post-disaster reconstruction, and outdoor exploration.

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

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

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

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

[0007] 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 of sending the second test packet, and the first historical position information and historical RSSI (received signal strength) 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 adjacent drone node receiving the first test packet sent by the drone node;

[0008] When the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node according to 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, 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;

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

[0010] 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, the network topology of the drone node is updated, and 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;

[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, the network topology of the drone node is updated 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 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, including:

[0014] Calculate the predicted straight-line distance between the drone node and the adjacent drone node according to 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 according to 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;

[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 the predicted RSSI signal values ​​of the drone node and the adjacent drone node includes:

[0018] Calculate the environmental attenuation parameter according to 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 of the drone node and the adjacent drone node at a unit distance;

[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, 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 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 also 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 drone node and the adjacent drone node;

[0023] Calculate the elevation of the line between the drone node and the adjacent drone node according to the predicted position information of the adjacent drone node and the current position information of the drone node;

[0024] An area where the elevation of the connection is less than the elevation of the terrain is set as an obstacle area. When the connection 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 drone node changes, sending the second test packet at the start time point of the position change 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 of the routing line of the data packet according to the target node of the data packet and the global network topology;

[0034] The first routing priority of the adjacent UAV 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 also 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 the third routing priority of the adjacent drone node according to 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 also 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 node and the distribution information of the suspected obstacle area maintained by the adjacent drone node, and 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 node;

[0044] 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;

[0045] The fourth routing priority of the adjacent UAV node is determined according to the number of suspected obstacle areas on the routing line of the data packet.

[0046] According to an embodiment of the present disclosure, it also 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, including:

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

[0051] The second sending module is configured to receive, by the drone node, 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 of sending the second test packet, and the first historical position information and historical RSSI (received signal strength) 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 adjacent drone node receiving the first test packet sent by the drone node;

[0052] The first determination module is configured to determine the predicted position information of the adjacent drone node according to 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;

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

[0054] An updating module is configured to update 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, 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;

[0055] A third sending module is configured to cause 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 according to 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 according to 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;

[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 drone node and the adjacent drone node according to the terrain map;

[0064] A third calculation submodule is configured to calculate the line elevation of the line between the drone node and the adjacent drone node according to 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 the area where the connection elevation is less than the terrain elevation as an obstacle area, and when the connection 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 drone node changes, sending the second test packet at the start time point of the position change 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 device further includes:

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

[0074] A third determination 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 also includes the traffic load value of the adjacent drone node, and the device also 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 judge the connection area between the drone node and any adjacent drone node as a suspected obstacle area when the drone node fails to send a data packet to any adjacent drone node.

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

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

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

[0085] A ninth determination 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 a 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 a drone ad hoc network is disclosed, comprising:

[0092] 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 actual speed, actual acceleration, and actual location information of the adjacent drone node at the time when the second test packet is sent, as well as the first historical location information and historical RSSI signal value of the adjacent drone node at the time when 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 location 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, as well as the 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, the most recently received test packet is selected as the second test packet; the current location information of the drone node is obtained ;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, the network topology of the drone node is updated, and 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; 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 fails 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. The technical solution disclosed in the present invention can predict the network topology at the current moment according to the test packet information at the previous moment, reduce the occurrence of drone self-organizing network routing errors without increasing the test packet sending frequency, and improve the 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 present 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 showing an application scenario of a drone self-organizing network routing method according to an embodiment of the present disclosure is shown.

[0096] Figure 2 A flow chart of a drone self-organizing network routing method according to an embodiment of the present disclosure is shown.

[0097] Figure 3 A flowchart of step S105 in the drone self-organizing 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 coverage of a drone network 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] Fig. 9 A structural block diagram of a drone self-organizing network routing device according to an embodiment of the present disclosure is shown.

[0103] Fig.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] Fig.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] Fig.12 A structural block diagram of a drone according to an embodiment of the present disclosure is shown.

[0106] Fig.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, behaviors, components, parts, or a combination thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or a combination thereof exist or are added.

