Collision avoidance method and device of unmanned aerial vehicle, server, medium and product

Abstract

This application provides a collision avoidance method, apparatus, server, medium, and product for unmanned aerial vehicles (UAVs). Applied to a satellite network server, the satellite network server receives flight information from a first UAV and a second UAV over multiple cycles. For any given cycle, the satellite network server determines the minimum distance between the first and second UAVs based on their flight information, thus obtaining the minimum distance between them for each cycle. Based on the minimum distance between the first and second UAVs for each cycle, the server predicts the cumulative number of collisions between them within a first preset time period. Then, based on the cumulative number of collisions, it generates a detection and avoidance analysis result, enabling the first and second UAVs to avoid collisions based on this analysis. This application reduces transmission latency.

Description

Technical Field

[0001] This application relates to unmanned aerial vehicle (UAV) technology, and more particularly to a collision avoidance method, device, server, medium, and product for UAVs. Background Technology

[0002] With the rapid development and widespread application of drone technology, scenarios involving multiple drones working together are becoming increasingly common. In multi-drone environments, due to the increased number of drones and their intersecting flight paths, collisions may occur between them, thus requiring collision avoidance measures.

[0003] In related technologies, when avoiding collisions with drones, the drone to be avoided typically transmits the perception data between itself and other drones to the ground access gateway based on the perception data detected by the drone to be avoided. After processing by the core network, a Detect and Avoid (DAA) result is generated, and then the DAA result is sent to the drone to be avoided.

[0004] However, the above methods have high transmission latency. Summary of the Invention

[0005] This application provides a collision avoidance method, device, server, medium, and product for unmanned aerial vehicles (UAVs) to achieve the technical effect of reducing transmission latency.

[0006] In a first aspect, embodiments of this application provide a collision avoidance method for unmanned aerial vehicles (UAVs), applied to a satellite network server, wherein the satellite network server is located on a satellite, comprising:

[0007] Receive flight information from the first UAV at multiple cycles and flight information from the second UAV at multiple cycles;

[0008] Based on the flight information of the first UAV and the second UAV in each cycle, determine the minimum distance between the first UAV and the second UAV in each cycle;

[0009] Based on the minimum distance between the first UAV and the second UAV in each cycle, the cumulative number of collisions within a first preset duration is predicted, where the first preset duration is longer than the duration corresponding to multiple cycles.

[0010] Based on the cumulative number of collisions, the detection and avoidance analysis results of the first UAV and the second UAV are generated, and the detection and avoidance analysis results are sent to the first UAV and the second UAV for collision avoidance.

[0011] In one possible implementation, determining the minimum distance between the first UAV and the second UAV in each period, based on the flight information of the first UAV and the second UAV in each period, includes:

[0012] For any given period, based on the flight information of the first UAV and the second UAV during that period, the position information of the first UAV and the second UAV is determined at each moment predicted every second preset duration, where the second preset duration is less than the first preset duration.

[0013] Based on the predicted position information of the first UAV and the second UAV at each time point, the distance between the first UAV and the second UAV at each time point is determined;

[0014] Based on the distance between the first UAV and the second UAV at each time point, the minimum distance between the first UAV and the second UAV during the period is determined.

[0015] In one possible implementation, the step of predicting the cumulative number of collisions within a first preset time period based on the minimum distance between the first UAV and the second UAV in each period includes:

[0016] Based on the minimum distance between the first UAV and the second UAV in each cycle, predict the number of collisions in each cycle;

[0017] The number of collisions in each period within the first preset time period is accumulated to obtain the cumulative number of collisions.

[0018] In one possible implementation, predicting the number of collisions in each cycle based on the minimum distance between the first UAV and the second UAV in each cycle includes:

[0019] For any given period, if the minimum distance between the first UAV and the second UAV during that period is less than a preset safe distance threshold, then the number of collisions is incremented by 1 to obtain the predicted number of collisions during that period.

[0020] In one possible implementation, generating the detection and avoidance analysis results for the first UAV and the second UAV based on the cumulative number of collisions includes:

[0021] Determine the target period when the cumulative number of collisions is 1;

[0022] Based on the minimum distance between the first UAV and the second UAV during the target period, the predicted target time corresponding to the minimum distance is determined;

[0023] Based on the predicted target time, the first preset duration, and the second preset duration, a cumulative threshold for the number of collisions is determined;

[0024] The target collision event type is determined based on the cumulative number of collisions and the cumulative collision number threshold. The collision event type includes low-probability collision events and high-probability collision events.

[0025] Based on the target collision event type, the detection and avoidance analysis results of the first UAV and the second UAV are generated.

[0026] In one possible implementation, determining the target collision event type based on the cumulative number of collisions and the cumulative collision count threshold includes:

[0027] If the cumulative number of collisions is less than or equal to the cumulative collision threshold, then the target collision event type is determined to be the low-probability collision event.

[0028] If the cumulative number of collisions is greater than the cumulative collision count threshold, then the target collision event type is determined to be the high-probability collision event.

[0029] In one possible implementation, if the target collision event type is the high-probability collision event, then the second preset duration is reduced to obtain a new second preset duration;

[0030] Based on the new second preset duration, the step of determining the position information of the first UAV and the second UAV at each predicted time interval every second preset duration is re-executed, based on the flight information of the first UAV and the second UAV in any period, until the detection and avoidance analysis results of the first UAV and the second UAV are generated.

[0031] In one possible implementation, it also includes:

[0032] Based on the detection and avoidance analysis results, an early warning message is generated;

[0033] Send the warning information to the first drone and the second drone; or...