[0109] It should also be noted that, in the absence of conflict, the embodiments and features in 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 technology and communication technology, a new communication mode, drone wireless ad hoc network communication, has emerged. As a node in the communication network, drones have the characteristics of self-organization, self-configuration, and self-healing. They can quickly establish a wireless communication network and provide wireless transmission services for multimedia services such as voice, data, and video. At the same time, due to the characteristics of fast movement and easy deployment, drones can be deployed more quickly in the required area than mobile communication vehicles, and are widely used in various scenarios such as emergency assistance, post-disaster reconstruction, and outdoor exploration.

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

[0112] In order to solve the above technical problems, the present invention discloses a UAV self-organizing network routing method, comprising: a UAV node sends a 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 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; when the UAV node sends a data packet, the UAV node determines the predicted position information of the adjacent UAV node based on the time difference between the current time and the time of sending the second test packet, and the actual speed, actual acceleration, and actual position information of the adjacent UAV node in the second test packet, wherein, when the UAV node receives multiple test packets from the adjacent UAV 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 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, 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 at present; 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 fails 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. The technical solution disclosed in the present invention can predict the network topology at the current moment according to the test packet information at the previous moment, reduce the occurrence of drone self-organizing network routing errors without increasing the test packet sending frequency, and improve the routing speed.

[0113] Figure 1 A schematic diagram showing an application scenario of a drone self-organizing 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 to W12, and each drone node can be implemented by a drone. The drone nodes in the drone network serve as relay nodes for wireless communication and are used to implement wireless communication between different communication terminals. For example, drone node W1 can receive a data packet sent by communication terminal A, and send the data packet to communication terminal B via drone nodes W3 and W5, thereby implementing communication between communication terminal A and communication terminal 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, resulting in changes in the connection relationship between drone nodes, which increases the difficulty of determining communication routes. In view of the difficulties in this practical application, the present disclosure provides a drone self-organizing network routing method that can effectively adapt to the frequent and unpredictable changes in the location and connection relationship of drone nodes in a drone self-organizing network, and realize 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, the drone node receives a second test packet sent by an adjacent drone node; the second test packet includes the actual speed, actual acceleration, and actual position information of the adjacent drone node at the time when 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 drone node at the time when the first test packet is received; the historical RSSI signal value is the signal strength of the first test packet sent by the adjacent drone node when the adjacent drone node receives it. The second test packet is used to locate the position of the adjacent drone node at the time when the second test packet is sent and predict the position at future times. The historical RSSI signal value is the signal strength of the first test packet sent by the adjacent drone node when the adjacent drone node receives it. The second test packet sent by the adjacent drone node includes the RSSI signal values ​​of the first test packets sent by all drone nodes received by the adjacent drone node and the corresponding identity codes of all drone nodes. The drone node obtains the corresponding first test packet RSSI signal value as the historical RSSI signal value according to the second test packet sent by the adjacent drone node and the local identity code of the drone node.

[0119] In the 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 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 according to 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 the connection with all other drone nodes, the global network topology obtained by the drone node W12 does not include any other drone nodes, the distance between drone nodes W11 and W8 is close and they can be connected, but the distance between W12 and other drone nodes is far and therefore cannot be established, 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 added drone node, the second test packet is sent.

[0123] Although the test package 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 cause the network topology to be untimely updated, affecting communication stability. Therefore, the timing of sending test packages needs to be set according to actual conditions, especially 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, 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.

[0125] When a drone node wants to send a data packet to an adjacent drone node, since there is an interval between the sending time of the second test packet it receives and the current time, the relative position change between the drone node and the adjacent drone node during the interval may cause a change in the connection state. For example, the adjacent drone node that could have established a connection with it cannot establish a connection due to flying far away, but the drone node does not find the connection interruption, resulting in data transmission failure. Therefore, it is necessary to predict the position of the adjacent drone node and update the network topology and routing selection of the drone based on the prediction results. The drone node can calculate the predicted position information of the adjacent drone node at the current moment based on the actual position, actual speed, actual acceleration of the received adjacent drone node and the time difference between the current moment and the sending moment of the second test packet. 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, and improve the success rate of data packet transmission and the 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 package, the historical RSSI signal value in the second test package, the second historical position information of the drone node at the time of sending the first test package, 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. 1 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 position information of the adjacent drone node and the current position information of the drone node. The predicted position 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 position 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 values ​​of the drone node and the adjacent drone node are 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 value of the drone node and the adjacent drone node includes: calculating the environmental attenuation parameter according to 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 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; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

[0132] Specifically, according to the first historical position information and the second historical position 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, and the environmental attenuation parameter is determined according to 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, according to 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 predicted RSSI signal value is determined. 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 state of the drone node and the adjacent drone node, and the network topology of the drone node is updated according to the predicted connection state. 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, it is considered that the drone node is in a connected state with the adjacent drone node. When the predicted RSSI signal value is less than the minimum RSSI signal value, it is considered that the drone node is in a disconnected state with the adjacent drone node.

[0137] Figure 4 FIG. 5 is a flow chart showing step S105 in a drone ad hoc 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 locations, and shapes on a horizontal plane, including height data of the surface undulations.

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

[0140] In step S1056, the line elevation of the line 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 the continuous height data of the line between the drone node and the adjacent drone node, which is used to indicate the highest effective height of each position on the signal transmission path between the drone node and the adjacent drone node. When the height of an obstacle exceeds the line elevation, it means that there is an obstruction in the signal transmission and the stability of the signal transmission cannot be guaranteed.

[0141] In step S1057, the area where the elevation of the connection is less than the elevation of the terrain is set as an obstacle area. When the connection 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 disconnected. The obstacle area means that there are high obstacles or terrains, such as buildings, hills, etc., between the drone node and the adjacent drone node, which block the straight-line signal transmission between the drone node and the adjacent drone node, and may cause the stability of signal transmission to decrease.

[0142] 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. Figure 5 As shown in the figure, the drone W1 and the drone W2 are separated by a mountain peak, and the terrain elevation of the mountain peak is greater than the elevation of the connection line between the drone node and the adjacent drone node at the mountain peak, forming an obstacle area, indicating that the communication between drone W1 and drone W2 will be interrupted by the mountain peak. Therefore, in order to ensure the signal transmission speed, based on the predicted position information of the adjacent drone node and the current position information of the drone node, the terrain elevation and the connection elevation are calculated to divide the obstructed area into an obstacle area, and determine in advance that the drone node is disconnected from the adjacent drone node, reduce routing line selection errors, and improve routing speed.

[0143] The technical solution disclosed in the present invention is particularly suitable for application scenarios such as natural disasters and post-disaster reconstruction. After a natural disaster occurs, normal ground communications are often interrupted and cannot be used. 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 and 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 implementation manner 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 means 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 according to 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 according to 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 topology of the adjacent 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 itself 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 prevent a drone node from 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 a 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 coverage of a drone network is automatically expanded or contracted in a 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 the W11 designated destination which is far away and exceeds the maximum connection distance with drone node W9 to achieve connection with communication terminal B. Before flying to the W11 designated destination, drone node W11 notifies the adjacent drone node W9 to move to a location where it can connect to drone nodes W11 and W4 at the same time. Furthermore, after drone node W9 receives the movement request from W11, it believes that the W9 designated destination exceeds the maximum communication distance between drone node W9 and drone node W4. Therefore, before drone node W9 goes to the W9 designated destination, it notifies drone node W4 to move to the W4 designated destination which can ensure communication between drone node W9 and W4. In this way, the automatic expansion and contraction of the drone network coverage can be achieved while ensuring the connectivity of the drone network.

[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 communication, 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 are planned to be sorted from small to large, 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 lines with a small number of 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 also includes a traffic load value of the adjacent drone node, and the method further includes:

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

[0157] According to an embodiment of the present disclosure, the method further includes: determining the third routing priority of the adjacent drone node according to 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 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 line 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 line between the drone node and the adjacent drone nodes passes through, and prioritize the adjacent drone nodes with the fewest suspected obstacle areas as the first adjacent drone nodes and / or the second adjacent drone nodes.