[0034] The warning information is sent to a first drone controller that controls the first drone and a second drone controller that controls the second drone, so that the warning information is sent to the first drone through the first drone controller and to the second drone through the second drone controller.

[0035] Secondly, embodiments of this application provide a collision avoidance device for unmanned aerial vehicles (UAVs), applied to a satellite network server, wherein the satellite network server is located on a satellite, and includes:

[0036] The receiving module is used to receive flight information from the first UAV for multiple cycles and flight information from the second UAV for multiple cycles.

[0037] The determination module is used to determine the minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the second UAV in each cycle.

[0038] The prediction module is used to predict the cumulative number of collisions within a first preset time period based on the minimum distance between the first UAV and the second UAV in each cycle, wherein the first preset time period is longer than the duration corresponding to multiple cycles.

[0039] The processing module is used to generate detection and avoidance analysis results for the first UAV and the second UAV based on the cumulative number of collisions, and send the detection and avoidance analysis results to the first UAV and the second UAV for collision avoidance.

[0040] Thirdly, embodiments of this application provide a satellite network server, including: a memory and a processor;

[0041] The memory stores computer-executed instructions;

[0042] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0043] Fourthly, embodiments of this application provide a satellite, including the satellite network server described in the third aspect.

[0044] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0045] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0046] This application provides a collision avoidance method, apparatus, server, medium, and product for unmanned aerial vehicles (UAVs). Applied to a satellite network server, this server is mounted on a satellite and receives flight information from a first UAV and a second UAV over multiple cycles. For any given cycle, the satellite network server determines the minimum distance between the first and second UAVs based on their respective flight information, thus obtaining the minimum distance between them for each cycle. Based on the minimum distance between the first and second UAVs in each cycle, the satellite network server predicts the cumulative number of collisions between them within a first preset time period and generates a detection and avoidance analysis result based on this cumulative collision count. This allows the first and second UAVs to perform collision avoidance based on the detection and avoidance analysis result. In this application, the satellite network server can directly obtain the flight information of the first UAV and the second UAV, and generate detection and avoidance analysis results based on the flight information. This eliminates the need for the process in related technologies where the perception data of the UAV to be avoided is first transmitted to the ground access gateway and then to the core network, and the core network generates the detection and avoidance analysis results. Transmission is carried out through the satellite network, reducing the number of nodes that the data passes through when transmitted via the ground network, thereby reducing transmission latency. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application;

[0049] Figure 2 A flowchart illustrating a collision avoidance method for an unmanned aerial vehicle (UAV) provided in an embodiment of this application;

[0050] Figure 3 A flowchart illustrating a method for determining the minimum distance between a first UAV and a second UAV in each cycle, provided in an embodiment of this application;

[0051] Figure 4 A flowchart illustrating a method for generating detection and avoidance analysis results for a first UAV and a second UAV, provided in an embodiment of this application;

[0052] Figure 5 A signaling flowchart of a collision avoidance method for an unmanned aerial vehicle (UAV) provided in an embodiment of this application;

[0053] Figure 6A Signaling flowchart of another collision avoidance method for unmanned aerial vehicles provided in this application embodiment;

[0054] Figure 6B This is another application scenario diagram provided by an embodiment of this application;

[0055] Figure 7 A schematic diagram of the structure of a collision avoidance device for an unmanned aerial vehicle (UAV) provided in an embodiment of this application;

[0056] Figure 8 This is a schematic diagram of the structure of a satellite network server provided in an embodiment of this application.

[0057] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0058] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0059] In the description of the embodiments of this application, the terms "inner" and "outer", etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or component must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this application.

[0060] In the description of the embodiments of this application, unless otherwise expressly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0061] With the increasingly widespread application of drones in various fields, the number of drones in the air is also increasing. In some scenarios, such as urban air traffic, security monitoring for large-scale events, and transportation and delivery, it is becoming more and more common for multiple drones to operate simultaneously. If drones cannot effectively avoid collisions, a collision accident could not only damage the drones themselves but also potentially cause damage to people and buildings on the ground. Therefore, collision avoidance between drones is of great significance for ensuring flight safety.

[0062] In related technologies, collision avoidance between drones utilizes various sensors installed on the drones. During flight, the drones obtain perception data by detecting collisions with other drones. These sensors include, but are not limited to, ordinary radar, lidar, cameras, and ultrasonic sensors. The perception data includes, but is not limited to, position data, speed data, and distance data. After detecting the perception data, the drone transmits the data to a ground access gateway, which then transmits it to the core network. The core network performs collision analysis based on the perception data to obtain a collision avoidance assessment (DAA) result, which is then forwarded to the drone so that it can perform collision avoidance based on the DAA result.

[0063] However, in the above method, the transmission process needs to go through the terrestrial network, that is, through the terrestrial access gateway to the core network. There are many intermediate links in the transmission, resulting in high transmission latency.

[0064] Therefore, in response to the aforementioned technical problems in related technologies, the inventors discovered during their research that the high transmission latency is caused by the long transmission process via terrestrial networks. Sensing data is first transmitted from the UAV to the ground access gateway, then from the ground access gateway to the core network, ultimately yielding the DAA result. If a satellite network is used instead of a terrestrial network, the above process can be omitted, allowing direct acquisition of the UAV's flight information, and then generating the DAA result based on that information. Specifically, the first and second UAVs send their respective flight information to the satellite network server at preset intervals. Therefore, the satellite network server can receive flight information from both the first and second UAVs over multiple cycles, determine the minimum distance between the two UAVs in each cycle, predict the cumulative collision count within a first preset time interval based on the minimum distance between the two UAVs in each cycle, and finally generate the DAA result based on the cumulative collision count and send it to both the first and second UAVs, thus reducing transmission latency. Therefore, this application proposes a collision avoidance method, device, server, medium, and product for UAVs.

[0065] To facilitate understanding of this application, the following description uses exemplary application scenarios. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application, which includes: a first UAV 01, a second UAV 02, a satellite network server 03, and a satellite 04. The satellite network server 03 is mounted on the satellite 04.

[0066] The first UAV 01 and the second UAV 02 periodically transmit flight information to the satellite network server 03. For any given period, the satellite network server 03 determines the minimum distance between the first UAV 01 and the second UAV 02 based on the flight information of the first UAV 01 and the second UAV 02 in that period, thus obtaining the minimum distance between the two UAVs for each period. Based on the minimum distance between the two UAVs for each period, the satellite network server 03 predicts the cumulative number of collisions between the first UAV 01 and the second UAV 02 within a first preset time period, generates a Direct Collision Avoidance (DAA) result based on the cumulative number of collisions, and sends the DAA result to the first UAV 01 and the second UAV 02 so that the first UAV 01 and the second UAV 02 can perform collision avoidance based on the DAA result.

[0067] It is understood that the above application scenarios are only for illustrative purposes. This application does not limit the type, function, or purpose of the first UAV 01, the second UAV 02, the satellite network server 03, and the satellite 04. These can be determined based on actual application conditions.

[0068] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0069] Please see Figure 2 , Figure 2 This is a flowchart illustrating a collision avoidance method for an unmanned aerial vehicle (UAV) provided in an embodiment of this application. The execution entity of this method can be a satellite network server, which is located on a satellite, such as... Figure 2 As shown, the method includes:

[0070] S201, Receive flight information from the first UAV for multiple cycles and flight information from the second UAV for multiple cycles.

[0071] In this embodiment, the first UAV and the second UAV can be any two UAVs.

[0072] In this embodiment, the first UAV and the second UAV periodically send their respective flight information to the satellite network server. For example, the first UAV and the second UAV can send their current flight information to the satellite network server every 3 seconds.

[0073] Optionally,

[0074] Flight information can be seen in Table 1 below:

[0075] Table 1

[0076]

[0077] The information that must be included in the flight information sent by the drone can be specified in advance by the user. The contents of Table 1 above are only for illustrative purposes and do not limit this application.

[0078] Since the first and second UAVs periodically send flight information to the satellite network server, the satellite network server can obtain the flight information of the first UAV in each period, as well as the flight information of the second UAV in each period.

[0079] S202. Based on the flight information of the first UAV and the second UAV in each cycle, determine the minimum distance between the first UAV and the second UAV in each cycle.

[0080] After receiving the flight information corresponding to each cycle sent by the UAV, the satellite network server predicts the UAV's position information at multiple future moments based on the flight information corresponding to any given cycle.

[0081] For the first UAV, the satellite network server predicts the location information of the first UAV at multiple future moments based on the flight information sent by the first UAV in each cycle.

[0082] Accordingly, for the second UAV, the satellite network server predicts the location information of the second UAV at multiple future moments based on the flight information sent by the second UAV in each cycle.

[0083] For any given period, the satellite network server determines the minimum distance between the first and second drones in that period based on the predicted location information of the first drone at multiple future moments and the location information of the second drone at multiple future moments in that period.

[0084] Based on the above method, the minimum distance between the first UAV and the second UAV in each cycle can be determined.

[0085] Optionally, the number of location information points of the first UAV predicted by the satellite network server in each cycle is the same as the number of location information points of the second UAV predicted.

[0086] S203. Based on the minimum distance between the first UAV and the second UAV in each cycle, predict the cumulative number of collisions within the first preset time period.

[0087] Among them, the first preset duration It is longer than the duration corresponding to multiple cycles.

[0088] The satellite network server determines the specific number of cycles included within the first preset duration based on the duration of the cycle, the transmission order of each cycle, and the first preset duration.

[0089] Based on the relationship between the minimum distance between the first and second drones corresponding to each cycle and the preset safe distance threshold, the cumulative number of collisions within the first preset time period is predicted.

[0090] S204. Based on the cumulative number of collisions, generate the detection and avoidance analysis results of the first UAV and the second UAV, and send the detection and avoidance analysis results to the first UAV and the second UAV for collision avoidance.

[0091] The satellite network server determines the collision event type based on the cumulative number of collisions. The collision event type includes low-probability collision events and high-probability collision events. Based on the target collision event type, the DAA results for the first UAV and the second UAV are generated.

[0092] After the satellite network server generates the DAA result, it can directly send the DAA result to the first and second UAVs.

[0093] After receiving the DAA result, the first and second UAVs perform collision avoidance by adjusting their speed, changing their course, or changing their altitude.

[0094] Optionally, after the satellite network server generates the DAA result, it can first send the DAA result to the first UAV controller that controls the first UAV and the second UAV controller that controls the second UAV. Then, the first UAV controller sends the DAA result to the first UAV, and the second UAV controller sends the DAA result to the second UAV.

[0095] Optionally, the satellite network server can also generate warning information based on the DAA result, and then directly send the warning information to the first UAV and the second UAV. Alternatively, the warning information can be sent to the first UAV controller that controls the first UAV and the second UAV controller that controls the second UAV, so that the warning information is sent to the first UAV through the first UAV controller and to the second UAV through the second UAV controller.