[0158] 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. Figure 7 As shown, 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, the line connecting drone nodes W9 and W10 passes through the first suspected obstacle area, and 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, and 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, 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 second 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. As Figure 8 As shown, UAV 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 UAV node W4. When a data packet needs to be transmitted from UAV 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 other routing routes do not pass through the suspected obstacle area. Then, the fourth routing priority of the adjacent UAV node W9 in the network topology of UAV node W3 is the lowest. UAV node W3 gives priority to the routing routes W3-W1-W2-W10 or W3-W1-W4-W10 that do not pass through the obstacle area to communicate with UAV node W10 to avoid the influence of suspected obstacle areas on communication.

[0161] According to an embodiment of the present disclosure, the method also 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 in the drone ad hoc network.

[0163] Fig. 9 The structural block diagram of the UAV self-organizing network routing device according to the embodiment of the present disclosure is shown. The device can be implemented as part or all of the electronic device through software, hardware or a combination of both.

[0164] like Fig. 9 As shown, the drone self-organizing network routing device 500 includes a first sending module 510, a second sending module 520, a first determination module 530, an acquisition module 540, an update 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 receive the second test packet sent by the adjacent drone node by the drone node; the second test packet includes the actual speed, actual acceleration, actual position information of the adjacent drone 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 drone node at the time of receiving the first test packet; the historical RSSI signal value is the signal strength of the adjacent drone node receiving the first test packet sent by the drone node. The second test packet is used to locate the position of the adjacent drone node at the time of sending the second test packet and predict the position at future times. The historical RSSI signal value is the signal strength of the first test packet sent by the adjacent drone node when the adjacent drone node receives it. The second test packet sent by the adjacent drone node includes the first test packet RSSI signal values ​​sent by all drone nodes received by the adjacent drone node and the corresponding identity codes of all drone nodes. The drone node obtains the corresponding first test packet RSSI signal value as the historical RSSI signal value according to the second test packet sent by the adjacent drone node and the local identity code of the drone node.

[0167] In the 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, 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. 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 according to 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 the connection with all other drone nodes, the global network topology obtained by the drone node W12 does not include any other drone nodes, the distance between drone nodes W11 and W8 is close and they can be connected, but the distance between W12 and other drone nodes is far and therefore cannot be established, 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 added drone node, the second test packet is sent.

[0171] Although the test package 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 cause the network topology to be untimely updated, affecting communication stability. Therefore, the timing of sending test packages needs to be set according to actual conditions, especially 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, since there is an interval between the sending time of the second test packet it receives and the current time, the relative position change between the drone node and the adjacent drone node during the interval may cause a change in the connection state. For example, the adjacent drone node that could have established a connection with it cannot establish a connection due to flying far away, but the drone node does not find the connection interruption, resulting in data transmission failure. Therefore, it is necessary to predict the position of the adjacent drone node and update the network topology and routing selection of the drone based on the prediction results. The drone node can calculate the predicted position information of the adjacent drone node at the current moment based on the actual position, actual speed, actual acceleration of the received adjacent drone node and the time difference between the current moment and the sending moment of the second test packet. 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, and improve the success rate of data packet transmission and the 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] Fig.10 FIG. 5 is a block diagram showing a structure of an update module 550 in a drone ad hoc network routing device according to an embodiment of the present disclosure. Fig.10 As shown, the updating module 550 includes a first calculating submodule 551, a second calculating submodule 552, and a first determining 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 according to the predicted position information of the adjacent drone node and the current position information of the drone node. The predicted position information of the adjacent drone node includes the latitude and longitude information and the altitude information of the adjacent drone node; the current position information of the drone node includes the latitude and longitude information and the altitude information of the current drone node.

[0178] The second calculation submodule 552 is configured to calculate the predicted RSSI signal value of the drone node and the adjacent drone node according to 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. According to an embodiment of the present disclosure, the calculation of the predicted RSSI signal value of the drone node and the adjacent drone node includes: calculating the environmental attenuation parameter according to 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 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; wherein the signal strength per unit distance between the drone node and the adjacent drone node is a preset value.