[0096] The information that may be included in the early warning information is shown in Table 2:

[0097] Table 2

[0098]

[0099] The information that must be included in the warning message can be specified by the user in advance, but it must at least include the collision event type. The contents in Table 2 above are for illustrative purposes only and do not limit this application.

[0100] In the above embodiments of this application, the satellite network server receives flight information of the first and second drones in multiple cycles. For any given cycle, the satellite network server determines the minimum distance between the first and second drones in that cycle based on the flight information of the first and second drones, thereby obtaining the minimum distance between the first and second drones for each cycle. Based on the minimum distance between the first and second drones in each cycle, the satellite network server predicts the cumulative number of collisions between the first and second drones within a first preset time period, and generates a detection and avoidance analysis result based on the cumulative number of collisions, enabling the first and second drones to avoid collisions based on this analysis result. Due to the wide coverage of the satellite network, the satellite network server can more quickly and directly obtain the flight information of the first and second drones and generate detection and avoidance analysis results based on the flight information. This eliminates the need for the process in related technologies where the sensing data of the drone to be avoided is first transmitted to the ground access gateway and then to the core network for the core network to generate the detection and avoidance analysis result. Transmission via the satellite network reduces the number of nodes involved in transmission through the ground network, thereby reducing transmission latency.

[0101] Furthermore, based on the above embodiments, the following embodiments illustrate the process of determining the minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the second UAV in each cycle.

[0102] Please see Figure 3 , Figure 3This application provides a flowchart illustrating a method for determining the minimum distance between a first UAV and a second UAV in each cycle. The method includes the following steps:

[0103] S301. For any given period, based on the flight information of the first UAV and the second UAV in that period, determine the position information of the first UAV and the second UAV at each time point predicted every second preset time interval.

[0104] Among them, the second preset duration Less than the first preset duration .

[0105] In this embodiment, the second preset duration It can be 10s, 30s, 5min, or 10min, etc.

[0106] In related technologies, there is also a method that utilizes a direct communication interface between drones (PC5 interface) to periodically broadcast signals for collision avoidance. However, the periodic broadcasting via the PC5 interface consumes a significant amount of drone power and has poor flexibility. More importantly, because the PC5 interface is a short-range communication interface, it is limited by transmission power and reception sensitivity, making long-distance transmission impossible. Therefore, a smaller broadcast time step, typically 1 or 2 seconds, is usually required for better signal transmission, ensuring that nearby drones can receive the broadcast signal and perform collision avoidance accordingly.

[0107] In this application, the second preset duration used when predicting location information... It can be flexibly configured; for example, in high-speed drones used for low-Earth orbit satellite applications, by setting... A prediction time of 10-30 seconds can predict relatively long distances, such as in transport drone applications covered by medium- and high-orbit satellites, by setting... A timeframe of 5-10 minutes allows for prediction of longer distances, enabling collision avoidance for drones at greater distances.

[0108] In this embodiment, the flight information may include flight speed and flight position. The flight speed is a velocity vector, which includes the speed magnitude and the speed direction. The speed direction can be represented by an angle or heading. The flight position is latitude and longitude, or a position vector relative to a preset reference point. It may also include the flight direction, which is the same as the direction of the velocity vector, and may also include heading angle, etc.

[0109] For example, taking the first cycle as an example, that is, taking the first and second UAVs sending their respective flight information to the satellite network server for the first time, assuming that the current velocity vector of the first UAV A is... The current velocity vector of the second drone B is The current location of the first drone A is The current location of the second drone B is The satellite network server, based on a preset artificial intelligence algorithm, such as a prediction algorithm, will then, every second preset time interval... The predicted position information of the first UAV A and the second UAV B at each time point is shown below:

[0110] First drone A:

[0111] The first position information for the first predicted moment is: ;

[0112] The second position information for the predicted second time point is: ;

[0113] The predicted position information at time n is: ;

[0114] Second drone B:

[0115] The first position information for the first predicted moment is: ;

[0116] The second position information for the predicted second time point is: ;

[0117] The predicted position information at time n is: ;

[0118] S302. Based on the predicted position information of the first and second UAVs at each time point, determine the distance between the first and second UAVs at each time point.

[0119] Taking the first cycle in the above example as an example, the satellite network server determines the distance between the first and second drones at each predicted time point based on the predicted position information of the first and second drones, as shown below:

[0120] The distance between the first UAV A and the second UAV B at the first determined moment is: ;

[0121] At the determined second moment, the distance between the first UAV A and the second UAV B is: And so on, the distance between the first UAV A and the second UAV B at the determined nth time is... .

[0122] S303. Based on the distance between the first UAV and the second UAV at each time point, determine the minimum distance between the first UAV and the second UAV in that period.

[0123] Taking the first cycle in the above example as an example, after the satellite network server determines the distance between the first UAV and the second UAV at each moment in the cycle, it judges the distance and thus determines the minimum distance in the cycle.

[0124] According to the method in this embodiment, the satellite network server can determine the minimum distance between the first UAV and the second UAV in each period included within the first preset time period.

[0125] In the above embodiments of this application, for any given period, the satellite network server determines the position information of the first and second drones at each predicted time interval every second preset time interval based on the flight information of the first drone and the second drone during that period. Based on the predicted position information of the first and second drones at each time interval, the distance between the first and second drones at each time interval is determined. Furthermore, based on the distance between the first and second drones at each time interval, the minimum distance between the first and second drones during that period is determined. The method of this embodiment, by predicting the position information at multiple times based on the flight information of the first and second drones, and obtaining the minimum distance between the first and second drones based on the predicted position information, is more accurate.

[0126] Furthermore, based on the above embodiments, the process of the satellite network server determining the minimum distance between the first drone and the second drone in each period within the first preset time period, and predicting the number of collisions in each period based on the minimum distance between the first drone and the second drone in each period is described.

[0127] Optionally, for any given period, if the minimum distance between the first UAV and the second UAV during that period is less than or equal to a preset safe distance threshold... If it is determined that the first and second drones are likely to collide, then the collision count is incremented by 1. If the minimum distance between the first and second drones in that period is greater than a preset safe distance threshold, then it is determined that the first and second drones are unlikely to collide, and the collision count is not incremented by 1, or it can be understood as incrementing the collision count by 0, thus obtaining the predicted collision count for that period. This represents a preset distance range centered on point A of the first drone. This indicates a preset distance range centered on point B of the second drone.

[0128] The satellite network server accumulates the number of collisions in each period within the first preset time period to obtain the cumulative number of collisions.

[0129] Then, based on the cumulative number of collisions, the satellite network server generates detection and avoidance analysis results for the first and second UAVs. Figure 4 This application provides a flowchart illustrating a method for generating detection and avoidance analysis results for a first UAV and a second UAV, as shown in the embodiments of this application. Figure 4 As shown, the method includes the following steps:

[0130] S401. Determine the target period when the cumulative number of collisions is 1.

[0131] If the minimum distance between the first drone and the second drone in the first cycle is less than or equal to the preset safe distance threshold, then the cumulative number of collisions is 1, and the target cycle is the first cycle.

[0132] If the minimum distance between the first and second drones in the first cycle is greater than the preset safe distance threshold, the cumulative number of collisions is 0. If the minimum distance between the first and second drones in the second cycle is less than or equal to the preset safe distance threshold, the cumulative number of collisions is 1, and the target cycle is the second cycle.

[0133] Using the method described above, the period corresponding to a cumulative collision count of 1 is determined as the target period.

[0134] S402. Based on the minimum distance between the first UAV and the second UAV during the target period, determine the predicted target time corresponding to the minimum distance.

[0135] For example, assuming the first cycle is the target cycle, based on the minimum distance between the first UAV and the second UAV determined in the first cycle, and assuming the time corresponding to the minimum distance is the second time, then the second time is the target time.

[0136] S403. Determine the cumulative threshold for the number of collisions based on the predicted target time, the first preset duration, and the second preset duration.

[0137] The cumulative threshold for the number of collisions is determined according to the following preset formula.

[0138]

[0139] in, Indicates the first A target time. Taking the above example, then... .

[0140] Substituting the predicted target time, the first preset duration, and the second preset duration into the above formula, the cumulative threshold for the number of collisions is determined.

[0141] S404. Determine the target collision event type based on the cumulative number of collisions and the cumulative threshold for the number of collisions.

[0142] The collision event types are divided into low-probability collision events and high-probability collision events. High-probability collision events indicate that there is a relatively high chance of a collision between drones, while low-probability collision events indicate that there is a relatively low chance of a collision between drones.

[0143] If the cumulative number of collisions is less than the cumulative collision count threshold, the target collision event type is determined to be a low-probability collision event. If the cumulative number of collisions is greater than or equal to the cumulative collision count threshold, the target collision event type is determined to be a high-probability collision event.

[0144] S405. Based on the target collision event type, generate the detection and avoidance analysis results for the first UAV and the second UAV.

[0145] After the satellite network server determines the type of target collision event, it generates the detection and avoidance analysis results for the first and second UAVs.

[0146] In the above embodiments of this application, the satellite network server determines the target period corresponding to a cumulative collision count of 1, determines the predicted target time corresponding to the minimum distance between the first UAV and the second UAV within the target period, and determines the cumulative collision count threshold based on the predicted target time, a first preset duration, and a second preset duration. Then, based on the cumulative collision count and the cumulative collision count threshold, the target collision event type is determined, and finally, a detection and avoidance analysis result is generated. The method of this embodiment, by determining the cumulative collision count threshold and the target collision event type determined based on the cumulative collision count and the cumulative collision count threshold, is more accurate, and thus the detection and avoidance analysis result generated based on the target collision event type is more accurate.

[0147] In this application, if the determined target collision event type is a high-probability collision event, the second preset duration is reduced to obtain a new second preset duration. Based on the new second preset duration, the step of determining the position information of the first UAV and the second UAV at each time point predicted every second preset duration is re-executed for any period, according to the flight information of the first UAV and the second UAV in the period, until the detection and avoidance analysis results of the first UAV and the second UAV are generated.

[0148] The specific implementation process is similar to the above embodiments. Please refer to the above embodiments. To avoid redundancy, the description will not be repeated.

[0149] In this application, by reducing the second preset duration, a new second preset duration is obtained, and the detection and avoidance analysis results of the first UAV and the second UAV are obtained again based on the new second preset duration, so that the obtained detection and avoidance analysis results are more accurate, and thus the collision avoidance performed by the UAV based on the accurate detection and avoidance analysis results is more accurate.

[0150] To facilitate a better understanding of this application, a brief explanation is provided below. Please refer to [link / reference]. Figure 5 , Figure 5 A signaling flowchart of a collision avoidance method for an unmanned aerial vehicle (UAV) provided in this application embodiment includes the following steps:

[0151] S501, The first UAV sends a first request message to the Unmanned Aircraft System-Network Function (UAS-NF).

[0152] The first request message includes, but is not limited to, identification information and credentials of the first UAV. Identification information may be, for example, an identification code or electronic serial number, and credentials may be, for example, a digital certificate or password.