[0179] Specifically, according to the first historical position information and the second historical position 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, and the environmental attenuation parameter is determined according to 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, according to 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 predicted RSSI signal value is determined. 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, determine the predicted connection state of the drone node and the adjacent drone node, and update the network topology of the drone node according to the predicted connection state. 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, it is considered that the drone node is in a connected state with the adjacent drone node. When the predicted RSSI signal value is less than the minimum RSSI signal value, it is considered that the drone node is in a disconnected state with the adjacent drone node.

[0184] Fig.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. Fig.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 locations, and shapes 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 drone node and the adjacent drone node according to the terrain map. The terrain elevation between the drone node and the adjacent drone node is continuous surface height data of the vertical plane where the line between the drone node and the adjacent drone node is located, and the terrain elevation is used to indicate the surface height of the drone node and the adjacent drone node in the signal transmission direction.

[0187] The third calculation submodule 556 is configured to calculate the line elevation of the line between the drone node and the adjacent drone node according to the predicted position information of the adjacent drone node and the current position information of the drone node. The line elevation is the continuous height data of the line between the drone node and the adjacent drone node, which is used to indicate the highest effective height of each position on the signal transmission path between the drone node and the adjacent drone node. When the height of an obstacle exceeds the line elevation, it means that there is a blockage in the signal transmission and the stability of the signal transmission cannot be guaranteed.

[0188] The determination submodule 557 is configured to set the area where the elevation of the connection line is less than the elevation of the terrain as an obstacle area, and 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 disconnected. The obstacle area means that there are high obstacles or terrains, such as buildings, hills, etc., between the drone node and the adjacent drone node, which block the straight-line signal transmission between the drone node and the adjacent drone node, and may cause the signal transmission stability to decrease.

[0189] The technical solution disclosed in the present invention is particularly suitable for application scenarios such as natural disasters and post-disaster reconstruction. After a natural disaster occurs, normal ground communications are often interrupted and cannot be used. 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 and 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 for 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 a second adjacent drone node in the network topology of the drone node, and no longer send the data packet to the first adjacent 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.

[0192] According to an implementation manner 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 through the route of the first adjacent drone node, and the data packet cannot be transmitted to the target node. In this case, 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 according to 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 according to 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 drone 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 the global network topology based on the network topology of the adjacent 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 itself 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 prevent a drone node from 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 a 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 determination 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.

[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 communication, 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 are planned to be sorted from small to large, 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 lines with a small number of 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 also includes the traffic load value of the adjacent drone node, and the device also includes:

[0202] The fifth determination module is configured to determine the second routing priority of the adjacent drone node according to the traffic load value of the adjacent drone node. For example, in the application scenario of emergency rescue, a certain area may have a high traffic load value of the drone node responsible for communication in the area due to the gathering of people. The traffic load of the drone node in the area should not be increased to prevent network congestion. The second routing priority sorts the adjacent drone nodes from low to high according to the traffic load value of the adjacent drone nodes, and prioritizes the adjacent drone nodes with low traffic load values ​​as the first adjacent drone nodes and / or the second adjacent drone nodes.

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

[0204] The sixth determination module is configured to determine the third routing priority of the adjacent drone node according to 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 reflected in the terrain map but affects the communication between the drone nodes.

[0205] The first judgment module is configured to judge the connection area between the drone node and any adjacent drone node as a suspected obstacle area when the drone node fails to send a data packet to any adjacent drone node. For example, in the application scenario of emergency rescue in mountainous areas, there may be a forest area with 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 the drone node fails to send a data packet to any other drone node, the connection 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 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 passed by the connection between the drone node and the adjacent drone node, and give priority to the adjacent drone nodes with fewer suspected obstacle areas as the first adjacent drone node and / or the second adjacent drone node.

[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, and 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 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 that pass through the entire routing line, 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 the routing line. By adding the network topology of the adjacent drone nodes and the distribution information of the suspected obstacle areas maintained by the 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 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.