[0153] S502, the second UAV sends a second request message to UAS-NF.

[0154] The second request message contains similar information to the first request message, except that it contains information about the second drone.

[0155] S503 and UAS-NF authenticate and authorize the first request message.

[0156] The UAS-NF verifies the identification information in the first request message to confirm whether its format or encoding conforms to regulations and whether it matches the registered drone identity information. Alternatively, the UAS-NF decrypts and verifies the credential information in the first request message based on a preset key and verification algorithm to further confirm the identity information of the first drone.

[0157] After UAS-NF confirms the validity of the first UAV's identity, it authorizes the preset operations that the first UAV can perform.

[0158] S504 and UAS-NF authenticate and authorize the second request message.

[0159] The UAS-NF authenticates and authorizes the second request message in the same way as in step S503, and will not be described again.

[0160] UAS-NF can authenticate and authorize the first request message after receiving it, or it can authenticate and authorize the first request message and the second request message after receiving them. This application does not limit the execution steps of authentication and authorization.

[0161] S505, the satellite network server sends a notification request to UAS-NF to subscribe to the first and second drones.

[0162] The satellite network server, also known as the collision avoidance application server, periodically or periodically sends notification requests to the UAS-NF to subscribe to the first and second drones.

[0163] S506 and UAS-NF send notification messages to the satellite network server allowing subscription.

[0164] After receiving the notification request from the satellite network server, UAS-NF sends a notification message allowing subscription to the satellite network server, since the authentication of the first and second UAVs has been successful, so that the satellite network server can receive the flight information sent by the first and second UAVs.

[0165] S507, the first UAV sends flight information to the satellite network server.

[0166] The first UAV periodically sends its current flight information to the satellite network server. The contents of the flight information are shown in Table 1.

[0167] S508, the second UAV, sends flight information to the satellite network server.

[0168] The second UAV periodically sends its current flight information to the satellite network server. The contents of the flight information are shown in Table 1.

[0169] S509: The satellite network server generates detection and avoidance analysis results based on the flight information sent by the first and second UAVs.

[0170] The satellite network server analyzes flight information from multiple cycles transmitted by the first UAV and the second UAV, based on preset artificial intelligence algorithms, to generate DAA results. For specific implementation details, please refer to the aforementioned embodiments.

[0171] The S510 satellite network server sends the detection and avoidance analysis results to the first UAV.

[0172] After receiving the DAA result, the first UAV performs collision avoidance based on the DAA result.

[0173] The S511 satellite network server sends the detection and avoidance analysis results to the second UAV.

[0174] After receiving the DAA result, the second UAV performs collision avoidance based on the DAA result.

[0175] It is understood that this embodiment does not limit the execution order of the above steps. For example, S501 and S502 can be executed simultaneously.

[0176] Please see Figure 6A , Figure 6A A signaling flowchart for another collision avoidance method for unmanned aerial vehicles provided in this application embodiment. Figure 6B This application provides another application scenario diagram, which includes: a first drone 01, a second drone 02, a satellite network server 03, a satellite 04, a first drone controller 011, and a second drone controller 021. The satellite network server 03 is mounted on the satellite 04. The following description, in conjunction with... Figure 6B ,right Figure 6A The signaling flowchart in the diagram can be explained by the following steps:

[0177] S601, The first UAV controller sends a first request message to the UAS-NF.

[0178] The first request message includes, but is not limited to, identification information and credential information of the first drone controller. Identification information may be, for example, an identification code or electronic serial number, and credential information may be, for example, a digital certificate or password.

[0179] S602, the first UAV sends a second request message to the UAS-NF.

[0180] The second request message includes, but is not limited to, the identification information and credentials of the first UAV. The identification information may be, for example, an identification code or an electronic serial number, and the credentials may be, for example, a digital certificate or a password.

[0181] S603, the second UAV controller sends a third request message to the UAS-NF.

[0182] The third request message includes, but is not limited to, the identification information and credential information of the second drone controller. The identification information may be, for example, an identification code or an electronic serial number, and the credential information may be, for example, a digital certificate or a password.

[0183] S604, the second UAV sends a fourth request message to UAS-NF.

[0184] The fourth request message includes, but is not limited to, the identification information and credentials of the second UAV. The identification information may be, for example, an identification code or an electronic serial number, and the credentials may be, for example, a digital certificate or a password.

[0185] S605 and UAS-NF authenticate and authorize the corresponding drones or drone controllers based on the first request message, the second request message, the third request message, and the fourth request message, respectively.

[0186] The specific implementation process is similar to the steps in the above embodiments, and will not be repeated to avoid redundancy.

[0187] Optionally, the UAS-NF can authenticate and authorize the UAV or UAV controller that sent the request message upon receiving any request message. Alternatively, it can authenticate and authorize the corresponding UAV or UAV controller after receiving all request messages. This application does not limit the timing of these execution steps.

[0188] S606, the satellite network server sends a notification request to UAS-NF to subscribe to the first and second drones.

[0189] S607 and UAS-NF send notification messages to the satellite network server allowing subscription.

[0190] S608, the first UAV sends flight information to the satellite network server.

[0191] S609, the second UAV, sends flight information to the satellite network server.

[0192] The S610 and satellite network server generate early warning information based on the flight information sent by the first and second UAVs.

[0193] The satellite network server analyzes flight information from multiple cycles transmitted by the first UAV and the second UAV, using pre-defined artificial intelligence algorithms, to generate DAA (Data Acquisition and Analysis) results. Based on these DAA results, it generates early warning information, the contents of which are shown in Table 2.

[0194] S611, the satellite network server sends the early warning information to the first unmanned aerial vehicle (UAV) controller.

[0195] S612, the satellite network server sends the early warning information to the second human-machine controller.

[0196] S613, the first UAV controller generates an avoidance strategy based on the early warning information.

[0197] The first UAV controller generates an avoidance strategy based on the early warning information. The avoidance strategy may include speed adjustment, heading change or altitude change.

[0198] S614, The first drone controller sends the evasion strategy to the first drone.

[0199] After receiving the avoidance strategy, the first UAV performs collision avoidance according to the content of the avoidance strategy.

[0200] S615, the second UAV controller generates an avoidance strategy based on the early warning information.

[0201] The second UAV controller generates an evasion strategy based on the early warning information. The evasion strategy may include speed adjustment, heading change or altitude change.

[0202] S616, the second drone controller sends the evasion strategy to the second drone.

[0203] After receiving the avoidance strategy, the second drone performs collision avoidance according to the content of the avoidance strategy.

[0204] It is understood that the execution order of the above steps is not limited in this embodiment.

[0205] In the above embodiments of this application, the satellite network server can directly obtain the flight information of the first and second UAVs and generate detection and avoidance analysis results based on the flight information. This eliminates the need for the process in related technologies where the sensing data of the UAV to be avoided is first transmitted to the ground access gateway and then to the core network, where the core network generates the detection and avoidance analysis results. Transmission via the satellite network reduces the number of nodes involved in the transmission through the ground network, thereby reducing transmission latency. Furthermore, compared to related technologies that rely solely on a single UAV application layer mechanism to avoid collisions with only one UAV, this application can perform collision avoidance for multiple UAVs based on the flight information of multiple UAVs, thus making the method of this application more applicable.

[0206] Figure 7 This is a schematic diagram of a collision avoidance device for a drone provided in an embodiment of this application. It is applied to a satellite network server, which is located on a satellite. Figure 7 As shown, it includes:

[0207] The receiving module 701 is used to receive flight information from the first UAV at multiple cycles and flight information from the second UAV at multiple cycles.

[0208] The determination module 702 is used to determine the minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the second UAV in each cycle.

[0209] The prediction module 703 is used to predict the cumulative number of collisions within a first preset time period based on the minimum distance between the first UAV and the second UAV in each cycle. The first preset time period is longer than the duration corresponding to multiple cycles.

[0210] The processing module 704 is used to generate detection and avoidance analysis results for the first UAV and the second UAV based on the cumulative number of collisions, and send the detection and avoidance analysis results to the first UAV and the second UAV for collision avoidance.

[0211] One possible implementation is to determine module 702, specifically used for

[0212] For any given period, based on the flight information of the first UAV and the second UAV during that period, the position information of the first UAV and the second UAV at each time point predicted every second preset duration is determined, where the second preset duration is less than the first preset duration.

[0213] Based on the predicted position information of the first and second UAVs at each time point, the distance between the first and second UAVs at each time point is determined.

[0214] Based on the distance between the first and second drones at each time point, determine the minimum distance between the first and second drones during the period.

[0215] One possible implementation is that the prediction module 703 is specifically used for:

[0216] Based on the minimum distance between the first and second drones in each cycle, the number of collisions in each cycle is predicted.

[0217] The number of collisions in each cycle within the first preset time period is accumulated to obtain the cumulative number of collisions.

[0218] One possible implementation is that the prediction module 703 is specifically used for:

[0219] For any given period, if the minimum distance between the first UAV and the second UAV is less than or equal to a preset safe distance threshold, then the number of collisions is incremented by 1 to obtain the predicted number of collisions for that period.

[0220] One possible implementation is that processing module 704 is specifically used for:

[0221] Determine the target period when the cumulative number of collisions is 1.

[0222] Based on the minimum distance between the first UAV and the second UAV during the target period, the predicted target time corresponding to the minimum distance is determined.

[0223] The cumulative threshold for the number of collisions is determined based on the predicted target time, the first preset duration, and the second preset duration.

[0224] The target collision event type is determined based on the cumulative number of collisions and the cumulative collision threshold. The collision event type includes low-probability collision events and high-probability collision events.

[0225] Based on the target collision event type, the detection and avoidance analysis results of the first and second UAVs are generated.

[0226] One possible implementation is that processing module 704 is specifically used for:

[0227] If the cumulative number of collisions is less than the cumulative collision count threshold, the target collision event type is determined to be a low-probability collision event.

[0228] If the cumulative number of collisions is greater than or equal to the cumulative collision count threshold, then the target collision event type is determined to be a high-probability collision event.

[0229] One possible implementation is that processing module 704 is also used for:

[0230] If the target collision event type is a high-probability collision event, then the second preset duration is reduced to obtain a new second preset duration.

[0231] Based on the new second preset duration, the process of determining the position information of the first and second UAVs at each predicted time interval every second preset duration is repeated for any given period, based on the flight information of the first UAV and the second UAV during that period, until the detection and avoidance analysis results of the first and second UAVs are generated.

[0232] One possible implementation is that processing module 704 is also used for:

[0233] Early warning information is generated based on the detection and avoidance analysis results.

[0234] Send the warning information to both the first and second drones. Alternatively,

[0235] The warning information is sent to a first drone controller that controls a first drone and a second drone controller that controls a second drone, so that the warning information is sent to the first drone through the first drone controller and to the second drone through the second drone controller.