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

[0210] like Fig.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 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 of sending the second test packet, and the first historical position information and historical 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 adjacent drone node receiving the first test packet sent by the drone node;

[0214] When the drone node sends a data packet, the drone node determines the predicted position information of the adjacent drone node according to 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, 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;

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

[0216] 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, the network topology of the drone node is updated, and 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;

[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 a 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 according to 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 according to 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;

[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 according to 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 of the drone node and the adjacent drone node at a unit distance;

[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 drone node and the adjacent drone node;

[0229] Calculate the elevation of the line between the drone node and the adjacent drone node according to the predicted position information of the adjacent drone node and the current position information of the drone node;

[0230] An area where the elevation of the connection is less than the elevation of the terrain is set as an obstacle area. When the connection 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 drone node changes, sending the second test packet at the start time point of the position change 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 of the routing line of the data packet according to the target node of the data packet and the global network topology;

[0240] The first routing priority of the adjacent UAV 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 the third routing priority of the adjacent drone node according to 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, and 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 node;

[0250] 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;

[0251] The fourth routing priority of the adjacent UAV node is determined according to 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 self-organizing network.

[0255] Fig.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 Fig.13 As shown, the computer system includes a processing unit, which can perform the various methods in the above-mentioned embodiments according to the program stored in the read-only memory (ROM) or the program loaded from the storage part into the 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 through a bus. The 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), etc., and a speaker; 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 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 an embodiment of the present disclosure, the method described above can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes a program code for executing the above method. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium.

[0259] The flowcharts and block diagrams in the accompanying drawings illustrate the possible 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, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order 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 with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

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

[0261] As another aspect, the present disclosure further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the drone or computer system in the above embodiment; or a computer-readable storage medium that exists independently and is not assembled into a device. The computer-readable storage medium stores one or more programs, and the programs are used by one or more processors to execute the method described in the present disclosure.

[0262] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other.

Claims

1. A UAV self-organizing network routing method, characterized in that: include: The drone node sends the 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 of sending the second test packet, and the first historical position 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 according to 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, 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; Obtaining the current location information 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, the network topology of the drone node is updated, and 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; 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 according to 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 according to 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 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 according to 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 of the drone node and the adjacent drone node at a unit distance; 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 drone node and the adjacent drone node; Calculate the elevation of the line between the drone node and the adjacent drone node according to the predicted position information of the adjacent drone node and the current position information of the drone node; An area where the elevation of the connection is less than the elevation of the terrain is set as an obstacle area. When the connection 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 self-organizing 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 drone node changes, sending the second test packet 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 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 of the routing line of the data packet according to the target node of the data packet and the global network topology; The first routing priority of the adjacent UAV 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 the third routing priority of the adjacent drone node according to 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, and 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 node; 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 fourth routing priority of the adjacent UAV node is determined according to 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 self-organizing 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 drone node; The second sending module is configured to receive, by the drone node, 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 of sending the second test packet, and the first historical position information and historical 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 adjacent drone node receiving the first test packet sent by the drone node; The first determination module is configured to determine the predicted position information of the adjacent drone node according to 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; An acquisition module is configured to acquire the current location information of the drone node; An updating module is configured to update 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, 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; A third sending module is configured to cause 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 comprises: A first calculation submodule is configured to calculate a predicted straight-line distance between the drone node and the adjacent drone node according to 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 according to 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; 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 drone node and the adjacent drone node according to the terrain map; A third calculation submodule is configured to calculate the line elevation of the line between the drone node and the adjacent drone node according to 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 the area where the connection elevation is less than the terrain elevation as an obstacle area, and when the connection 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 drone node changes, sending the second test packet 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 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 device also includes: A second determination module is configured to determine a global network topology based on the network topology of the adjacent drone nodes; A third determination 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 also includes: The fifth determination module is configured to determine the second routing priority of the adjacent drone node according to 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 also includes: 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 also includes: The first judgment module is configured to judge the connection area between the drone node and any adjacent drone node as a suspected obstacle area when the drone node fails to send a data packet to any adjacent drone node.

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, and the device also includes: a seventh determination module, configured to determine a global network topology according to the network topology of the adjacent drone nodes; an eighth determination module, configured to determine global suspected obstacle area distribution information according to the suspected obstacle area distribution information maintained by the adjacent UAV node; A ninth determination 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 also includes: 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 1-13.

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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