[0236] The collision avoidance device for drones provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0237] This application also provides a satellite that includes the satellite network server mentioned in any of the above embodiments. It is understood that this application does not limit the type or function of the satellite.

[0238] Figure 8 This is a schematic diagram of the structure of a satellite network server provided in an embodiment of this application. Figure 8 As shown, the device 80 includes at least one processor 801 and a memory 802. Optionally, the device 80 also includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0239] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0240] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0241] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0242] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0243] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0244] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0245] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0246] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0247] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0248] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0249] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0250] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0251] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0252] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0253] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A collision avoidance method for unmanned aerial vehicles (UAVs), characterized in that, Applied to a satellite network server, wherein the satellite network server is located on a satellite, the method includes: Receive flight information from the first UAV for multiple cycles and flight information from the second UAV for multiple cycles; Based on the flight information of the first UAV and the second UAV in each cycle, determine the minimum distance between the first UAV and the second UAV in each cycle; Based on the minimum distance between the first UAV and the second UAV in each cycle, the cumulative number of collisions within a first preset duration is predicted, where the first preset duration is longer than the duration corresponding to multiple cycles. Based on the cumulative number of collisions, the detection and avoidance analysis results of the first UAV and the second UAV are generated, and the detection and avoidance analysis results are sent to the first UAV and the second UAV for collision avoidance.

2. The method according to claim 1, characterized in that, The step of determining the minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the second UAV in each cycle includes: For any given period, based on the flight information of the first UAV and the second UAV during that period, the position information of the first UAV and the second UAV is determined at each moment predicted every second preset duration, where the second preset duration is less than the first preset duration. Based on the predicted position information of the first UAV and the second UAV at each time point, the distance between the first UAV and the second UAV at each time point is determined; Based on the distance between the first UAV and the second UAV at each time point, the minimum distance between the first UAV and the second UAV during the period is determined.

3. The method according to claim 1, characterized in that, The step of predicting the cumulative number of collisions within a first preset time period based on the minimum distance between the first UAV and the second UAV in each period includes: Based on the minimum distance between the first UAV and the second UAV in each cycle, predict the number of collisions in each cycle; The number of collisions in each period within the first preset time period is accumulated to obtain the cumulative number of collisions.

4. The method according to claim 3, characterized in that, The method of predicting the number of collisions in each cycle based on the minimum distance between the first UAV and the second UAV in each cycle includes: For any given period, if the minimum distance between the first UAV and the second UAV during that period is less than or equal to a preset safe distance threshold, then the number of collisions is incremented by 1 to obtain the predicted number of collisions during that period.

5. The method according to claim 2, characterized in that, The step of generating detection and avoidance analysis results for the first UAV and the second UAV based on the cumulative number of collisions includes: Determine the target period when the cumulative number of collisions is 1; Based on the minimum distance between the first UAV and the second UAV during the target period, the predicted target time corresponding to the minimum distance is determined; Based on the predicted target time, the first preset duration, and the second preset duration, a cumulative threshold for the number of collisions is determined; The target collision event type is determined based on the cumulative number of collisions and the cumulative threshold of the number of collisions. The collision event type includes low-probability collision events and high-probability collision events. Based on the target collision event type, the detection and avoidance analysis results of the first UAV and the second UAV are generated.

6. The method according to claim 5, characterized in that, The step of determining the target collision event type based on the cumulative number of collisions and the cumulative threshold number of collisions includes: If the cumulative number of collisions is less than the cumulative threshold for the number of collisions, then the target collision event type is determined to be the low-probability collision event; If the cumulative number of collisions is greater than or equal to the cumulative collision threshold, then the target collision event type is determined to be the high-probability collision event.

7. The method according to claim 6, characterized in that, If the target collision event type is the high-probability collision event, then the second preset duration is reduced to obtain a new second preset duration; Based on the new second preset duration, the step of determining the position information of the first UAV and the second UAV at each predicted time interval every second preset duration is re-executed, based on the flight information of the first UAV and the second UAV in any period, until the detection and avoidance analysis results of the first UAV and the second UAV are generated.

8. The method according to any one of claims 1-7, characterized in that, Also includes: Based on the detection and avoidance analysis results, an early warning message is generated; The warning information is sent to the first drone and the second drone; or, The warning information is sent to a first drone controller that controls the first drone and a second drone controller that controls the second drone, so that the warning information is sent to the first drone through the first drone controller and to the second drone through the second drone controller.

9. A collision avoidance device for an unmanned aerial vehicle (UAV), characterized in that, An application for satellite network servers, wherein the satellite network server is located on a satellite, comprising: The receiving module is used to receive flight information from the first UAV for multiple cycles and flight information from the second UAV for multiple cycles. The determination module is used to determine the minimum distance between the first UAV and the second UAV in each cycle based on the flight information of the first UAV and the second UAV in each cycle. The prediction module is used to predict the cumulative number of collisions within a first preset time period based on the minimum distance between the first UAV and the second UAV in each cycle, wherein the first preset time period is longer than the duration corresponding to multiple cycles. The processing module is used to generate detection and avoidance analysis results for the first UAV and the second UAV based on the cumulative number of collisions, and send the detection and avoidance analysis results to the first UAV and the second UAV for collision avoidance.

10. A satellite network server, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the collision avoidance method for the unmanned aerial vehicle as described in any one of claims 1-8.

11. A satellite, characterized in that, Includes the satellite network server as described in claim 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the collision avoidance method for the unmanned aerial vehicle as described in any one of claims 1-8.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the collision avoidance method for the unmanned aerial vehicle as described in any one of claims 1-8.

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